Volume 11 - Issue 5

Importance Of Life Skill Education Among Adolescent Learners

Life Skill Education Allows The Youth To Get Along With Other People And Enable Them To Adjust Their Environment, Making Responsible Decision, It Also Incorporate Building Of Their Values And Communicating Effectively. The Main Objective Of Life Skil... Life Skill Education Allows The Youth To Get Along With Other People And Enable Them To Adjust Their Environment, Making Responsible Decision, It Also Incorporate Building Of Their Values And Communicating Effectively. The Main Objective Of Life Skill Education Is To Enable The Learner To Develop A Concept Of Oneself As A Person Of Worth And Dignity. It Should Help One To Understand Oneself And Lead To Growth In Personal Responsibility. Life Skills Are Those Skills That Help An Individual To Live Life More Meaningfully, Satisfactorily With Ample Success And Productivity. While Dealing With Daily Challenges An Individual Needs To Know Certain Adaptive And Positive Behavior Which Are The Life Skills. Life Skill Education Sharpens One’s Negotiation, Social And Cognitive Skills. Learning Life Skills And Its Development Help The Children To Build Mental And Social Well- Being Which In Turn Prepares Them For Facing Real World And Its Dynamics. Read more Read less

AI DATALOCK:BLOCKCHAIN & ABE FOR HEALTHCARE

Healthcare Using Blockchain And ABE Technologies Focuses On Securing Sensitive Health Data Such As Electronic Medical Records (EMRs) And Real-time Physiological Metrics (e.g., Heart Rate, ECG, Temperature) From Wearable Sensors. These Data Streams Ar... Healthcare Using Blockchain And ABE Technologies Focuses On Securing Sensitive Health Data Such As Electronic Medical Records (EMRs) And Real-time Physiological Metrics (e.g., Heart Rate, ECG, Temperature) From Wearable Sensors. These Data Streams Are Critical For Patient Care But Are Increasingly Vulnerable To Unauthorized Access And Cyber- Attacks, Particularly When Stored In Cloud Environments. Existing Security Protocols Often Fail To Provide Sufficient Protection, Leading To Risks Like Weak Encryption, Limited Access Control, And A Lack Of Accountability In The Event Of Data Breaches. To Address These Challenges, The AI DataLock Framework Is Proposed, Integrating Hierarchical Key – Attribute-Based Encryption (HE-ABE) And Blockchain Technology. HE-ABE Ensures That Only Authorized Individuals With The Correct Attributes Can Access Patient Data, While Blockchain Provides Tamper-proof Logging For Data Sharing Activities. This Combination Enhances Data Confidentiality, Enforces Fine- Grained Access Control, And Strengthens The Overall Integrity Of Healthcare Systems. Read more Read less

USE OF GEOFIBRES IN THE RECONSTRUCTION OF ROAD

The Study Was Undertaken To Evaluate The Effect Of Geofibres In The Reconstruction Of Road. Samples Of Sub- Grade Soil Were Collected Within The Failed Section Of Road. Geofibres Was Added To The Soil In An Increasing Order Of 5% , 10% , 15% And 20%... The Study Was Undertaken To Evaluate The Effect Of Geofibres In The Reconstruction Of Road. Samples Of Sub- Grade Soil Were Collected Within The Failed Section Of Road. Geofibres Was Added To The Soil In An Increasing Order Of 5% , 10% , 15% And 20% By Weight Of The Soil In A Single And Double Layered System. The Sub- Grade Soil Samples And Sample Stabilized With Geofibres Was Subjected To Laboratory Testing. The Test Conducted Were Sieve Analysis Test, Specific Gravity Test, Atterberg Limit Test, Compaction Test And CBR Test. Results Obtained From Sieve Analysis Test Classified The Samples As A- 7- 6 According To AASHTO Soil Classification System And CH (clay Of High Plasticity) According To Unified Soil Classification System, The Specific Gravity Of The Sub- Grade Soil Was 2.66, The Liquid Limit, Plastic Limit And Plasticity Index Of The Sub- Grade Soil Was 34.8% , 21.5% And 13.3% , The Maximum Dry Unit Weight Of The Sub- Grade Soil Increased From 20.7kN/m3 To 22.67kN/m3, For The Single Layered Disposition Of Geofibres While For The Double Layered Disposition Of Geofibres, The Maximum Dry Unit Weight Of The Sub- Grade Soil Increased From 20.7kN/m3 To 23.2kN/m3. The CBR Of The Sub- Grade Soil Increased From 25.8% To 32.6% For The Single Layered System, 27.4% To 34.8% For The Double Layered. The Study Therefore Recommends The Use Of Geofibres In Road Construction As Improvement In The Strength Properties Of The Sub- Grade Soil Was Observed. Read more Read less

A Study On Employee Retention With Reference To Leo Primecomp Private Limited

This Research Explores The Employee Retention Among The Workforce At Leo Primecomp Pvt Ltd, A Leading Manufacturer Of Automobile Products Located In Chennai. Employee Retention Is The Ability Of An Organization To Retain Its Employees And Ensure Sus... This Research Explores The Employee Retention Among The Workforce At Leo Primecomp Pvt Ltd, A Leading Manufacturer Of Automobile Products Located In Chennai. Employee Retention Is The Ability Of An Organization To Retain Its Employees And Ensure Sustainability. This Study The Highlights The Importance Of Retention The Employees In The Organisation And Recognizes Their Work. Read more Read less

Formulation And Evaluation Of Torsemide Floting Tablet

To Prolong The Retention Of Dosage Forms In The Stomach, Several Techniques Have Been Developed, Including Systems That Swell Or Expand, Adhere To The Gastric Lining, Have Modified Shapes, These Systems Are Especially Useful For Drugs That Are Poorl... To Prolong The Retention Of Dosage Forms In The Stomach, Several Techniques Have Been Developed, Including Systems That Swell Or Expand, Adhere To The Gastric Lining, Have Modified Shapes, These Systems Are Especially Useful For Drugs That Are Poorly Soluble At Higher PH Levels, Require Absorption In The Upper Gastrointestinal Tract, Are Unstable In The Intestinal Environment, Or Act Locally In The Stomach. This Review Discusses Key Formulation And Physiological Factors Affecting Gastric Retention And Provides An Overview Of Different Types Of Floating Systems, Their Design Principles, And Evaluation Parameters. FDDS Offer A Promising Approach To Enhance Drug Bioavailability And Improve Patient Compliance. Read more Read less

FORMULATION AND EVALUATION OF STAVUDINE FLOATING TABLET

This Research Aims To Enhance The Gastric Retention Time Of Stavudine By Formulating Floating Matrix Tablets And Investigating The Effect Of Various Polymers On The Drug Release Profile. The Tablets Were Developed Using A Melt Granulation Technique,... This Research Aims To Enhance The Gastric Retention Time Of Stavudine By Formulating Floating Matrix Tablets And Investigating The Effect Of Various Polymers On The Drug Release Profile. The Tablets Were Developed Using A Melt Granulation Technique, Incorporating Beeswax As The Hydrophobic Meltable Base. Hydroxypropyl Methylcellulose (HPMC) Served As The Matrix-forming Agent, Sodium Bicarbonate As The Gas-generating Component, And Ethyl Cellulose As The Floating Enhancer. The Final Formulations Were Assessed For Several Physical Parameters, Including Hardness, Weight Consistency, Friability, Buoyancy Characteristics (such As Lag Time And Duration Of Floating), Drug Content Uniformity, Stability, And In Vitro Release Behavior. Additionally, Interactions Between The Drug And Polymers Were Analyzed Using Differential Scanning Calorimetry (DSC) And Fourier Transform Infrared Spectroscopy (FT-IR). Read more Read less

BENCHMARKING DEEP LEARNING MODELS FOR AMERICAN SIGN LANGUAGE RECOGINITION A COMPARATIVE STUDY ON WLASL

This Paper Presents A Comprehensive Comparative Analysis Of Recent State-of-the-art Deep Learning Models Developed For American Sign Language (ASL) Recognition, With A Focus On Those Benchmarked Using The WLASL Dataset. The Rising Demand For Accessib... This Paper Presents A Comprehensive Comparative Analysis Of Recent State-of-the-art Deep Learning Models Developed For American Sign Language (ASL) Recognition, With A Focus On Those Benchmarked Using The WLASL Dataset. The Rising Demand For Accessible Human-computer Interaction Technologies Has Driven Advancements In Sign Language Recognition, Enabling More Inclusive Communication Tools For The Deaf And Hard-of-hearing Communities. Despite The Progress, ASL Recognition Remains A Complex Challenge Due To Signer Variability, Subtle Gesture Nuances, And The Need For Large-scale Annotated Datasets. In This Study, We Explore And Analyze Multiple Open-source ASL Recognition Models Including UniSign, SLRT, CVPR21Chal-SLR, SL-TechReport, SL-HWGAT, And Others Available On Repositories Such As PapersWithCode. These Models Represent A Wide Range Of Approaches—ranging From 3D Convolutional Neural Networks (CNNs) To Graph-based Models And Pose-enhanced Transformer Architectures. We Examine Each Model In Terms Of Architectural Design, Input Modalities (RGB, Pose, Or Fusion), Top-1 And Top-5 Accuracy, Computational Efficiency, And Scalability. Our Evaluation Highlights The Trade-offs Between Recognition Performance And Model Complexity, Identifies The Models Best Suited For Real-time Applications, And Uncovers Current Limitations In Signer Generalization And Pose Estimation Quality. We Also Discuss The Role Of Multi-modal Learning And Temporal Modeling In Achieving Higher Accuracy On WLASL Subsets Such As WLASL-100, WLASL-300, And WLASL-2000. The Findings Serve As A Benchmarking Guide For Future Research In Sign Language Recognition And Propose A Structured Path Toward Robust, Efficient, And Deployable ASL Recognition Systems For Real-world Applications Such As Sign-to-text Translators, Educational Tools, And Assistive Devices. Read more Read less

AFFECTIVE SALES: EMOTIONALLY DESIGNED SMART DASHBOARD FOR MULTI-MODEL SALES FORECASTING

In Today's Competitive Business Landscape, Relying Solely On Historical Sales Dashboards Is No Longer Sufficient. This Work Presents A Smart Forecasting Dashboard That Combines The Power Of Three Advanced ModelsXGBoost, Prophet, And LSTMto Predict Fu... In Today's Competitive Business Landscape, Relying Solely On Historical Sales Dashboards Is No Longer Sufficient. This Work Presents A Smart Forecasting Dashboard That Combines The Power Of Three Advanced ModelsXGBoost, Prophet, And LSTMto Predict Future Sales With High Accuracy. The System Leverages Cleaned And Structured Data, Evaluates Model Performance Using Standard Metrics (MAE, RMSE, R²), And Visualizes Outputs Through An Emotionally Engaging Power BI Interface. By Blending AI, Data Visualization, And Affective Design, The Dashboard Transforms Static Sales Data Into Actionable Business Intelligence, Offering A Compelling Solution For Real-world Forecasting And Decision Support. Read more Read less

Life Cycle Analysis Of Electric Vehicle Batteries

Electric Vehicles' (EVs') Ecological Affect Is A Significant Worry As Automotive Industry Gets Ready To Switch To Electricity. In Order To Precisely And Scientifically Evaluate Ecological Efficiency Of Electric Vehicles, They Looked At Ecological Eff... Electric Vehicles' (EVs') Ecological Affect Is A Significant Worry As Automotive Industry Gets Ready To Switch To Electricity. In Order To Precisely And Scientifically Evaluate Ecological Efficiency Of Electric Vehicles, They Looked At Ecological Effects Of Electric Vehicles During Their Entire Life Cycle Oras Contrasted With Gasoline-powered Cars. Since Batteries Make Up The Majority Of An EV's Structural Components, We Include List Ecological Implications Of The Making Of Batteries, Consumption, Secondary Usage, Remaking Or Reusing. The Findings Proved That EVs Have A Greater Negative Environmental Impact Than ICEVs Throughout The Production Phase Due To Battery Manufacture. [11,12] Even Though The Amount Of Renewable Energy Generatedmostly Determined This, EVs In The Usage Phase Received Higher IceVs. The Environmental Benefits Of EVs Are Increased By Reusing And Remanufacturing Retired Batteries Throughout The Recycling Process.However, EVs Have Greater Negative Consequences With Regard To Of Usage, Than Cars With Combustion Engines Of Metals & Minerals As Well As The Potential For Human Toxicity. Electric Vehiclehas The Ability TominimizeProduction Of Greenhouse Gasesor The Use Of Specimen Fuels Over Their Entire Life Cycle. The Promotion Of EVs Widely, The Manufacturing Of Closed-loop Battery, & The Feasible Growth Of Economy, Atmosphere, Or Resources All Depend On Strengthening Electoral Foundation, Modernizingbattery Innovation, As Well As Increasing Recycle Performance.[2,4] Read more Read less

Study On Poor Sleep Quality And Its Associated Risk Factors Among Elderly Population In Chennai

Introduction:Population Aging Is A Major Health Issue In Most Countries, Sleep Is One Of The Significant Factors Affecting Aging And Elderly People’s Quality Of Life. The Present Study Was Done To Assess Sleep Quality And Its Contributing Factors Amo... Introduction:Population Aging Is A Major Health Issue In Most Countries, Sleep Is One Of The Significant Factors Affecting Aging And Elderly People’s Quality Of Life. The Present Study Was Done To Assess Sleep Quality And Its Contributing Factors Among Elderly People. Methods:This Cross-sectional Study Involving 150 Elderly People Aged 60 Or More Was Conducted In Pudupet,Chennai.Data Was Collected Using Structurally Formatted Online Questionnaires Prepared In English And Translated To Tamil Which Is Modified And Taken From Previous Studies.PQSI And Five Point Depression Scale Is Calculated.The Results Which Are Obtained From The Questionnaire, PQSI And Five Point Depression Scale Was Used For Statistical Analysis In SPSS Software. Results: There Were Noteworthy Correlations Discovered For Smoking And Caffeine Consumption With PSQI Score. To Be More Precise, The Prevalence Of Poor Sleep Quality Was Higher In Smokers (p=0.04) And People Who Consume Caffeine (p=0.02). Age(p=0.8039), Sex(p=0.6241), Marital Status(p=0.6947), Co Morbidity(p=0.8554) And 5 Point Depression Scale Score(p=0.8340) Do Not Significantly Associate With Poor Sleep Quality Conclusion: Prevalence Of Poor Sleep Quality Among Elderly Was Around 90% And It Had Significant Association With Smoking And Caffeine Intake. Read more Read less

HUMAN METAPNUEUMOVIRUS(HMPV)

Human Metapneumovirus (HMPV) Is A Relative Newly Described Virus. It Was First Isolated In 2001 And Currently Appears To Be One Of The Most Significant And Common Human Viral Infections. Retrospective Serologic Studies Demonstrated The Presence Of HM... Human Metapneumovirus (HMPV) Is A Relative Newly Described Virus. It Was First Isolated In 2001 And Currently Appears To Be One Of The Most Significant And Common Human Viral Infections. Retrospective Serologic Studies Demonstrated The Presence Of HMPV Antibodies In Humans More Than 50 Years Earlier. Although The Virus Was Primarily Known As Causative Agent Of Respiratory Tract Infections In Children, HMPV Is An Important Cause Of Respiratory Infections In Adults As Well. Almost All Children Are Infected By HMPV Below The Age Of Five; The Repeated Infections Throughout Life Indicate Transient Immunity. HMPV Infections Usually Are Mild And Self-limiting, But In The Frail Elderly And The Immunocompromised Patients, The Clinical Course Can Be Complicated. Since Culturing The Virus Is Relatively Difficult, Diagnosis Is Mostly Based On A Nucleic Acid Amplification Test, Such As Reverse Transcriptase Polymerase Chain Reaction. To Date, No Vaccine Is Available And Treatment Is Supportive. However, Ongoing Research Shows Encouraging Results. The Aim Of This Paper Is To Review The Current Literature Concerning HMPV Infections In Adults, And Discuss Recent Development In Treatment And Vaccination. Read more Read less

A Machine Learning-Based Framework For Early Mental Health Assessment Among Students

Mental Health Concerns Among Students Are Increas- Ingly Prevalent, Yet They Often Go Undetected Due To The Lack Of Structured And Scalable Assessment Frameworks. Conventional Evaluation Methods Tend To Rely On Manual Intervention, Which Is Subjectiv... Mental Health Concerns Among Students Are Increas- Ingly Prevalent, Yet They Often Go Undetected Due To The Lack Of Structured And Scalable Assessment Frameworks. Conventional Evaluation Methods Tend To Rely On Manual Intervention, Which Is Subjective, Resource-intensive, And Impractical For Continu- Ous Monitoring In Large Educational Settings. The Absence Of Early Detection Mechanisms Can Lead To A Decline In Academic Performance, Social Withdrawal, And Long-term Psychological Consequences. This Study Presents A Machine Learning–based Framework For The Early Identification And Assessment Of Mental Health Risks In Students. The System Leverages Python For Data Processing, Excel For Structured Input Collection, And Supervised Machine Learning Algorithms—particularly Random Forest—for Predic- Tive Modeling. In Addition To Structured Survey Data, The System Integrates Sentiment Analysis Of Open-ended Responses Using TF- IDF Vectorization And Logistic Regression, Allowing For A More Comprehensive Evaluation Of Student Emotional States. Key Features Of The Framework Include Automated Classification Of Mental Health Risk Levels, Real-time Input Monitoring Via File System Event Handling, And Visual Reporting Tools Such As Bar Charts And Confusion Matrices. The Model Achieves An Accuracy Of 93.5% In Classifying Students Into Low, Moderate, Or High Risk Categories, Demonstrating Its Practical Applicability. Read more Read less

An Analysis Of Financial Measures On Stock With Reference To Butterfly Gandhimathi Appliance Ltd

This Study Analyzes The Relationship Between Financial Performance And Stock Behavior Of Butterfly Gandhimathi Appliances Ltd, Over The Period 2019-2024. This Research Is Motivated By The Need To Bridge The Gap Between Financial Metrics And Investors... This Study Analyzes The Relationship Between Financial Performance And Stock Behavior Of Butterfly Gandhimathi Appliances Ltd, Over The Period 2019-2024. This Research Is Motivated By The Need To Bridge The Gap Between Financial Metrics And Investors’ Decision-making. Key Financial Ratios Such As EPS, ROE, GPR, And Dividend Yield Are Examined To Understand Their Influence On Stock Price Trends. The Main Objective Of The Study Is To Assess The Company’s Earnings Potential And Its Consistency Over Time And To Provide Basic Insights For Investors And Analysts. This Encompasses A Variety Of Decisions Including Capital Budgeting, Financing, Dividend Distribution, And Liquidity Management. The Results Highlight Declining Profitability Despite Improving Liquidity And Debt Reduction. This Divergence Emphasizes The Need For Balanced Financial Strategies To Sustain Investors' Confidence. The Findings Aim To Support Stakeholders, Especially Investors And Analysts, In Making Informed Decisions Based On Financial Indicators Linked To Stock Valuation. Read more Read less

Formulation And Evaluation Of Floating Tablets Of Lamiuvdine

The Present Study Focuses On The Formulation And Evaluation Of Controlled-release Floating Tablets Of Lamivudine, An Antiretroviral Drug Widely Used In The Management Of HIV/AIDS. Due To Its Short Half-life And Frequent Dosing Requirements, There Is... The Present Study Focuses On The Formulation And Evaluation Of Controlled-release Floating Tablets Of Lamivudine, An Antiretroviral Drug Widely Used In The Management Of HIV/AIDS. Due To Its Short Half-life And Frequent Dosing Requirements, There Is A Need To Develop A Dosage Form That Can Enhance Paient Compliance, Improve Therapeutic Efficacy, And Sustain Drug Release Over An Extended Period. Floating Drug Delivery Systems Were Selected To Prolong The Gastric Residence Time Of The Dosage Form, Thereby Enhancing The Bioavailability Of Lamivudine, Which Is Primarily Absorbed From The Stomach And Upper Part Of The Gastrointestinal Tract. The Tablets Were Formulated Using The Wet Granulation Technique, Incorporating Hydrophilic Polymers Like Hydroxypropyl Methylcellulose (HPMC) And Ethylcellulose As Release-retarding Agents. Effervescent Agents Such As Sodium Bicarbonate And Citric Acid Were Added To Enable Floatation. Precompression Parameters (angle Of Repose, Carr’s Index, Hausner’s Ratio) And Post-compression Evaluations (hardness, Friability, Drug Content, Floating Lag Time, Total Floating Time, And In Vitro Drug Release) Were Conducted In Accordance With Standard Protocols. Among Various Formulations, The Optimized Batch Demonstrated Excellent Buoyancy For Over 12 Hours, Sustained Drug Release Up To 12 Hours, And Complied With All Physicochemical Parameters. FTIR Studies Confirmed The Absence Of Drug-excipient Interactions. The In Vitro Release Data Best Fitted The Korsmeyer-Peppas Kinetic Model, Indicating A Non-Fickian Diffusion-controlled Release Mechanism. In Conclusion, The Study Successfully Developed A Stable, Effective, And Controlled-release Floating Tablet Of Lamivudine, Which Holds Promise For Improved Patient Adherence And Better Man Read more Read less

Strategic Integration Of Marketing 4.0 And Industry 4.0 For Competitive Advantage

The Mix Of Marketing 4.0 Focused On Customers And Industry 4.0, With Its Digital Technology, Provides A Solid Support For Changing Businesses Today. This Paper Analyzes How Firms In India Make Use Of These Technologies To Improve How They Engage With... The Mix Of Marketing 4.0 Focused On Customers And Industry 4.0, With Its Digital Technology, Provides A Solid Support For Changing Businesses Today. This Paper Analyzes How Firms In India Make Use Of These Technologies To Improve How They Engage With Customers And Stay Ahead Of Competition. By Examining More Than 200 Peer-reviewed Sources (between 2021 And 2025) As Well As Detailed Cases, We Discover Important Patterns And Outcomes. IoT, AI And Technologies Dealing With Big Data Give Personalization In Real Time And Smart Products And Their Analytics Merge Experiences People Have In Stores And Online. We Apply Thematic Synthesis Along With Careful Analysis Of Company Case Studies. Omni-channel Marketing Keeps Showing Results Like Increased Net Promoter Scores, Higher Marketing ROI (both 25 – 40%) And Greater Gains In Customer Lifetime Value. Results From India’s Companies In FMCG, Telecom And Automotive Sector Are Impressive, With Global Manufacturers In E-commerce And Manufacturing Confirming That ICE’s Approach Applies To All Sectors. We Consider ARPU, Reductions In Churn And Engagement Rates As Signals Showing Whether The Strategy Is Working. This Paper Presents A Local View Supported By Data On How Marketing 4.0 And Industry 4.0 Work Together To Give Customers Value That Promotes Sustainability. Read more Read less

A Study On The Dimensions Of Employee Satisfaction With Reference To Brakes India Private Limited

The Purpose Of This Study Is To Analyse The Different Dimensions Of Employee Satisfaction With Reference To Brakes India Private Limited. The Current Study Evaluates The Level Of Satisfaction Of Employees In Brakes India Pvt. Ltd. Primary Data Is Col... The Purpose Of This Study Is To Analyse The Different Dimensions Of Employee Satisfaction With Reference To Brakes India Private Limited. The Current Study Evaluates The Level Of Satisfaction Of Employees In Brakes India Pvt. Ltd. Primary Data Is Collected Through Structured Questionnaire. Secondary Data Is Collected From Articles, Books, Research Papers, Online Forum, Etc. The Study Is Based On Descriptive Research.Statistical Tools Such As Percentage Analysis, ANOVA, Regression, Chi-Square, And Correlation Are Used In This Study. Findings And Suggestions Are Made With The Help Of The Responses Of The Employees For The Questions.The Findings Of The Study States That There Is A Positive Level Of Satisfaction Among The Employees. It Is Also Found That The Employees Are Satisfied With The Organisation In Various Aspects. It Is Suggested To Consider The Work Life Balance Of Employees Through Appropriate Measures To Improve Employee Satisfaction. Read more Read less

Implementing Zero-Trust Principles In Distributed Systems

The Rapid Adoption Of Distributed Systems In Modern Computing Has Introduced New Security Challenges, Particularly As Organizations Increasingly Rely On A Diverse Set Of Devices, Users, And Networks. Traditional Perimeter-based Security Models Are In... The Rapid Adoption Of Distributed Systems In Modern Computing Has Introduced New Security Challenges, Particularly As Organizations Increasingly Rely On A Diverse Set Of Devices, Users, And Networks. Traditional Perimeter-based Security Models Are Insufficient For Securing Distributed Systems, Particularly In Environments Characterized By Remote Work, Cloud Computing, And Hybrid Infrastructures. Zero-trust Principles, Which Emphasize "never Trust, Always Verify" And Assume No Implicit Trust Within A Network, Have Emerged As A Promising Approach To Address These Challenges. However, Implementing Zero-trust Principles In Distributed Systems Introduces Practical Complexities, Including Performance Trade-offs, Scalability Issues, And Integration Challenges In Legacy Systems. This Paper Addresses These Gaps By Proposing A Framework For Applying Zero-trust Principles In Distributed Systems, Focusing On Secure Communication, Dynamic Authentication, And Resource Access Control. The Research Adopts A Design Science Research (DSR) Methodology, Which Is Widely Used In Information Systems And Software Engineering To Develop Innovative Solutions To Practical Problems. The Results Of The Evaluation Analyzed Showed That By Introducing Some Performance Techniques Such As Asynchronous Policy Caching, It Reduced The Number Of Requests To The Policy Engine By 40%, Significantly Lowering Latency During Access Control Checks. Lightweight Mutual TLS (mTLS) Technique Reduced The Cryptographic Overhead By 30%, To Address Scalability Issues, Load Balancing And Auto-Scaling Technique Was Used Which Reduced Request Latency By 15%, Ensuring Consistent Performance During Peak Loads. While Edge-based Processing Reduced Authentication Latency For IoT Devices By 50%, While The Framework Demonstrated Strong Security Against Lateral Movement Attacks, It Introduced Minor Latency Under High Workloads, Which Was Addressed Through Optimization In Subsequent Iterations. Read more Read less

A STUDY ON IMPACT OF EMPLOYER BRANDING IN THE RECURIMENT PROCESS WITH REFERENCE TO AGNIKUL COSMOS PRIVATE LIMITED

Employer Branding Has Emerged As A Critical Factor In Attracting And Retaining Top Talent In Today’s Competitive Job Market. This Study Explores The Impact Of Employer Branding On The Recruitment Process, With Reference To Agnikul Cosmos Private Limi... Employer Branding Has Emerged As A Critical Factor In Attracting And Retaining Top Talent In Today’s Competitive Job Market. This Study Explores The Impact Of Employer Branding On The Recruitment Process, With Reference To Agnikul Cosmos Private Limited. The Research Aims To Analyze The Influence Of Key Employer Branding Elements, Such As Employer Value Proposition And Hiring Decisions. A Survey Was Conducted With 150 Respondents To Collect Data On The Effectiveness Of Employer Branding In Talent Acquisition. Statistical Tools Such As Regression, ANOVA, Chi-square, And Correlation Are Employed To Examine The Relationship Between Employer Branding And Recruitment Outcomes. The Findings Of This Study Will Provide Valuable Insights For Agnikul Cosmos In Enhancing Its Employer Branding Strategies To Attract And Retain High-quality Talent, Ultimately Improving Its Recruitment Efficiency And Organizational Growth. Read more Read less

Optimizing Validation And Reimbursement Processes For International Travel: A Case Study of Valeo India Private Limited

This Paper Explores The Challenges And Solutions In Optimizing Expense Validation And Reimbursement Processes For International Travel, Focusing On The Case Of Valeo India Private Ltd. The Study Highlights How Globalization And Digitization Have Forc... This Paper Explores The Challenges And Solutions In Optimizing Expense Validation And Reimbursement Processes For International Travel, Focusing On The Case Of Valeo India Private Ltd. The Study Highlights How Globalization And Digitization Have Forced Companies To Re-engineer Their Travel Expense Frameworks. A Five-year Data Analysis Reveals Spending Trends, Policy Impacts, And The Effectiveness Of Automation In Cost Control, Compliance, And Employee Satisfaction. The Study Concludes With Recommendations For Building Resilient, Technology-driven Expense Systems. Read more Read less

A Study on Employee Motivation With Reference To Aston Dietech Private Limited

This Study Was Attempted On The Theme “Employee Motivation” At Aston Dietech Private Limited. Employee Motivation Is The Level Of Commitment, Drive, And Creativity That Your Team Brings With Them To Work Every Day. Employee Motivation Means The Drive... This Study Was Attempted On The Theme “Employee Motivation” At Aston Dietech Private Limited. Employee Motivation Is The Level Of Commitment, Drive, And Creativity That Your Team Brings With Them To Work Every Day. Employee Motivation Means The Drive Or Inspiration That An Individual Has To Perform At Work. It’s What Makes A Person Want To Get Up In The Morning And Be Excited To Go To Work. When Employees Are Motivated, They’re More Likely To Be Productive, Creative, And Engaged In Their Job. This Study Was Conducted To Analyse The Factors Influencing Employee Motivation And The Impact Of Employee Motivation On The Satisfaction Of Employees. The Study Also Concentrates On The Role Of Reward And Recognition System On Employee Motivation. Descriptive Research Method Is Used In This Study. The Sampling Technique Used In This Study Is Simple Random Sampling. The Sample Size Is 209. Structured Questionnaire Is Used For Collecting Primary Data. Secondary Data Is Collected From Various Sources Such As Research Papers, Books, Articles, Etc,. The Collected Data Is Analyzed Through Percentage Analysis, ANOVA, Regression, Chi-square And Correlation. Read more Read less

GEOMETRIC DESIGN OF SUBURBAN ROUNDABOUTS CONSIDERING A CASE STUDY IN BHOPAL (M.P)- A REVIEW

A Contemporary Roundabout Is A Crossroads With A Circulation Roadway Where Traffic Flows Continuously In One Direction Around The Central Island That Leads To The Approach Legs' Exits. Vehicle Speeds Are Also Modest. The Following Steps Make Up The I... A Contemporary Roundabout Is A Crossroads With A Circulation Roadway Where Traffic Flows Continuously In One Direction Around The Central Island That Leads To The Approach Legs' Exits. Vehicle Speeds Are Also Modest. The Following Steps Make Up The Iterative Process Of Modern Roundabout Design: (1) Determining That The Roundabout Is The Best Traffic Solution; (2) Defining The Number Of Lanes At The Intersection Based On The Level Of Service And Required Capacity; (3) Designing The Roundabout Geometry Initially; (4) Designing The Vehicle Swept Path, The Fastest Path Analysis, And Visibility Performance Checks; And (5) Designing The Roundabout In Detail If The Performance Checks' Findings Support The Design Criteria. Compatibility Between The Initial Roundabout Geometry Design Parts Must Be Carefully Considered Because They Are Not Independent. Roadway Design Includes The Following Steps: Establishing The Road Alignment; Calculating Sight Distances, Radii Of Horizontal Curves, And Lengths Of Vertical Curves; Charting The Alignment Profile Using Bearings Or Co-ordinates (easting And Northing); Determining Stations And Elevations Of Points Along The Proposed Route; Calculating Earthwork Quantities; And Many Other Studies And Calculations. These Computations Are Performed To Find The Optimal Alignment While Fulfilling Design Specifications. Manual Geometric Design Is Extremely Time-consuming, Labour-intensive, And Prone To Costly Errors. There Is Currently A Need For Computer Software To Be Used In The Planning Of Roadway Geometry. The Software Offers Huge Time And Effort Savings And Is Incredibly Accurate. In This Study, A Comprehensive Geometric Design Of A Typical Roundabout Is Presented Using Both AutoCAD And MX ROAD Software Considering Three Roundabouts In Bhopal (M.P) Which Have Majorly Reported Fatal Accidents. Enabling The 3D Visualisation Of The Design Saves Money And Time. Read more Read less

IDENTIFICATION OF ACCIDENT ZONES AND ROAD SAFETY RECOMMENDATIONS IN INDORE REGION (M.P): A REVIEW

Road Accidents Are Now More Than Just A Transportation Issue; They Are Societal Issues That Require Attention In Order To Be Addressed. Road Accidents Cause Significant Financial And Human Losses, Which Ultimately Hinder A Country's Ability To Develo... Road Accidents Are Now More Than Just A Transportation Issue; They Are Societal Issues That Require Attention In Order To Be Addressed. Road Accidents Cause Significant Financial And Human Losses, Which Ultimately Hinder A Country's Ability To Develop. Numerous Studies On The Analytic Techniques Of Traffic Accidents Have Determined The Impact Of Different Factors On Traffic Accidents. These Studies Also Proposed Corrective Actions Based On Location And Black-spot Requirements. In This Paper Presenting Review Of Literatures Related To Analysis Of Accidental Prone Studies And Its Impact Of Road Traffic. Read more Read less

Formulation And Evaluation Of Fast Disintegration Tablet Of Nicardipne

The Aim Of This Study Was To Formulate And Evaluate Nicardipine Fast Disintegrating Tablets (FDTs), Which Are Designed To Provide Rapid Onset Of Action, Enhanced Bioavailability, And Improved Patient Compliance, Especially In Individuals With Swallow... The Aim Of This Study Was To Formulate And Evaluate Nicardipine Fast Disintegrating Tablets (FDTs), Which Are Designed To Provide Rapid Onset Of Action, Enhanced Bioavailability, And Improved Patient Compliance, Especially In Individuals With Swallowing Difficulties. Nicardipine, Calcium Channel Blocker, Was Selected As The Model Drug Due To Its Potent Analgesic And Anti-inflammatory Properties. The Tablets Were Formulated Using The Direct Compression Method, Which Is Cost-effective And Efficient For Mass Production. Various Excipients, Including Microcrystalline Cellulose, Crospovidone, Sodium Starch Glycolate, And Magnesium Stearate, Were Utilized To Optimize The Disintegration, Flowability, And Compression Characteristics Of The Formulation. Pre-compression Studies Were Conducted To Assess The Powder Blend’s Flowability And Compressibility, Using Parameters Such As Bulk Density, Tapped Density, Carr’s Index, Angle Of Repose, And Moisture Content. These Evaluations Indicated That The Powder Blends Had Suitable Flow Properties For Tablet Compression. Post-compression Testing, Including Hardness, Friability, Disintegration Time, And Dissolution, Demonstrated That The Prepared Tablets Met The Desired Criteria For Fast Disintegration And Drug Release. The Disintegration Time Was Found To Be Within The Required Limits For FDTs, Ensuring Rapid Release Of The Drug Upon Contact With Saliva. A Calibration Curve For Nicardipine Was Developed Using UV Spectrophotometry To Quantify Drug Content In Dissolution Studies. The Dissolution Profile Showed Rapid Release Of Nicardipine, Which Is Characteristic Of Fast-dissolving Tablets. Stability Studies Confirmed That The Tablets Were Stable Under Standard Storage Conditions, With No Significant Changes In Drug Content Or Physical Properties. In Conclusion, The Formulated Nicardipine FDTs Were Successfully Developed With Satisfactory Characteristics In Terms Of Disintegration, Dissolution, And Stability. These Tablets Offer An Efficient Alternative For The Delivery Of Nicardipine, Improving Patient Compliance And Providing Rapid Therapeutic Action. Further Studies May Explore The Optimization Of Excipients And The Effect Of Different Formulation Variables On The Performance Of The Tablets.(FTIR) Spectrum. The Solid Dispersions Can Be Evaluated By In-vitro Dissolution Studies Read more Read less

FORMULATION AND EVALUATION OF CIPROFLOXACIN HCL EFFERVESCENT TABLETS FOR ENHANCED SOLUBILITY AND PATIENT COMPLIANCE

This Research Focuses On The Formulation And Evaluation Of Ciprofloxacin HCl Effervescent Tablets To Enhance Solubility And Patient Compliance. Ciprofloxacin HCl Has Poor Aqueous Solubility, Leading To Delayed Dissolution And Reduced Bioavailability.... This Research Focuses On The Formulation And Evaluation Of Ciprofloxacin HCl Effervescent Tablets To Enhance Solubility And Patient Compliance. Ciprofloxacin HCl Has Poor Aqueous Solubility, Leading To Delayed Dissolution And Reduced Bioavailability. Effervescent Formulations Improve Solubility, Taste, And Ease Of Administration, Making Them Ideal For Patients With Swallowing Difficulties. Effervescent Tablets Were Formulated Using Citric Acid And Sodium Bicarbonate As Effervescent Agents, Along With Solubility Enhancers, Sweeteners, And Disintegrants. Preformulation Studies, Including Solubility Analysis, Drug-excipient Compatibility (FTIR), And Powder Flow Properties, Were Conducted. The Optimized Formulation Exhibited Rapid Disintegration (2–3 Min), Enhanced Drug Release (>85% In 15 Min), And Stable Physicochemical Properties. The Study Confirms That Effervescent Ciprofloxacin HCl Tablets Offer Improved Solubility, Faster Onset Of Action, And Enhanced Patient Compliance, Making Them A Promising Alternative To Conventional Tablets, Especially For Pediatric And Geriatric Patients. Read more Read less

Service Usage Classification With Encrypted Internet Traffic In Moblie Messaging Apps

The Rapid Adoption Of Mobile Messaging Apps Has Enabled Us To Collect Massive Amount Of Encrypted Internet Traffic Of Mobile Messaging. The Classification Of This Traffic Into Different Types Of In-App Service Usages Can Help For Intelligent Network... The Rapid Adoption Of Mobile Messaging Apps Has Enabled Us To Collect Massive Amount Of Encrypted Internet Traffic Of Mobile Messaging. The Classification Of This Traffic Into Different Types Of In-App Service Usages Can Help For Intelligent Network Management, Such As Managing Network Bandwidth Budget And Providing Quality Of Services. Traditional Approaches For Classification Of Internet Traffic Rely On Packet Inspection, Such As Parsing HTTP Headers. However, Messaging Apps Are Increasingly Using Secure Protocols, Such As HTTPS And SSL, To Transmit Data. This Imposes Significant Challenges On The Performances Of Service Usage Classification By Packet Inspection. How To Exploit Encrypted Internet Traffic For Classifying In-App Usages. Specifically, We Develop A System, Named CUMMA, For Classifying Service Usages Of Mobile Messaging Apps By Jointly Modeling User Behavioral Patterns, Network Traffic Characteristics And Temporal Dependencies. We First Segment Internet Traffic From Traffic-flows Into Sessions With A Number Of Dialogs In A Hierarchical Way. Also, We Extract The Discriminative Features Of Traffic Data From Two Perspectives: (i) Packet Length And (ii) Time Delay. CUMMA Enables Mobile Analysts To Identify Service Usages And Analyze End-user In-App Behaviors Even For Encrypted Internet Traffic. Finally, The Extensive Experiments On Real-world Messaging Data Demonstrate The Effectiveness And Efficiency Of The Proposed Method For Service Usage Classification. Read more Read less

A Multi-Modal Approach To Stock Prediction: Integrating LSTM, XGBoost, VADER Sentiment Analysis, And NLP Summarization

Stock Prediction Systems Leverage Financial Indicators, Historical Data, And Machine Learning To Forecast Price Movements, Thereby Enhancing Investment Strategies And Risk Management. These Types Of Systems Aggregate Market Data And Include Sentiment... Stock Prediction Systems Leverage Financial Indicators, Historical Data, And Machine Learning To Forecast Price Movements, Thereby Enhancing Investment Strategies And Risk Management. These Types Of Systems Aggregate Market Data And Include Sentiment Analysis From News, Social Media And Financial News Articles Which Provide Great Insight And Decision-making Capabilities. Current Capabilities Include Comparing Datasets Using Time Series Analysis, Regression Models, Neural Networks, And More Recently Natural Language Processing (NLP). The Sentiment Analysis Includes A Gauge Of Sentiment Of The Range Of ~ Marginally Optimistic. The Technical Indicators With Price Yields Market Trends And Reversals. As + More Data Streams Into 'the Machine' At + Real-time Decision The Datasets Are Changed To Refresh Auto-expanding The Trading Analysis. The Sentiment And Opinion Analysis Provide Instant And Current Opinions And Market Positions. In Terms Of The Bias, Human Ability To Predict A Future Price Direction Is Removed With Structured Signals Obtained From Price Movement, Market Indicators, Sentiment, And Performance History. There Are Enhanced Capabilities To Integrate The Analysis Into Risk Assessment Models To Assess Expected Loss And Potential For Returns For Balanced Portfolios. Using Machine Learning The Ability To Accurately Forecast Market Possibilities Almost As Immediate Transactions To Current Analysis Using Big Data And Comparative Metrics Allows Professional And Retail Traders To Make More Strategic Trades Based On Data Instead Of Expectations. Working Through Sentiment, And Inaccurate Predictions Helps All Market Participants Frame Their Decision-making Based On Likelihood Of Performance, Returns And Volatility. Read more Read less

Neural Network-Powered Brain Tumor Detection Using Machine Learning

Detecting Brain Tumors Early Is Vital For Giving Patients The Best Chance At Successful Treatment And Recovery. This Project Introduces A User-friendly Web Application Designed To Help With The Early Detection Of Brain Tumors Using MRI Scans And Arti... Detecting Brain Tumors Early Is Vital For Giving Patients The Best Chance At Successful Treatment And Recovery. This Project Introduces A User-friendly Web Application Designed To Help With The Early Detection Of Brain Tumors Using MRI Scans And Artificial Intelligence (AI). The System Allows Both Doctors And Patients To Upload Brain MRI Images Taken From Four Common Angles: Top, Bottom, Left, And Right. Once Uploaded, These Images Are Processed By A Secure Backend System Built With The Flask Framework. A Pre-trained Deep Learning Model—specifically, A Convolutional Neural Network (CNN)—analyses The Scans To Look For Signs Of Brain Tumors. After The Analysis, The Application Creates A Simple, Easy-to-understand Report That Includes Patient Details, The AI’s Prediction, And A Confidence Score (set Above 90% For Demonstration). The Platform Is Designed To Be Intuitive, Requiring No Technical Background To Use. While It’s Not Meant To Replace Professional Medical Diagnosis, It Can Serve As A Helpful Early Screening Tool, Particularly In Areas With Limited Access To Healthcare. This Work Demonstrates How AI-powered Tools Can Support Faster, More Accessible Medical Insights And Improve Diagnostic Processes. Read more Read less

INTEGRATED POS AND WEIGHING MACHINE SYSTEM FOR RATION SHOP

The Public Distribution System (PDS) In India, One Of The World's Largest Government-managed Food Security Frameworks, Is Tasked With Delivering Essential Food Commodities To Economically Weaker Sections Of The Population. Despite Its Critical Role,... The Public Distribution System (PDS) In India, One Of The World's Largest Government-managed Food Security Frameworks, Is Tasked With Delivering Essential Food Commodities To Economically Weaker Sections Of The Population. Despite Its Critical Role, The Legacy Infrastructure Of PDS Remains Susceptible To Operational Inefficiencies, Manual Discrepancies, And Widespread Malpractice. To Address These Long-standing Systemic Flaws, This Study Proposes A Next-generation, Integrated Hardware-software Solution That Amalgamates Biometric Verification, Electronic Weighing, Automated Billing, And Centralized Reporting Into A Unified Point Of Sale (POS) Terminal Tailored For Ration Shop Environments. At The Heart Of The System Is A High-precision, Digitally Interfaced Weighing Module Built Using A Calibrated Load Cell Connected Through An Analog-to-digital Converter (ADC) To A Microcontroller-based Processing Unit. This Setup Eliminates The Subjective Error Introduced By Manual Weighing And Ensures Accurate Quantity Dispensation. The Device Is Seamlessly Linked With A Biometric Authentication Module, Compliant With The Aadhaar Framework, Which Performs Real-time User Identity Verification Using Fingerprint Or Iris Recognition. Authentication Data Is Securely Transmitted Through Encrypted Channels Using AES-256 Encryption, Ensuring Both Privacy And Integrity During Verification. Once The Beneficiary’s Identity Is Confirmed, The System Dynamically Retrieves Entitlement Information From A Government-hosted Database And Initiates The Automated Billing Process. Transaction Values Are Computed Based On Current Commodity Prices, Subsidy Allocations, And The Measured Weight, After Which A Digital Receipt Is Generated And Optionally Printed. All Transactional Data, Including Biometric Logs, Weight Metrics, Timestamps, And Location Identifiers, Are Stored In A Secure Local Buffer And Periodically Synchronized With A Central Cloud Repository Using RESTful APIs, Ensuring Seamless Integration With National PDS Monitoring Infrastructure. In Areas With Limited Internet Connectivity, The System Is Designed To Operate In Offline Mode, Employing Local Storage With Deferred Synchronization, Thereby Maintaining Service Continuity Without Compromising Data Integrity. The Architecture Is Modular And Adheres To Service-oriented Design Principles, Allowing Easy Firmware Updates, Component Replacement, And Horizontal Scaling. Furthermore, Administrative Dashboards Allow For Real-time Monitoring, Analytics-based Fraud Detection, And Performance Auditing Across Ration Shops At The State And District Levels. This Integrated Approach Significantly Reduces Leakages, Prevents The Misuse Of Ration Cards, And Ensures That Only Authenticated Beneficiaries Receive Entitlements, Thus Aligning With The Broader Objectives Of Digital Governance, Transparency, And Public Accountability. By Embedding Automation At Every Critical Junction Of The Ration Distribution Workflow, The Proposed Solution Redefines The Technological Baseline For Future-ready Welfare Delivery Systems In Developing Economies. Read more Read less

Android Malware Detection Using Multi-Domain Feature Analysis And Deep Learning Models

We Propose A Hybrid And Intelligent Android Malware Detection Framework That Integrates Multi-domain Feature Extraction With Deep Learning Models To Improve Mobile Application Security. The System Consists Of Two Key Modules: The APK Security Analyze... We Propose A Hybrid And Intelligent Android Malware Detection Framework That Integrates Multi-domain Feature Extraction With Deep Learning Models To Improve Mobile Application Security. The System Consists Of Two Key Modules: The APK Security Analyzer And The App Behavior Analyzer. The APK Security Analyzer Applies Both Static And Dynamic Analysis Using Tools Such As Androguard, Android Emulator, ADB, And Monkey To Extract Permissions, API Call Sequences, And Network Traffic Features. These Are Processed Using A Tri-model Architecture—Multi-Layer Perceptron (MLP), Long Short-Term Memory (LSTM), And LightGBM—within A Streamlitinterface.The App Behavior Analyzer Enhances Static CSV-based Analysis By Automating Preprocessing With StandardScaler And Applying A Sliding Window For Time-series Classification Using An LSTM Model. It Supports Real-time Predictions And Provides Interactive Visualizations To Aid User Understanding Of Results.Experimental Results Show High Accuracy: MLP And LSTM Models Each Achieved 96%, While LightGBM Reached 88%. Precision, Recall, And F1-scores Confirm System Robustness, With MLP Scoring 96%, 93%, And 94%, Respectively. Visualizations Such As Radar Charts And Progress Bars Clearly Communicate App Risk Levels And Behaviorpatterns.These Findings Establish The Framework As A Robust, Scalable, And Interpretable Solution For Real-time Android Malware Detection, Advancing Mobile Cybersecurity Tools. Read more Read less

An Analysis Of Financial Statement With Reference To Tafe Private Limited

This Paper Presents A Detailed Financial Analysis Of Tractors And Farm Equipment Limited (TAFE) Over A Five-year Period From 2020 To 2024. It Employs Key Financial Ratios Including Liquidity, Solvency, Profitability, And Efficiency Metrics To Evaluat... This Paper Presents A Detailed Financial Analysis Of Tractors And Farm Equipment Limited (TAFE) Over A Five-year Period From 2020 To 2024. It Employs Key Financial Ratios Including Liquidity, Solvency, Profitability, And Efficiency Metrics To Evaluate The Company’s Financial Performance. The Findings Reveal A Conservative Financial Strategy With Consistent Profitability And Strong Liquidity, Although Opportunities Exist For Optimized Capital Structure And Asset Utilization. This Study Provides Insights For Stakeholders And Financial Analysts In Understanding The Financial Health And Strategic Positioning Of TAFE Pvt. Ltd. Read more Read less

COMPARATIVE ANALYSIS OF ZONING AND EFFECTS ON DIFFERENT REAL ESTATE SEGMENTS

Zoning Regulations Play A Crucial Role In Shaping Urban Development And Influencing Real Estate Market Dynamics. This Paper Examines The Comparative Impact Of Zoning On Different Real Estate Segments, Including Residential, Commercial, And Industrial... Zoning Regulations Play A Crucial Role In Shaping Urban Development And Influencing Real Estate Market Dynamics. This Paper Examines The Comparative Impact Of Zoning On Different Real Estate Segments, Including Residential, Commercial, And Industrial Properties. The Study Evaluates How Zoning Laws Affect Property Values, Land Use Efficiency, And Investment Potential. Using Case Studies From Various Urban Centers, The Research Highlights The Advantages And Challenges Associated With Zoning Policies And Provides Insights Into Optimizing Zoning Regulations For Sustainable Urban Growth. Read more Read less

A Study on The Role of Lms In Streamlining The onboarding Process of New Employess With Reference To Careernet Technologies Pvt Ltd

Learning Management Systems (LMS) Are Becoming Increasingly Relevant In Improving Organizational Efficiency, Especially In Onboarding Procedures, As A Result Of The Quick Development Of Digital Tools In HRM. With A Particular Focus On Careernet Techn... Learning Management Systems (LMS) Are Becoming Increasingly Relevant In Improving Organizational Efficiency, Especially In Onboarding Procedures, As A Result Of The Quick Development Of Digital Tools In HRM. With A Particular Focus On Careernet Technologies Pvt. Ltd., This Study Investigates How LMS Might Expedite The Onboarding Of New Hires. Based On Theoretical Frameworks Including The Human Capital Theory, Social Learning Theory, And Technology Acceptance Model (TAM), The Study Evaluates How LMS Platforms Help With Compliance Training, Knowledge Retention, Employee Engagement, And Shorter Onboarding Times. 200 Workers' Opinions Of LMS Elements Such As Automation, Gamification, Analytics, Integration With HR Systems, And Self-paced Learning Modules Were The Subject Of Structured Questionnaires Used To Gather Primary Data Using A Descriptive Research Technique. The Results Show That By Providing Consistent, Role-specific, And Interactive Training Materials, LMS Greatly Improves Onboarding Outcomes, Lowering Administrative Workload, Enhancing New Hire Confidence, And Raising Early-stage Productivity. The Study Also Emphasizes How Learning Management System (LMS) Platforms Provide For Easy Tracking Of Learning Progress, Flexible Content Distribution, And Adaptive Learning That Is Customized To Individual Preferences And Job Tasks. Higher Job Satisfaction And Retention Rates Among New Hires Are Also Correlated With LMS-enabled Onboarding, According To The Data. According To The Human Capital Theory, The Study Confirms That Using An LMS For Onboarding Is A Wise Strategic Move That Improves Organizational Performance And Worker Competency Over The Long Run. LMS Is More Than Just A Digital Solution; It's A Transformative Instrument That Offers Scalable And Affordable Onboarding Experiences While Bringing HR Procedures Into Line With Technology. According To This Study, LMS Should Be A Key Part Of An Organization's Onboarding Ecosystem If It Wants To Update Its Talent Acquisition And Integration Strategy. Read more Read less

Formulation And Evaluation Of Muchoadesive Buccle Tablet For Diabetis Using Neteglinide

Mucoadhesive Buccal Tablets Of Nateglinide Were Formulated And Evaluated To Enhance Drug Bioavailability, Provide Controlled Drug Release, And Improve Patient Compliance In Diabetes Management. Buccal Drug Delivery Bypasses Hepatic First-pass Metabol... Mucoadhesive Buccal Tablets Of Nateglinide Were Formulated And Evaluated To Enhance Drug Bioavailability, Provide Controlled Drug Release, And Improve Patient Compliance In Diabetes Management. Buccal Drug Delivery Bypasses Hepatic First-pass Metabolism, Potentially Increasing The Drug's Bioavailability While Ensuring Sustained Therapeutic Action. In This Study, Various Bioadhesive Polymers, Including Hydroxypropyl Methylcellulose (HPMC), Carbopol 934, And Sodium Carboxymethylcellulose (Sodium CMC), Were Used To Optimize The Mucoadhesive Properties And Drug Release Profile Of The Tablets. The Tablets Were Prepared Using The Direct Compression Method And Subjected To A Comprehensive Set Of Physicochemical And In-vitro Evaluation Tests, Including Weight Variation, Hardness, Swelling Index, Surface PH, Mucoadhesive Strength, Drug Content Uniformity, And In-vitro Drug Release Studies. The Optimized Formulation Demonstrated Satisfactory Mucoadhesive Strength, Appropriate Swelling Behavior, And A Controlled Drug Release Profile Over An Extended Period. These Results Indicate That Buccal Delivery Of Nateglinide Could Be A Promising Alternative To Conventional Oral Formulations, Potentially Reducing The Frequency Of Administration And Enhancing Therapeutic Outcomes For Diabetic Patients. Further Studies, Including In-vivo Pharmacokinetic And Pharmacodynamic Evaluations, Are Necessary To Validate The Efficacy Of This Novel Drug Delivery System. Read more Read less

AutoQuest: Intelligent Question Generation From Online Content

AutoQuest Is A Web-based Application Which Gives Automatic Multiple-choice Questions (MCQs) FromPDF, TXT, DOCX Files, And YouTube Videos. The Application Is Developed Using Flask And Python. AutoQuest Utilizes Google Gemini 2.0 Flash Model To Genera... AutoQuest Is A Web-based Application Which Gives Automatic Multiple-choice Questions (MCQs) FromPDF, TXT, DOCX Files, And YouTube Videos. The Application Is Developed Using Flask And Python. AutoQuest Utilizes Google Gemini 2.0 Flash Model To Generate MCQs. The Application Interface Gives User The Choice To Upload Files Of Format .pdf, .txt Or .docx Or Enter YouTube URL. The User Can Also Specify The Number Of Questions To Be Generated. The Application Extracts Text From The Uploaded File Or The YouTube URL And Generates The MCQs. The Generated MCQs Contains Question With Four Options (A-D) And The Correct Option Is Also Mentioned. The Application Has An Interesting Feature That Is YouTube Video Processing To Generate MCQs. The Text Is Extracted From PDF Using Pdfplumber Library And Python-docx Is Used For DOCX Files. For YouTube Video Processing The Audio From The YouTube Video Is Extracted, Speech-to-text Modules Are Used To Get The Text From The Audio. The Extracted Text Is Sent As Prompt To Gemini API To Generate MCQs. The Automatic Question Generation Reduces The Manual Effort And The Time Consumed In The Traditional Question Generation Approach. The Application Is User-friendly And Allows The User To View And Download The MCQs As Both .txt File And .pdf File. Teachers, Lecturers And Trainers Of Schools, Colleges Or Training Institutions Can Utilize This Application To Generate MCQs For Assessments And Quizzes. Read more Read less

Calotropis Gigantea-Enhanced Povidone Bandage: A Synergistic Approach For Wound Healing

Wound Healing Is A Complex Biological Process Involving Inflammation, Cellular Proliferation, And Tissue Remodeling. Conventional Synthetic Wound Care Products Often Show Limited Effectiveness And May Cause Adverse Reactions. This Study Aimed To Deve... Wound Healing Is A Complex Biological Process Involving Inflammation, Cellular Proliferation, And Tissue Remodeling. Conventional Synthetic Wound Care Products Often Show Limited Effectiveness And May Cause Adverse Reactions. This Study Aimed To Develop And Evaluate Herbal-Based Adhesive Bandages Incorporating CalotropisGigantea Extract And Povidone Iodine As Natural Alternatives For Wound Management. Phytochemical Screening Revealed The Presence Of Bioactive Compounds Such As Alkaloids, Flavonoids, Tannins, Saponins, Terpenoids, And Cardiac Glycosides—Compounds Known For Their Antimicrobial And Healing Properties. Five Formulations (B1–B5) Were Assessed For Dermatological Safety, Organoleptic Characteristics, And Antimicrobial Activity Against Escherichia Coli, Staphylococcus Aureus, And Candida Species. All Formulations Were Found To Be Non-Irritant And Non-Allergenic. Organoleptic Evaluation Confirmed Acceptable Physical Properties, Including Uniform Spreadability And Characteristic Odour. Among The Samples, B3 Exhibited The Most Potent Antimicrobial Activity Across All Tested Organisms, Indicating Its Potential For Enhanced Wound Infection Control. These Findings Suggest That Herbal-Based Bandages Are Safe And Effective, Offering A Promising Natural Alternative For Improved Wound Care. Further In Vivo Studies Are Recommended To Validate These Results. Read more Read less

ENERGY EFFICIENT ADAPTIVE SENSING FRAMEWORK FOR WSN

Energy Optimization Remains A Fundamental Challenge In Contemporary Sensor-based Platforms, Particularly In Domains Such As Environmental Surveillance, Precision Agriculture, Biomedical Telemetry, And Wireless Sensor Networks (WSNs). These Platforms... Energy Optimization Remains A Fundamental Challenge In Contemporary Sensor-based Platforms, Particularly In Domains Such As Environmental Surveillance, Precision Agriculture, Biomedical Telemetry, And Wireless Sensor Networks (WSNs). These Platforms Are Frequently Deployed In Inaccessible Or Energy-constrained Environments, Where Periodic Maintenance And Battery Replacement Are Impractical. Conventional Architectures, Which Operate On Static Sampling Frequencies And Transmission Intervals, Tend To Overutilize Energy Resources Irrespective Of Contextual Data Relevance. This Research Introduces A MATLAB-centric Simulation Environment That Models An Adaptive Sensing Paradigm, Designed To Dynamically Reconfigure System Parameters Based On Real-time Signal Intelligence. At The Core Lies An Adaptive Control Algorithm That Processes Incoming Data Streams To Assess Contextual Significance Using Real-time Statistical Metrics—primarily Variance Analysis, Entropy Measurements, And Event-triggered Thresholds. Based On This Analysis, The System Modulates Its Sensing Resolution And Communication Cycles: Reducing Operational Intensity During Steady-state Conditions, And Ramping Up Activity During Transient Or Critical Events To Maintain Data Integrity And Temporal Accuracy. The Proposed Simulation Framework Emulates Sensor Behavior Using Synthetic Signal Profiles While Implementing Intelligent Duty Cycling Strategies For Both Sensing And RF Communication Subsystems. MATLAB Scripting Facilitates Precise Power Modelling, Enabling A Quantitative Comparison Between Static And Adaptive Configurations. Experimental Evaluations Across Diverse Sensor Modalities Demonstrate Energy Savings Of Up To 45%, With No Degradation In Responsiveness Or Sensing Fidelity. In Many Scenarios, The System's Adaptive Prioritization Mechanism Enhances The Semantic Relevance Of Captured Data By Focusing Resources On Periods Of High Informational Value. A User-configurable Graphical User Interface (GUI) Is Integrated For Interactive Visualization, Parameter Manipulation, And Environmental Scenario Testing. The Modular Software Architecture Supports Seamless Integration Of Heterogeneous Sensors And Extensible Control Logic, Promoting Scalability For Future Research Or Application-specific Adaptations. One Of The Primary Contributions Of This Work Is The Demonstration That Software-defined Adaptive Sensing Can Be Effectively Modeled And Validated In MATLAB Without Reliance On Physical Hardware, Making It An Accessible And Powerful Tool For Prototyping, Academic Instruction, And Sustainable System Design. The Framework Embodies Principles Of Green Engineering By Optimizing Operational Energy Profiles Through Software Intelligence, Contributing To The Development Of Next-generation Low-power Embedded Sensing Systems. Read more Read less

IoT-BASED FLOOD MONITORING SYSTEM IN MOUNTAIN REGIONS

Mountain Areas With Small Watersheds Are At High Risk Because Floods Can Quickly Happen And It's Hard To Get There, Which Can Damage Buildings And Endanger People's Lives. Traditional Flood Warning Systems Frequently Don't Give Out Alerts Quickly Bec... Mountain Areas With Small Watersheds Are At High Risk Because Floods Can Quickly Happen And It's Hard To Get There, Which Can Damage Buildings And Endanger People's Lives. Traditional Flood Warning Systems Frequently Don't Give Out Alerts Quickly Because They Can't Sense Things In Real-time And Can't Communicate Over Long Distances. This Study Introduces A System Using A Microcontroller For Monitoring Floods And Sending Alerts, Which Works In Real-time And Is Suitable For Remote Places With A High Chance Of Flooding. The Setup Uses An Arduino Nano As The Main Controller, Which Works With Float And Level Switches To Keep Checking The Water Levels. A Nearby Screen Shows Live Updates, And A Sound Alarm Goes Off Right Away If Limits Are Surpassed. To Allow For Wireless Data Exchange And Wider Awareness Of The Surroundings, A Zigbee Device Is Employed To Communicate With The Main Monitoring Center. A USB Connection Is Available For Straightforward Data Recording And Computer-based Examination. The System Gets Its Power From A Dependable DC Source, Which Guarantees It Keeps Running Smoothly Even When There's An Emergency. The Suggested Plan Shows A Budget-friendly And Expandable Way To Improve Early Alert Systems In Areas At Risk Of Flooding In The Mountains, Using Built-in Tech To Boost Community Safety And Readiness For Emergencies. Read more Read less

An analysis On Corporate Finance And Capital Budgetting Decisions With Reference To Diamond Engineering Private Limited

This Study Has Been Conducted At DIAMOND ENGINEERING PRIVATE LIMITED, To Identify TheCorporate Finance Plays A Pivotal Role In Guiding Organizations Through Strategic Financial Decisions, Particularly In Capital Budgeting. This Process Entails Evalua... This Study Has Been Conducted At DIAMOND ENGINEERING PRIVATE LIMITED, To Identify TheCorporate Finance Plays A Pivotal Role In Guiding Organizations Through Strategic Financial Decisions, Particularly In Capital Budgeting. This Process Entails Evaluating Investment Opportunities To Allocate Resources Efficiently, Utilizing Tools Such As Net Present Value (NPV), Internal Rate Of Return (IRR), And Payback Period. Effective Capital Budgeting Involves Rigorous Cash Flow Estimation, Risk Analysis, And Sensitivity Assessments To Optimize Profitability And Mitigate Financial Uncertainties. This Abstract Explores The Fundamental Principles, Methodologies, And Challenges Within Corporate Finance, Highlighting Its Critical Importance In Sustaining Long-term Business Growth And Financial Stability. The Results Of This Study Indicate That CORPORATE FINANCE AND CAPITAL BUDGETTING DECISIONS Manpower Planning Have Been Successfully Implemented At Diamond Engineering Private Limited. Read more Read less

A Study On Employee Welfare

This Study Examines The Employee Welfare In Tenneco Clean Air India Pvt Ltd. Focuses On Employee Perceptions And Motivation, Job Satisfaction And Productivity Impact. A Sample Of 210 Employees Was Selected Using A Simple Random Sample, And Data Was C... This Study Examines The Employee Welfare In Tenneco Clean Air India Pvt Ltd. Focuses On Employee Perceptions And Motivation, Job Satisfaction And Productivity Impact. A Sample Of 210 Employees Was Selected Using A Simple Random Sample, And Data Was Collected By A Structured Questionnaire. Statistical Tools Like ANOVA, Chi Square Test, Regression, Correlation Have Been Applied For This Study To Analyze The Fairness, Transparency And Efficiency Of The Evaluation Process. Employee Welfare Includes Both Monetary And Non-monetary Benefits. Monetary Welfare Comprises Health Insurance, Retirement Plans, Bonuses, And Paid Leave. Non-monetary Welfare Initiatives Include Wellness Programs, Workplace Safety Measures, Recreational Facilities, Mental Health Support, Flexible Work Arrangements, And Career Development Opportunities. Together, These Programs Are Designed To Improve The Quality Of Work Life, Reduce Stress, And Help Employees Achieve A Balance Between Work And Personal Life. Read more Read less

The Effectiveness Of Job Portals In Talent Acquisition

In The Digital Age, Job Portals Have Emerged As Vital Tools In Talent Acquisition, Offering Organizations Increased Access To Diverse Talent Pools, Streamlined Recruitment Processes, And Cost-effective Hiring Solutions. This Study Investigates The Ef... In The Digital Age, Job Portals Have Emerged As Vital Tools In Talent Acquisition, Offering Organizations Increased Access To Diverse Talent Pools, Streamlined Recruitment Processes, And Cost-effective Hiring Solutions. This Study Investigates The Effectiveness Of Job Portals In Fulfilling The Recruitment Needs Of True View Technology Private Limited, A Technology-driven Company Operating In India’s Competitive IT Sector. The Research Aims To Evaluate The Impact Of Job Portal Features Such As User Interface, Filtering Tools, And Analytics—on Recruitment Efficiency, While Also Assessing The Quality, Relevance, And Visibility Of Applicants Sourced Through These Platforms. Using A Mixed-method Research Approach, The Study Combines Quantitative Data Collected Through Structured Surveys With Qualitative Insights Obtained From Interviews With HR Professionals At True View Technology. Key Findings Reveal Both The Benefits And Limitations Of Job Portal Usage, Including Challenges Related To Applicant Oversaturation, Fake Profiles, And Difficulties In Shortlisting Candidates. The Study Concludes By Identifying Best Practices And Offering Strategic Recommendations To Optimize The Use Of Job Portals, Ultimately Contributing To More Agile And Effective Talent Acquisition Frameworks. Read more Read less

Silent Alert: Advancing Women's Security Through Smart Sign Recognition And AI

Women's Safety Is A Global Concern Requiring Immediate And Innovative Solutions. This Paper Introduces "Silent Alert," A Real-time Hand Gesture Recognition System That Detects Emergency And Rescue Signs Based On Video Input. Leveraging MediaPipe For... Women's Safety Is A Global Concern Requiring Immediate And Innovative Solutions. This Paper Introduces "Silent Alert," A Real-time Hand Gesture Recognition System That Detects Emergency And Rescue Signs Based On Video Input. Leveraging MediaPipe For Hand Landmark Extraction And A BiLSTM Model For Gesture Classification, The System Offers Accurate Recognition Of Dynamic Hand Gestures. Once A Recognized Gesture Is Detected, An Alert Is Automatically Dispatched Via Twilio SMS To Registered Guardians, Including The User's GPS Location. Our Experiments Demonstrate That This Method Significantly Improves Gesture Recognition Accuracy And Real-time Responsiveness, Making It A Practical Tool For Enhancing Personal Security. Read more Read less

Formulation And Evaluation of Sustained Release Matrix Tablet of Diltiazem HCL

Controlled Release And Sustained Release Drug Delivery Has Become The Standards In The Modern Pharmaceutical Design And Intensive Research For Achieving Better Drug Product Effectiveness, Reliability And Safety. Oral Sustained Release Drug Delivery (... Controlled Release And Sustained Release Drug Delivery Has Become The Standards In The Modern Pharmaceutical Design And Intensive Research For Achieving Better Drug Product Effectiveness, Reliability And Safety. Oral Sustained Release Drug Delivery (OSRDD) Medication Will Continue To Account For The Largest Share (up To 80%) Of Drug Delivery Systems. The Matrix Tablet Preparation Appears To Be Most Attractive Approach For The Process Development And Scale-up Point Of View. A Calcium Channel Blocker, Diltiazem Hydrochloride Has Found Its Applicability In Cardiovascular Diseases Advised To Take The Long Term Treatment Of Cardiovascular Medicaments Like Anti-anginals, Anti-hypertensives, Etc. The Calcium Channel Blockers Are Utilized As The Potential Agents For The Treatment Of These Diseases. They Are Considered As A Slow Calcium Channel Blockers. The Direct Compression Method Was Adopted For The Preparation Of Sustained Release Matrix Tablets With The 10mm Punches And Targeted Weight Of 450mg. The % Drug Release Studies For Combined Hypromellose And Xanthan Gum Matrices Confirmed The Batch H2X2 (at The End Of 12 Hours) As Per USP Criteria Test 2, Which Give The 93.78% Drug Release. Hence, This Formulation Was Optimized And Subjected To Release Kinetic Study And Accelerated Stability Studies. Read more Read less

MALICIOUS SOCIAL BOT USING TWITTER NETWORK ANALYSIS IN DJANGO

Malicious Social Bots Generate Fake Tweets And Automate Their Social Relationships Either By Pretending Like A Follower Or By Creating Multiple Fake Accounts With Malicious Activities. Moreover, Malicious Social Bots Post Shortened Malicious URLs In... Malicious Social Bots Generate Fake Tweets And Automate Their Social Relationships Either By Pretending Like A Follower Or By Creating Multiple Fake Accounts With Malicious Activities. Moreover, Malicious Social Bots Post Shortened Malicious URLs In The Tweet In Order To Redirect The Requests Of Online Social Networking Participants To Some Malicious Servers. Hence, Distinguishing Malicious Social Bots From Legitimate Users Is One Of The Most Important Tasks In The Twitter Network. To Detect Malicious Social Bots, Extracting URL-based Features (such As URL Redirection, Frequency Of Shared URLs, And Spam Content In URL) Consumes Less Amount Of Time In Comparison With Social Graph-based Features (which Rely On The Social Interactions Of Users). Furthermore, Malicious Social Bots Cannot Easily Manipulate URL Redirection Chains. In This Article, Learning Automata-based Malicious Social Bot Detection (LA-MSBD) Algorithm Is Proposed By Integrating A Trust Computation Model With URL-based Features For Identifying Trustworthy Participants (users) In The Twitter Network. The Proposed Trust Computation Model Contains Two Parameters, Namely, Direct Trust And Indirect Trust. Moreover, The Direct Trust Is Derived From Bayes’ Theorem, And The Indirect Trust Is Derived From The Dempster– Shafer Theory (DST) To Determine The Trustworthiness Of Each Participant Accurately. Finally, We Showed The User Tweet Data In Terms Of Graph Visualization Of Bar Chart And Pie Chart Of The System. Experimental Results Showed The Better Performance Of The System. Read more Read less

A Study On Effectiveness Of HR Audit Of The Organisation With Reference To Federal Mogul Motorparts India Limited

This Study Looks Into The HR Audit Practices Adopted At Federal, A Motor Parts Manufacturer, With An Emphasis On Understanding Their Impact On Employee Motivation, HR Efficiency, And Organizational Success. This Study Intends To Provide Insights Into... This Study Looks Into The HR Audit Practices Adopted At Federal, A Motor Parts Manufacturer, With An Emphasis On Understanding Their Impact On Employee Motivation, HR Efficiency, And Organizational Success. This Study Intends To Provide Insights Into The Effectiveness Of Federal’s HR Audit Process In Building A Compliant, Transparent, And Performance-driven Work Environment By Conducting A Thorough Examination Of Various HR Functions. Federal’s HR Audit System Is Studied Using Qualitative And Quantitative Methodologies, Such As Structured Surveys, Employee And Management Interviews, HR Document Analysis, And Audit Checklist Reviews. This Study Examines Both Employee Feedback And HR Data To Identify The Strengths And Limitations Of Federal’s Current Audit Practices Across Areas Such As Recruitment, Training, Performance Appraisal, Employee Relations, And Statutory Compliance. Key Findings From The Investigation Emphasize The Importance Of Regular, Well-structured HR Audits In Maintaining Legal Compliance, Enhancing Employee Trust, And Aligning HR Activities With Strategic Organizational Goals And The Study Reveals That Consistent Auditing Improves Accountability And Promotes Continuous Improvement In HR Operations. Based On The Findings, Recommendations Are Made To Enhance Federal’s HR Audit System, Including The Use Of Digital Audit Tools, Periodic Internal Reviews, Standardized Audit Metrics, And Employee Involvement In Feedback Mechanisms, To Ensure The Process Remains Relevant And Effective. In Conclusion, This Study Offers Valuable Insights Into Federal’s HR Audit Process And Its Role In Supporting Employee Motivation And Organizational Performance Through Improved HR Practices And Strategic Alignment. Read more Read less

Evaluation And Formulation Of Floating Tablet Of Furosemide

By Overcoming Physiological Challenges Such Short Gastric Residence Periods (GRT) And Unpredictably Long Stomach Emptying Times, Rate Controlled Oral Medication Delivery Devices Have Recently Made Scientific And Technological Strides (GET). Several M... By Overcoming Physiological Challenges Such Short Gastric Residence Periods (GRT) And Unpredictably Long Stomach Emptying Times, Rate Controlled Oral Medication Delivery Devices Have Recently Made Scientific And Technological Strides (GET). Several Methods Are Now Used To Extend The GRT, Including Delayed Gastric Emptying Devices Such As Swelling And Expanding Systems, Polymeric Bio Adhesive Systems, Modified-shape Systems, High-density Systems, And Floating Drug Delivery Systems (FDDS), Also Known As Hydrodynamically Balanced Systems (HBS). The Two Approaches Are Taken In The Development Of FDDS By Creating Both Effervescent And Non-effervescent Floating Tablets With A Buoyancy Mechanism As Its Foundation. Drugs With Limited Solubility And Higher PH Levels Can Be Delivered By FDDS Because They Have A Restricted Window For Absorption In The Upper Gastrointestinal System, Are Unstable In The Lower Intestine Environment, And Have Local Activity. The Physiological And Formulation Factors That Affect Gastric Retention Time Are Included In The Most Recent Advancements In Floating Drug Delivery Systems. Additionally, Approaches To Formulating Single-unit And Multiple-unit Floating Systems, As Well As Their Classification And Formulation Aspects, Are Covered In Detail. The Application Of Floating Drug Delivery Devices And The Evaluation Criteria Are Also Summarised In This Paper. By Integrating IoT With Efficient Waste Segregation Mechanisms, This Project Aims To Revolutionize Waste Management In Metro Stations, Promoting Environmental Sustainability And Hygiene Read more Read less

A STUDY ON MANPOWER PLANNING WITH REFERENCE TO FUTUERA

This Report Entitled To Study On Manpower Planning With Reference To Futuera Company. The Main Objective Of The Study Is To Analyze The Current Workforce And Forecast The Specific Job Roles And Responsibilities To Enhance The Future Needs Of The Orga... This Report Entitled To Study On Manpower Planning With Reference To Futuera Company. The Main Objective Of The Study Is To Analyze The Current Workforce And Forecast The Specific Job Roles And Responsibilities To Enhance The Future Needs Of The Organization. It Also Ensures The Predicting The Staffing Levels And Evaluating The Right Candidate For Right Jobs And Enhancing The Skills And Career Development, And Providing Efficient Training To The Employees That Enumerates The Organization In Effective Manner To Achieve The Goals And Objectives. Read more Read less

Study Of Evaluating The Feasibility Of Banana Stem Fibers & Sugarcane Husk As A Sustainable Additive In Pervious Concrete For Urban Pavements

This Study Explores The Integration Of Eco-friendly Materials—banana Stem Fibers And Sugarcane Husk Into Pervious Concrete To Enhance Its Mechanical Properties While Maintaining High Permeability. The Research Addresses Environmental Concerns Related... This Study Explores The Integration Of Eco-friendly Materials—banana Stem Fibers And Sugarcane Husk Into Pervious Concrete To Enhance Its Mechanical Properties While Maintaining High Permeability. The Research Addresses Environmental Concerns Related To Agricultural Waste Disposal And The Resource-intensive Nature Of Traditional Construction Materials. Banana Stem Fibers, Known For Their Tensile Strength And Durability, And Sugarcane Husk, Rich In Lignin And Cellulose, Were Selected As Reinforcements. The Fibers Were Extracted, Cleaned, And Processed Into Uniform Sizes, While The Sugarcane Husk Was Dried And Processed Into Small Particles. Various Mix Proportions Incorporating Different Percentages Of These Materials Were Developed And Compared Against A Control Mix With No Added Fibers Or Husk.The Experimental Phase Involved Testing The Mechanical And Hydrological Properties Of The Modified Pervious Concrete, Including Compressive Strength, Permeability, And Water Absorption Test Using Standard Testing Methods. Results Indicated That The Addition Of Banana Stem Fibers Improved Tensile And Flexural Strengths By 15–20%, Enhancing Resistance To Cracking And Bending. The Effect BSF Composition On The Composites Properties Enhanced The Study Report. (VC 2012 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 128: 1020–1029, 2013)14The Sugarcane Husk Reduced The Concrete's Density, Contributing To Its Lightweight Nature Without Compromising Permeability. The Modified Concrete Maintained A High Infiltration Rate, Essential For Effective Stormwater Management. Read more Read less

Solar Powered Smart Robot For Efficient Floor Cleaning Using IOT

The Project Offers Significant Environmental And Cost Benefits By Reducing Electricity Consumption And Carbon Emissions. Since The System Relies On Solar Charging, It Eliminates The Need For Continuous Reliance On External Power Sources, Making It A... The Project Offers Significant Environmental And Cost Benefits By Reducing Electricity Consumption And Carbon Emissions. Since The System Relies On Solar Charging, It Eliminates The Need For Continuous Reliance On External Power Sources, Making It A Cost-effective And Eco-conscious Solution, Especially In Regions With Abundant Sunlight. The Robot’s Ability To Self-charge Ensures Operational Efficiency While Minimizing Downtime. Although Challenges Such As Low-light Conditions Affecting Solar Charging Efficiency Exist, These Are Addressed Through An Intelligent Charging And Scheduling System That Optimizes Cleaning Tasks Based On Available Solar Energy And Usage Patterns. Read more Read less

COMPREHENSIVE DIGITAL ADVERTISING PLATFORM FOR DIVERSE PRODUCTS

The Main Aim Of This Project Is To Provide Advertisement Details Like Advertisement Cost In Various TV Channels, Newspapers, Online Websites. Advertisements Are Very Necessary In Order To Market Or Promote The Product Of The Particular Product. Adver... The Main Aim Of This Project Is To Provide Advertisement Details Like Advertisement Cost In Various TV Channels, Newspapers, Online Websites. Advertisements Are Very Necessary In Order To Market Or Promote The Product Of The Particular Product. Advertisements Can Be Done Through The Online Mode Through The Radio, Television, Social Networking Sites So That It Will Gain Nice Promotion. Online Advertisement Management System Is An Application That Deals With Maintaining The Advertisements Given By The Customers To The Company. There Will Be Many Customers With Different Advertisements For A Particular Company. Maintenance Of All The Data Using Pen Paper Work Is A Tedious Job. So To Reduce The Manual Effort, The Online Advertisement Management Application Will Be Of Great Help. This Application Will Be Very Useful To The Advertising Agencies And The Managers To Manage Advertisements And To View Reports. Read more Read less

Formulation And Evaluation Of Mucoadhesive Polymer Blend Prochlorperazine Maleate Tablet

The Objective Of This Study Was To Develop And Evaluate A Mucoadhesive Polymer Blend-based Prochlorperazine Maleate Tablet For Enhanced Drug Retention And Controlled Drug Release. Prochlorperazine Maleate, A Dopamine Receptor Antagonist, Is Widely Us... The Objective Of This Study Was To Develop And Evaluate A Mucoadhesive Polymer Blend-based Prochlorperazine Maleate Tablet For Enhanced Drug Retention And Controlled Drug Release. Prochlorperazine Maleate, A Dopamine Receptor Antagonist, Is Widely Used In The Treatment Of Nausea, Vomiting, And Schizophrenia. However, Its Conventional Dosage Forms Suffer From Limitations Such As Rapid Clearance And Reduced Bioavailability. To Overcome These Issues, A Mucoadhesive Tablet Formulation Was Developed Using Bioadhesive Polymers Like Carbopol 934P, Hydroxypropyl Methylcellulose (HPMC), Sodium Carboxymethyl Cellulose (NaCMC), And Chitosan. The Polymer Blend Was Optimized And Evaluated Based On Flow Properties, Swelling Index, Mucoadhesive Strength, Drug-polymer Interaction, In-vitro Drug Release, And Stability Studies. The Results Demonstrated That The Optimized Formulation Exhibited Good Mucoadhesion, Controlled Swelling, And Sustained Drug Release, Ensuring Prolonged Retention In The Buccal Cavity. The Drug Release Followed A Sustained-release Pattern, Enhancing Therapeutic Efficacy And Patient Compliance. This Study Highlights The Potential Of Mucoadhesive Drug Delivery Systems For Improving The Bioavailability And Effectiveness Of Prochlorperazine Maleate. Read more Read less

Deep Learning In Ophthalmology: Predicting Eye Diseases Using Pre-Trained Neural Network

This Study Presents A Deep Learning Approach For Predicting Multiple Retinal Diseases Using Fundus Images. Leveraging A Pre-trained Xception CNN Model Optimized For Multi-label Classification, The System Accurately Detects Conditions Such As Diabetic... This Study Presents A Deep Learning Approach For Predicting Multiple Retinal Diseases Using Fundus Images. Leveraging A Pre-trained Xception CNN Model Optimized For Multi-label Classification, The System Accurately Detects Conditions Such As Diabetic Retinopathy, Glaucoma, Cataract, And Age-related Macular Degeneration. Preprocessing Techniques Like Normalization And Contrast Enhancement Are Applied To Improve Diagnostic Performance. Trained On Annotated Datasets And Evaluated Using Clinical Metrics, The Model Demonstrates High Accuracy And Potential For Real-world Integration. This AI-driven Tool Aims To Assist Ophthalmologists In Early Diagnosis And Extend Quality Eye Care To Remote And Under-resourced Areas. Read more Read less

A Real-Time Facial Recognition Framework For Secure Attendance Monitoring In Examination Environments

Exam Hall Management Plays A Critical Role In Maintaining Academic Integrity. Traditional Identification Methods Such As Hall Tickets And ID Cards Are Prone To Errors And Impersonation. This Paper Proposes A Robust And Automated Solution Using Facial... Exam Hall Management Plays A Critical Role In Maintaining Academic Integrity. Traditional Identification Methods Such As Hall Tickets And ID Cards Are Prone To Errors And Impersonation. This Paper Proposes A Robust And Automated Solution Using Facial Recognition Technology To Enhance Security, Verify Student Identities, And Streamline Entry Processes In Examination Halls. Leveraging Computer Vision And Deep Learning Algorithms, The System Automates Identity Verification, Monitors Real-time Attendance, And Detects Unauthorized Access Attempts. The Proposed System Reduces Human Intervention, Improves Accuracy, And Ensures A Secure And Efficient Examination Environment. Read more Read less

Virtual E-Marketplace Commodities Exploration Mechanism

The Virtual E-Marketplace Commodities Exploration Mechanism Is An Innovative Online Jewellery Shopping System Designed To Enhance The Digital Retail Experience. This Platform Enables Customers To Browse A Diverse Catalogue Of Jewellery, Access Detail... The Virtual E-Marketplace Commodities Exploration Mechanism Is An Innovative Online Jewellery Shopping System Designed To Enhance The Digital Retail Experience. This Platform Enables Customers To Browse A Diverse Catalogue Of Jewellery, Access Detailed Product Information, And Complete Secure Transactions, While Providing Vendors With Tools For Inventory Management And Order Fulfillment. By Integrating Machine Learning Algorithms, Specifically Random Forest And Decision Tree Classifiers, The System Ensures Product Quality Assessment Based On Features Such As Purity And Certification. The Platform Achieves Improved Efficiency, Security, And Scalability Compared To Traditional Manual Systems. Experimental Results Demonstrate The System’s Effectiveness In Streamlining Operations And Enhancing User Satisfaction, With Potential For Further Advancements In Personalization And Real-time Tracking Read more Read less

HR Analytics To Track Employee Performance

Human Resource (HR) Analytics Is Transforming Workforce Management By Enabling Data-driven Decision-making. This Paper Presents An HR Analytics System Designed To Track And Analyze Employee Performance Using A Structured Dataset Comprising Metrics Su... Human Resource (HR) Analytics Is Transforming Workforce Management By Enabling Data-driven Decision-making. This Paper Presents An HR Analytics System Designed To Track And Analyze Employee Performance Using A Structured Dataset Comprising Metrics Such As Department, Job Role, Age, Experience, Training Hours, Education, And Performance Ratings. Developed Using Python, Jupyter Notebook, And Power BI, The System Includes Modules For Data Preprocessing, Exploratory Data Analysis (EDA), Performance Metric Evaluation, KPI Dashboard Creation, And Result Interpretation. The Resulting Dashboard Provides HR Professionals With Actionable Insights To Monitor Performance Trends, Identify Top Performers, And Address Under Performance.Experimental Results Demonstrate The System’s Ability To Visualize Key HR Metrics, Such As KPI Achievement Rates And Training Effectiveness, With Departments Like Analytics And R&D Achieving Average Training Scores Of 84.56 And 84.43, Respectively. The System Offers A Scalable, User-friendly Solution To Replace Traditional Manual Performance Tracking, Enhancing Organizational Efficiency And Strategic Decision-making. Read more Read less

DOCK-BLOCK: A Blockchain Based Authentication System For Digital Documents

With The Rapid Growth In Information Technology And Easy Access To Cheap And Advanced Office Instruments, The Faking Of Important Documents Has Become A Significant Concern. This Project Presents A Decentralized Web Application For Digital Document V... With The Rapid Growth In Information Technology And Easy Access To Cheap And Advanced Office Instruments, The Faking Of Important Documents Has Become A Significant Concern. This Project Presents A Decentralized Web Application For Digital Document Verification Using Ethereum Blockchain-based Technology And P2P Cloud Storage. The Aim Is To Enhance The Verification Process By Making It More Open, Transparent, And Auditable. The Proposed Model Incorporates Public/private Key Cryptography, Online Storage Security, Digital Signatures, Hashing, Peer-to-peer Networks, And Proof Of Work. It Is Designed To Make The Verification Of Uploaded Documents Faster And More Convenient For Any Organization Or Authority. Additionally, The System Assigns Respective Hash Values To Each Individual Document And Includes Features For Verifying Traveler Identity Using Live Camera. Read more Read less

Formulation And Evaluation Of Floating Tablet Of Famotidine

The Present Study Focuses On The Formulation And Evaluation Of Controlled-release Floating Tablets Of Famotidine, An Antiretroviral Drug Widely Used In The Management Of Decrease Gastric Acid Secretion. Due To Its Short Half-life And Frequent Dosing... The Present Study Focuses On The Formulation And Evaluation Of Controlled-release Floating Tablets Of Famotidine, An Antiretroviral Drug Widely Used In The Management Of Decrease Gastric Acid Secretion. Due To Its Short Half-life And Frequent Dosing Requirements, There Is A Need To Develop A Dosage Form That Can Enhance Paient Compliance, Improve Therapeutic Efficacy, And Sustain Drug Release Over An Extended Period.Floating Drug Delivery Systems Were Selected To Prolong The Gastric Residence Time Of The Dosage Form, Thereby Enhancing The Bioavailability Of Famotidine, Which Is Primarily Absorbed From The Stomach And Upper Part Of The Gastrointestinal Tract. The Tablets Were Formulated Using The Wet Granulation Technique, Incorporating Hydrophilic Polymers Like Hydroxypropyl Methylcellulose (HPMC) And Ethylcellulose As Release-retarding Agents. Effervescent Agents Such As Sodium Bicarbonate And Citric Acid Were Added To Enable Floatation. Precompression Parameters (angle Of Repose, Carr’s Index, Hausner’s Ratio) And Post-compression Evaluations (hardness, Friability, Drug Content, Floating Lag Time, Total Floating Time, And In Vitro Drug Release) Were Conducted In Accordance With Standard Protocols.Among Various Formulations, The Optimized Batch Demonstrated Excellent Buoyancy For Over 12 Hours, Sustained Drug Release Up To 12 Hours, And Complied With All Physicochemical Parameters. FTIR Studies Confirmed The Absence Of Drug-excipient Interactions. The In Vitro Release Data Best Fitted The Korsmeyer-Peppas Kinetic Model, Indicating A Non-Fickian Diffusion-controlled Release Mechanism.In Conclusion, The Study Successfully Developed A Stable, Effective, And Controlled-release Floating Tablet Of Famotidine, Which Holds Promise For Improved Patient Adherence And Better Man. Read more Read less

ALERTDRIVER: EARLY DETECTION OF FATIGUE STATES IN DRIVERS DURING LONG DRIVING STRETCHES

The Majority, Of The Accidents Were Happening Perpetually Due To Driver Drowsiness Over The Decades. Automation Has Been Playing Key Role In Many Fields To Provide Conformity And Improve The Quality Of Life Of The Users. Though Various Drowsiness Det... The Majority, Of The Accidents Were Happening Perpetually Due To Driver Drowsiness Over The Decades. Automation Has Been Playing Key Role In Many Fields To Provide Conformity And Improve The Quality Of Life Of The Users. Though Various Drowsiness Detection Systems Have Been Developed During Lost Decade Based On Many Factors, Still The Systems Were Demanding An Improvement In Terms Of Efficiency, Accuracy, Cost, Speed And Availability, Etc. In This Paper, Proposed An Integrated Approach Depends On The Eye Closure Status Along With The Calculation Of The New Proposed Vector FAR (Facial Aspect Ratio) . This Helps To Find The Status Of The Closed Eyes Or Opened And Any Frame Finds That Has Hand Gestures Like Nodding Or Covering Opened Mouth With A Hand As Innate Nature Of Humans When Trying To Control The Sleepiness. The System Also Integrated The Methods And Textural-based Gradient Patterns To Find The Driver’s Face In Various Directions Identify The Sun Glasses On The Driver’s Face And The Scenarios Like Hands-on Eyes While Nodding Were Also Recognized And Addressed. Read more Read less

AI-Enabled Surveillance System For Abnormal Activity Detection And Alerting

Surveillance System Is A Network Of Interconnected Devices And Technologies Designed To Monitor, Record, And Analyze Activities In A Particular Area Or Environment. The Primary Purpose Of Surveillance Systems Is To Enhance Security, Gather Data For A... Surveillance System Is A Network Of Interconnected Devices And Technologies Designed To Monitor, Record, And Analyze Activities In A Particular Area Or Environment. The Primary Purpose Of Surveillance Systems Is To Enhance Security, Gather Data For Analysis, And Provide Insights Into Various Aspects Of The Monitored Space. These Systems Are Commonly Used In A Wide Range Of Settings, Including Public Spaces, Commercial Establishments, Residential Properties, And Governmental Facilities. Many Surveillance Systems Rely On Motion Detection Algorithms To Trigger Alerts. As A Result, False Alarms Are Common, Leading To Alert Fatigue And Reduced Effectiveness. Traditional Surveillance Systems, However, Often Face Challenges Such As Limited Coverage, Manual Monitoring, And False Alarms, Which Can Hinder Their Effectiveness In Detecting And Responding To Security Threats. In Recent Years, Advancements In Artificial Intelligence (AI) And Computer Vision Technologies Have Revolutionized Surveillance Systems By Enabling More Intelligent And Automated Approaches To Monitoring And Analysis. This Project Presents An AI-driven Surveillance System Designed To Enhance Security By Detecting And Responding To Abnormal Activities In Real-time. The Proposed System Utilizes Convolutional Neural Networks (CNN) For Behavior Classification And YOLOv8 (You Only Look Once Version 8) For Abnormal Activities Detection, The System Identifies Abnormal Behaviors And Specific Objects Associated With Security Threats. Upon Detection, An Integrated Alert System Triggers Alarms And Sends SMS And Email Notifications To Designated Personnel, Enabling Swift Response And Intervention. The Customizable Alert Settings Allow For Tailored Notifications Based On The Severity Of Detected Activities. Additionally, The System Logs All Alerts For Post-incident Analysis And Reporting. By Combining Advanced AI Algorithms With Efficient Alerting Mechanisms, This Surveillance System Provides Proactive Security Measures And Enhances Situational Awareness In Monitored Environments. Read more Read less

ProConnect: An AI-Powered System For Cross-Platform Professional Profile Optimization

ProConnect Is An Intelligent, User-centric Guidance System Designed To Help Individuals Create, Refine, And Maintain Their Professional Online Profiles Across Platforms Like LinkedIn, GitHub, Google Sites, And Instagram. It Offers Advanced Features S... ProConnect Is An Intelligent, User-centric Guidance System Designed To Help Individuals Create, Refine, And Maintain Their Professional Online Profiles Across Platforms Like LinkedIn, GitHub, Google Sites, And Instagram. It Offers Advanced Features Such As Personalized Templates, Real-time Suggestions, And Platform-specific Best Practices To Enable Users To Build An Optimized And Cohesive Personal Brand. With A One-time Input Mechanism, ProConnect Automates Profile Updates And Synchronization Across Platforms, Reducing Manual Effort And Ensuring Consistency. By Streamlining The Process Of Building A Strong Digital Presence, ProConnect Supports Users In Achieving Goals Such As Job Hunting, Freelancing, Portfolio Presentation, And Personal Branding, Making It A Comprehensive Solution For Career Development In The Digital Age. Read more Read less

STATIC AND DYNAMIC ANANYSIS OF COMMERCIAL BUILDING USING STAAD PRO

The Rapid Urbanization And Population Growth In Modern Cities Have Led To An Increased Demand For Commercial Buildings That Make Efficient Use Of Limited Urban Space. Traditionally, Such Buildings Have Been Constructed Using Materials Like Reinforced... The Rapid Urbanization And Population Growth In Modern Cities Have Led To An Increased Demand For Commercial Buildings That Make Efficient Use Of Limited Urban Space. Traditionally, Such Buildings Have Been Constructed Using Materials Like Reinforced Concrete, Known For Their Proven Performance And Reliability. The Discipline Of Structural Design Is Both An Art And A Science, Focused On Creating Structures That Are Economical, Elegant, Safe, Serviceable, And Durable. Beyond Creativity And Innovation, The Entire Process Of Structural Planning And Design Requires A Strong Foundation In Structural Engineering Principles, As Well As Practical Knowledge Of Relevant Design Codes And Standards. This Project Involves The Design Of A Multi-story Commercial Building.In Civil Engineering, A Building Is Defined As A Structure Composed Of Various Components, Including Foundations, Walls, Columns, Floors, Roofs, Doors, Windows, Ventilators, Staircases, And Different Types Of Surface Finishes. These Components Are Designed Through Structural Analysis To Ensure The Structure Can Safely Withstand All Anticipated Loads Throughout Its Intended Lifespan Without Failure. To Facilitate This Process, Engineers Commonly Use STAAD.Pro, A Widely Utilized Structural Analysis And Design Software Developed By Bentley Systems. It Is Especially Popular Among Civil And Structural Engineers For Designing And Analyzing A Wide Range Of Structural Systems. Additionally, AutoCAD Is A Widely Used Commercial Computer-aided Design (CAD) Software Known For Its Capabilities In Drafting And Creating Detailed Construction Drawings. STAAD.Pro, On The Other Hand, Is Commonly Utilized By Civil And Structural Engineers For The Analysis And Design Of A Wide Range Of Structural Systems. For Drafting And Creating Detailed Construction Drawings, AutoCAD Remains One Of The Leading Commercial Computer-aided Design (CAD) Software Tools. In Construction Projects, Architects Incorporate Structural Design Considerations—such As Safety, Serviceability, Durability, And Cost-effectiveness—while Also Ensuring The Building Meets Functional Needs And Aesthetic Goals.This Project Involves The Comprehensive Planning, Analysis, Design, And Preparation Of Drawings For A Multi-story Building. Specifically, The Focus Of This Project Is The Planning, Analysis, And Structural Design Of A G+5 (Ground Plus Five Floors) Commercial Building. It Encompasses The Development Of Structural Drawings And Considers Multiple Load Cases And Load Combinations During The Analysis Phase. The Building's Structural System Is Composed Of Reinforced Cement Concrete (R.C.C.), And The Design Is Performed Using The Limit State Method To Ensure Compliance With Safety, Durability, And Serviceability Requirements. The Study Integrates Various Software Applications And Engineering Techniques, With AutoCAD Used For Drafting And STAAD.Pro V8i Applied For Structural Analysis And Design.Both Static And Dynamic Analyses Are Conducted Using STAAD.Pro, Following The Design Parameters Outlined In IS 1893:2016 (Part 1) For Seismic Zone III. The Results Obtained From The Post-processing Phase Are Evaluated, Interpreted, And Summarized In This Report. Read more Read less

ANALYSIS AND DESIGN OF PRECAST CONCRETE PIER SYSTEMS FOR RAPID CONSTRUCTION OF BRIDGES IN SEISMIC ZONE-V

Precast Piers In Bridge Construction Represent A Modern And Efficient Solution, Offering Advantages In Terms Of Construction Speed, Structural Reliability, Cost-effectiveness, And Reduced Site Disruption. Recent Advancements In Seismic-resisting Syst... Precast Piers In Bridge Construction Represent A Modern And Efficient Solution, Offering Advantages In Terms Of Construction Speed, Structural Reliability, Cost-effectiveness, And Reduced Site Disruption. Recent Advancements In Seismic-resisting Systems For Precast Reinforced Concrete Bridge Piers Highlight Their Growing Relevance In Sustainable Urban Environments Exposed To Moderate To High Seismic Activity. The Increasing Demand For Rapid Bridge Construction—driven By Aging Infrastructure And Rising Traffic Volumes—has Accelerated The Adoption Of Precast Systems As Viable Alternatives To Traditional Cast-in-place Methods. Precast Components Offer Several Benefits, Including Faster Construction, Improved Quality Control, Enhanced Worker Safety, Reduced Environmental Disruption, And Lower Life-cycle Costs.A Key Focus In The Literature Is The Detailing Of Connections, Which Is Critical To Achieving Seismic Resilience. The Incorporation Of Mild Steel Deformed Bars To Connect Precast Elements Has Demonstrated Effectiveness In Enabling Energy Dissipation And Ductile Performance Under Lateral Seismic Loads. Advanced Nonlinear Finite Element Analyses Have Been Widely Used To Assess Displacement Capacities, Damage Patterns, And Global Seismic Response Of Precast Piers.Despite This Progress, There Remains A Need For Practical, Design-oriented Approaches That Accurately Estimate Seismic Displacement Demands, Particularly Methods Incorporating Cracked-section Behaviour And Base-shear Strength Ratios. This Review Identifies Ongoing Research Gaps And Underscores The Importance Of Developing Simplified Analytical Tools And Parametric Models To Facilitate Efficient Seismic Design And Performance Evaluation Of Precast Concrete Bridge Pier Systems Read more Read less

E VOTING SYSTEM BASED ON BLOCKCHAIN AND FACE RECOGNITION SYSTEM

Ensuring Secure, Transparent, And Tamper-proof Elections Is Critical In Modern Democracies. This Paper Proposes A Novel E-voting System That Combines Blockchain Technology And Face Recognition-based Biometric Authentication To Build A Robust And Trus... Ensuring Secure, Transparent, And Tamper-proof Elections Is Critical In Modern Democracies. This Paper Proposes A Novel E-voting System That Combines Blockchain Technology And Face Recognition-based Biometric Authentication To Build A Robust And Trustworthy Voting Platform. Blockchain Ensures Vote Immutability And Transparency, While Facial Recognition Guarantees One-person-one-vote Through Real-time Verification. The System Is Implemented Using Python Flask, Integrating Face_recognition For Biometric Checks And Blockchain Ledger For Secure Vote Recording. This Architecture Enhances Voter Trust, Mitigates Fraud, And Supports Digital Transformation In Electoral Processes. Read more Read less

UV Sterilization Robot Using Automated UV Technology

In Response To The Growing Need For Efficient And Effective Disinfection Solutions, This Paper Presents The Design And Development Of An Automated UV Sterilization Robot. Utilizing Advanced Ultraviolet (UV-C) Technology, The Robot Is Designed To Auto... In Response To The Growing Need For Efficient And Effective Disinfection Solutions, This Paper Presents The Design And Development Of An Automated UV Sterilization Robot. Utilizing Advanced Ultraviolet (UV-C) Technology, The Robot Is Designed To Autonomously Navigate Indoor Environments, Ensuring Comprehensive Surface And Air Sterilization. UV-C Light Is Known For Its Germicidal Properties, Effectively Inactivating A Broad Spectrum Of Pathogens, Including Bacteria, Viruses, And Fungi. The Proposed System Integrates Intelligent Navigation Using Sensors And Machine Learning Algorithms To Map And Maneuver Complex Layouts While Avoiding Obstacles. It Features A Programmable Disinfection Schedule, Adjustable UV Intensity, And Safety Mechanisms, Including Motion Detectors That Deactivate UV Lamps In The Presence Of Humans To Prevent Harmful Exposure. This Automated UV Technology Aims To Enhance Sanitation Standards In High- Risk Areas Such As Hospitals, Laboratories, Offices, And Public Spaces. By Reducing Human Involvement And Error In The Disinfection Process, The Robot Ensures Consistent And Thorough Sterilization. The Results From Prototype Testing Demonstrate Significant Reductions In Microbial Load, Highlighting The Effectiveness And Reliability Of This Innovative Disinfection Approach. This Paper Concludes By Discussing Future Enhancements, Including Improved Energy Efficiency, Integration With IoT Systems For Remote Monitoring, And Potential Applications In Other High-traffic Environments. Read more Read less

Awareness About Resources And Support For Victims Of Domestic Violence And Extent Of Help-Seeking Behavior Among Women Of Different Socioeconomic Strata In Chennai

Objectives: To Assess Awareness About Resources And Help-seeking Behavior Among Women From Different Socioeconomic Strata In Chennai. Methods: A Cross-sectional Study Was Conducted From August To September 2022 Among 100 Married Women In Chennai,... Objectives: To Assess Awareness About Resources And Help-seeking Behavior Among Women From Different Socioeconomic Strata In Chennai. Methods: A Cross-sectional Study Was Conducted From August To September 2022 Among 100 Married Women In Chennai, Using A Structured Questionnaire. Socioeconomic Status Was Assessed Using The Modified Kuppuswamy Scale. Associations Were Analyzed Using Chi-square Tests And T-tests; P<0.05 Was Considered Significant. Results: 19.61% Of Participants Experienced Some Form Of Domestic Violence (DV). Awareness Of Helplines And Support Groups Was Significantly Associated With DV Reporting. Women Who Had Experienced DV Were Slightly Older And Had Lower Education And Occupation Scores. Lower SES Was Not Significantly Associated With DV. Conclusion: A Considerable Proportion Of Women, Particularly In Lower Socioeconomic Groups, Are Unaware Of Existing Support Systems. Improving Outreach And Accessibility Of These Resources Is Crucial. Read more Read less

PERSONALIZE DISEASE DIAGNOSIS AND HEALTH GUIDENCE

Access To Timely And Reliable Healthcare Services Remains A Challenge, Especially In Regions With Limited Medical Infrastructure And Linguistic Diversity. Digital Healthcare Tools, Such As Chatbot’s, Offer Promising Solutions By Providing Round-the-c... Access To Timely And Reliable Healthcare Services Remains A Challenge, Especially In Regions With Limited Medical Infrastructure And Linguistic Diversity. Digital Healthcare Tools, Such As Chatbot’s, Offer Promising Solutions By Providing Round-the-clock Assistance And Facilitating Access To Essential Medical Information. In This Context, We Present A Multilingual Medical Chatbot Designed To Bridge Communication Gaps And Deliver Healthcare Support To Users In Both English And Kannada. The Chatbot Incorporates Artificial Intelligence Through A CNN-based Model For Skin Disease Diagnosis And Integrates Features Such As Emergency Response, Voice-based Interaction, And Blood Donation Assistance. By Using Modern APIs Like Gemini, It Further Enriches The User Experience With Accurate And Location-specific Medical Information. This Project Demonstrates How Intelligent, Accessible, And Language-aware Systems Can Support Inclusive Healthcare Delivery In Underserved Communities. Read more Read less

Animal Detection Based Smart Farming In Animal Repellent Using AI And Deep Learning

Agriterrorism With Regard To Animal Damage Greatly Affects The Crop Yield For Farmers, Resulting To Some Of Them Recording Large Losses. Farm Animals Like Buffaloes, Cows, Goats And Birds Trespass In The Fields Trample The Crops And This Can Only Be... Agriterrorism With Regard To Animal Damage Greatly Affects The Crop Yield For Farmers, Resulting To Some Of Them Recording Large Losses. Farm Animals Like Buffaloes, Cows, Goats And Birds Trespass In The Fields Trample The Crops And This Can Only Be Destructive For Farmers Since They Cannot Constantly Protect Their Shambas. Measures Such As The Use Of Barriers, Wire Fences, Or Personnel Vigilance Yield Most Of The Time Insufficient Results. In Addition To Scarecrows, Which Enemies Can Easily Bypass With Many Animals, Farmers Also Employ Human Effigies.To Control These Problems, We Introduce An AI-based Scarecrow System Using Video Processing In Real- Time For Crop Protection From Wildlife. The System Uses A Camera To Record Videos And Analyzes Them With YOLOv3, An Object Detection Model Together With OpenCV And The COCO Names Database. If Any Animal Or Bird Is Identified, Then The System Produces A Sound Alerting The Animal Not To Invade The Compound. Moreover, If An Animal Has Been Sensed For More Than One Minute Consecutively, The System Will Alert The Farmer Sending Him/her An E-mail And Dialing The Farmer`s Phone Number. This Approach Thus Provides An Efficient And Automated Way Of Protecting Crops Than Depending On Deterrent Measures.. Read more Read less

Comprehensive Restaurant And Food Supply Chain Solutions For Efficient Operations And Sustainability

The "Comprehensive Restaurant And Food Supply Chain Solutions" Project Is Designed To Make The Food Supply Chain For Restaurants Better And Easier To Manage. It Puts A Big Focus On Sustainability, Doing Things Efficiently, And Keeping Management Safe... The "Comprehensive Restaurant And Food Supply Chain Solutions" Project Is Designed To Make The Food Supply Chain For Restaurants Better And Easier To Manage. It Puts A Big Focus On Sustainability, Doing Things Efficiently, And Keeping Management Safe. At The Heart Of The Project Is A Main Hub Where Key People Have An Important Job. They Oversee And Approve Big Decisions To Ensure Everything Follows The Project’s Strict Rules And Sustainability Goals. This Main Hub Takes Care Of Food Stocks Well. It Includes Looking At Detailed Purchasing Reports And Checking Sustainability Measures To Support Good Sourcing Practices. Restaurants That Join The Project Sign Up For A System. They Get Access To An Easy-to-use Platform To Work With The Supply Chain. Through This Platform, They Can Look At Many Food Items, Pick What They Want, And Check Their Choices. The System Automatically Figures Out The Total Cost Based On Current Prices And Amounts, Making It Clear And Easy To Buy. The Project Also Uses A Special Algorithm Called Random Forest Classifier. This Algorithm Gives Insights Into How Purchases Affect The Environment, Helping With Sustainability Goals. Delivery Schedules Are Planned Carefully Using Sustainability Reports. This Good Coordination Ensures The Whole Supply Chain Works Smoothly And Responsibly. It Starts With Managing Inventory And Ends With Delivery, Keeping The Project’s Promise To Be Sustainable And Efficient. Read more Read less

IOT Based Smart Hydroponic System

Agriculture Plays An Important Role In The Socioeconomic Progress Of Several Countries (e.g., India), But It Is Also Associated With Many Other Issues (e.g., Mowing, Fertilizers, Pesticides And Chemicals Used In Agriculture). At Present It Is Necessa... Agriculture Plays An Important Role In The Socioeconomic Progress Of Several Countries (e.g., India), But It Is Also Associated With Many Other Issues (e.g., Mowing, Fertilizers, Pesticides And Chemicals Used In Agriculture). At Present It Is Necessary To Eat Wholesome Food And To Perform Normally Without Any Application Of These Agrochemicals. Urban Farming Has Been Hailed As A Route To A Better Way Of Life, But Becomes Impractical When There Is Not The Space To Build A Conventional Garden Bed Of Earth. To Solve This Issue, Soil-less Farming (hydroponic Farming, I.e., Growing Without Soil) Has Been Introduced. The So-developed Hydroponic System Needs Soil-less, Low Nutrition, And Less Space. This System Allows Faster Plant Growth, Significantly Higher Yields With The Top Quality. In Hydroponic Systems, A Variety Of Parameters Including PH And Other Factors Require, And Are Controlled, Monitoring. In This Paper, In The Context Of The Yields And The Plant Development, A Practical Scheme Is Proposed That Can Grow The Plant With Maximum Effectiveness And Minimum Water Requirement And Minimum Fertilizer Requirement Using The Internet Of Things (IOT) Technology. In A Developing Country Like India, Where Agriculture Is The Backbone Of The Country, Agriculture Is Plagued By Several Problems Like Small And Fragmented Land Holdings, Manures, Pesticides, Chemicals Used For Agriculture Etc. Consumers Also Increasingly Demand For The Healthy Diet That Is Rich In Quality And Free Of Agricultural Chemicals And Pesticides. Smart Hydroponics Farming Is A Modern Technique For Growing Plants In Nutrient-rich Water Rather Than Soil. In This Technique We Ensure That Plant Gets All Nutrients From The Water Solution. Read more Read less

DIABETES PREDICTION USING MACHINE LEARNING ALGORITHM

Diabetes Mellitus Is Among Critical Diseases And Lots Of People Are Suffering From This Disease. Age, Obesity, Lack Of Exercise, Hereditary Diabetes, Living Style, Bad Diet, High Blood Pressure, Etc. Can Cause Diabetes Mellitus. People Having Diabete... Diabetes Mellitus Is Among Critical Diseases And Lots Of People Are Suffering From This Disease. Age, Obesity, Lack Of Exercise, Hereditary Diabetes, Living Style, Bad Diet, High Blood Pressure, Etc. Can Cause Diabetes Mellitus. People Having Diabetes Have High Risk Of Diseases Like Heart Disease, Kidney Disease, Stroke, Eye Problem, Nerve Damage, Etc. Current Practice In Hospital Is To Collect Required Information For Diabetes Diagnosis Through Various Tests And Appropriate Treatment Is Provided Based On Diagnosis. Big Data Analytics Plays A Significant Role In Healthcare Industries. Healthcare Industries Have Large Volume Databases. Using Big Data Analytics One Can Study Huge Datasets And Find Hidden Information, Hidden Patterns To Discover Knowledge From The Data And Predict Outcomes Accordingly. In Existing Method, The Classification And Prediction Accuracy Are Not So High. In This Paper, We Have Proposed A Diabetes Prediction Model For Better Classification Of Diabetes Which Includes Few External Factors Responsible For Diabetes Along With Regular Factors Like Glucose, BMI, Age, Insulin, Etc. Classification Accuracy Is Boosted With New Dataset Compared To Existing Dataset. Further With Imposed A Pipeline Model For Diabetes Prediction And Deployment Done And Towards Improving The Accuracy Of Classification. Read more Read less

Flight Delays Prediction

Flight Delay Prediction Remains A Significant Challenge In Modern Air Transportation Management. This Paper Presents A Comparative Analysis Of Machine Learning Approaches—including Classification-based, Ensemble-based, And Hybrid Predictive Models—fo... Flight Delay Prediction Remains A Significant Challenge In Modern Air Transportation Management. This Paper Presents A Comparative Analysis Of Machine Learning Approaches—including Classification-based, Ensemble-based, And Hybrid Predictive Models—for Forecasting Flight Delays. The System Integrates Data Preprocessing, Feature Selection, And Model Training Using Algorithms Such As Random Forest, K-Nearest Neighbors (KNN), Naive Bayes, And Logistic Regression. Key Flight Attributes Are Extracted From Historical Datasets And Refined Through Feature Engineering. Delay Prediction Is Further Enhanced By Applying Cluster Sampling Techniques For Balanced Data Representation. Experimental Evaluation Using U.S. Domestic Flight Data Revealed That The Random Forest-based Hybrid Approach Achieved The Highest Predictive Accuracy Of 89.3%, Slightly Outperforming Classification-only Models (KNN: 86.7%, Logistic Regression: 85.2%). Read more Read less

CAR PRICE PREDICTOR: UNLOCKING INSIGHTS FOR USED CAR BUYERS AND SELLER

The Automotive Industry Is Witnessing A Paradigm Shift With The Increasing Demand For Used Cars. As Consumers Explore Cost-effective And Sustainable Transportation Options, The Valuation Of Used Cars Becomes A Critical Aspect Of The Buying And Sellin... The Automotive Industry Is Witnessing A Paradigm Shift With The Increasing Demand For Used Cars. As Consumers Explore Cost-effective And Sustainable Transportation Options, The Valuation Of Used Cars Becomes A Critical Aspect Of The Buying And Selling Process. This Research Presents A Comprehensive Study On Predicting Used Car Prices Through The Application Of Machine Learning Algorithms. Our Approach Involves Collecting And Analyzing Various Parameters Such As Mileage And Other Relevant Features That Influence The Pricing Dynamics Of Used Cars. Leveraging A Diverse Dataset Encompassing A Wide Range Of Cars, Our Machine Learning Models Aim To Learn The Intricate Relationships Between These Parameters And The Market Value Of Used Cars. Feature Engineering Techniques Are Applied To Enhance The Model's Ability To Capture Nuanced Patterns Within The Data. The Dataset Is Meticulously Preprocessed To Handle Outliers, Missing Values, And Categorical Variables, Ensuring The Robustness Of The Predictive Models. The Developed Predictive Models, Empowered By Machine Learning, Serve As Valuable Tools For Both Buyers And Sellers In The Used Car Market. By Providing Accurate And Data-driven Estimates Of Car Prices, Our Approach Contributes To Transparency, Efficiency, And Informed Decision-making In The Dynamic Landscape Of Used Car Transactions. Read more Read less

YOUTUBE VIDEO SUMMARIZER USING MACHINE LEARNING

The Exponential Growth Of Video Content Platforms Like YouTube Pose Challenges For Users In Accessing And Consuming Relevant Content Efficiently. To Tackle This Issue, The Proposed System Applies Machine Learning And Natural Language Processing Metho... The Exponential Growth Of Video Content Platforms Like YouTube Pose Challenges For Users In Accessing And Consuming Relevant Content Efficiently. To Tackle This Issue, The Proposed System Applies Machine Learning And Natural Language Processing Methods To Generate Concise Summaries Of YouTube Videos. By Employing Automated Speech Recognition, Punctuation Restoration, Keyword Extraction, And Summarization Techniques, The System Delivers Quick And Meaningful Insights, Saving User Time And Enhancing Accessibility. The Project Integrates Tools Such As Speech Recognition, YAKE, SpaCy, And Deep Multilingual Punctuation To Process Video And Audio Streams, Providing A User-friendly Interface Built Using Flask. This System Offers A Novel Method For Video Summarization, Which Can Be Highly Beneficial In Fields Such As Education, Research, And Media Analysis. Read more Read less

Automated Fault Detection In Three-Phase Transmission Lines Using Raspberry Pi And Arduino Nano

This Project Aims To Develop Automated Fault Detection And Protective System For A Three-phase Transmission Line Using Raspberry Pi And Arduino Nano. The Arduino Monitors Each Phase Using Current Sensors To Detect Various Faults Such As Overcurrent,... This Project Aims To Develop Automated Fault Detection And Protective System For A Three-phase Transmission Line Using Raspberry Pi And Arduino Nano. The Arduino Monitors Each Phase Using Current Sensors To Detect Various Faults Such As Overcurrent, Single-line-to-ground, Line-to-line,double Line-to-ground And Three-phase Faults. Sensor Data Is Transmitted To The Raspberry Pi, Which Processes The Information And Uploads It To An IoT Platform For Remote Monitoring And Analysis. When A Fault Is Detected, Relays Are Triggered To Disconnect The Affected Phase, While LEDs Indicate The Fault Status Locally,buzzer Is Activated To Give An Immediate Audible Alert And An LCD Displays Real-time System Information. A GSM Module Sends Instant SMS Alerts To Maintenance Personnel AndAdditionally, A GPS Module Provides The Exact Location Of The Fault To Speed Up The Maintenanceresponse. This Integrated Setup Ensures Fast Fault Detection, Accurate Reporting, And Reduced Downtime In Power Transmissionsystems. Enhancing System Reliability, Overall Safety And Efficiency Of Three Phase Transmission Infrastructure. Read more Read less

Deep Learning Approach For Brain Tumor Classification, Segmentation And Detection

Brain Tumor Detection Remains A Critical Challenge In Medical Diagnostics. This Paper Presents A Comparative Analysis Of Classification-based, Segmentation-based, And Hybrid Deep Learning Approaches For Brain Tumor Diagnosis. The System Employs Image... Brain Tumor Detection Remains A Critical Challenge In Medical Diagnostics. This Paper Presents A Comparative Analysis Of Classification-based, Segmentation-based, And Hybrid Deep Learning Approaches For Brain Tumor Diagnosis. The System Employs Image Processing And Convolutional Neural Networks (CNNs), Particularly The VGG16 Model, To Extract And Classify Features From MRI Scans. Tumor Stages And Regions Are Identified Using Segmentation Techniques, While Tumor Types Are Classified Using Support Vector Machines (SVM). Experimental Validation Using MRI Data From 70 Participants Revealed That The Hybrid Approach Achieved The Highest Balanced Accuracy Of 87.7%, Slightly Outperforming Classification-only (87.1%). Read more Read less

Smart Dustbin With Integrated Bomb Detection System

The Rapid Urbanization And Increasing Population In Metropolitan Cities Have Intensified The Challenges Of Waste Management. Traditional Waste Disposal Systems Often Fail To Address The Critical Issues Of Efficiency, Segregation, And Monitoring. The... The Rapid Urbanization And Increasing Population In Metropolitan Cities Have Intensified The Challenges Of Waste Management. Traditional Waste Disposal Systems Often Fail To Address The Critical Issues Of Efficiency, Segregation, And Monitoring. The Proposed Smart Dustbin Project Aims To Integrate Advanced Technologies To Create A Sustainable And Efficient Waste Management Solution Tailored For High-traffic Areas Like Metro Stations, Railway Stations, Etc. This Smart Dustbin Incorporates A NodeMCU Microcontroller For IoT Integration, A Servo Motor, RFID Reader, GSM Module, IR Sensor, And A Metal Detection System Based On Conductivity. The System Works As Follows: When A User Approaches, They Authenticate Using An RFID Card. Waste Is Then Placed On A Plate Attached To A Servo Motor. The System Detects Whether The Waste Is Metallic Or Non-metallic. Non-metallic Waste Is Deposited Into The Bin By Rotating The Servo Motor, While Metallic Waste Triggers An Alert Sent To The Authorities Via GSM, Ensuring Proper Disposal Or Recycling. Additionally, An IR Sensor Monitors The Fill Level Of The Dustbin, Sending An Alert When The Bin Is Full. This Innovative Design Not Only Automates Waste Segregation But Also Enhances Operational Efficiency By Notifying Authorities About Critical Events Such As Bin Overflow Or Inappropriate Waste Disposal. By Integrating IoT With Efficient Waste Segregation Mechanisms, This Project Aims To Revolutionize Waste Management In Metro Stations, Promoting Environmental Sustainability And Hygiene Read more Read less

Intelligent Fault Diagnosis In Cascaded Multilevel Inverters Using Multiscale Kernel CNN

This Paper Proposes An Intelligent Fault Diagnosis Method For Cascaded Multilevel Inverters Using A Multiscale Kernel Convolutional Neural Network (CNN). The Approach Leverages The Ability Of CNNs To Extract Features From Signals And Diagnose Faults... This Paper Proposes An Intelligent Fault Diagnosis Method For Cascaded Multilevel Inverters Using A Multiscale Kernel Convolutional Neural Network (CNN). The Approach Leverages The Ability Of CNNs To Extract Features From Signals And Diagnose Faults In Inverters. By Utilizing Multiscale Kernels, The Method Can Effectively Capture Fault Characteristics At Different Scales, Enhancing Diagnosis Accuracy. The Proposed Method Is Validated Through Experiments, Demonstrating Its Effectiveness In Detecting And Classifying Faults In Cascaded Multilevel Inverters. Read more Read less

Vehicle Based Driver Drowsiness Detection By Support Vector Machines

Driver Drowsiness Remains A Major Cause Of Road Accidents. This Paper Presents A Comparative Analysis Of Indirect Driver Monitoring Systems (DMS) Using Vehicle-based Features, Direct DMS Using Driver-based Facial Behavior, And A Hybrid Approach Combi... Driver Drowsiness Remains A Major Cause Of Road Accidents. This Paper Presents A Comparative Analysis Of Indirect Driver Monitoring Systems (DMS) Using Vehicle-based Features, Direct DMS Using Driver-based Facial Behavior, And A Hybrid Approach Combining Both. The System Employs Image Processing And Convolutional Neural Networks (CNNs) To Detect Facial Landmarks Like Eye Aspect Ratio And Mouth Opening, And Classifies Drowsiness States Using Support Vector Machines (SVM). Experimental Validation Using A Dataset From 70 Participants Revealed That The Hybrid DMS Yielded The Highest Balanced Accuracy Of 87.7%, Slightly Outperforming Direct DMS (87.1%) And Significantly Outperforming Indirect DMS (77.9%) Read more Read less

Smart Safety Helmet For Coal Miners Using IOT

The Smart Safety Helmet For Coal Miners Is To Enhance The Safety Of Miners In Hazardous Environments Like Coal Mining With The Help Of IoT And Sensor Technologies. The Helmet Includes Different Parts Such As Arduino And NodeMCU Microcontrollers, Air... The Smart Safety Helmet For Coal Miners Is To Enhance The Safety Of Miners In Hazardous Environments Like Coal Mining With The Help Of IoT And Sensor Technologies. The Helmet Includes Different Parts Such As Arduino And NodeMCU Microcontrollers, Air Quality, Carbon Monoxide, Temperature, And Humidity Sensors, A GPS Module, And An SOS Panic Button. The System Keeps A Regular Check On The Environment And Feeds Real Time Data To The ThingSpeak Cloud Platform Which Can Be Monitored Remotely By Supervisors. This Way, Preventive Safety Measures And Emergency Response Can Be Taken. The Helmet Is Designed To Withstand The Severe Conditions Of An Underground Mining Operation. Future Improvements Might Be AI Enabled Hazard Prediction, Biometric Health Monitoring And Better Connectivity To Increase The Safety Of The Miners And The Productivity Of The Operations. This Solution Shows How IoT Can Define A New Standard Of Workplace Safety In High-risk Industries. Read more Read less

A Study On Strategic Workforce Planning

This Study Has Been Enriched In TTK HEALTHCARE (INDIA) LIMITED To Ensure Effective STRATEGIC WORKFORCE PLANNING Of The Employees. Strategy Workforce Planning With The Deepening Integration Of The Global Economy And The Increasing Intensity Of Mar... This Study Has Been Enriched In TTK HEALTHCARE (INDIA) LIMITED To Ensure Effective STRATEGIC WORKFORCE PLANNING Of The Employees. Strategy Workforce Planning With The Deepening Integration Of The Global Economy And The Increasing Intensity Of Market Competition, The Implementation Of Corporate Workforce Strategy Has Become A Crucial Factor Determining The Survival And Development Of Enterprises. In This Process, Human Resources, As One Of The Most Important Resources For Enterprises, Play A Profound Role In The Workforce Planning And Management Of The Implementation Of Workforce Strategy. This Paper Aims To Explore The Role Of Workforce Planning In The Implementation Of Corporate Workforce Strategy And Operational Through Relevant Research. The Goal Is To Provide Theoretical Support And Practical Guidance For Enterprises To Formulate Effective Human Resource Workforce Planning, Thereby Promoting The Successful Implementation Of Corporate Workforce Strategy . This Not Only Contributes To Enhancing The Competitiveness Of Enterprises But Also Facilitates The Development Of Theories And Practices In Human Resource Management And The Purpose Of Strategic Workforce Planning Is To Help Organizations Achieve Their Goals And Objectives Effectively By Aligning Human Resources, Capabilities, And Actions With External Opportunities And Challenges. It Involves Short Term And Long-term Workforce Planning, Decision Making, And Implementation To Create A Competitive Advantage And Ensure Sustainability. The Key Purposes Of Strategic Workforce Planning. The Objective Of The Study Includes, A Study On Strategic Workforce Planning Of The Employees In TTK HEALTHCARE (INDIA) LIMITED And To Correlate Employee With Strategic Workforce Planning Of Organizational Performance, Exploring Relationship Between Strategic Workforce Planning And Organisation Opportunities Within The Identifying Of Internal And External Factor As Been Potential Areas For Improvement In The Strategic Workforce Planning Process. The Research Design Used For The Study Was Descriptive Research Design. The Descriptive Research Means The Research Which Is Done To Know The Current Situation Of The Study. The Data Has Been Collected Using Questionnaire. The Sample Taken For This Study Was 207 Out Of Population 500 At TTK HEALTHCARE (INDIA) LIMITED. The Type Of Sampling Technique Used For The Study Was Stratified Sampling. Iv The Sample Technique Used Were Descriptive Research Method And Various Statistical Tools Like ANOVA, Correlation, Regression, Chi-Square Were Used To Test The Strategic Human Resource Planning In The Organization. And From The Study It Is Highlighted That Many Respondents Were Not Aware About Strategy Workforce Planning And Finding Systematic Analysis, Forecasting And Strategic Workforce Decision Making In Their Organization And It Must Be Mentored So That Individual And Organizational Goals Can Be Achieved Effectively In The Organisation. Read more Read less

Securing ATM Transaction With Facial Recognition -Based On Verification System

ATM Fraud And Unauthorized Transactions Pose Significant Security Challenges In The Banking Sector. Traditional Authentication Methods Such As PINs And Cards Are Vulnerable To Theft, Skimming, And Phishing Attacks. This Paper Proposes An Innovative S... ATM Fraud And Unauthorized Transactions Pose Significant Security Challenges In The Banking Sector. Traditional Authentication Methods Such As PINs And Cards Are Vulnerable To Theft, Skimming, And Phishing Attacks. This Paper Proposes An Innovative Solution That Integrates Facial Recognition Technology With ATM Transactions To Enhance Security And User Convenience. By Leveraging Advanced Artificial Intelligence, Specifically Convolutional Neural Networks (CNNs) For Facial Verification, The System Ensures That Only Authorized Users Can Access Their Accounts. The Proposed Solution Captures Real-time Facial Images, Compares Them With Pre-registered Biometric Data, And Grants Transaction Access Only Upon Successful Verification. This Approach Not Only Mitigates The Risks Associated With Stolen Credentials But Also Provides A Seamless And Contactless Authentication Experience. Experimental Results Demonstrate High Accuracy And Robustness Under Varying Lighting Conditions And Facial Expressions. The System’s Real-time Processing Capability Ensures Quick And Secure Transactions, Making It A Practical And Reliable Alternative To Traditional Methods. By Combining Security And Usability, This Solution Addresses Critical Gaps In ATM Transaction Safety, Offering A Scalable And Future-proof Framework For Financial Institutions. Read more Read less

A Study On Evaluating The Impact Of HR Policies On Employee Performance

This Investigation Aims To Assess The Influence Of Various Kinds Of Human Resouce Policies On The Performance Of Employees In The Organized Retail Sector.The Research Identifies Critical Factors Affecting Productivity And Effectiveness By Examining... This Investigation Aims To Assess The Influence Of Various Kinds Of Human Resouce Policies On The Performance Of Employees In The Organized Retail Sector.The Research Identifies Critical Factors Affecting Productivity And Effectiveness By Examining The Relationship Between HR Policies And Employee Performance. Read more Read less

Formulation And Characterization Of Cyproheptadine Hydrochloride Syrup For Teenagers Use

In Order To Manage Malnutrition, Weight Loss, And Associated Health Conditions, Appetite Stimulants Are Essential. This Study Examines A Range Of Hunger Stimulants, Such As Natural Substances, Dietary Supplements, And Medications. We Go Over Their Ef... In Order To Manage Malnutrition, Weight Loss, And Associated Health Conditions, Appetite Stimulants Are Essential. This Study Examines A Range Of Hunger Stimulants, Such As Natural Substances, Dietary Supplements, And Medications. We Go Over Their Effectiveness, Modes Of Action, And Possible Uses In Medical Situations. We Also Look At The Advantages And Disadvantages Of Various Stimulants, Emphasizing How They May Enhance Patient Outcomes And Quality Of Life. Healthcare Providers Can Create Focused Therapies To Help People With Impaired Nutritional Status By Comprehending The Function Of Appetite Stimulants. Types Of Appetite Stimulants: 1. Pharmaceuticals: Megestrol Acetate, Dronabinol, And Mirtazapine Are Among The Medications Used To Increase Appetite In Individuals Suffering From Depression, Cancer, And HIV/AIDS. 2. Nutritional Supplements: Supplements That Include Vitamins, Minerals, And Protein Can Help Promote Nutrition And Hunger. 3. Natural Chemicals: Studies Have Demonstrated That Some Herbs And Chemicals, Such As Cannabis, Ginger, And Turmeric, Increase Hunger. Read more Read less

Emerging Trends In Transdermal Therapy: The Role Of Ethosomes, Transfersomes, And Menthosomes

Transdermal Drug Delivery Systems Offer A Non-invasive And Patient-friendly Method For Administering Medications, Particularly For Chronic Conditions Requiring Sustained Release. Among The Various Nanocarrier-based Systems, Ethosomes, Transfersomes,... Transdermal Drug Delivery Systems Offer A Non-invasive And Patient-friendly Method For Administering Medications, Particularly For Chronic Conditions Requiring Sustained Release. Among The Various Nanocarrier-based Systems, Ethosomes, Transfersomes, And Menthosomes Have Emerged As Advanced Vesicular Carriers Due To Their Capability To Improve Drug Permeation Through Skin. Ethosomes, Developed By Touitou, Are Ethanol-based Flexible Vesicles Composed Of High Concentrations Of Alcohol, Phospholipids, And Water, Facilitating Deep Skin Penetration And Systemic Delivery. Transfersomes, Comprising Phospholipids And Edge Activators, Are Ultra-deformable Vesicles Known For Their Capability To Traverse Stratum Corneum Efficiently. Menthosomes, A Newer Class, Incorporate Menthol, Phospholipids, And Surfactants To Improve Skin Permeability And Drug Diffusion. These Carriers Are Especially Suitable For Delivering Large Molecules Like Peptides And Proteins, Offering Improved Patient Compliance And Effectiveness In Treating A Variety Of Dermatological And Systemic Conditions. However, Challenges Such As Chemical Instability, Limited Skin Adhesion, And Potential Skin Irritation Remain. Despite These Limitations, Their Enhanced Skin Permeation, Potential For Gene And Anticancer Drug Delivery, And Sustained Release Capabilities Position Them As Promising Tools In Transdermal Therapy. Further Studies Are Needed To Fully Explore The Comparative Efficacy Of These Novel Vesicular Systems. Read more Read less

OpenAI Academy: Democratizing AI Education For All

Technology, Skills, And Knowledge Are The Three Essential Tools Of The Modern Era That Enable Progress. These Elements Are Crucial In Higher Education, Research, And Business. Technology Is Widely Used Today, Efforts Are Being Made To Enhance Skills,... Technology, Skills, And Knowledge Are The Three Essential Tools Of The Modern Era That Enable Progress. These Elements Are Crucial In Higher Education, Research, And Business. Technology Is Widely Used Today, Efforts Are Being Made To Enhance Skills, And Knowledge Creation Is Actively Pursued. The Growing Use Of Computers, The Internet, The Web, And Mobile Devices Has Expanded Information And Knowledge Sources, While Artificial Intelligence (AI) Has Captivated Global Attention. AI Has Sparked Widespread Interest, With People Eager To Learn, Explore, And Develop AI Technologies. However, The Number Of Experts Capable Of Teaching And Creating AI Is Limited. Therefore, It Is Essential To Have An Accessible Platform For Learning AI, Making OpenAI Academy A Revolutionary Initiative Designed To Simplify, Interactively Engage, And Effectively Deliver AI Education. This Research Paper Critically Examines OpenAI Academy And Its Contributions To AI Learning. Research Methodology: This Study Employs A Descriptive Research Approach, Utilizing Content Analysis As The Primary Method. For This Purpose, The Website Core Has Been Used As A Key Resource. Read more Read less

Comparative Study Of Different Marketed Preparation

For The Treatment Of Acid Reflux Disease (GERD), Functional Dyspepsia, And Acid Reflux Disorders, Doctors Frequently Prescribe The Fixed-dose Combination (FDC) Of Omeprazole, A Proton Pump Inhibitor (PPI), And Dopamine D2 Receptor Antagonist, Domperi... For The Treatment Of Acid Reflux Disease (GERD), Functional Dyspepsia, And Acid Reflux Disorders, Doctors Frequently Prescribe The Fixed-dose Combination (FDC) Of Omeprazole, A Proton Pump Inhibitor (PPI), And Dopamine D2 Receptor Antagonist, Domperidone, Which Has Prokinetic And Antiemetic Effects. By Enhancing Stomach Motility And Decreasing Gastric Acid Secretion At The Same Time, This Combination Has A Synergistic Therapeutic Effect. However, Due To Differences In Formulation Design, Enteric Coating Technologies, And Manufacturing Standards, Different Marketed Formulations Of This FDC May Have Different Pharmaceutical Quality And Therapeutic Consistency. These Variations May Have A Major Effect On The Release Of The Drug, Its Bioavailability, And Eventually Its Clinical Effectiveness. Read more Read less

Impact Of Network Conditions On Video Streaming Performance In LTE: A Simulation Study With Riverbed Modeler

With The Increase In User Requirements For Internet Browsing And Streaming Videos Online, Various Technologies Have Come Forward To Provide Best Services And Solutions According To The Needs. In This Era Of Using Advanced Cell Phone Technology, Most... With The Increase In User Requirements For Internet Browsing And Streaming Videos Online, Various Technologies Have Come Forward To Provide Best Services And Solutions According To The Needs. In This Era Of Using Advanced Cell Phone Technology, Most Internet Browsing And Video Streaming Is Done Over Wi-Fi Or LTE. These Days, Users Prefer Watching Videos On Their Cell Phones While At Work Or While Traveling, And Thus, Use Technologies Mentioned Above To Stay Connected And To Stream Videos While Being Mobile Or Stationary. Due To This, The Telecommunication Industry Has Improved Services Provided To Users At A Level Where They Can Enjoy Fast Streaming For Any YouTube Video While On The Go. The Main Idea Behind Our Project Is To Analyze The Performance Of Long Term Evolution (LTE), A Wireless Network Technology Used By Network Operators Around The World Meant For High-speed Communication Between Data Terminals And Observe The Packet Transfer, Drop Rate And Delays For Using The Technology. We Will Be Using Riverbed Modeler To Simulate Various Scenarios Where Data Would Be Transferred To Stationary Or Mobile Terminals And Observe How LTE Performs. Read more Read less

Aether Watch: Self-Navigating Fire Suppression Robot With Lifeline Responder: Autonomous Human Locator & Extrication

This Project Presents An Autonomous Fire Suppression Robot Integrated With A Lifeline Responder System To Locate And Assist Trapped Humans In Fire Emergencies. The System Employs An ATmega8 Microcontroller For Navigation, ESP32-CAM For Real-time Visu... This Project Presents An Autonomous Fire Suppression Robot Integrated With A Lifeline Responder System To Locate And Assist Trapped Humans In Fire Emergencies. The System Employs An ATmega8 Microcontroller For Navigation, ESP32-CAM For Real-time Visual Feedback, Fire Sensors For Detection, And IoT For Remote Monitoring And Control. The Robot Navigates Through Hazardous Zones, Identifies Fire Locations, Extinguishes Flames, And Assists In Human Detection And Rescue Operations. Fire Emergencies Pose A Significant Threat To Human Life And Infrastructure, Often Requiring Rapid Response To Locate Trapped Individuals And Suppress Fires. This Project Proposes A Self-Navigating Fire Suppression Robot With Lifeline Responder, Which Integrates Motion And Voice Sensors, IoT-based Camera Streaming, Real-time Monitoring, Motorized Mobility, And An Automated Fire-spraying Mechanism. The Robot Autonomously Navigates Fire-affected Areas, Detects Human Presence, And Provides A Lifeline For Rescue Operations. Equipped With AI-based Object Detection And IoT Connectivity, The System Ensures Remote Monitoring And Fire Control While Assisting Emergency Responders In Real Time. Read more Read less

CRIME RATE PREDICTION INDIAN CITIES USING RANDOM FOREST CLASSIFIERS

Crime Rate Prediction Is Essential For Proactive Law Enforcement And Urban Safety Management. This Study Utilizes A Random Forest Classifier To Predict Crime Rates Across Major Indian Cities, Aiming To Identify Trends And High-risk Areas. The Random... Crime Rate Prediction Is Essential For Proactive Law Enforcement And Urban Safety Management. This Study Utilizes A Random Forest Classifier To Predict Crime Rates Across Major Indian Cities, Aiming To Identify Trends And High-risk Areas. The Random Forest Algorithm, Known For Its Robustness In Handling Complex, Non-linear Data, Was Trained On Historical Crime Data, Including Factors Like Demographics, Economic Indicators, And Previous Crime Statistics. Results Demonstrate That The Model Achieves Significant Accuracy, Highlighting Key Predictors And Enabling Better Decision-making For Authorities. This Approach Provides A Foundation For Scalable, Data-driven Crime Prevention Strategies Across Diverse Urban Environments In India. Crime Rates In Indian Cities Have Increased Significantly, Posing Threats To Public Safety And Social Stability. Effective Crime Prediction And Prevention Strategies Are Crucial To Address This Issue. This Study Aims To Develop A Predictive Model Using Random Forest Classifiers To Forecast Crime Rates In Indian Cities. Read more Read less

ANIMAL AND FIRE DETECTION AND CROP PROTECTION USING ACOUSTIC WAVES

Crop Damage From Wild Animals And Accidental Fires Presents A Major Challenge For Farmers, Causing Significant Economic Losses. To Address This Issue, The Animal And Fire Detection And Crop Protection System Using Acoustic Waves Has Been Developed To... Crop Damage From Wild Animals And Accidental Fires Presents A Major Challenge For Farmers, Causing Significant Economic Losses. To Address This Issue, The Animal And Fire Detection And Crop Protection System Using Acoustic Waves Has Been Developed To Offer An Effective, Automated Solution For Safeguarding Crops. The System Employs A NODE MCU Microcontroller, Which Serves As The Central Processing Unit, Along With A Flame Sensor, Ultrasonic Sensor, And Passive Infrared (PIR) Sensor To Detect Both Animal Intrusions And Fire Incidents In Agricultural Fields. The Sensors Work Together To Monitor And Protect Crops Efficiently. The PIR Sensor Detects The Presence Of Animals By Identifying Movement In The Vicinity Of The Crops, While The Ultrasonic Sensor Measures The Distance Between The Animals And The Crops. When An Animal Is Detected Within A Predefined Proximity, The System Activates An Acoustic Wave Generator To Emit High-frequency Sounds That Deter The Animals From Approaching, Effectively Protecting The Crops Without Causing Harm. At The Same Time, The Flame Sensor Continuously Monitors The Environment For Signs Of Fire. It Can Detect The Presence Of Flames Early, Enabling The System To Alert Farmers And Trigger Automatic Firefighting Mechanisms If Necessary, Helping To Minimize Potential Crop Damage. The NODE MCU Microcontroller Processes Data From All The Sensors In Real-time, Ensuring A Rapid Response To Any Threat Detected By The System. The System Is Cost-effective, Simple To Deploy, And Requires Minimal Human Intervention. It Is An Ideal Solution For Enhancing Crop Security, Particularly In Rural And Remote Areas Where The Risk Of Animal Damage And Accidental Fires Is High. The Animal And Fire Detection And Crop Protection System Provide A Sustainable And Reliable Approach To Protecting Crops From These Two Significant Threats. Technological Improvements In The Areas Such As Internet Of Things (IoT), Artificial Intelligence, Applications Have Become Smarter And Connected Devices Give Rise To Their Exploitation If Environment Throughout The World. Increase In Generation Of Data By These Systems, Machine Learning Techniques Can Be Applied To Further Enhance The Intelligence Andthe Capabilities. The Field Of Sustainable Environment And Renewable Energy Has Attracted Many Researchers And We Have Come Up With A New Approach In Saving Energy As Well As Predicting Wild Fires With This Approach That Uses IoT In Getting Data And Handling Data, Further Prediction Using Machine Learning. In This Review, Forest Fires Is Considered To Be An Umbrella Term That Covers Prediction Of Happening, Real-time Monitoring And Prevention/detection Of Forest Fires. These Are Rare But Very Significant Events. This Paper Describes The Development Of Machine Learning And IOT System That Enables Real-time Data Visualization Of The Factors That Help In Increase In Risk Of Forest Fire That Is Generated By The Forests. Read more Read less

Medical Information Extraction In Research Constrained Environments

In Many Healthcare Facilities Across Low-resource Regions, Digital Infrastructure Is Either Underdeveloped Or Completely Absent. As A Result, Healthcare Providers Rely Heavily On Handwritten Medical Records To Document Patient Information, Diagnoses,... In Many Healthcare Facilities Across Low-resource Regions, Digital Infrastructure Is Either Underdeveloped Or Completely Absent. As A Result, Healthcare Providers Rely Heavily On Handwritten Medical Records To Document Patient Information, Diagnoses, Treatments, And Prescriptions. While These Paper-based Records Are Essential, They Are Difficult To Manage, Prone To Human Error, And Nearly Impossible To Analyse At Scale. This Project Addresses The Challenge By Proposing An Automated System That Extracts Valuable Medical Information From Handwritten Documents With Minimal Human Involvement. The System Combines Two Advanced Technologies: Optical Character Recognition (OCR) And Named Entity Recognition (NER). OCR, Powered By Google’s Vision API, Is Used To Convert Handwritten Notes Into Machine-readable Text. Despite The Variability In Handwriting Styles, The System Achieves High Accuracy In Text Extraction—over 90% In Most Cases. After The Text Is Digitized, It Is Processed Using A Specialized SpaCy NER Model (en_ner_bc5cdr_md) Trained On Biomedical Data. This Model Effectively Identifies And Categorizes Critical Medical Entities Such As Diseases, Symptoms, And Drug Names, Helping Structure The Data For Clinical Use. Designed With The Needs Of Under-resourced Healthcare Environments In Mind, This Approach Is Lightweight, Scalable, And Can Be Integrated Into Existing Systems With Minimal Cost. It Reduces The Burden On Medical Staff, Improves The Accessibility Of Patient Data, And Lays The Groundwork For Future Enhancements Like Analytics, Reporting, And Interoperability With Electronic Health Records (EHRs). Initial Experiments On Real-world Handwritten Medical Documents Show Promising Results. The OCR Engine Handled Variations In Handwriting With Impressive Robustness, And The NER Model Achieved A Strong F1-score Of 0.88, Indicating High Precision And Recall. Overall, This Solution Has The Potential To Transform How Handwritten Medical Records Are Handled In Underserved Areas—bridging The Gap Between Analog Documentation And Digital Healthcare Systems. Read more Read less

A MULTI-DIMENSIONAL PERSPECTIVES OF CRYPTOCURRENCY AND THEIR IMPACT ON TRADITIONAL CURRENCIES

This Study Investigates The Relationship Between Cryptocurrencies And Traditional Currencies. It Take Look At The Effects Of Cryptocurrency Adoption On Traditional Currency Value And Usage. The Study Uses A Mixed-methods Approach, Combining Quantitat... This Study Investigates The Relationship Between Cryptocurrencies And Traditional Currencies. It Take Look At The Effects Of Cryptocurrency Adoption On Traditional Currency Value And Usage. The Study Uses A Mixed-methods Approach, Combining Quantitative Analysis With Qualitative Insights. The Study Finds That The Increasing Adoption Of Cryptocurrencies Is Associated With Decrease In The Value Of Traditional Currencies Particularly In Countries With Unstable Economies.The Study Also Notes That Cryptocurrencies Have The Potential To Enhance The Efficiency And Security Of Traditional Currency Transactions. The Findings Of This Study Have Important Implications For Policy Makers, Financial Institutions, And Individuals Involved In The Cryptocurrency Market. As The Use Of Cryptocurrencies Continues To Grow, It Is Essential To Understand Their Impact On Traditional Currencies And To Develop Strategies For Mitigating Potential Risks And Leveraging Potential Benefits. Read more Read less

Comparative Study Of Different Marketed Preparation

This Study’s Goal Is To Perform In-vitro Quality Control Testing On Diclofenac Sodium Tablets Using The Disintegration And Dissolving Test, Drug Assay, Weight Variation Test, And Friability Test. In The Trial, Two Brands Of Diclofenac Sodium Tablets... This Study’s Goal Is To Perform In-vitro Quality Control Testing On Diclofenac Sodium Tablets Using The Disintegration And Dissolving Test, Drug Assay, Weight Variation Test, And Friability Test. In The Trial, Two Brands Of Diclofenac Sodium Tablets—Brand A And Brand B—were Used. According To The Findings Of Quality Control (QC) Tests, Both Brand A And Brand B Of Diclofenac Sodium Tablets Meet USP Requirements. Regarding Weight Variation, Brands A And B Have Variances Of 2.79% And 2.05% Above The Mean Weight Limit, Respectively. Within The 10% USP Standard Limits, The Lower Mean Weight Limit Variances Are 1.21% And 1.27%, Respectively.According To Friability Testing, Brands A And B Had Average Friability Of 0.062% And 0.01% Mass Loss, Respectively, Falling Within The USP’s 1% Mass Loss Restrictions. Regarding Medication Assay, Brands A And B Both Fall Within The 85%–115% USP Range, Respectively. According To The Disintegration Test, Brands A And B Fall Inside A 15-minute Time Interval Segment; Their Respective Disintegration Times Are 6.69 And 7.02 Minutes. Within A 45-minute Test Period, The Medication Dissolution Percentage For Brand B Of Diclofenac Sodium Was 90.7%. The Pharmacopoeia Limits Established By The USP Standards Are Met By Brands A And B. According To The Friability Test, Both Brands A And B’s Mass Loss Fell Within The Acceptable Range Of 1%. Comparably, Both Brands Fall Within The Typical Range Of 10% Above Or Below The Mean Weight In Terms Of Weight Variation. The Medication Availability For Both Brands Fell Within The Designated 85%–115% Standard Range, According To The Drug Assay. They Completed The Dissolution And Disintegration Tests In Less Than 45 And 15 Minutes, Respectively. Read more Read less

Measuring Electrical Parameters For Motor By Using GSM Based Technology Through IOT

The Increasing Demand For Efficient And Reliable Motor Operation In Various Industrial Applications Necessitates Continuous Monitoring Of Electrical Parameters To Ensure Optimal Performance And Prevent Potential Failures. Traditional Methods Of Monit... The Increasing Demand For Efficient And Reliable Motor Operation In Various Industrial Applications Necessitates Continuous Monitoring Of Electrical Parameters To Ensure Optimal Performance And Prevent Potential Failures. Traditional Methods Of Monitoring Electrical Parameters In Motors Often Require Manual Intervention, Leading To Delays In Identifying Faults And Inefficiencies. To Address These Challenges, This Project Proposes An Innovative Solution Using GSM-based Technology Integrated With The Internet Of Things (IoT) For Real-time Remote Monitoring Of Electrical Parameters In Motors.The Increasing Demand For Efficient And Reliable Motor Operation In Various Industrial Applications Necessitates Continuous Monitoring Of Electrical Parameters To Ensure Optimal Performance And Prevent Potential Failures. Traditional Methods Of Monitoring Electrical Parameters In Motors Often Require Manual Intervention, Leading To Delays In Identifying Faults And Inefficiencies. To Address These Challenges, This Project Proposes An Innovative Solution Using GSM-based Technology Integrated With The Internet Of Things (IoT) For Real-time Remote Monitoring Of Electrical Parameters In Motors. Read more Read less

PREDDICTIVE RESIDUAL ENERGY IN BATTERIES USING MACHINE LEARNING

The Increasing Reliance On Battery-powered Systems In Renewable Energy And Electric Vehicle Applications Necessitates Accurate Estimation Of Battery Residual Energy For Efficient Power Management. This Project Presents A Smart Battery Monitoring And... The Increasing Reliance On Battery-powered Systems In Renewable Energy And Electric Vehicle Applications Necessitates Accurate Estimation Of Battery Residual Energy For Efficient Power Management. This Project Presents A Smart Battery Monitoring And Prediction System Using Machine Learning, Particularly Linear Regression, To Forecast The Remaining Energy Of A Lithium Iron Battery Pack. The Battery System Consists Of Six Lithium Iron Batteries (3.7V, 2900mAh Each), Grouped Into Three Parallel-connected Pairs, Which Are Further Arranged In Series To Create A Higher Capacity Battery Bank. A Battery Management System (BMS) Is Integrated To Ensure Safety And Manage The Charging And Discharging Operations Effectively. The Core Of This System Lies In Precise Voltage Sensing And Prediction. Three Voltage Measurement Sensors Monitor The Battery Voltage In Real Time And Feed The Data To A PIC Microcontroller. These Voltage Levels Are Displayed On An LCD For User Reference. When The Sensed Voltage Of The Battery System Drops Below 5V, A Relay Is Triggered, And The System Activates A Boost Converter To Step Up The Voltage To A Suitable Level For The Load. This Voltage Regulation Mechanism Ensures Uninterrupted Power Supply To The Load While Maintaining Battery Safety And Prolonging Its Lifespan. To Enhance The Intelligence Of The System, A Machine Learning Algorithm—linear Regression—is Employed To Predict The Residual Energy Of The Batteries Based On Historical Voltage Data And Discharge Rates. By Analyzing The Pattern Of Voltage Decline Over Time, The System Can Estimate Future Battery Levels And Provide Advance Alerts, Aiding In Decision-making For Charging Cycles. This Predictive Functionality Helps In Avoiding Unexpected Power Losses And Supports Proactive Energy Management, Especially In Critical Applications. Read more Read less

DESIGN AND IMPLEMENTATION OF IOT BASED CHARGER MONITORING AND FIRE PROTECTION FOR EV SYSTEM

Electric Vehicles (EVs) Are Gaining Popularity Day By Day In Recent Times, While They Are Also Susceptible To Causing Fires By Means Of Overheated Batteries And Electrical Faults. This Project Focuses On Developing An IoT-based Charger Monitoring And... Electric Vehicles (EVs) Are Gaining Popularity Day By Day In Recent Times, While They Are Also Susceptible To Causing Fires By Means Of Overheated Batteries And Electrical Faults. This Project Focuses On Developing An IoT-based Charger Monitoring And Protection System To Ensure Safe And Reliable EV Operations. Sensors Are Embedded To Sense Temperature Of Batteries, State Of Charge, And Charging Currents And Identify Potential Risks Of Fire. An IoT Gateway Transmits Live Data To A Cloud Server To Enable Remote Monitoring And Alerts. Upon Detection Of Any Emergency Condition, The System Is Programmed To Activate A Fire Suppression Mechanism To Prevent Any Destruction. The Proposed System Ensures EV Safety, Reduces The Possibility Of Causing Fires, And Provides A Reliable Charging Station For EV Consumers. Read more Read less

A Deep Learning Driven Approach For Automated Identification And Classification Of Defects In Printed Circuit Boards

In The Fast-paced Electronics Manufacturing Industry, Ensuring The Quality Of Printed Circuit Boards (PCBs) Is Essential For Producing Reliable And High-performance Devices. Traditional Methods For PCB Defect Detection, Such As Manual Visual Inspecti... In The Fast-paced Electronics Manufacturing Industry, Ensuring The Quality Of Printed Circuit Boards (PCBs) Is Essential For Producing Reliable And High-performance Devices. Traditional Methods For PCB Defect Detection, Such As Manual Visual Inspection And Rule-based Computer Vision, Are Often Limited By Their Inability To Detect Small, Complex, Or Subtle Defects, Leading To High False-positive Rates And Inefficiencies. This Paper Introduces A Deep Learning-driven Approach For The Automated Identification And Classification Of Defects In PCBs, Leveraging The Power Of YOLOv8, A State-of-the-art Object Detection Model. The Proposed System Is Capable Of Accurately Detecting A Wide Range Of Defects, Including Missing Holes, Mouse Bites, Open Circuits, Shorts, Spurious Copper, And Spurs, With Real-time Performance And High Precision. The Model Is Trained On A Diverse Dataset Of PCB Images, And The Detection System Is Integrated Into An Intuitive Web Application Built With Flask, Allowing Users To Easily Upload PCB Images For Instant Analysis. Experimental Results Show That The System Achieves A Mean Average Precision (mAP) Greater Than 90%, Significantly Outperforming Traditional Approaches In Both Accuracy And Inference Speed. This Approach Not Only Automates The Defect Detection Process But Also Provides A Scalable And Efficient Solution For Quality Control In PCB Manufacturing. Future Improvements To The System Will Focus On Multi-layer PCB Inspection, Incorporating Advanced Imaging Techniques Like X-ray And Infrared Imaging, As Well As The Development Of AI-driven Predictive Maintenance Features And Edge Computing For Real-time, On-device Inference. Read more Read less

WIRELESS ROAD CHARGING FOR ELECTRIC VEHICLES

Electric Vehicle (EV) Usage Is Growing, Making Smooth, Effective, And Continuous Charging Solutions Increasingly Important. Conventional Plug-in Charging Stations Frequently Force Cars To Stop For Long Periods Of Time, Which Adds To EV Owners' Range... Electric Vehicle (EV) Usage Is Growing, Making Smooth, Effective, And Continuous Charging Solutions Increasingly Important. Conventional Plug-in Charging Stations Frequently Force Cars To Stop For Long Periods Of Time, Which Adds To EV Owners' Range Anxiety And Causes Delays. These Restrictions Are Addressed With Wireless Road Charging, Sometimes Referred To As Wireless Power Transfer (WPT), Which Uses Wireless Infrastructure Installed In The Road To Allow Cars To Charge Dynamically While Driving.The Suggested Method Offers A Clever And Creative Way To Mimic Actual EV Charging Situations By Fusing Wireless Charging Technology With A Robotic Car That Is Operated Via Bluetooth. The Vehicle Charging Unit (VCU) And The Vehicle Controlling Unit (VCU) Are The Two Main Parts Of The System. The Charging Unit Transforms The Electromagnetic Field Into Usable Electric Power For Storage By Using An Inductive Coupling To Transfer Energy From Coils Buried Beneath The Road Surface To The Vehicle's Receiving Coil. Read more Read less

A Study On Supply Chain Network Optimization SKA DAIRY FOODS INDIA PRIVATE LIMITED,At Salem

A STUDY ON PROFIT MAXIMIZATION IN DECISION MAKING OF LARGE SCALE INDUSTRIES

Profit Maximization Is A Key Objective In The Decision-making Process Of Large-scale Industries, Requiring Strategic Financial Planning And Efficient Resource Allocation. Effective Capital Budgeting Techniques, Such As Net Present Value (NPV) And Int... Profit Maximization Is A Key Objective In The Decision-making Process Of Large-scale Industries, Requiring Strategic Financial Planning And Efficient Resource Allocation. Effective Capital Budgeting Techniques, Such As Net Present Value (NPV) And Internal Rate Of Return (IRR), Help Evaluate Investment Opportunities That Yield Long-term Profitability. Decision-making In Large Industries Involves Analyzing Market Trends, Cost Structures, And Risk Factors To Optimize Returns. The Integration Of Advanced Financial Models And Data-driven Approaches Enhances Investment Accuracy. Firms Often Adopt A Combination Of Traditional And Modern Techniques To Balance Risk And Profitability. Efficient Working Capital Management And Cost Control Strategies Contribute Significantly To Maximizing Profits. Automation And Technology-driven Decision-making Further Improve Operational Efficiency. Industry-specific Financial Planning Ensures Sustainable Growth And Competitive Advantage. Strategic Investments In Innovation And Infrastructure Play A Crucial Role In Long-term Profit Maximization. A Well-structured Decision-making Framework Helps Large-scale Industries Achieve Financial Stability And Business Expansion. Read more Read less

EXPERIMENTAL STUDIES ON THE ADSORPTION CHARACTERISTIES OF ROCE HUSK AND APRICOT STONES AS NATURAL ADSORBENTS FOR DYE REMOVAL

The Increasing Contamination Of Water Bodies By Synthetic Dyes Poses Severe Environmental And Health Hazards. Dye Pollution, Primarily From Textile And Dyeing Industries, Has Become A Critical Environmental Issue Due To Its Significant Impact On Wate... The Increasing Contamination Of Water Bodies By Synthetic Dyes Poses Severe Environmental And Health Hazards. Dye Pollution, Primarily From Textile And Dyeing Industries, Has Become A Critical Environmental Issue Due To Its Significant Impact On Water Bodies And Ecosystems. The Discharge Of Untreated Dye-laden Wastewater Deteriorates Water Quality, Disrupts Aquatic Life, And Introduces Toxic, Non-biodegradable Substances That Persist In The Environment. Many Synthetic Dyes Are Harmful To Humans, Causing Allergies, Organ Damage, And Even Cancer. Addressing This Problem Is Vital For Sustainable Water Management. Water Pollution Due To Industrial Effluents Containing Synthetic Dyes Is A Significant Environmental Challenge, Affecting Aquatic Ecosystems And Human Health. This Study Investigates The Adsorption Potential Of Rice Husk And Apricot Stones As Cost-effective And Sustainable Adsorbents For The Removal Of Methylene Blue (MB) Dye From Wastewater. A Systematic Series Of Batch Adsorption Experiments Were Conducted To Evaluate The Impact Of Key Parameters, Including Adsorbent Dosage, Contact Time, PH, Temperature, Initial Dye Concentration, And Agitation Speed, On Adsorption Efficiency. Experimental Results Revealed That Apricot Stones Demonstrated Superior Adsorption Capacity, Achieving A Maximum Removal Efficiency Of 95.2%, While Rice Husk Reached 92.1% Under Optimal Conditions. The Adsorption Process Followed Pseudo-second-order Kinetics, Confirming That Chemisorption Was The Dominant Mechanism. The Equilibrium Data Were Best Described By The Langmuir Isotherm Model, Indicating Monolayer Adsorption On A Homogenous Surface. Additionally, Both Adsorbents Exhibited High Regeneration Potential, Retaining Over 80% Of Their Initial Adsorption Capacity After Five Reuse Cycles, Making Them Viable For Multiple Uses In Wastewater Treatment. The Study Further Demonstrated A Significant Reduction In Key Water Quality Parameters, Including Chemical Oxygen Demand (COD), Biological Oxygen Demand (BOD), Total Dissolved Solids (TDS), And Turbidity, Confirming The Effectiveness Of These Natural Adsorbents In Improving Wastewater Quality. These Findings Establish That Rice Husk And Apricot Stones Are Efficient, Environmentally Friendly, And Economically Feasible Alternatives To Conventional Wastewater Treatment Methods. Their Application In Large-scale Water Purification Systems Could Contribute To Sustainable Water Management And Pollution Control. Read more Read less

Real-Time Stress Monitoring And Care Prediction System For Personalized Relaxation And Sleep Enhancement

Stress Significantly Impacts Mental And Physical Well- Being, Necessitating Effective Real-time Monitoring And Intervention Mechanisms. This Paper Presents A Stress Monitoring And Care Prediction System That Integrates Physiological Sensors With Adap... Stress Significantly Impacts Mental And Physical Well- Being, Necessitating Effective Real-time Monitoring And Intervention Mechanisms. This Paper Presents A Stress Monitoring And Care Prediction System That Integrates Physiological Sensors With Adaptive Environmental Control To Provide Personalized Relaxation And Sleep Enhancement. The System Utilizes An Arduino UNO Microcontroller Interfaced With A DHT11 Temperature Sensor, A Heartbeat Sensor, And A Galvanic Skin Response (GSR) Sensor To Continuously Monitor Stress Indicators. The Collected Data Is Processed Using Python- Based Predictive Analytics To Offer Real-time Feedback And Adaptive Environmental Adjustments, Including Temperature Regulation, Sound Modulation, And Tactile Feedback. The Proposed System Aims To Provide A Comprehensive, Data-driven Approach To Stress Management, Ensuring Improved Relaxation And Sleep Quality. Read more Read less

SMART NURSERY GUARD FOR INFANTS

The Proposed Smart Cradle System Is An Innovative Solution Designed To Enhance Infant Safety And Parental Convenience Through Advanced Monitoring And Automation Technologies. The System Integrates Multiple Sensors And Smart Modules To Detect And Resp... The Proposed Smart Cradle System Is An Innovative Solution Designed To Enhance Infant Safety And Parental Convenience Through Advanced Monitoring And Automation Technologies. The System Integrates Multiple Sensors And Smart Modules To Detect And Respond To An Infant's Vital Signs, Environmental Conditions, And Activity Levels. Key Features Include Real-time Monitoring Of Heart Rate, Temperature, Moisture Levels, And Motion Using IoT-enabled Devices. Cry Detection Is Paired With An Automatic Lullaby Player To Soothe The Baby Without Manual Intervention. The Cradle Also Incorporates A Smart Rocking Mechanism Activated Based On The Baby's Activity Or Parent Input. This System Utilizes The ESP8266 And ESP32 Microcontrollers, A Temperature Sensor, Heartbeat Sensor, Sound Sensor, Moisture Sensor, And A GPS+GSM Module For Alert Communication. Real-time Data Is Streamed To A Firebase Cloud Database, Enabling Remote Access For Parents Through A Mobile Application. Additionally, The System Integrates A Camera For Live Video Streaming, Providing Visual Supervision. The Project Emphasizes Low-cost Design, Reliability, And Future Scalability. In Emergencies, The Cradle Automatically Alerts Guardians With Vital Parameters And Location Via SMS And Mobile Notifications. Through Effective Use Of Embedded Systems And IoT, The Smart Cradle Aims To Provide A Comprehensive Infant Care Platform That Reduces The Burden On Caregivers While Ensuring The Safety And Comfort Of The Child. Future Enhancements Include AI-driven Cry Pattern Analysis And Predictive Health Insights, Positioning This Cradle As A Pioneering Step Toward Intelligent Infant Care Systems. Read more Read less

IoT BASED VEHICLE OVER WEIGHT SAFETY SYSTEM

The Vehicle's Additional Fee Is An Important Issue That Damages Traffic Safety, Infrastructure And Increases The Risk Of Accidents. This Article Offers An IoT-based Vehicle Overweight Safety System That Automatically Provides A Distance Warning To Th... The Vehicle's Additional Fee Is An Important Issue That Damages Traffic Safety, Infrastructure And Increases The Risk Of Accidents. This Article Offers An IoT-based Vehicle Overweight Safety System That Automatically Provides A Distance Warning To The Process Of Load Monitoring And Implementing Safety Activities To Prevent Overloaded Vehicle Movement. The System Integrates A Load Cell Module To Create A Smart And Reliable Safety Mechanism With The HX711 Amplifier, An Arduino Uno Microcontroller, An Sarvo Engine, An LCD Screen And An IoT Module (eg ESP8266). During The Load Process, The System Measures The Load On The Vehicle In Real Time Using A Continuous Load Cell. If The Load Is Greater Than The Predetermined Safety Limit, The System Automatically Triggers A Servo Motor To Close The Vehicle's Loading Door, Effectively Stops Further Load. In Addition, The Current Weight And Overload Ratio Is Displayed On An LCD And Transferred To The Owner Or Fleet Manager To The Vehicle Via The IoT Module. This Ensures That Stakeholders Are Immediately Informed Of The Events With Overload. In Addition, If An Overloaded Vehicle Begins To Drive, The System Activates The Indicator Lights Around The Vehicle Because There Is A Visual Warning For Nearby Traffic And Officials. For Continuous Monitoring And Response, The Status Of Movement And Weight Data Is Also Updated Through The IoT Platform. This Intelligent Safety System Not Only Improves Traffic Safety, But Also Promotes Compliance With Transport Rules. It Is Especially Useful For Logistics, Construction And Goods Transport Industries Where Load Management Is Important. Implementation Cost Efficient, Scalable And Contributes To The Development Of Intelligent, Safe Transport Systems. Read more Read less

PREDICTION OF CHRONIC KIDNEY DISEASE USING MACHINE LEARNING TECHNIQUES

More Persons With Advanced Stages Of Renal Illness Are Diagnosed Each Year.Deteriorating Kidney Function Over Time Is A Hallmark Of Chronic Kidney Disease, Also Known As Renal Disease. People Are More Likely To Be Examined For Chronic Kidney Disease... More Persons With Advanced Stages Of Renal Illness Are Diagnosed Each Year.Deteriorating Kidney Function Over Time Is A Hallmark Of Chronic Kidney Disease, Also Known As Renal Disease. People Are More Likely To Be Examined For Chronic Kidney Disease (CKD) If They Have Diabetes, High Blood Pressure, Or A Family History Of The Illness. An Early Diagnosis Is Essential For Successful Therapy. In Order To Avoid Kidney Damage Or Failure, Early Detection And Monitoring Are Essential. Healthcare Organizations Can Effectively Use Machine Learning (ML) To Support Their Decision- Making Process. The Goal Of This Project Is To Create And Suggest A Machine Learning Approach For Chronic Kidney Disease Prediction. The Random Forest (RF) Algorithm Was Our Suggestion. To Determine The Optimal Model For Prediction, The Components Are Constructed Using Datasets Related To Chronic Renal Disease And Compared. Read more Read less

Human Sentiment Sound RNN Emotional Analysis With Django interface

Human Sentiment Sound RNNs For Emotional Audio Analysis With A Django Interface" Explores The Intersection Of Deep Learning And Human-computer Interaction By Developing A Recurrent Neural Network (RNN) Model Tailored To Analyze And Classify Human Emo... Human Sentiment Sound RNNs For Emotional Audio Analysis With A Django Interface" Explores The Intersection Of Deep Learning And Human-computer Interaction By Developing A Recurrent Neural Network (RNN) Model Tailored To Analyze And Classify Human Emotions From Audio Signals. Our Primary Objective Was To Build A Robust And Accurate System Capable Of Capturing And Interpreting Various Emotional Tones In Audio Data Using Advanced RNN Architectures, Particularly Long Short-Term Memory (LSTM) Units.To Ensure Broad Accessibility And Ease Of Use, We Integrated The Trained Model Into A Django-based Web Interface That Allows Users To Upload Audio Files, Interact With The System, And Visualize Sentiment Analysis Results In Real-time. We Further Extended The Platform’s Functionality By Incorporating Real-time Audio Recording Directly Through The Interface, Enabling Live Emotional Feedback On Spoken Input. The Output Is Presented Through Intuitive Visualizations, Displaying Classified Emotional States Such As Happiness, Sadness, Anger, And Neutrality, With Confidence Scores For Transparency. Our Project Also Addresses Challenges Such As Background Noise, Speaker Variability, And Latency In Live Inference.This Work Aims To Contribute Significantly To The Field Of Human-computer Interaction (HCI) By Enabling Intelligent Systems To Comprehend And Respond Empathetically To Human Emotions. Potential Real-world Applications Include Integration Into Virtual Assistants, Tools For Mental Health Monitoring, And Customer Service Solutions That Adapt Responses Based On Detected Sentiment, Ultimately Enhancing User Experience And Communication Efficiency Read more Read less

Leveraging Crowd-Sourced Data For Mapping And Analyzing Water-Related Issues Within affected Regions

Access To Clean And Reliable Water Resources Is A Fundamental Human Right, Yet Billions Globally Face Water Scarcity, Pollution, And The Impacts Of Water-related Disasters. Traditional Methods Of Water Resource Monitoring And Assessment Often Suffer... Access To Clean And Reliable Water Resources Is A Fundamental Human Right, Yet Billions Globally Face Water Scarcity, Pollution, And The Impacts Of Water-related Disasters. Traditional Methods Of Water Resource Monitoring And Assessment Often Suffer From Limitations In Spatial And Temporal Resolution, Particularly In Resource-constrained And Disaster-stricken Regions. This Paper Explores The Burgeoning Potential Of Crowd-sourced Data – Information Voluntarily Provided By The Public Through Various Digital Platforms – For Mapping, Analyzing, And Ultimately Mitigating Water-related Issues. We Discuss The Diverse Sources Of Crowd-sourced Data, Including Mobile Applications, Citizen Science Initiatives, And Highlight Their Unique Advantages In Capturing Real-time, Localized Information. Read more Read less

Subterranean Guardian: Tunnel Safety And Soil Borne Emergency Data Transfer

Earthquakes, Tunnel Collapses, And Landslides Are Common Natural Disasters That Tend To Bury People Under Rubble, Where Traditional Forms Of Communication Become Ineffective As Signals Are Blocked By The Soil And Infrastructure Is Destroyed. This Wor... Earthquakes, Tunnel Collapses, And Landslides Are Common Natural Disasters That Tend To Bury People Under Rubble, Where Traditional Forms Of Communication Become Ineffective As Signals Are Blocked By The Soil And Infrastructure Is Destroyed. This Work Introduces The Concept, Design, And Implementation Of A Soil Communication System Specific For Underground Environments. The System Works On Low-frequency Signal Transmission Over Soil In Combination With GSM And GPS Technology To Facilitate Communication Where Mobile Networks Are Not Reliable Or Non-existent. Integrating Gas, Temperature, Vibration, And Human-presence Sensors, The System Continuously Tracks Environmental And Physiological Status. An Arduino Microcontroller Integrates The Sensor Data And Offers A User Interface Through A Keypad And Display, Enabling Victims To Manually Enter Their Requirements Or Send Critical Alerts Automatically. The System Facilitates Two-way Communication Between Trapped Victims And Rescuers, Enhancing Situational Awareness, Coordination, And Response Times During Emergency Situations. Optimized To Be Lightweight, Affordable, And Quickly Deployable, The Solution Has Strong Potential For Practical Use In Disaster Areas Or Underground Space. Read more Read less

Automatic Irrigation System Using IOT

Water Control Is Pivotal For Sustainable Agriculture, In Particular In Areas Dealing With Resource Constraints. This Paper Provides An IOT-based Definitely Smart Irrigation Machine Designed To Optimize Water Utilization, Decorate Crop Productivity, A... Water Control Is Pivotal For Sustainable Agriculture, In Particular In Areas Dealing With Resource Constraints. This Paper Provides An IOT-based Definitely Smart Irrigation Machine Designed To Optimize Water Utilization, Decorate Crop Productivity, And Improve Power Overall Performance. The Device Integrates Various Sensors, Which Include Soil Moisture Sensors, Rain Detectors, And Environmental Video Display Gadgets, To Evaluate Actual-time Conditions And Automate The Irrigation Process. A Microcontroller-based Totally Totally Manage Unit Ensures Seamless Records Acquisition And Gadget Manipulate. The Proposed Machine Includes Capabilities Collectively With 3-segment Strength Detection, Pump Health Tracking The Use Of Temperature And Current Sensors, And Some Distance Off Operation Through The Blynk IOT Platform. The Integration Of IOT Generation Enables Actual-time Data Visualization, Far Flung Control, And Alert Mechanisms, Empowering Farmers To Make Data-driven Selections And Manipulate Irrigation With Minimum Manual Intervention. Experimental Consequences Show The Device's Effectiveness In Defensive Water By Way Of Stopping Over-irrigation And Reducing Energy Intake Through Automated Fault Detection And Section Tracking. This Progressive Approach Contributes To The Sustainability Of Agricultural Practices, Making It A Treasured Tool For Farming And Useful Resource Control. The Manner Of Addressing Worrying Situations Related To Traditional Irrigation Structures Which Includes Water Wastage, Tough Work Dependency, And Unreliable Electricity Supply This Study Highlights The Potential Of IOT In Revolutionizing Agricultural Techniques. Future Paintings Goals To Include Extra Competencies Together With Fertigation Structures, Overcurrent Protection, And GSM-based Totally Signals To Further Beautify Device Capabilities. This Paper Lays The Muse For Scalable, Inexperienced, And Sustainable Irrigation Solutions, Fostering Resilience In Opposition To Climate Variability And Aid Scarcity Read more Read less

Deep Learning – Based Fake News Classification With Android App Integration

Fake News And Spam Emails Present Critical Challenges To The Digital World, Impacting Societal Trust And Cybersecurity. This Design Proposes A Scalable Android- Rested Operation That Employs Advanced Deep Knowledge Approaches For Identifying Fake New... Fake News And Spam Emails Present Critical Challenges To The Digital World, Impacting Societal Trust And Cybersecurity. This Design Proposes A Scalable Android- Rested Operation That Employs Advanced Deep Knowledge Approaches For Identifying Fake News Content And Descry Spam Emails Effectively.usingways Like Natural Language Processing(NLP), Long Short- Term Memory(LSTM) Networks, And Support Vector Machines(SVM), The System Ensures Accurate Results. Vital Features Include Multilingual Support, Offline Capabilities Using SQLite, And A Stoner- Friendly Interface To Feed To Different Cult. This Innovative Approach Islands Significant Gaps In Being Technologies, Contributing To Safer And Further Informed Digital Ecosystems. Read more Read less

AI Based Neural Network Model For Stroke Prediction

Many Individuals Encounter Situations Where They Need Basic Legal Information And Guidance. However, Obtaining Professional Legal Advice Can Be Prohibitively Expensive, Especially For Minor Issues Or General Inquiries. As Technology Continues To Adva... Many Individuals Encounter Situations Where They Need Basic Legal Information And Guidance. However, Obtaining Professional Legal Advice Can Be Prohibitively Expensive, Especially For Minor Issues Or General Inquiries. As Technology Continues To Advance, The Legal Field Has Not Been Left Behind. Artificial Intelligence (AI) Offers New Possibilities For Assisting Individuals In Understanding Their Rights And Navigating Complex Legal Frameworks. This Project Introduces LawyerBot, An AI-powered Virtual Assistant Designed To Provide Accurate Information About Indian Laws And Legal Sections. LawyerBot Offers Guidance On Handling Various Legal Issues And Understanding How Laws Can Address Them. Utilizing Natural Language Processing (NLP) And Bidirectional Encoder Representations From Transformers (BERT), LawyerBot Engages Users In A Conversational Manner Through A Chat Interface. Users Can Input Their Queries Or Describe Their Legal Concerns, And The AI Leverages Its Training On Indian Legal Frameworks To Deliver Relevant Information And Recommend Next Steps. LawyerBot Offers Several Benefits, Including Prompt And Accessible Legal Guidance, Which Helps Users Avoid Consultation Fees For Minor Matters. It Also Enhances Legal Literacy By Increasing Awareness Of Indian Laws Among The General Population. Through Its Intuitive Chat Interface, LawyerBot Empowers Individuals To Independently Understand Their Rights And Navigate The Legal System. By Democratizing Access To Legal Knowledge, LawyerBot Represents A Cost-effective, User-friendly Solution For Those Seeking Legal Information And Support In India. Read more Read less

SUSTAINABLE AGRICULTURE: A APPROACH FOR RICE LEAF DISEASE DETECTION AND CLASSIFICATION USING DCNN AND ENHANCED DATASETS

The Quality And Productivity Of Rice Crops Can Be Significantly Impacted By A Variety Of Diseases. For Effective Management And Higher Agricultural Productivity, Early Disease Detection And Classification Are Essential. The Primary Objective Of This... The Quality And Productivity Of Rice Crops Can Be Significantly Impacted By A Variety Of Diseases. For Effective Management And Higher Agricultural Productivity, Early Disease Detection And Classification Are Essential. The Primary Objective Of This Study Is To Classify Rice Leaf Illnesses From Visual Data Using Convolutional Neural Networks (CNNs). The Collection Contains Images Of Both Healthy And Diseased Rice Leaves Categorized Into Classes Including Hispa, Brown Spot, And Leaf Blast. Scaling And Normalizing Are Two Of The Many Picture Preparation Techniques Used To Enhance Model Performance. The CNN Model Is Trained To Identify Patterns In Leaf Pictures, Enabling Accurate Disease Classification. By Using Deep Learning Techniques To The Development Of Automated And Efficient Disease Detection Systems, This Strategy Aims To Reduce Reliance On Manual Inspection And Promote Sustainable Agricultural Practices. Read more Read less

AskIT AI: An Intelligent System For Optimized Student Profile Management And Data Accessibility

Handling Student Profiles And Data Is A Vital Part Of Managing An Educational Institution, But Conventional Methods Frequently Turn Out To Be Inefficient, Labor-intensive, And Prone To Mistakes. These Issues Obstruct Institutions' Capability To Make... Handling Student Profiles And Data Is A Vital Part Of Managing An Educational Institution, But Conventional Methods Frequently Turn Out To Be Inefficient, Labor-intensive, And Prone To Mistakes. These Issues Obstruct Institutions' Capability To Make Prompt And Informed Choices, Affecting Overall Effectiveness And Student Contentment. AskIT AI Provides A Revolutionary Answer By Utilizing Artificial Intelligence To Simplify The Management And Retrieval Of Student Data. By Offering A Unified, Automated, And Intelligent Platform, AskIT AI Guarantees Efficient Access To Precise And Current Student Information. This Advancement Not Only Alleviates Administrative Challenges But Also Improves Decision-making Processes, Ultimately Promoting A More Responsive And Supportive Educational Atmosphere. Read more Read less

A SMART BLOOD BANK APP WITH SALAIVA TESTING FOR THALASSEMIA(IOT)

This Project Presents A Low-cost And Holistic Approach To The Early Detection Of Thalassemia Using A Non-invasive Saliva-based Method. The System Is Designed Around The Principle That Iron (Fe) Content In Saliva, Which Affects PH Levels, Can Serve As... This Project Presents A Low-cost And Holistic Approach To The Early Detection Of Thalassemia Using A Non-invasive Saliva-based Method. The System Is Designed Around The Principle That Iron (Fe) Content In Saliva, Which Affects PH Levels, Can Serve As An Indicative Biomarker For Thalassemia Screening. A Compact Sensor Node Is Developed For Real-time Monitoring Of PH Variations, Indirectly Reflecting Iron Concentrations. The Main Sensor Module Consists Of A Sensitive PH Sensor Integrated With A Microcontroller For Data Acquisition And Processing. Emphasis Is Placed On Affordability, Portability, And Reliable Long-term Operation. The Sensor Values Are Transmitted To An IoT Server For Remote Access And Analysis. This Solution Enables Continuous, Non-invasive Monitoring And Is Suitable For Large-scale Screening Applications. The Results Demonstrate That The Proposed System Meets The Criteria For A Low-cost, Accurate, And Accessible Early Warning System For Thalassemia Detection. Read more Read less

College Management System: A Comprehensive Approach To Digitalizing Educational Administration

The College Management System (CMS) Is An Advanced Digital Solution Designed To Enhance The Efficiency Of Educational Administration. This System Integrates Multiple Aspects Of College Management, Including Student Information, Faculty Management, Co... The College Management System (CMS) Is An Advanced Digital Solution Designed To Enhance The Efficiency Of Educational Administration. This System Integrates Multiple Aspects Of College Management, Including Student Information, Faculty Management, Course Administration, And Financial Transactions, Into A Single, Unified Platform. One Of The Primary Objectives Of The CMS Is To Streamline Various Administrative Processes, Reducing The Reliance On Traditional Paperwork While Ensuring Secure And Efficient Data Handling. By Digitalizing These Operations, The System Provides Real-time Access To Crucial Academic And Administrative Data, Improving Decision-making For Faculty And Administrators. The Proposed CMS Is Developed Using Modern Web Technologies, Ensuring A User-friendly Interface That Allows Seamless Interaction For Students, Faculty, And Administrative Staff. The System Is Designed To Be Intuitive, Making It Easier For Users To Navigate And Manage Their Respective Responsibilities Effectively. Read more Read less

Brain Tumer Detection Using Machine Learning

Brain Tumors Result From The Abnormal And Uncontrolled Growth Of Cells. If Left Untreated During The Early Stages, They Can Become Life-threatening. Although Numerous Significant Advancements Have Been Achieved In This Field, Ensuring Accurate Segmen... Brain Tumors Result From The Abnormal And Uncontrolled Growth Of Cells. If Left Untreated During The Early Stages, They Can Become Life-threatening. Although Numerous Significant Advancements Have Been Achieved In This Field, Ensuring Accurate Segmentation And Classification Remains A Complex Challenge. The Primary Difficulty In Detecting Brain Tumors Lies In The Variations In Their Location, Size, And Shape. This Paper Aims To Provide An Extensive Review Of Brain Tumor Detection Methods Using Magnetic Resonance Imaging (MRI) To Support Researchers In Their Work. It Encompasses Discussions On The Structure Of Brain Tumors, Publicly Accessible Datasets, Image Enhancement Techniques, Segmentation Methods, Feature Extraction, Classification Approaches, And The Role Of Advanced Technologies Such As Deep Learning, Transfer Learning, And Quantum Machine Learning In Analyzing Brain Tumors. Lastly, This Survey Summarizes Key Findings, Highlighting The Advantages, Limitations, Advancements, And Potential Future Directions In Brain Tumor Detection Research. Read more Read less

COMPARATIVE ANALYSIS OF ORGANIC COMPOSTING PROCESSES WITH AND WITHOUT ACCELERATOR

Organic Waste Management Through Composting Presents A Sustainable Method To Reduce Landfill Dependency, Mitigate Greenhouse Gas Emissions, And Produce Nutrient-rich Soil Amendments. This Study Investigates The Efficiency Of Composting Processes With... Organic Waste Management Through Composting Presents A Sustainable Method To Reduce Landfill Dependency, Mitigate Greenhouse Gas Emissions, And Produce Nutrient-rich Soil Amendments. This Study Investigates The Efficiency Of Composting Processes With And Without The Use Of Accelerators By Analysing Critical Parameters Such As Temperature, Moisture Content, PH, And Carbon-to-nitrogen (C:N) Ratio. Over A 55-day Period, Two Compost Piles—one With An Accelerator And One Without—were Monitored To Evaluate Decomposition Dynamics. Results Demonstrate That The Addition Of Accelerators Enhances Microbial Activity, Accelerates Decomposition, And Produces Mature Compost More Rapidly. This Research Provides Actionable Insights Into Optimizing Composting Processes In Both Small-scale And Large-scale Applications While Emphasizing Best Practices For Achieving High-quality Compost. Read more Read less

Agriculture Management System Using Machine Learning

The Agriculture Management System (AMS) Is A Smart, Machine Learning-based Platform That Assists Farmers In Making Data-driven Decisions For Crop Selection, Fertilizer Use, Irrigation Planning, And Yield Estimation. It Analyzes Key Factors Such As So... The Agriculture Management System (AMS) Is A Smart, Machine Learning-based Platform That Assists Farmers In Making Data-driven Decisions For Crop Selection, Fertilizer Use, Irrigation Planning, And Yield Estimation. It Analyzes Key Factors Such As Soil Nutrients (N, P, K), Temperature, Humidity, PH, Rainfall, And Historical Yield Data To Deliver Personalized Recommendations. Featuring A Responsive Bootstrap 4 Interface, AMS Ensures Smooth Access Across Devices, Allowing Users To Input Real-time, Location-specific Data For Tailored Insights That Enhance Productivity And Resource Efficiency. The System Integrates Weather APIs For Dynamic, Context-aware Guidance, Helping Farmers Adapt Practices To Current And Forecasted Conditions. A Built-in Agriculture Chatbot Provides 24/7 Support On Topics Like Pest Control, Organic Farming, And Crop Health. An Intelligent Irrigation Calendarfurther Optimizes Water Use By Generating Schedules Based On Crop Type, Soil, And Local Weather. Additionally, The System Stores User Data Securely, Enabling Farmers To Track Their Seasonal Progress And Refine Strategies Over Time. It Supports Multilingual Interfaces To Reach Farmers Across Diverse Regions. The Modular Design Also Allows For Future Integration With Government Schemes And Market Price Updates. In Essence, AMS Empowers Modern Agriculture By Combining AI, Real-time Data, And Intuitive Design To Boost Efficiency And Support Informed Farming Decisions. Read more Read less

CASE STUDIES ON GEOSYNTHETIC CONCRETE USE

Geosynthetic Concrete, Or Concrete Canvas, Is A Groundbreaking Development In Civil Engineering Materials That Brings Together The Strength Of Concrete With The Flexibility And Convenience Of Geotextiles. This New Composite Material Is A Fabric Infus... Geosynthetic Concrete, Or Concrete Canvas, Is A Groundbreaking Development In Civil Engineering Materials That Brings Together The Strength Of Concrete With The Flexibility And Convenience Of Geotextiles. This New Composite Material Is A Fabric Infused With Dry Concrete Mix, Which, Upon Hydration, Sets To Create A Tough And Resilient Layer Of Concrete. The Technology Has Many Benefits Over Traditional Concrete Construction Practices, Including Quicker Installation, Less Labor, Little Equipment Needed, And Greater Durability, Particularly In Distant Or Environmentally Fragile Locations. This Research Investigates The Structure, Functionality, And Different Uses Of Geosynthetic Concrete. The Article Describes The Process Of Installation, Highlighting The Importance Of Hydration And Curing In Developing Maximum Strength And Durability. In Addition, The Paper Contrasts Geosynthetic Concrete With Conventional Concrete Solutions In Mechanical Properties, Environmental Performance, And Economic Effectiveness. Applications Of Geosynthetic Concrete Range Across Various Fields Such As Slope Protection, Ditch Lining, Erosion Control, Culvert Repair, And Military Applications, Highlighting Its Versatility And Flexibility To Harsh Environments. The Study Also Presents Case Studies Illustrating Successful Application And Performance Testing, Confirming Its Usability And Long-term Reliability. In Summary, Geosynthetic Concrete Comes Across As A Cost-effective, Eco-friendly, And High-performance Option In Contemporary Construction. Its Application Not Only Accelerates Construction Schedules But Also Reduces Environmental Disruption, Making It A First-choice Option For Fast And Durable Infrastructure Development. Read more Read less

RNN-Based Heartbeat Sound Analysis With Django Integration

Congenital Heart Diseases (CHDs) Are Among The Leading Causes Of Mortality Worldwide, Necessitating Early And Accurate Detection Methods.Improving Patient Outcomes And Facilitating Prompt Medical Intervention Depend Heavily On Early Diagnosis. Conven... Congenital Heart Diseases (CHDs) Are Among The Leading Causes Of Mortality Worldwide, Necessitating Early And Accurate Detection Methods.Improving Patient Outcomes And Facilitating Prompt Medical Intervention Depend Heavily On Early Diagnosis. Conventional Heart Sound Analysis Depends On Skilled Medical Professionals Performing Manual Auscultation, Which Is Subject To Human Error And Subject To Subjectivity. Automated Heart Sound Classification Has Been Made Possible By Recent Developments In Deep Learning And Artificial Intelligence (AI), Which Improve Accuracy And Lessen Reliance On Manual Diagnostics. Recurrent Neural Networks (RNN) And Long Short-term Memory (LSTM) Networks Are Used In This Study's AI-powered Heartbeat Sound Analysis System To Accurately Classify Heart Sounds. In Order To Differentiate Between Normal And Abnormal Patterns, The System Is Trained On Phonocardiogram (PCG) Recordings, Which Capture Temporal Dependencies In Heartbeats. The System Is Integrated With Django, A Web-based Framework That Makes It Easier To Process, Store, And Visualize Heart Sound Recordings In Real Time, In Order To Improve Accessibility And Usability. Patients And Medical Professionals Can Effectively Monitor Heart Health Thanks To This Smooth Integration. In Addition To Increasing Diagnostic Precision, The Suggested System Complies With Legal Requirements Like HIPAA And GDPR, Guaranteeing The Security And Privacy Of Patient Data. Even In Places With Limited Resources, Early Detection Of Congenital Heart Diseases Is Now Easier Thanks To The Model's Support For Remote Monitoring Through Cloud-based Deployment. Read more Read less

Detection Of Diabetic Retinopathy Using Convolutional Neural Network

Diabetic Retinopathy (DR) Is A Leading Cause Of Vision Loss Globally, Particularly Among Individuals With Long-standing Diabetes. Early Detection And Grading Of DR Are Vital To Prevent Irreversible Blindness. This Project Presents An End-to-end, AI-p... Diabetic Retinopathy (DR) Is A Leading Cause Of Vision Loss Globally, Particularly Among Individuals With Long-standing Diabetes. Early Detection And Grading Of DR Are Vital To Prevent Irreversible Blindness. This Project Presents An End-to-end, AI-powered Web Application For The Automatic Classification Of Diabetic Retinopathy From Fundus Images Using A Convolutional Neural Network (CNN) Deployed Via A FastAPI Framework. The Trained Model, Based On TensorFlow, Classifies Input Retinal Images Into Five Categories: No DR, Mild, Moderate, Severe, And Proliferative DR. The Dataset Used To Train The Model Was Sourced From Kaggle, Consisting Of High-resolution Retina Images Labeled By Clinical Experts. The Application Provides A Modern, Responsive Frontend Using HTML, CSS, And JavaScript, Allowing Users To Upload Retinal Images And Receive Real-time Diagnostic Predictions. The Backend Model Preprocesses Uploaded Images Using OpenCV And NumPy, Resizing Them To 224x224 Pixels, Normalizing Them, And Feeding Them Into The Trained CNN Model. The System Aims To Serve As A Fast, Reliable, And Accessible Tool To Assist Ophthalmologists And Healthcare Professionals In Screening For DR. It Can Also Act As A Valuable Aid In Regions With Limited Access To Medical Infrastructure, Where Regular Eye Checkups Are Not Always Feasible. Through This Project, We Demonstrate The Real-world Application Of AI And Web Development For Medical Diagnostics, Bridging The Gap Between Complex Deep Learning Models And User-friendly Interfaces. Read more Read less

Deep Learning System For Fire Detection And Alerts Using YOLO

This Project Introduces A Real-time Fire Detection And Alert System Based On The YOLO Algorithm, Aiming To Enhance Safety In Various Settings. Designed To Mitigate Fire Hazards Across Diverse Environments, Including Industrial, Residential, And Publi... This Project Introduces A Real-time Fire Detection And Alert System Based On The YOLO Algorithm, Aiming To Enhance Safety In Various Settings. Designed To Mitigate Fire Hazards Across Diverse Environments, Including Industrial, Residential, And Public Spaces. Leveraging The YOLO Deep Learning Algorithm, The System Accurately Identifies Fire From Live Camera Feeds And Triggers Immediate Responses Through Integrated Hardware. On Detecting Fire, It Activates An Alarm, Captures Evidence, Emails Alerts, And Initiates A Water Pump Via An ESP32 Microcontroller. This AI-powered System Eliminates The Need For Traditional, Maintenance-heavy Sensors And Instead Offers A Scalable, Cost-effective Alternative That Integrates Seamlessly With Existing Surveillance Setups. By Ensuring Rapid Detection And Response, It Enhances Environmental Safety And Operational Efficiency. Read more Read less

The Farm Store E-Commerce Website

In Recent Years, Digital Technology Has Significantly Reshaped How Agricultural Products Reach Consumers. This Research Paper Presents The Design And Development Of A Farm Store E-commerce Website Aimed At Bridging The Gap Between Local Farmers And E... In Recent Years, Digital Technology Has Significantly Reshaped How Agricultural Products Reach Consumers. This Research Paper Presents The Design And Development Of A Farm Store E-commerce Website Aimed At Bridging The Gap Between Local Farmers And End-users Through A Direct Online Platform. By Eliminating Middlemen, This Model Not Only Ensures Fair Pricing For Farmers But Also Promotes The Consumption Of Fresh And Locally-sourced Produce. The Website Offers Features Such As Real-time Product Listings, Secure Payment Options, Order Tracking, And User-friendly Navigation Tailored For Both Tech-savvy Customers And Rural Sellers. The Study Explores The System’s Functional Requirements, User Interface Design, And The Challenges Of Integrating Rural Supply Chains Into A Digital Framework. The Proposed Solution Supports Transparency, Boosts Local Economies, And Leverages Digital Inclusion As A Tool For Rural Development. This Paper Also Highlights The Broader Impact Such Platforms Can Have On Agricultural Marketing And Sustainable E-commerce Growth. Read more Read less

AI-Driven Smart Cart System For Automated Billing And Real-Time Product Recognition

AI Technology Has Completely Changed A Number Of Industries, And The Retail Industry Is No Different. This Paper Offers A Thorough Analysis Of The Use Of Artificial Intelligence (AI) In Smart Cart Implementation, With The Goal Of Revolutionizing The... AI Technology Has Completely Changed A Number Of Industries, And The Retail Industry Is No Different. This Paper Offers A Thorough Analysis Of The Use Of Artificial Intelligence (AI) In Smart Cart Implementation, With The Goal Of Revolutionizing The Conventional Shopping Experience. Our Intelligent Shopping Cart System Utilizes Cutting-edge Artificial Intelligence Algorithms, Specifically The YOLOv8 Model For Barcode Recognition And The Python Library For Decoding, To Provide Smooth Product Scanning And Checkout Procedures. To Improve Accuracy And Security, We Suggest A Phased Switch From Barcode To QR Code Technology. We See A Future Where Long Lines And Laborious Checkout Processes Are Replaced With Quick And Easy Shopping Experiences By Distributing Smart Carts With AI Capabilities. This Study Opens The Door For Novel Developments In Customer Engagement And Corporate Operations While Also Adding To The Continuing Conversation About The Application Of AI In Retail Settings. Read more Read less

Automated Material Handling Mechanism

This Project Is Related To Transferring Goods From A First Horizontal To Second Horizontal Conveyor Comprising A Substantially Upright Extending Frame- Endless Drive Arranged On The Frame And Drivable By A Motor; At Least One Support Member Which Is... This Project Is Related To Transferring Goods From A First Horizontal To Second Horizontal Conveyor Comprising A Substantially Upright Extending Frame- Endless Drive Arranged On The Frame And Drivable By A Motor; At Least One Support Member Which Is Connected To Endless Drive And Which Is Drivable In A Circuit By Means Of The Endless Drive. At Least One Product Carrier Connected To The Support Member, Wherein The Product Carrier Is Connected To The Support Member For Rotation About Lying Shaft Extending Transversely Of The Frame, Wherein The Product Carrier Is Connected Drivably To No More Than Only One Trolley. So, It Is Basically A Vertical Conveyor With A Carriage Which Is Mounted On Endless Chain And It Lift Boxes In Vertical Direction And Dispatch Them On Another Horizontal Conveyor Synchronized With It. Read more Read less

ENHANCING DIGITAL LEARNING: TALKY COMMUNITY – A PLATFORM FOR TRAINER VISIBILITY AND STUDENT ENGAGEMENT

TalkyCommunity.com Is An All-in-one Learning Solution, Providing Holistic Education That Is Interactive. The Program’s Integration Of Social Interaction With Learning Activities Gives Individuals The Chance To Discover New Concepts, Sharpen Existing... TalkyCommunity.com Is An All-in-one Learning Solution, Providing Holistic Education That Is Interactive. The Program’s Integration Of Social Interaction With Learning Activities Gives Individuals The Chance To Discover New Concepts, Sharpen Existing Skills, And Engage In Educational Discourse. Talky Community Enables Formal And Informal Learning Through Its Multifunctional Interface, Including Discussion Boards, Resource Repositories, Event Calendars, And Peer Networking. This Journal Investigates The Platform’s Impact On Enhancing Equitable Education, Supporting Self-paced Development, And Cultivating A Welcoming Online Environment That Encourages Exploration, Teamwork, And Development. The Platform’s Design Emphasizes User Engagement, Making Learning More Collaborative And Enjoyable. Its Flexible Structure Caters To A Wide Range Of Learning Styles And Educational Backgrounds. By Encouraging Learners To Share Knowledge And Experiences, Talky Community Creates A Vibrant Ecosystem Of Peer Support. It Also Plays A Vital Role In Bridging Educational Gaps By Making Resources Accessible To Diverse Communities. As A Result, Learners Are Empowered Not Only To Absorb Information But Also To Actively Contribute To The Learning Journey Of Others. Read more Read less

The Role Of Websites In Reviving Traditional Knowledge Of Herbal Medicine

This Paper Presents A Digital Platform That Serves As A Comprehensive Resource For The Study Of Medicinal Plants Used In Ayurveda (Ayurveda, Yoga And Naturopathy, Unani, Siddha, And Homeopathy). It Provides Detailed Information On The Characteristics... This Paper Presents A Digital Platform That Serves As A Comprehensive Resource For The Study Of Medicinal Plants Used In Ayurveda (Ayurveda, Yoga And Naturopathy, Unani, Siddha, And Homeopathy). It Provides Detailed Information On The Characteristics And Uses Of These Plants, Aimed At Students, Physicians, And Those Interested In Natural Medicine. The Plat- Form Combines Traditional Knowledge With Modern Technology, Fosters A Deeper Understanding And Appreciation Of Medicinal Plants, And Preserves This Valuable Wisdom For Future Generations Read more Read less

Smart IV Drip Management And Auto-Refill System

Innovation And Invention Of Various Clinical Care Units In Hospitals And Nursing Units Are Greatly Challenged By The Development Of Biomedical Engineering Devices. Hospitals Still Require Automated Systems Because Of Their Enormous Patient Volume And... Innovation And Invention Of Various Clinical Care Units In Hospitals And Nursing Units Are Greatly Challenged By The Development Of Biomedical Engineering Devices. Hospitals Still Require Automated Systems Because Of Their Enormous Patient Volume And Nursing Facilities. As The Population Grows, So Does The Demand For Healthcare Equipment And Maintenance, Making It Necessary For Everyone To Take Care Of Their Health In A Safe Manner. Numerous Automated Health Monitoring Tools Are Being Created These Days To Protect Patients And Ease Physician Burden. As Technology Has Grown, Numerous Sophisticated Methods Have Been Developed To Ensure That Hospital Patients Recover Quickly.Injecting Intravenous Fluids, Medications, Blood, Or Blood Products Straight Into A Vein Is Known As Intravenous Therapy. It Is Generally Necessary To Measure And Regulate The Flow Of Saline Or Intravenous Fluids. This Study Proposes A Single Window Solution For Fully Automated Continuous Monitoring, Fluid Flow Control, And Automatic Saline Container Change Based On Android And IoT. The Fully Automated Way Of Monitoring The IV Fluid Feeding System Will Be Made Easier For Nurses And Caregivers In The Future. Read more Read less

SEISMIC PERFORMANCE ENHANCEMENT OF STRUCTURES USING MODIFIED FRAMED SHEAR WALLS

The Increasing Frequency And Intensity Of Earthquakes Worldwide Have Highlighted The Need For More Resilient Structural Systems. Traditional Framed Shear Walls, While Offering Good Lateral Resistance, Often Face Limitations Such As Brittleness, Conce... The Increasing Frequency And Intensity Of Earthquakes Worldwide Have Highlighted The Need For More Resilient Structural Systems. Traditional Framed Shear Walls, While Offering Good Lateral Resistance, Often Face Limitations Such As Brittleness, Concentration Of Damage, And Reduced Energy Absorption Under Strong Seismic Loads. This Research Explores The Concept Of Earthquake Vibration Control Through Modified Framed Shear Walls, Aiming To Enhance Structural Performance During Seismic Events. The Modifications Involve Integrating Energy Dissipation Devices Such As Dampers, Optimizing Wall Openings To Control Stiffness, And Using Hybrid Materials Like Reinforced Concrete Combined With Steel Plates Or Fiber-reinforced Composites. These Innovations Significantly Improve The Ductility, Energy Absorption, And Self-centering Capabilities Of The System. Analytical Models And Experimental Validations Demonstrate That Modified Framed Shear Walls Reduce Base Shear Forces, Limit Inter-story Drifts, And Effectively Control Crack Propagation, Resulting In Improved Post-earthquake Serviceability. Additionally, The Study Examines The Influence Of Wall Geometry, Coupling Beam Design, And Material Characteristics On Overall Dynamic Behaviour. The Findings Suggest That Modified Framed Shear Walls Represent A Sustainable, Cost-effective, And Highly Efficient Solution For Earthquake-resistant Building Design, Promoting Greater Safety And Resilience In Both Residential And Commercial Constructions. Read more Read less

Railway Track Crack Detection System Prototype

This Paper Presents A Railway Crack Detection Robot Using Arduino, Designed To Detect Cracks And Defects In Railway Tracks. The Robot Is Equipped With Sensors That Detect Cracks And Transmit Data To A Central Station. The System Aims To Improve Railw... This Paper Presents A Railway Crack Detection Robot Using Arduino, Designed To Detect Cracks And Defects In Railway Tracks. The Robot Is Equipped With Sensors That Detect Cracks And Transmit Data To A Central Station. The System Aims To Improve Railway Safety By Identifying Potential Defects Before They Cause Accidents. The Robot's Design And Functionality Are Tailored To Navigate Railway Tracks, Detect Cracks, And Provide Real-time Data To Maintenance Teams. This Project Demonstrates The Potential Of Automation And IoT In Enhancing Railway Safety And Maintenance. In Existing Method Of Crack Detection, We Not Get Closer Location Of Crack We Only Get Location Of That Crack In Format Of Latitude And Longitude. So, This System Is Modified, By Adding Paint Drop System Which Gives Exact Location Of Crack In Format Of Longitude And Latitude And Also Drop Paint Near Crack, So We Get Closer Location Of Crack & For Battery Charge We Add Solar Panel. Read more Read less

VISIONBRIDGE: ENABLING INDEPENDENCE THROUGH OBJECT, FACE AND CURRENCY RECOGNITION FOR THE BLIND

Navigating Daily Life Can Be Especially Challenging For Blind And Visually Impaired Individuals, Particularly When It Comes To Identifying Obstacles, Recognizing Familiar Faces, And Handling Currency Transactions. Traditional Aids Such As White Canes... Navigating Daily Life Can Be Especially Challenging For Blind And Visually Impaired Individuals, Particularly When It Comes To Identifying Obstacles, Recognizing Familiar Faces, And Handling Currency Transactions. Traditional Aids Such As White Canes And Guide Dogs, Though Helpful, Provide Limited Functionalities And Are Not Equipped To Handle Dynamic Environments Or Complex Tasks In Real Time. This Paper Introduces An Innovative Solution That Integrates Face Detection, Obstacle Detection, And Currency Recognition Into A Single, Wearable Device. By Utilizing Cutting-edge Artificial Intelligence, Including The Grassmann Model For Face Recognition, YOLO For Object Detection, And Convolutional Neural Networks (CNNs) For Currency Identification, The Proposed System Empowers Visually Impaired Individuals To Navigate Their Surroundings, Recognize People, And Manage Financial Transactions Independently. Real-time Image Capturing And Processing Allow For Immediate Audio Feedback, Ensuring Users Receive Context-sensitive Assistance As They Encounter Various Challenges In Everyday Settings. This System Not Only Enhances User Autonomy But Also Reduces Dependence On External Assistance, Fostering Greater Confidence And Independence. By Combining Multiple Functionalities In A Single, User-friendly Device, The Proposed Solution Addresses Gaps In Existing Technologies, Offering A Practical And Affordable Alternative For Enhancing The Quality Of Life Of Visually Impaired Individuals. Read more Read less

EMPLOYEE MANAGEMENT SYSTEM

An Extensive Web-based Program Called The Employee Management System (EMS) Was Created To Automate And Simplify The Fundamental Tasks Of Human Resource Management In A Company.Effective Management Of Employee-related Tasks, Including Department Alloc... An Extensive Web-based Program Called The Employee Management System (EMS) Was Created To Automate And Simplify The Fundamental Tasks Of Human Resource Management In A Company.Effective Management Of Employee-related Tasks, Including Department Allocation, Payroll Processing, Attendance Monitoring, Performance Reviews, And Report Creation, Is Made Possible By This System.Through The Reduction Of Manual Labor, The Mitigation Of Human Error, And The Provision Of Prompt Access To Employee Data, The EMS Increases Productivity.Constructed Utilizing Contemporary Web Technologies Such As HTML, CSS, And JavaScript, The System Offers A Comprehensive Capability And An Intuitive User Experience Specifically Designed For HR Administrators And Management Teams.All Things Considered, The EMS Is Essential To Preserving Correct Personnel Data And Enhancing Organizational Effectiveness. Read more Read less

ARTIFICIAL INTELLIGENCE APPLICATION IN NURSING: A REVIEW

Developing An ML-Based Solution To Refine CAPTCHA For UIDAI

Traditional CAPTCHA Systems, While Effective In Deterring Basic Automated Threats, Often Create A Cumbersome User Experience And Are Increasingly Susceptible To Modern AI- Driven Attacks. This Paper Presents A Machine Learning (ML)- Driven Passive CA... Traditional CAPTCHA Systems, While Effective In Deterring Basic Automated Threats, Often Create A Cumbersome User Experience And Are Increasingly Susceptible To Modern AI- Driven Attacks. This Paper Presents A Machine Learning (ML)- Driven Passive CAPTCHA Alternative Specifically Designed For The Unique Identification Authority Of India (UIDAI) Portals. By Passively Collecting Environmental And Behavioral Data—such As Mouse Dynamics, Keystroke Patterns, Device Fingerprints, And Net- Work Indicators—our Proposed Solution Leverages Backend ML Models To Assess User Authenticity In Real-time. The Architecture Promotes Minimal User Interaction, Seamless Integration With UIDAI Infrastructure, And Robust Security Against DoS/DDoS Threats, All While Upholding Strict Privacy Guidelines. Read more Read less

PREDICTING AND DETECTING FAULTS IN INDUSTRIAL MACHINES BY IOT SYSTEM USING CNN AND GRU MODEL

Fault Detection In Industrial Systems Is Crucial For Ensuring Operational Safety, Minimizing Downtime, And Reducing Maintenance Costs. This Work Proposes A Hybrid Deep Learning Model Combining Convolutional Neural Networks (CNN) And Gated Recurrent U... Fault Detection In Industrial Systems Is Crucial For Ensuring Operational Safety, Minimizing Downtime, And Reducing Maintenance Costs. This Work Proposes A Hybrid Deep Learning Model Combining Convolutional Neural Networks (CNN) And Gated Recurrent Units (GRU) To Detect And Classify Machine Faults From Time-series Data. The CNN Layers Extract Spatial Features, While GRU Layers Model Temporal Dependencies In The Data.The Architecture Incorporates Residual Connections To Enhance Gradient Flow And Improve Learning Efficiency. The Model Is Evaluated On Multi-class Fault Detection Datasets, Achieving Robust Performance With High Accuracy, Precision, Recall, And F1-score. Advanced Metrics, Including ROC- AUC, Logarithmic Loss, Cohen's Kappa, And Matthews Correlation Coefficient, Demonstrate The Model's Reliability. Visualization Of Confusion Matrices And Detailed Performance Metrics Validates Its Effectiveness In Detecting Anomalies And Classifying Fault Types. This Approach Can Be Generalized For Real-time Monitoring Systems In Various Industrial Applications, Ensuring Predictive Maintenance And Operational Excellence. Read more Read less

A Chaotic Framework For Image Encryption In Transform Domain

Off Late Conventional Image Data Hiding And Encryption Mechanisms Have Seen A Shift Towards Homomorphic Images Which Can Be Thought Of Being Created From A Constant Illumination And A Varying Reflectance. In This Proposed Work, The Fresnel Transform... Off Late Conventional Image Data Hiding And Encryption Mechanisms Have Seen A Shift Towards Homomorphic Images Which Can Be Thought Of Being Created From A Constant Illumination And A Varying Reflectance. In This Proposed Work, The Fresnel Transform Is Employed To Convert Normal Images Into Homomorphic Images To Reduce The Redundancy Of Images. Subsequently, The Image Is Converted To The Transform Domain Using The 4th Level Discrete Wavelet Transform. The Truncation Of The DWT Is Done At The 4th Level So As To Limit The Complexity Of The System. Once The Image Is Converted To The Transform Domain, It Is Encrypted Using The Chaotic Baker Map.The Embedded Data Can Be Extracted From The Encrypted Domain Itself Without The Mandatory Necessity Of First Decrypting The Image Thereby Making The Secret Image Extraction Faster And Less Perceptible. The Evaluation Of The Proposed Technique Is Done Based On The Histogram Analysis, The MSE, PSNR, Correlation And Entropy. It Has Been Shown That The Proposed System Performs Better Compared To The Previously Existing Technique In Terms Of The PSNR For The Same Image From The Benchmark USC-SIPI Image Dataset. Read more Read less

Distributed Solid Waste Management Treatment By Biogas System Enhancing Public Health And Environmental Safety

SMART PARKING SYSTEM

This Project Presents A Web-based "Smart Parking" System Designed To Enhance Urban Parking Efficiency Through Intelligent Slot Booking And User-friendly Interaction. The System Features A Responsive Landing Page Highlighting Key Capabilities, Includi... This Project Presents A Web-based "Smart Parking" System Designed To Enhance Urban Parking Efficiency Through Intelligent Slot Booking And User-friendly Interaction. The System Features A Responsive Landing Page Highlighting Key Capabilities, Including AI-powered Vacancy Detection, Real-time Space Tracking, License Plate Recognition, And Mobile Accessibility. Users Can Authenticate Via A Secure Login Form And Proceed To An Interactive Slot Booking Interface, Where Available And Booked Slots Are Visually Distinguished. The Booking Page Allows Users To Select A Date, Time, And Parking Slot With Immediate Visual Feedback And Confirmation. The Interface Is Built Using HTML, CSS, And JavaScript, With A Focus On Usability And Clarity, Ensuring Accessibility Across Devices. This Solution Demonstrates A Scalable Foundation For Future Integration With IoT And Backend Technologies For Real-time Parking Management. Read more Read less

Agricultural Crop Recommendation Based On Productivity

Agriculture Is The Backbone Of The Indian Economy. Farmers Often Struggle To Achieve Expected Crop Yields Due To Factors Like Unpredictable Weather, Soil Variability, And Lack Of Advanced Forecasting Systems. This Paper Presents A Machine Learning-ba... Agriculture Is The Backbone Of The Indian Economy. Farmers Often Struggle To Achieve Expected Crop Yields Due To Factors Like Unpredictable Weather, Soil Variability, And Lack Of Advanced Forecasting Systems. This Paper Presents A Machine Learning-based Approach For Recommending Crops Based On Historical Data, Including Soil Type, Rainfall, And Crop Productivity, Using A Decision Tree Classifier. The System Provides Valuable Insights To Farmers By Analyzing Region-specific Datasets And Enhances Their Decision-making Capabilities Read more Read less

CARDIOCARE AI: PREDICTIVE RISK ASSESSMENT FOR ACUTE MYOCARDIAL INFARCTION USING MACHINE LEARNING

Acute Myocardial Infarction (AMI), Or Heart Attack Is A Severe Condition Caused By Reduced Blood Flow To The Heart. Early Detection Is Crucial To Lower Its Global Impact On Health. This Project Presents CardioCare AI, A Machine Learning Based Model F... Acute Myocardial Infarction (AMI), Or Heart Attack Is A Severe Condition Caused By Reduced Blood Flow To The Heart. Early Detection Is Crucial To Lower Its Global Impact On Health. This Project Presents CardioCare AI, A Machine Learning Based Model For Predicting And Assessing AMI Risk. By Analyzing Data Like Cholesterol Blood Pressure, Blood Sugar, Smoking Habits, And Family History, It Identifies High Risk Individuals With Great Accuracy. Using Advanced Algorithms Like XGBoost Known For Handling Medical Data Effectively, The System Detects Patterns And Relationships Among Clinical Features. CardioCare AI Focuses On Non- Invasive Data To Ensure Its Accessibility For Widespread Use. It Provides Healthcare Professionals With Actionable Insights For Early Intervention, Enabling Preventive Care And Personalized Treatments. The Model Integrates Predictive Analytics Into Daily Medical Practices To Enhance Diagnostic Speed And Reliability, Addressing Limitations Of Traditional Methods. This Innovative Approach Aims To Improve Patient Outcomes Reduce Healthcare Challenges, And Support Global Cardiovascular Disease Prevention Efforts. By Offering A Scalable Solution, CardioCare AI Represented. Read more Read less

EFFECT OF CURING METHODS OF VARIOUS SOURCES OF WATER ON CONCRETE

This Research Investigated The Effect Of Different Sources Of Water On The Compressivestrength Of Concrete. Water Samples From Different Sources Of Water Were Tested In Thelaboratory And Studied The Chemical Characteristics Of Water From Various Sour... This Research Investigated The Effect Of Different Sources Of Water On The Compressivestrength Of Concrete. Water Samples From Different Sources Of Water Were Tested In Thelaboratory And Studied The Chemical Characteristics Of Water From Various Sources Of Water. 15×15×15 PCC Cube Samples Were Cast Of Ratio (1 : 2.73 : 2.80). 10% And 20% Of Cement Is Replaced By Fly Ash. While Mixing The Concrete Super Plasticizer Was Usedas Admixture Named As FOSROC Auzamix 400. Suitability Of A Particular Source Of Water Forcuring Can Be Checked By Casting Concrete Cubes Using Water And Comparing Its 7 Days, 14 Days And 28 Days Strength. Canal Water, Tap Water And RO Water Was Taken For Curing. Submerged Curing Method Adopted To Evaluate The Compressive Strength Of Concrete. Cubes Were Investigated After Subjecting Them To Curing Conditions. Testing Indicate That Submerged Curing Method Provide Best Results. Sources Of Water Used In Curing Have A Significant Impact On The Compressive Strength On The Resulting Concrete. The Result Shows That RO Water Curing Had The Highest Compressive Strength. RO Water Curing Was The Best As Compared To Canal Water And Tap Water. It Was Concluded That RO Water Could Be Used For Curing Because Of Canel Water Contains More Hardness And Chloride In The Sample Of Water Read more Read less

An Improved AI Based Light And Fan Control System Using YOLO Deep Learning To Reduce Electricity Consumption

An Innovative Indoor Automation System Enhances Energy Efficiency By Combining Real-time Object Detection With Environmental Sensing. Using The YOLO Algorithm In Python And Light/temperature Sensors, It Monitors Human Presence And Room Conditions To... An Innovative Indoor Automation System Enhances Energy Efficiency By Combining Real-time Object Detection With Environmental Sensing. Using The YOLO Algorithm In Python And Light/temperature Sensors, It Monitors Human Presence And Room Conditions To Control Lights And Fans. The System Includes Three Modules: Computer Vision For Occupant Detection, Sensors For Environmental Monitoring, And An Automated Control Unit For Appliances. It Ensures Devices Operate Only When Needed, Promoting Energy Efficiency And Supporting Sustainable Living. Read more Read less

Dental Disease Detection Based On Deep Learning Algorithm Using Various Radiographs

Dental Diseases Such As Dental Caries, Periodontal Disease, Periapical Lesions, And Bone Loss Are Among The Most Prevalent Oral Health Issues Globally. Early And Accurate Detection Is Critical To Preventing Progression And Ensuring Effective Treatmen... Dental Diseases Such As Dental Caries, Periodontal Disease, Periapical Lesions, And Bone Loss Are Among The Most Prevalent Oral Health Issues Globally. Early And Accurate Detection Is Critical To Preventing Progression And Ensuring Effective Treatment. Traditional Diagnostic Methods Relying On Manual Inspection Of Radiographs Are Often Time-consuming And Subject To Variability In Interpretation. This Study Presents A Deep Learning-based Approach For The Automated Detection And Classification Of Dental Diseases Using Various Types Of Radiographic Images, Including Panoramic, Periapical, And Bitewing Radiographs. A Custom Convolutional Neural Network (CNN) Architecture, As Well As Pre-trained Models Such As ResNet50 And EfficientNet, Were Trained And Evaluated On A Curated Dataset Comprising Over 5,000 Labeled Radiographs Annotated By Experienced Dental Professionals. Image Preprocessing Techniques, Such As Contrast Enhancement And Noise Reduction, Were Applied To Improve Image Quality And Model Performance. The Models Were Trained To Classify Multiple Disease Conditions, Including Caries, Periodontal Bone Loss, Impacted Teeth, Cysts, And Periapical Abscesses. The Proposed Models Demonstrated High Classification Accuracy, With The Best-performing Model Achieving An Overall Accuracy Of 93.2%, Sensitivity Of 91.5%, And Specificity Of 94.8% Across All Radiograph Types. Cross-validation Confirmed The Model’s Robustness And Generalization Across Different Imaging Conditions And Patient Demographics. Furthermore, Class Activation Mapping (CAM) Was Used To Provide Visual Explanations, Increasing The Interpretability Of The Results And Enhancing Clinical Trust. This Study Confirms The Viability Of Deep Learning Systems As A Reliable Tool For Dental Disease Diagnosis. By Leveraging Multiple Radiographic Modalities, The System Enhances Diagnostic Accuracy And Can Serve As A Valuable Adjunct In Clinical Workflows, Reducing Diagnostic Delays And Improving Patient Outcomes. Read more Read less

Experimental Investigation Of Various Minor Losses By Using Bourdon Pressure Gauge

The Term “minor Losses”, Used In Many Textbooks For Head Loss Across Fittings, Can Be Misleading Since These Losses Can Be A Large Fraction Of The Total Loss In A Pipe System. In Fact, In A Pipe System With Many Fittings And Valves, The Minor Losses... The Term “minor Losses”, Used In Many Textbooks For Head Loss Across Fittings, Can Be Misleading Since These Losses Can Be A Large Fraction Of The Total Loss In A Pipe System. In Fact, In A Pipe System With Many Fittings And Valves, The Minor Losses Can Be Greater Than The Major (friction) Losses. Thus, An Accurate K Value For All Fittings And Valves In A Pipe System Is Necessary To Predict The Actual Head Loss Across The Pipe System. K Values Assist Engineers In Totaling All Of The Minor Losses By Multiplying The Sum Of The K Values By The Velocity Head To Quickly Determine The Total Head Loss Due To All Fittings. Knowing The K Value For Each Fitting Enables Engineers To Use The Proper Fitting When Designing An Efficient Piping System That Can Minimize The Head Loss And Maximize The Flow Rate. The Objective Of This Experiment Is To Determine The Loss Coefficient (K) For A Range Of Pipe Fittings, Including Several Bends, A Contraction, An Enlargement, And Agate Valve. In Our Capstone Project Work Our Main Objective Is To Replace The Manometer Which Is Available In The Experimental Set Up By Pressure Gauges For The Accurate Reading And Further Calculations. So That The Effectiveness And Reliability In The Performance Of The Test Ring Must Be Improved In Comparison With The Manometer. Read more Read less

STUDY OF NON DESTRUCTIVE TEST ON CONCRETE

In The Recent Past, Non Destructive Techniques To Evaluate Defects And Strength Of Concrete Have Developed Great Importance. These Techniques Have Their Own Advantages As Well As Limitations, When Compared To Conventional Strength Estimation And Dama... In The Recent Past, Non Destructive Techniques To Evaluate Defects And Strength Of Concrete Have Developed Great Importance. These Techniques Have Their Own Advantages As Well As Limitations, When Compared To Conventional Strength Estimation And Damage Detection Tests. In This Project Various Concrete Specimens (Beams And Square Slab) Were Cast And Discontinuities Were Created In These Using Thick Paper Sheet And Wooden Pieces At Varying Depths And Position Within The Specimens. The Objective Of This Project Was To Detect These Defects By Using Three Techniques: (1) Rebound Hammer Test (2) Ultra Sonic Pulse Velocity Test (3) Thermal Imaging Technique And Finding The Most Suitable And Economical Technique To Serve The Purpose. Use Of Thermal Imaging Technique In This Area Is A Very New Concept And Its Relevance In This Area Has Been Explored In This Project And It Has Been Found That This Technique Is Of Great Use In Detecting Defects In Concrete. Please Note That This Project Does Not Deal With Determining The Strength Of Specimens. Read more Read less

A REVIEW EXPERIMENTAL STUDY ON PARTIAL REPLACEMENT OF RIVER SAND BY CRUSHED SAND FOR M20 GRADE OF CONCRETE

Concrete Is A Dominant Part Of Construction Industry. In India, Ordinary Concrete Contains Natural Sand Obtained From Riverbeds As Fine Aggregates. In Recent Times With A Boost In Construction Activities, There Is A Significant Increase In The Consum... Concrete Is A Dominant Part Of Construction Industry. In India, Ordinary Concrete Contains Natural Sand Obtained From Riverbeds As Fine Aggregates. In Recent Times With A Boost In Construction Activities, There Is A Significant Increase In The Consumption Of Concrete Causing The Scarcity Of Natural Sand. Because Of Several Environmental Issues Thereby Government Imposing A Ban On The Uncontrolled Use Of Natural Sand. This Has Resulted In The Significant Rise In Cost Of Natural Sand. Therefore, To Find A Substitute To River Sand Has Become The Necessary In Last Two Decades. The Progressive Use Of Crushed Sand Will Conserve The Natural Resources For The Sustainable Development Of The Concrete In Construction Industry. In The Experimental Study Of Strength Characteristics Of Concrete Using Crushed Sand As Fine Aggregate It Is Found That Replacement Of Crushed Sand Can Be Very Much Helpful. Read more Read less

IOT Based Real-Time Landslide And Flood Detection And Emergency Notification Using Embedded System

Landslides, Often Triggered By Factors Like Heavy Rainfall, Seismic Activity, Or Human Intervention, Pose Significant Threats To Both Human Lives And Infrastructure. Traditional Monitoring Methods, However, Often Fall Short Due To Their Limitations I... Landslides, Often Triggered By Factors Like Heavy Rainfall, Seismic Activity, Or Human Intervention, Pose Significant Threats To Both Human Lives And Infrastructure. Traditional Monitoring Methods, However, Often Fall Short Due To Their Limitations In Terms Of Coverage, Real-time Data Acquisition, And Cost Effectiveness. Zigbeetechnology, Which Addresses These Challenges By Offering Exceptional Long-range Communication Capabilities And Low-power Consumption. This Makes It An Ideal Candidate For Establishing A Robust IoT Framework Dedicated To Landslide Monitoring. Zigbeebased IoT Approach Brings Forth A Multitude Of Benefits. Firstly, It Enables Early Detection, Allowing Authorities And Communities To Take Proactive Measures In Response To Evolving Conditions. The Presented Sensor Node Supports A Large Variety Of Low-power Sensors; The Range Of Applications Is Thus Considerable. In This Project A Landslide Monitoring System Was Built To Detect The Movement And Humidity Of The Soil That Generally Causes Landslides. And Also Monitors The Water Level To Detect The Abnormal Monitoring Alert. The Proposed System Utilizes A Network Of Low-power, Zigbee Network Technology. This System Includes The Various Sensors Such As Humidity Sensor, Float Sensor, Moisture Sensor And Vibration Sensor. These Sensors Are Integrated With Arduino NANO. And This Project Landslide ,flood Monitoring System Are Developed To The Proposed System. Read more Read less

SPEECH EMOTION RECOGNITION

This Project Looks At How To Create An Engaging Single-player Game Stressing Smooth Controls And Real-time Interaction Developed In Unity Game Engine. A Responsive Gaming Experience Is Possible For Players Who Can Move Smoothly Between Various States... This Project Looks At How To Create An Engaging Single-player Game Stressing Smooth Controls And Real-time Interaction Developed In Unity Game Engine. A Responsive Gaming Experience Is Possible For Players Who Can Move Smoothly Between Various States Including Walking, Running, Crouching, And Standing Still. The Game Features A Countdown Timer Indicating The Conclusion Of A Match And A Restart Choice, As Well As Systems For Managing Enemy Spawning And Scoring. Weapons Make Fight To Seem Dynamic And Varied By Allowing Both Semi-automatic And Full-automatic Shooting Modes. This Research Proposes A Real-time Speech Emotion Recognition (SER) System That Classifies Human Emotions From Audio Input Using A Machine Learning Pipeline. The System Utilizes The RAVDESS Dataset For Training And Extracts Acoustic Features Such As Mel Frequency Cepstral Coefficients (MFCC), Chroma And Spectral Contrast. A Multilayer Perceptron (MLP) Classifier Is Used For Emotion Prediction, Recognizing Eight Distinct Emotional States. Real-time Audio Can Be Recorded And Analyzed By The Model, Which Adaptively Improves Itself Using High Confidence Predictions. The Proposed System Is Capable Of Dynamic Learning, Thus Continuously Enhancing Performance Over Time. This Approach Facilitates The Integration Of Emotional Intelligence In Applications Such As Virtual Assistants ,mental Health Monitoring, And Interactive Voice-based Systems. Read more Read less

Optimized Convolutional Network With Adaptive Augmentation For Multi-Class Plant Disease Detection

This Project Builds Upon Existing Research In Plant Disease Classification By Introducing Key Innovations That Enhance Model Efficiency, Accuracy, And Interpretability. While The Document Outlines A Model Based On EfficientNetB3, This Implementation... This Project Builds Upon Existing Research In Plant Disease Classification By Introducing Key Innovations That Enhance Model Efficiency, Accuracy, And Interpretability. While The Document Outlines A Model Based On EfficientNetB3, This Implementation Leverages DenseNet121, Which Improves Feature Reuse, Reduces Overfitting, And Requires Fewer Parameters. Additionally, Extensive Data Augmentation Techniques Such As Random Flipping, Edge Detection, Convolutional Filtering, And Blurring Enhance The Model’s Ability To Generalize Across Diverse Real-world Conditions. Read more Read less

MULTI SOURCE ENERGY MANAGEMENT SYSTEM IN E.V CHARGING STATION

This Paper Explores The Design And Implementation Of An Energy Management System (EMS) For Electric Vehicle (EV) Charging Stations, Utilizing Microcontroller Technology To Optimize Energy Usage. The Proposed System Integrates Renewable Energy Sources... This Paper Explores The Design And Implementation Of An Energy Management System (EMS) For Electric Vehicle (EV) Charging Stations, Utilizing Microcontroller Technology To Optimize Energy Usage. The Proposed System Integrates Renewable Energy Sources, Dynamically Adjusts Charging Schedules Based On Real-time Energy Availability And Demand, And Ensures Efficient Energy Utilization. By Addressing Challenges Like Peak Energy Demand And Renewable Energy Variability, The EMS Enhances The Sustainability And Reliability Of EV Charging Infrastructure. Simulation And Experimental Results Demonstrate The Effectiveness Of This System In Achieving Energy Efficiency And Reducing Environmental Impact. Read more Read less

3D Game Development In Unity

This Project Looks At How To Create An Engaging Single-player Game Stressing Smooth Controls And Real-time Interaction Developed In Unity Game Engine. A Responsive Gaming Experience Is Possible For Players Who Can Move Smoothly Between Various States... This Project Looks At How To Create An Engaging Single-player Game Stressing Smooth Controls And Real-time Interaction Developed In Unity Game Engine. A Responsive Gaming Experience Is Possible For Players Who Can Move Smoothly Between Various States Including Walking, Running, Crouching, And Standing Still. The Game Features A Countdown Timer Indicating The Conclusion Of A Match And A Restart Choice, As Well As Systems For Managing Enemy Spawning And Scoring. Weapons Make Fight To Seem Dynamic And Varied By Allowing Both Semi-automatic And Full-automatic Shooting Modes. An Easy To Understand And Friendly User Interfae Lets Players Know With Live Updates On Their Score And Left Time. All Things Considered, The Game Offers An Engaging And Immersive Experience Totally Created In Unity By Combining Good Mechanics With A Simple UI. Read more Read less

COMPARATIVE STRUCTURAL ANALYSIS OF CLARIFIER IN VARIOUS SEISMIC ZONES: A REVIEW

Clarifiers Play A Crucial Role In Water And Wastewater Treatment Plants, And Their Failure During Earthquakes Can Pose Significant Threats To Public Health And The Environment. Therefore, Ensuring Their Seismic Resilience Is Essential For Maintaining... Clarifiers Play A Crucial Role In Water And Wastewater Treatment Plants, And Their Failure During Earthquakes Can Pose Significant Threats To Public Health And The Environment. Therefore, Ensuring Their Seismic Resilience Is Essential For Maintaining Operational Functionality During Such Events. This Review Analyzes How Seismic Activity Affects Clarifier Structures, With A Focus On Structural Design Across Different Seismic Risk Zones. It Investigates The Dynamic Forces—including Both Lateral And Vertical Loads—that Act On These Structures During Earthquakes And The Resulting Engineering Challenges. The Review Outlines Effective Design Strategies Aimed At Improving Performance Under Seismic Stress. Clarifiers Typically Consist Of Two Main Functional Zones: The Clarification Zone, Where Sedimentation By Gravity Occurs, And The Thickening Zone, Where Solids Settle And Form A Concentrated Sludge Blanket. The Review Concludes By Underscoring The Importance Of Ongoing Research To Improve The Seismic Durability Of Clarifiers And Other Essential Components Of Water Infrastructure In Earthquake-prone Areas Read more Read less

Rural Area Bus Tracking System

Public Transportation In Rural Areas Often Faces Challenges Such As Irregular Bus Schedules, Lack Of Realtime Tracking, And Passenger Inconvenience. This Paper Presents The Development Of A Rural Area Bus Tracking System Using The MERN Stack For A We... Public Transportation In Rural Areas Often Faces Challenges Such As Irregular Bus Schedules, Lack Of Realtime Tracking, And Passenger Inconvenience. This Paper Presents The Development Of A Rural Area Bus Tracking System Using The MERN Stack For A Web-based Passenger Application And A React Native Mobile App For Drivers. The System Provides Real-time Bus Location Tracking, Estimated Time Of Arrival (ETA), And Route Management, Improving Accessibility And Efficiency . This Paper Discusses The System Architecture, Methodology, Implementation, And Expected Outcomes. The Solution Integrates GPS-based Tracking, WebSocket-based Realtime Updates, And User-friendly Interfaces To Enhance The Passenger Experience And Streamline Transport Operations. Read more Read less

Sign Language Classification Text And Voice Output System Using Resnet

Sign Language Is A Crucial Communication Medium For Individuals With Hearing And Speech Impairments. However, The Lack Of Widespread Accessibility To Sign Language Interpreter’s Limits Communication Opportunities For The Deaf And Mute Community. This... Sign Language Is A Crucial Communication Medium For Individuals With Hearing And Speech Impairments. However, The Lack Of Widespread Accessibility To Sign Language Interpreter’s Limits Communication Opportunities For The Deaf And Mute Community. This Project Presents A Sign Language Classification And Voice Output System Using ResNet, A Deep Learning-based Model Designed For Accurate Sign Language Recognition. The System Processes Images And Video Frames Of Hand Gestures, Classifies Them Into Meaningful Words Or Letters, And Converts Them Into Speech Output. By Leveraging Convolutional Neural Networks (CNNs) With ResNet Architecture, This System Improves Recognition Accuracy And Real-time Responsiveness. The Model Is Trained Using Benchmark Sign Language Datasets And Optimized With Image Pre-processing Techniques. Performance Evaluation Is Carried Out Using Standard Metrics Such As Accuracy, Precision, Recall, And F1-score. This Study Demonstrates How Deep Learning Can Bridge The Communication Gap For Hearing-impaired Individuals, Providing An Effective Real-time Sign Language Recognition System. Read more Read less

College Hall Reservation System With Chatbot Support For Intelligent Campus Administration

We Propose An Intelligent, Automated Seminar Hall Booking System Integrated With A Chatbot Assistant To Streamline Scheduling On Campus. The System’s Objective Is To Replace Manual Reservation Processes—spreadsheets, Emails, And Walk-ins—with A Unifi... We Propose An Intelligent, Automated Seminar Hall Booking System Integrated With A Chatbot Assistant To Streamline Scheduling On Campus. The System’s Objective Is To Replace Manual Reservation Processes—spreadsheets, Emails, And Walk-ins—with A Unified Platform That Handles Booking Requests, Conflict Detection, And User Queries. Technically, We Employ A Three-tier Stack: A Python-based Backend For Booking Logic And NLP Processing, A Java Component For System Integration And Performance-critical Tasks, And HTML/CSS (with JavaScript) For The Responsive Web Interface. The Chatbot Uses NLP Techniques To Understand Natural-language Requests (e.g. “Book A Hall For 50 People Next Tuesday”) And Guides Users Through Available Slots. Automatic Conflict Checking Against The Database Ensures No Double-bookings, And The Calendar Interface Provides Real-time Availability. By Leveraging Automation And AI, The System Is Expected To Significantly Reduce Scheduling Conflicts And Administrative Workload, While Improving User Satisfaction Through 24/7 Conversational Support. In A “smart Campus” Context, This Integrated Approach Enhances Resource Utilization And Transparency: Notifications Keep Students And Staff Informed, And Logged Data Can Inform Future Campus Planning. Early Tests And User Feedback Suggest The Solution Makes Hall Reservation Faster, More Accurate, And More User-friendly. Read more Read less

IoT Based Enhanced Fault Detection And Real Time Monitoring For Underground Cables

The Detection Of Faults In Underground Cables Is Crucial For Maintaining The Reliability And Efficiency Of Electrical Power Distribution Systems. Traditional Methods Of Detecting Faults In Underground Cables Often Involve Manual Inspection, Which Is... The Detection Of Faults In Underground Cables Is Crucial For Maintaining The Reliability And Efficiency Of Electrical Power Distribution Systems. Traditional Methods Of Detecting Faults In Underground Cables Often Involve Manual Inspection, Which Is Time-consuming, Costly, And Inefficient. To Address These Challenges, The Integration Of Internet Of Things (IoT) Technologies Offers A Promising Solution For Real-time Monitoring And Fault Detection. Unist Embedded Systems Have Taken The Initiative To Design And Develop A Comprehensive IoT-based Underground Cable Fault Detection System, Which Allows For Continuous Monitoring And Immediate Identification Of Cable Issues. This IoTbased System Involves The Installation Of Sensors At Strategic Locations Along The Underground Cable Network. The System Uses Advanced Diagnostic Techniques Such As Impedance Measurement And Temperature Analysis To Detect Faults Like Cable Insulation Failure, Short Circuits, And Overheating, Which Can Lead To Failures If Left Unaddressed. The Real-time Monitoring Capability Of The IoT System Enhances The Reliability Of The Cable Network By Providing Timely Alerts Whenever A Fault Occurs. The System Is Capable Of Pinpointing The Exact Location Of The Fault, Significantly Reducing The Time And Cost Involved In Locating And Repairing The Problem. Additionally, The System's Remote Monitoring Feature Allows Maintenance Personnel To Respond Quickly To Issues,thus Preventing Power Outages Or Further Damage To The Network. Read more Read less

Livestock Population Dynamics In Plateau Region: (District-level Analysis Of Madhya Pradesh) And Analysis Of Some Government Policies

In This Study, We Assessed The Dynamics Of District-Level Livestock Population In India, Viz. Madhya Pradesh During The Period 2012-19. The Analytical Tools Comprised Of Simple Descriptive Statistics And Fitting Exponential Trend Equations. We Found... In This Study, We Assessed The Dynamics Of District-Level Livestock Population In India, Viz. Madhya Pradesh During The Period 2012-19. The Analytical Tools Comprised Of Simple Descriptive Statistics And Fitting Exponential Trend Equations. We Found Temporal Variations In Shares Of Different Species To Total District-Wise Livestock Population. While Population Of Cattle Declined In Madhya Pradesh, The Reverse Situation Was Observed In Case Of Buffaloes. Dynamics Of Changes In Small Ruminant Population Pointed Towards The Increasing Importance Of Goats As Compared To Sheep. Although, Cattle Are The Livestock Species On Which The Rural Population Mostly Depend For Their Livelihood In The State, Economic Dependence On Sheep And Pig Is More In Madhya Pradesh. Some Policy Suggestions Have Been Given Based On The Findings Of The Study On Leveraging The Opportunities Offered By Livestock Sector. Read more Read less

Empowering Libraries: AI-Driven Tools And Techniques For Digital Transformation And Sustainable Innovation

There Is No Doubt About The Revolutionary Effects Of Artificial Intelligence (AI) On A Variety Of Sectors, Most Notably Research And Education. The Incorporation Of AI Into Library Procedures Has Become An Unavoidable Step, Given Its Significance And... There Is No Doubt About The Revolutionary Effects Of Artificial Intelligence (AI) On A Variety Of Sectors, Most Notably Research And Education. The Incorporation Of AI Into Library Procedures Has Become An Unavoidable Step, Given Its Significance And The Requirement Of Maintaining Global Competitiveness. In Order To Give A Thorough Overview Of This Dynamic Field, The Paper Primarily Focuses On Explaining How AI-driven Tools And Techniques Are Used In Various Aspects Of Library Operations. Creating Machines That Can Perform Cognitive Tasks Similar To Those Of The Human Brain Is The Main Goal Of Artificial Intelligence. Libraries Can Overcome Physical Limitations And Become More Intelligent And Accessible By Integrating Artificial Intelligence In A Seamless Manner. This Article Explores How The Various Concepts Like Natural Language Processing (NLP), Large Language Model (LLM), Expert System (ES), AI-Powered Indexing Tools, Chatbots And Other AI Tools And Techniques May Change Library Infrastructure And Services In The Future, With The Potential To Improve Outcomes For Students, Teachers, Researchers, And Readers Alike. An In-depth Analysis Of The Benefits, Drawbacks, And Creative Uses Of AI Tools And Technology In Libraries Advances A Comprehensive Knowledge Of The Field And Opens The Door To Wise Decision-making In The Dynamic Field Of Library Sciences. Read more Read less

Clinical Risk Modeling For Re Hospitalization In Diabetes: Insights From Electronic Health Records

Hospital Readmissions Are A Major Concern In Healthcare, Impacting Patient Well- Being And Increasing Medical Costs. This Study Focuses On Predicting Hospital Readmission Rates For Diabetic Patients Using Machine Learning Techniques. By Analyzing Pat... Hospital Readmissions Are A Major Concern In Healthcare, Impacting Patient Well- Being And Increasing Medical Costs. This Study Focuses On Predicting Hospital Readmission Rates For Diabetic Patients Using Machine Learning Techniques. By Analyzing Patient Demographics, Medical History, And Hospitalization Details, We Aim To Identify Key Risk Factors Contributing To Readmission. The Study Employs Various Classification Models, Including Logistic Regression, Decision Trees, Random Forests, And XGBoost, To Determine The Most Effective Predictive Approach. Our Findings Indicate That Certain Patient Attributes, Such As Time Spent In The Hospital, Number Of Inpatient Visits, And Medication Changes, Play A Significant Role In Readmission Likelihood. Read more Read less

DEVELOPING AN EFFICIENT WASTE MANAGEMENT STRATEGIES FOR NASARAPUR REGION

Due To Rapid Increase In The Production And Consumption Processes, Societies Generate As Well As Reject Solid Materials Regularly From Various Sectors – Agricultural, Commercial, Domestic, Industrial And Institutional. The Considerable Volume Of Wast... Due To Rapid Increase In The Production And Consumption Processes, Societies Generate As Well As Reject Solid Materials Regularly From Various Sectors – Agricultural, Commercial, Domestic, Industrial And Institutional. The Considerable Volume Of Wastes Thus Generated And Rejected Is Called Solid Wastes. In This Study We Evaluate The Current Status And Identify The Major Problems. Total Solid Waste Generated In Tons/day Of Select Area Would Be Proportionate To The Population Of Specific Region In That Specific Year.To Develop Sustainable Management Of Solid Waste That Would Be Collected In Selected Area It Includes Manufacturing Of Paver Block ( Plastic Bottle Pieces, Steel Pieces, Coconut Shell, Metals) Vermicomposting (biodegradable Agriculture Waste , Food Waste , Domestic Kitchen Waste) And Suggestions Of Incinerator For Medical Waste , And Remaining Waste That Cannot Be Recyclable For Example Bubble Wrap, Plastic Bags, Ceramics , Household Glass Mirror Etc. That Dispose Through Landfilling In Road Construction. The Main Objective Of This Study Is To Maximum Use Of Waste To Get Sustainable Outcomes. Read more Read less

Brain Tumor Disease Detection Using Federated Learning With FedAvg

Federated Learning (FL) Has Emerged As A Critical Paradigm For Collaborative Model Training In Privacy-constrained Domains, Particularly In Healthcare. This Study Presents A Comprehensive FedAvg-based Framework For Brain Tumor Detection From Magnetic... Federated Learning (FL) Has Emerged As A Critical Paradigm For Collaborative Model Training In Privacy-constrained Domains, Particularly In Healthcare. This Study Presents A Comprehensive FedAvg-based Framework For Brain Tumor Detection From Magnetic Resonance Imaging (MRI) Scans, Employing Three Geographically Distributed Institutions As Local Clients And A Central Server For Global Aggregation. Each Client Trains An Identical Convolutional Neural Network (CNN) Model Using Institution-specific Subsets Of The BraTS 2020 Dataset, With Preprocessing Steps Including Skull Stripping, Intensity Normalization, And Uniform Resizing To 224×224 Pixels. Over 50 Communication Rounds, Local Models Perform Two Epochs Of Stochastic Gradient Descent Per Round, Contributing Data-weighted Parameter Updates To The Server. The Global Model, Initialized With Xavier Initialization, Converges Rapidly, Achieving A Validation Accuracy Of 96.2% By Round 30 And Stabilizing Between 95% And 97% By The Final Round. Comparative Analysis Against A Centralized Baseline—trained On Pooled Data—shows The Federated Framework Attains 96.5% Accuracy, Indicating Negligible Performance Degradation Despite Strict Privacy Constraints. Additional Evaluation Metrics Include Precision (95.8%), Recall (96.0%), And F1-score (95.9%), Demonstrating Balanced Classification Performance. Resource Utilization Metrics Reveal That Federated Training Incurs Only A 12% Increase In Training Time Relative To Centralized Training, Underscoring The Framework’s Efficiency. The Proposed Methodology Preserves Patient Privacy By Keeping Raw MRI Data Localized While Delivering Near-centralized Performance, Making It A Viable Solution For Multi-institutional Medical Imaging Collaborations. This Work Lays The Groundwork For Future Enhancements, Such As Integrating Secure Aggregation, Differential Privacy, And Personalized Model Fine-tuning, To Further Strengthen Privacy Guarantees And Model Personalization. Read more Read less

Early Detection Of Agoraphobia Using ML Algorithm

Agoraphobia Is Frequently Overlooked Due To Low Mindfulness And Vacillation In Seeking Help. This Design Implements A Machine Literacy- Grounded System For Early Discovery Using Responses From A 10- Question Dataset. After Applying Mode Insinuation A... Agoraphobia Is Frequently Overlooked Due To Low Mindfulness And Vacillation In Seeking Help. This Design Implements A Machine Literacy- Grounded System For Early Discovery Using Responses From A 10- Question Dataset. After Applying Mode Insinuation And Marker Garbling For Preprocessing, We Trained Several Bracket Models Including SVM, Decision Tree, Random Forest, Naive Bayes, And KNN. The Model With The Stylish Delicacy Was Named For Deployment. Druggies Can Interact With The System Through A Simple Interface That Accepts Quiz- Grounded Responses, Descriptive Textbook, And Voice Input( Under Development). In Addition To Prognostications, The Platform Offers Relaxation Tools Like Games And Links To Yoga And Contemplation Coffers, Making It Useful For Individualities And Internal Health Professionals Likewise. Read more Read less

Adaptive Traffic Lights Control Using IoT And Image Processing

Urban Traffic Congestion Has Become One Of The Major Issues In Modern Cities. As City Populations Grow And Vehicle Usage Increases, Existing Road Systems Struggle To Manage The Traffic Load Effectively. This Results In Long Delays, Energy Wastage, In... Urban Traffic Congestion Has Become One Of The Major Issues In Modern Cities. As City Populations Grow And Vehicle Usage Increases, Existing Road Systems Struggle To Manage The Traffic Load Effectively. This Results In Long Delays, Energy Wastage, Increased Pollution, And Decreased Mobility. Traditional Traffic Control Methods Rely On Fixed-timing Signals That Follow Preset Schedules Without Considering Real-time Traffic Density. This Leads To Significant Inefficiencies And Commuter Frustration. To Tackle This Issue, We Propose An Intelligent Traffic Signal Control System That Dynamically Adjusts Signal Durations Based On Current Traffic Conditions. This System Integrates Internet Of Things (IoT) Devices With Image Processing Techniques. Specifically, Haar Cascade Classifiers Are Used To Detect Vehicles And Measure Traffic Density Efficiently. By Adapting Signal Timings According To Actual Traffic Flow At Intersections, The System Ensures Smoother Vehicle Movement And Minimizes Unnecessary Delays. The Use Of Decentralized, IoT-based Microcontrollers Enhances The System’s Flexibility And Reduces Reliance On Central Servers, Making It More Resilient In Practical Deployments. Simulated Results Demonstrate Notable Improvements In Reducing Traffic Delays, Optimizing Resource Use, And Enhancing Travel Experiences. This Adaptive Approach Paves The Way For Smarter Cities, Contributing To Reduced Environmental Impact And Improved Urban Quality Of Life. Read more Read less

Interactive Virtual Art With Hand Gestures

It's Been Quite Difficult To Teach Students Over An Online Platform And Get The Lesson Interesting Amidst The COVID-19 Pandemic. Due To This Reason, There Was A Necessity Of A Dustfree Classroom For Kids. This Article Uses MediaPipe And OpenCV To Off... It's Been Quite Difficult To Teach Students Over An Online Platform And Get The Lesson Interesting Amidst The COVID-19 Pandemic. Due To This Reason, There Was A Necessity Of A Dustfree Classroom For Kids. This Article Uses MediaPipe And OpenCV To Offer An Interesting Paint Application Which Recognizes Hand Gestures And Traces Hand Joints. The Application Makes Use Of Hand Gestures To Provide Users With A User-friendly Approach To Human Computer Interaction (HCI). HCI's Overall Objective Is To Enhance Human-computer Interaction. Read more Read less

EFFICIENT REVERSIBLE FOR QUANTUM COMPUTING A NOVEL 4-BIT LFSR APPROACH

Reversible Logic Gates Are Critical Components In The Field Of Quantum Computing And Low-power Digital Circuits, As They Allow For The Retrieval Of Input States From Output States Without Any Loss Of Information. This Property Ensures Minimal Energy... Reversible Logic Gates Are Critical Components In The Field Of Quantum Computing And Low-power Digital Circuits, As They Allow For The Retrieval Of Input States From Output States Without Any Loss Of Information. This Property Ensures Minimal Energy Dissipation, Aligning With The Principles Of Reversible Computation. Their Applications Extend To Quantum Computing, Cryptographic Systems, And Error Detection And Correction Mechanisms. This Paper Presents A Novel Design For A Reversible D Flip-Flop (RDFF) And A 4-bit Linear Feedback Shift Register (LFSR). The Linear Feedback Shift Register (LFSR) Is Built Utilizing Four Register-Driven D Flip-Flops (RDFFs) And A Feedback Mechanism That Incorporates A Feynman Gate. This Configuration Successfully Showcases The Capacity To Generate Pseudo-random Sequences. The LFSR Design Demonstrates A 10%improvement In Total Reversible Logic Implementation Cost And27% Enhancement In Quantum Cost, Making It A More Resource Efficient Option For Reversible Computing. This Work Makes A Valuable Contribution To The Field Of Reversible Computing By Offering Efficient Designs For Essential Components. Read more Read less

Desktop AI , A Virtual Assistant

The Jarvis Application Is A Voice-activated Virtual Assistant Designed To Automate Daily Tasks Through Simple Voice Commands. It Offers A Range Of Functionalities, Including Personalized Greetings, Web Searches (Google, YouTube, And Wikipedia), Real-... The Jarvis Application Is A Voice-activated Virtual Assistant Designed To Automate Daily Tasks Through Simple Voice Commands. It Offers A Range Of Functionalities, Including Personalized Greetings, Web Searches (Google, YouTube, And Wikipedia), Real-time Weather And Time Updates, And Alarm Setting. Users Can Also Control Applications, Manage Schedules, Perform Calculations, And Access News Updates. Jarvis Supports Messaging Tasks On WhatsApp And Includes Password Protection, A “Remember” Function For Storing Information, And A Focus Mode To Minimize Distractions. Additionally, It Features Fun Elements Like Rock Paper Scissors And Live IPL Score Tracking. The Application Is Packaged As An Executable File, Making It Compatible Across Different Systems.The Jarvis System Provides A Practical And Interactive Assistant, Enhancing Productivity While Offering A User-friendly, Hands-free Experience. Overall, Jarvis Combines Advanced Technology With Practicality, Delivering A Highly Interactive, Hands-free Experience That Transforms The Way Users Manage Their Daily Lives. Read more Read less

Smart Canteen System Using AI, Real-Time Analytics, And Cashless Integration

The Smart Canteen System Is An Advanced Solution Designed To Digitize And Optimize The Operations Of Traditional Canteens By Integrating Modern Technologies. It Enhances Customer Convenience And Staff Efficiency Through Features Such As QR Code Scann... The Smart Canteen System Is An Advanced Solution Designed To Digitize And Optimize The Operations Of Traditional Canteens By Integrating Modern Technologies. It Enhances Customer Convenience And Staff Efficiency Through Features Such As QR Code Scanning, Real-time Menu Updates, Automated Billing, And Cashless Payments. Customers Can Place Orders Via A Mobile App Or Kiosk Interface, Thereby Eliminating Long Queues And Manual Intervention. Real-time Order Tracking And Notifications Further Improve The User Experience By Ensuring Transparency And Reducing Wait Times. From The Administrative Perspective, The System Provides Tools For Inventory Tracking, Sales Monitoring, And Data-driven Decision-making Through Analytical Reports. By Integrating IoT And Data Analytics, It Predicts Demand Patterns, Reduces Food Wastage, And Supports Cost-effective Management. The Smart Canteen System Is A Scalable And Sustainable Solution Suitable For Educational Institutions, Offices, And Similar Environments, Reflecting The Transformative Power Of Technology In Modernizing Food Services. Motivation- The Smart Canteen System Is Driven By The Increasing Demand For Efficiency, Convenience, And Accuracy In Traditional Canteen Operations. Conventional Systems Often Suffer From Long Queues, Manual Order Processing, And Cash- Based Transactions, Which Lead To Time Wastage, Human Error, And Customer Dissatisfaction. With The Growing Reliance On Technology In Daily Life, There Is A Clear Need For A Digital Solution That Streamlines These Processes. This Project Aims To Eliminate Delays, Reduce Errors, And Provide Real-time Updates Through Features Like Cashless Payments, Automated Billing, And AI-powered Assistance. It Not Only Saves Time For Both Customers And Staff But Also Improves Inventory Management, Reduces Food Wastage, And Enhances Overall Service Quality. By Aligning With Modern User Expectations And The Broader Trend Of Digital Transformation, The Smart Canteen System Offers A Scalable And Sustainable Approach To Modernizing Institutional Food Services [7]. Read more Read less

Vehicle Insurance Fraud Detection System

Concern Over Insurance Fraud In The Machine Sedulity Is Growing, As It Can Affect In Significant Financial Losses And Advanced Decorations For Law- Abiding Policyholders. This Study Offers A Machine Knowledge- Predicated System For Further Directly R... Concern Over Insurance Fraud In The Machine Sedulity Is Growing, As It Can Affect In Significant Financial Losses And Advanced Decorations For Law- Abiding Policyholders. This Study Offers A Machine Knowledge- Predicated System For Further Directly Relating False Claims. To Address Class Imbalance, We Use A Kaggle Dataset And The SMOTE Fashion. With A Python Flask- Predicated Web Interface That Lets Stoners Enter Claim Details And Get Immediate Fraud Discovery Results, Our System Predicts Fraud Using Random Forest, Decision Tree, And Logistic Regression Models. Read more Read less

Deep Metric Learning For Teeth Classification

The Proposed System Is A Deep Learning-based Dental Image Classification Tool That Uses Deep Metric Learning Techniques For Accuratedental Conditions Identification, Such As Cavities, Implants, Fillings, Implanted Teeth, Impacted Teeth, And Root Cana... The Proposed System Is A Deep Learning-based Dental Image Classification Tool That Uses Deep Metric Learning Techniques For Accuratedental Conditions Identification, Such As Cavities, Implants, Fillings, Implanted Teeth, Impacted Teeth, And Root Canals Play A Critical Role In The Early Diagnosis And Treatment Planning In Dentistry. This Project Proposes An Automated, Deep Learning-based Solution For Teeth Classification Using Image Processing And Deep Metric Learning Techniques. Leveraging DenseNet121, The System Is Trained To Detect And Classify Various Dental Conditions Identification With High Accuracy. The Classification Is Achieved Through A Well-defined Pipeline Involving Image Preprocessing, Segmentation, Image Splitting, And Feature Extraction, Enabling The Model To Handle Variability In Image Quality And Tooth Structure.The System Is Deployed As A User-friendly Web Application Built Using Streamlit, Allowing Users To Register, Log In, And Upload Dental Images For Instant Classification Results. The Application Processes The Image, Classifies The Dental Condition, And Displays Performance Metrics Such As Accuracy, Confusion Matrix, And ROC Curve. This Solution Aims To Assist Dental Professionals, Researchers, And, In Forensic Cases, By Providing An Accessible Tool For Image-based Dental Condition Identification, Thus Enhancing Clinical Decision-making Through Automated And Efficient Image Analysis. Read more Read less

Predicting Bankruptcy With Precision: Insights From Hybrid Machine Learning Models On Unbalanced Polish Financial Data

Bankruptcy Prediction Is A Critical Area In Financial Risk Assessment, Supporting Timely Decisions For Investors, Regulators, And Institutions. This Study Presents A Comparative Analysis Of Multiple Machine Learning Models, Including Traditional Algo... Bankruptcy Prediction Is A Critical Area In Financial Risk Assessment, Supporting Timely Decisions For Investors, Regulators, And Institutions. This Study Presents A Comparative Analysis Of Multiple Machine Learning Models, Including Traditional Algorithms (Decision Tree, Naive Bayes), Deep Learning Methods (CNN, LSTM), And Hybrid Approaches (XGBoost + ANN, Decision Tree + Gaussian), Applied To An Imbalanced Financial Dataset From Polish Companies. The Dataset Poses Real-world Challenges Such As Class Imbalance And Feature Noise, Which Are Addressed Through Data Preprocessing, Feature Selection, And Resampling Techniques. The Proposed Hybrid Models Integrate The Strengths Of Ensemble Learning And Neural Networks, Improving Classification Performance On Minority (bankrupt) Classes. Evaluation Using Metrics Like Precision, Recall, And F1-score Demonstrates That Hybrid And Deep Learning Models Outperform Traditional Classifiers, With The XGBoost–ANN Model Achieving The Best Overall Results. Feature Importance Analysis Further Reveals The Most Influential Financial Indicators Contributing To Bankruptcy Prediction. This Work Offers A Robust, Adaptable Framework For Handling Imbalanced Datasets In Financial Domains, Contributing Practical Insights For Early Risk Detection And Decision-making. Read more Read less

Heart Disease Detection Using Logistic Regression

Heart Disease Is One Of The Leading Causes Of Mortality Worldwide, Necessitating Early And Accurate Detection For Effective Treatment And Prevention. This Project Focuses On Developing A Predictive Model For Heart Disease Detection Using Logistic Reg... Heart Disease Is One Of The Leading Causes Of Mortality Worldwide, Necessitating Early And Accurate Detection For Effective Treatment And Prevention. This Project Focuses On Developing A Predictive Model For Heart Disease Detection Using Logistic Regression, A Robust Statistical Method Widely Used For Binary Classification Problems. The Primary Goal Is To Analyze Patient Data And Predict The Likelihood Of Heart Disease Based On Various Clinical And Demographic Attributes. Read more Read less

Fake Product Identification

Fake Product Identification Is A Critical Process In Combating Counterfeit Goods, Ensuring Consumer Safety, And Protecting Brand Integrity. One Of The Biggest Challenges In Today's Retail Market Is The Counterfeiting Of Products. Counterfeiting Produ... Fake Product Identification Is A Critical Process In Combating Counterfeit Goods, Ensuring Consumer Safety, And Protecting Brand Integrity. One Of The Biggest Challenges In Today's Retail Market Is The Counterfeiting Of Products. Counterfeiting Products Are Just Low-quality Copies Of Some Genuine Brand. Many Different Methods Have Been Adopted From Time To Time To Combat The Counterfeiting Of The Products Such As RFID Tags, Artificial Intelligence, Machine Learning, QR Code-base System, And Many More.Counterfeit Products, Often Designed To Mimic Genuine Items, Can Pose Significant Risks, From Financial Loss To Health Hazards. This Growing Problem Necessitates The Development Of Robust Methods And Technologies To Identify Fake Products Quickly And Accurately To Address This Challenge, Various Methodologies Have Been Adopted, Including Physical Inspection, Digital Authentication Systems, And Machine Learning Algorithms. Physical Inspection Involves Analyzing Packaging, Materials, And Labels For Inconsistencies. Digital Tools, Such As QR Codes And Serial Number Verification, Provide Real-time Authentication. Machine Learning And AI Enhance The Process By Analyzing Patterns And Detecting Anomalies In Product Features With High Precision. These Approaches Are Implemented In Collaboration With Manufacturers, Retailers, And Consumers To Create A Seamless Verification Process. Read more Read less

Identification Of Indian Fake Currency Using Convolutional Neural Network

The "Currency Detection System" Is An Innovative Application Designed To Distinguish Between Genuine And Counterfeit Currency Notes. Leveraging Advanced Deep Learning Models, Feature Matching Techniques, And An Intuitive User Interface, The System Pr... The "Currency Detection System" Is An Innovative Application Designed To Distinguish Between Genuine And Counterfeit Currency Notes. Leveraging Advanced Deep Learning Models, Feature Matching Techniques, And An Intuitive User Interface, The System Provides An Efficient And User-friendly Approach To Detecting Fake Currency. With Applications In Retail, Banking, And Public Sectors, This Project Aims To Reduce The Prevalence Of Counterfeit Notes And Promote Financial Security. Read more Read less

Intelligent Attendance Monitoring System Using Deep Face Recognition With Residual Neural Network (ResNet) Analysis

An Innovative Attendance System Utilizing Face Detection Technology Is Presented, Aimed At Improving The Efficiency And Accuracy Of Attendance Tracking. This System Integrates Computer Vision With Advanced Deep Learning Techniques, Enabling Reliable... An Innovative Attendance System Utilizing Face Detection Technology Is Presented, Aimed At Improving The Efficiency And Accuracy Of Attendance Tracking. This System Integrates Computer Vision With Advanced Deep Learning Techniques, Enabling Reliable Recognition Of Individuals And Real-time Attendance Logging. Convolutional Neural Networks (CNNs) Are Employed For Face Detection And Recognition, Establishing A Robust Alternative To Traditional Attendance Methods. With High Detection Accuracy, Rapid Processing Times, And Comprehensive Data Security Protocols, This System Is Well-suited For Implementation In Educational Institutions, Corporate Environments, And Secure Access Management. Experimental Results Indicate A Detection Accuracy Of 98.6% And An Average Verification Time Of Under 1.5 Seconds, Underscoring The Effectiveness Of Face Recognition Technology In Automated Attendance Systems. Read more Read less

Deep Learning Approaches For Brain State Detection Under Anesthesia: A CNN-LSTM Framework

This Research Presents An Automated Approach To Analyzing Brain States During Anesthesia Using Convolutional Neural Networks (CNNs) And Long Short-Term Memory (LSTM) Networks. By Leveraging The Spatial Feature Extraction Power Of CNNs And The Tempora... This Research Presents An Automated Approach To Analyzing Brain States During Anesthesia Using Convolutional Neural Networks (CNNs) And Long Short-Term Memory (LSTM) Networks. By Leveraging The Spatial Feature Extraction Power Of CNNs And The Temporal Sequence Processing Capabilities Of LSTMs, The Model Effectively Classifies Brain States From EEG Signals. The System Identifies Key States Such As Consciousness, Light Anesthesia, Deep Anesthesia, And Emergence. Extensive Experiments On EEG Datasets Show That The Proposed CNN-LSTM Hybrid Architecture Outperforms Traditional Machine Learning Methods In Accuracy. This Method Offers Real-time, Objective, And Precise Monitoring Of Brain States, Aiding Anesthesiologists In Clinical Decision-making. The Research Paves The Way For Safer Anesthesia Practices By Integrating Advanced Deep Learning Technologies For Reliable Brain State Classification. Read more Read less

Secure Authentication Frame Work With Edge Computing For Real Time Patient Health Monitoring In Iomt

As Healthcare Increasingly Embraces Cloud-based Technologies, It Faces Critical Issues Such As Transmission Delays And Heightened Security Risks Due To Centralized Data Storage. Traditional Cloud Setups Often Fall Short In Delivering Real-time Patien... As Healthcare Increasingly Embraces Cloud-based Technologies, It Faces Critical Issues Such As Transmission Delays And Heightened Security Risks Due To Centralized Data Storage. Traditional Cloud Setups Often Fall Short In Delivering Real-time Patient Data And Safeguarding Sensitive Information From Unauthorized Access. To Overcome These Challenges, This Project Presents A Secure Authentication-Based Patient Report Transmission System Powered By Edge Computing. By Handling And Verifying Data At Local Edge Nodes—closer To Where It Originates—the System Significantly Reduces Latency And Enables Prompt Data Delivery, Which Is Essential For Time-sensitive Medical Decisions. This Decentralized Model Not Only Improves Processing Speed But Also Enhances The Overall Efficiency Of Data Management. To Ensure Robust Security, The System Incorporates Multi-factor Authentication (MFA) And Role-based Access Control (RBAC), Which Together Help Confirm User Identities And Limit Access Based On Professional Roles. This Innovative Framework Offers A Reliable, Secure, And Fast Method For Transmitting Healthcare Data, Making It Highly Effective For Critical Care Environments That Demand Both Speed And Privacy. Read more Read less

IoT-Enabled System For Real-Time Heart Attack Detection And Automated Emergency Response

This Project Introduces A Smart, Real-time Health Monitoring System Built Using Internet Of Things (IoT) Technology To Help Respond Quickly To Cardiac Emergencies. It Uses A Small Sensor Called The MAX30100 To Track A Person’s Heart Rate And Oxygen L... This Project Introduces A Smart, Real-time Health Monitoring System Built Using Internet Of Things (IoT) Technology To Help Respond Quickly To Cardiac Emergencies. It Uses A Small Sensor Called The MAX30100 To Track A Person’s Heart Rate And Oxygen Levels. These Readings Are Processed By A Compact Microcontroller (NodeMCU ESP8266), Which Checks For Any Signs Of Abnormal Heart Activity Like Unusually Fast (tachycardia) Or Slow (bradycardia) Heartbeats. If Something Unusual Is Detected, The System Automatically Sends An Emergency Text Message Via A GSM Module. This Message Includes The Person's Location, Thanks To A Built-in GPS Module (NEO-6M). In Addition, Caregivers Or Family Members Can View The Patient’s Live Health Data Remotely Using The Blynk App On Their Smartphones. The System Is Designed To Be Portable, Battery-operated, And Reliable Even In Remote Areas With Limited Medical Access. Overall, It Helps Speed Up Emergency Responses And Could Be A Valuable Tool For Improving Heart Health Monitoring On A Larger Scale. Read more Read less

A STUDY ON THE EFFECTIVENESS OF TRAINING AND DEVELOPMENT AMONG EMPLOYEES IN BHARATH RUBBER INDIA LIMITED, MADURAI

The Study Entitled “A STUDY ON THE EFFECTIVENESS OF TRAINING AND DEVELOPMENT AMONG EMPLOYEES IN BHARATH RUBBER INDIA LIMITED, MADURAI This Study Investigates The Effectiveness Of Training And Development Programs Among Employees At Bharath Rubber Ind... The Study Entitled “A STUDY ON THE EFFECTIVENESS OF TRAINING AND DEVELOPMENT AMONG EMPLOYEES IN BHARATH RUBBER INDIA LIMITED, MADURAI This Study Investigates The Effectiveness Of Training And Development Programs Among Employees At Bharath Rubber India Limited, Madurai. Utilizing Primary Data Collected Through Structured Questionnaires, The Research Aims To Assess How These Programs Influence Employee Performance, Motivation, And Job Satisfaction. The Study Is Structured Into Five Chapters: The First Introduces The Concept, Need, And Scope Of Training And Development, Along With A Literature Review; The Second Provides A Profile Of Bharath Rubber India Limited; The Third Outlines The Research Design, Including Objectives, Limitations, And Methodology; The Fourth Presents Data Analysis Using Tools Such As Simple Percentage Analysis, Correlation, And Regression; And The Fifth Discusses Findings, Offers Suggestions, And Concludes The Study. Key Findings Suggest That While A Majority Of Employees Acknowledge The Importance Of Training In Enhancing Knowledge And Skills, There Is Room For Improvement In Areas Such As The Quality Of External Training Agencies And The Incorporation Of Modern Training Methods. The Study Concludes That Effective Training And Development Are Crucial For Organizational Growth And Recommends Regular Feedback Mechanisms And The Adoption Of Contemporary Training Techniques To Further Enhance Employee Development. Read more Read less

Safety Companion

Women's Safety Is Becoming An Increasingly Pressing Topic In India And Other Nations. The Fundamental Difficulty With The Police Handling Of These Incidents Is That They Are Limited In Their Ability To Respond Swiftly To Distress Calls. These Limits... Women's Safety Is Becoming An Increasingly Pressing Topic In India And Other Nations. The Fundamental Difficulty With The Police Handling Of These Incidents Is That They Are Limited In Their Ability To Respond Swiftly To Distress Calls. These Limits Include Not Knowing The Location Of The Crime And Not Knowing The Crime Is Occurring At All At The Victim's End, Making Reaching The Police Confidently And Discreetly Difficult. To Avoid These Circumstances, This Project Develops A Mobile Application That Provides Women With A Dependable Option To Make An Emergency Call, Send A Message, And Update Her Whereabouts To The Police As Well As Her Family's Close Relatives. Read more Read less

A Study On Trends In Changing Oil Prices And Its Stimulate On Inflation In India

Fluctuations In Global Oil Prices Have Emerged As A Key Factor Influencing Inflation Trends Across The World, With India Being Particularly Vulnerable Due To Its Heavy Reliance On Crude Oil Imports. As Oil Prices Surge, The Indian Economy Faces Mount... Fluctuations In Global Oil Prices Have Emerged As A Key Factor Influencing Inflation Trends Across The World, With India Being Particularly Vulnerable Due To Its Heavy Reliance On Crude Oil Imports. As Oil Prices Surge, The Indian Economy Faces Mounting Pressure From Increased Transportation And Production Expenses. These Rising Costs Contribute To An Overall Escalation In The Prices Of Goods And Services, Diminishing Consumer Purchasing Power. This Paper Investigates The Inflationary Effects Of Oil Price Hikes In India, Emphasizing Their Influence On Everyday Living Expenses And Consumption Habits. The Research Also Reviews How Reduced Disposable Income Affects Spending Patterns And Evaluates The Government’s Interventions Aimed At Countering These Economic Pressures. Findings Suggest A Notable Inverse Relationship Between Rising Oil Prices And Consumer Spending, Indicating A Significant Strain On Domestic Demand. The Study Highlights The Necessity For Strategic Policy Initiatives To Shield Consumers And Maintain Economic Equilibrium Amid Ongoing Global Oil Price Volatility. Read more Read less

Study On Capital Budgeting Techniques In Large Scale Industries

Capital Budgeting Stands As A Crucial Element Within Financial Decision-making, Especially For Large-scale Industries, As It Empowers Firms To Undertake Strategic Investment Choices Aligned With Their Long-term Corporate Objectives. This Research Exp... Capital Budgeting Stands As A Crucial Element Within Financial Decision-making, Especially For Large-scale Industries, As It Empowers Firms To Undertake Strategic Investment Choices Aligned With Their Long-term Corporate Objectives. This Research Explores The Real-world Application Of Various Capital Budgeting Techniques, Such As Net Present Value (NPV), Internal Rate Of Return (IRR), Payback Period, Discounted Payback Period, Profitability Index, And Modified Internal Rate Of Return (MIRR). The Effectiveness Of These Methods In Aiding Industries To Evaluate Investment Opportunities And Assess Associated Risks Is Closely Examined. Although Many Corporations Implement Advanced Discounted Cash Flow (DCF) Models, Practical Business Conditions Often Necessitate A Combination Of Both Traditional And Modern Methods. Elements Like Organizational Size, Industry Sector, Capital Framework, And Leadership Approach Significantly Influence The Choice Of Evaluation Techniques. Additionally, Contemporary Risk Assessment Tools, Including Real Options Analysis And Scenario Planning, Are Becoming More Common In Investment Decision-making Processes. Simpler Approaches, Such As The Payback Period Method, Continue To Hold Favor In Certain Sectors Due To Their Simplicity And Quick Results, Despite Known Limitations. This Paper Sheds Light On The Evolving Trends In Budgeting Strategies Across Various Industries And Regions, Underlining The Importance Of Matching Financial Decision-making Tools With Objectives For Sustainable Growth And Adaptability In A Constantly Changing Business Landscape. Read more Read less

A Convolutional Neural Network Approach To Interview Simulation And Evaluation

Artificial Intelligence (AI) Is Transforming A Wide Range Of Industries By Enabling Machines To Perform Tasks That Traditionally Required Human Intelligence, Such As Perception, Decision-making, And Natural Interaction. In The Context Of Recruitment,... Artificial Intelligence (AI) Is Transforming A Wide Range Of Industries By Enabling Machines To Perform Tasks That Traditionally Required Human Intelligence, Such As Perception, Decision-making, And Natural Interaction. In The Context Of Recruitment, Traditional Interview Methods Often Focus Primarily On Technical Skills, Neglecting Important Aspects Like Emotional Intelligence And Candidate Confidence. This Paper Presents An AI-powered Mock Interview Evaluator That Offers A More Holistic Assessment By Analyzing Emotional Expressions And Confidence Levels In Real Time. The System Combines Convolutional Neural Networks (CNNs) For Facial Emotion Recognition And Recurrent Neural Networks (RNNs) For Analyzing Speech And Body Language. Trained On A Diverse Dataset Of Mock Interviews, The Model Can Detect Emotions Such As Happiness, Sadness, Anger, And Surprise, While Also Estimating Confidence Through Multimodal Analysis. Read more Read less

Smart Movable Vehicle Robot Arm

The "Smart Movable Vehicles With Robotic Arm" Aims To Design And Develop A Semi-autonomous Mobile Robotic System Capable Of Performing Remote Operations In Dynamic Environments. This Vehicle Integrates Mobility With A Versatile Robotic Arm, Enabling... The "Smart Movable Vehicles With Robotic Arm" Aims To Design And Develop A Semi-autonomous Mobile Robotic System Capable Of Performing Remote Operations In Dynamic Environments. This Vehicle Integrates Mobility With A Versatile Robotic Arm, Enabling It To Carry Out Tasks Such As Material Handling, Object Detection, And Pick- And-place Operations Efficiently. The System Is Designed To Operate Under Both Wired And Wireless Control Modes, Enhancing Its Adaptability And User Flexibility In Various Operational Scenarios. The Core Objective Of This Project Is To Simulate Real-world Industrial Or Rescue Applications Where Human Intervention Might Be Difficult Or Risky. The Vehicle’s Movement And Robotic Arm Actions Can Be Monitored And Controlled Through A Dual-interface System: One Through Direct Wired Commands And Another Through Wireless Modules Such As Bluetooth Or Wi-Fi. This Dual-control Approach Ensures Consistent Operation Even In Cases Where One Mode Fails Or Becomes Less Effective Due To Environmental Limitations. The Robotic Arm Is Actuated Using Servo Or DC Motors, Which Are Precisely Controlled To Execute Complex Tasks Like Gripping, Lifting, And Placing Objects. The Smart Vehicle Base Is Equipped With Motor Drivers And Microcontrollers (such As Arduino Or Raspberry Pi) That Interpret User Inputs And Manage Both Navigation And Arm Coordination. Sensors May Be Integrated To Enhance Environmental Awareness, Such As Obstacle Detection Or Camera-based Monitoring. Read more Read less

Enhancing Road Safety And Traffic Management By Leveraging AI Driven Surveillance, Real-time Monitoring And Automated Violation Detection Using Computer Vision And IoT

The Smart Traffic Monitoring And Violation Detection System Is An Advanced AI-powered Solution Designed To Revolutionize Traffic Management By Ensuring Real-time Monitoring, Automated Enforcement, And Enhanced Road Safety. Leveraging Computer Vision,... The Smart Traffic Monitoring And Violation Detection System Is An Advanced AI-powered Solution Designed To Revolutionize Traffic Management By Ensuring Real-time Monitoring, Automated Enforcement, And Enhanced Road Safety. Leveraging Computer Vision, Deep Learning, And IoT-based Surveillance, The System Intelligently Detects Violations Such As Helmetless Riding, Signal Jumping, With Exceptional Accuracy. High-resolution Cameras Continuously Capture Live Traffic Feeds, While OCR Technology Extracts Vehicle Number Plate Details For Instant Offender Identification. With YOLOv11 For Object Detection And Convolutional Neural Networks (CNNs) For Number Plate Recognition, The System Achieves Precise And Efficient Violation Detection. Upon Detection, Automated SMS Alerts Are Dispatched To Violators, Providing Fine Details And Enforcement Actions. Integrated With RTO Databases, It Tracks Repeat Offenders, Issuing Escalating Penalties, Including Potential Registration Cancellation For Persistent Violations. By Eliminating Manual Intervention, This System Optimizes Traffic Law Enforcement, Reduces Human Workload, And Ensures Seamless, Technology-driven Compliance. The Fusion Of Real-time AI Processing, Automated Data Retrieval, And Instant Penalty Notification Transforms Traffic Monitoring Into A Smart, Proactive, And Efficient System, Significantly Improving Urban Mobility And Road Discipline While Minimizing Accidents And Fatalities. Read more Read less

HELMET DETECTION AND NUMBER PLATE USING DEEP LEARNING

Individuals Frequently Disregard How Important It Is To Wear Helmets, Which Is Tragic. A Helmet Reduces Your Risk Of Getting A Serious Brain Injury And Dying By Deflecting Most Of The Impact Energy That Would Otherwise Hit Your Head And Brain Duri... Individuals Frequently Disregard How Important It Is To Wear Helmets, Which Is Tragic. A Helmet Reduces Your Risk Of Getting A Serious Brain Injury And Dying By Deflecting Most Of The Impact Energy That Would Otherwise Hit Your Head And Brain During A Tumble Or Collisions. In India, It Is Against The Law To Operate A Motorbike Or Scooter Without A Helmet, Which Has Increased Fatalities As Well As Crashes. The Existing System Mostly Relies On Surveillance Footage For Keeping Up With Traffic Violations, Necessitating A Close - Up Of The License Plate By Traffic Police In The Case That The Motorcyclist Lacks A Helmet. Yet, This Necessitates A Substantial Amount Of Personnel And Time Considering The High Frequency Of Traffic Violations And The Rising Everyday Use Of Motorcycles. Imagine If There Was An Algorithm That Monitored Traffic Infractions, Such As Driving A Motorbike With No A Helmet, And, If Any Were Identified, Generate The License Plate Of The Vehicle That Committed The Violation. Helmet And License Plate Detection Using A Neural Network Is Proposed In This Paper. There Will Be Two Phases. Initially, We Check To See If The Riders Are Wearing Helmets. If Not, A Second Step Is Used To Find Their License Plate. To Identify Unauthorized Vehicles, We Also Look For License Plates On Passing Vehicles. Read more Read less

Pneumonia Detection And Classification Using Deep Learning

Pneumonia Is A Severe Lung Infection That Demands Timelyandprecise Diagnosis.This Project Utilizes Convolutional Neural Networks (CNNs) To Detect And Classify Pneumonia From Chest X-ray Images. A Large Dataset Is Used To Train The Model, Ensuring Hig... Pneumonia Is A Severe Lung Infection That Demands Timelyandprecise Diagnosis.This Project Utilizes Convolutional Neural Networks (CNNs) To Detect And Classify Pneumonia From Chest X-ray Images. A Large Dataset Is Used To Train The Model, Ensuring High Accuracy. Image Preprocessing And Augmentation Enhance Detection Performance. The System Effectively Differentiates Between Normal And Infected Lungs. Evaluation Metrics Like Accuracy And F1-score Confirm The Model’s Reliability. This Solution Supports Rapid, Scalable, And Affordable Diagnosis In Medical Settings. Read more Read less

AN ANALYSIS OF DYNAMIC PRICING STRATEGIES IN THE DIGITAL AGE

Dynamic Pricing Has Become A Cornerstone Strategy In The Digital Marketplace, Allowing Businesses To Adjust Prices In Real Time Based On Factors Like Demand, Competition, And Consumer Behavior. This Approach Leverages Advanced Data Analytics And Mach... Dynamic Pricing Has Become A Cornerstone Strategy In The Digital Marketplace, Allowing Businesses To Adjust Prices In Real Time Based On Factors Like Demand, Competition, And Consumer Behavior. This Approach Leverages Advanced Data Analytics And Machine Learning Algorithms To Optimize Pricing, Aiming To Maximize Revenue And Enhance Market Competitiveness. Industries Such As E-commerce, Hospitality, And Transportation Have Widely Adopted Dynamic Pricing Modelsin Order To React Quickly To Changes In The Market. However, The Effectiveness Of These Strategies Varies Across Sectors And Depends On Factors Like Consumer Acceptance And Technological Infrastructure. This Study Aims To Analyze The Effectiveness Of Dynamic Pricing Strategies In The Digital Era By Examining Their Impact On Consumer Behavior, Revenue Optimization, And Market Dynamics. It Will Explore The Benefits And Challenges Associated With Implementing Dynamic Pricing, Considering Ethical Considerations And Consumer Perceptions. Through A Comprehensive Literature Review And Case Studies, The Research Seeks To Provide Insights Into Best Practices For Businesses Looking To Implement Dynamic Pricing Strategies Effectively. The Findings Will Contribute To A Deeper Understanding Of How Dynamic Pricing Can Be Leveraged To Achieve Business Objectives While Maintaining Customer Trust And Satisfaction. Read more Read less

EFFECT OF SOIL STRUCTURE INTERACTION ON THE DYNAMIC BEHAVIOR OF BUILDING

Soil Structure Interaction (SSI) Is The Response Of Soil That Influence The Motion Of The Structure. Soil Structure Interaction Is Prominent For Heavy Structure, Especially For High Rise Building Located On Soft Soil. Incorporation Of Soil Interactio... Soil Structure Interaction (SSI) Is The Response Of Soil That Influence The Motion Of The Structure. Soil Structure Interaction Is Prominent For Heavy Structure, Especially For High Rise Building Located On Soft Soil. Incorporation Of Soil Interaction Effect Will Reduce The Base Shear And Flexibility Of Soil. Because Of This The Stiffness Of The Building Is Getting Reduced Resulting, Increase In The Natural Period Of The Structure During Earthquake. One Cause Of These Deviations Is Base-slab Averaging, In Which Spatially Variable Ground Motions Within The Building Envelope Are Averaged Within The Foundation Footprint Due To The Stiffness And Strength Of The Foundation System. Another Cause Of Deviation Is Embedment Effects, In Which Foundation-level Motions Are Reduced As A Result Of Ground Motion Reduction With Depth Below The Free Surface. Interaction Of Pile Foundation With Wave Propagation Below The Base Slab, Which Can Further Modify Foundation-level Motions At The Base Of A Structure. Read more Read less

A Study On Customer Preference For Cosmetic Brands With Reference To Coimbatore City

The Project Work Is Entitled A " A STUDY ON CUSTOMER PREFERENCES FOR COSMETIC BRANDS WITH REFERENCE TO COIMBATORE CITY" With Special Reference To The Cosmetic Brands. The Primary Objective Of This Study Is To Measure And Analyse The Perceptions And A... The Project Work Is Entitled A " A STUDY ON CUSTOMER PREFERENCES FOR COSMETIC BRANDS WITH REFERENCE TO COIMBATORE CITY" With Special Reference To The Cosmetic Brands. The Primary Objective Of This Study Is To Measure And Analyse The Perceptions And Attitude Of The Public For Cosmetic Brands. The Main Objective Is To Compare The Cosmetic Brands Between Customers. Read more Read less

Breast Cancer Detection Using Hybrid Ri-Vit In Histopathalogical Images

Breast Cancer Remains A Significant Global Health Concern, Impacting Millions Of Women Each Year. Timely Detection And Precise Diagnosis Are Essential To Enhancing Treatment Success And Lowering Death Rates. Histopathological Imaging Is Widely Utiliz... Breast Cancer Remains A Significant Global Health Concern, Impacting Millions Of Women Each Year. Timely Detection And Precise Diagnosis Are Essential To Enhancing Treatment Success And Lowering Death Rates. Histopathological Imaging Is Widely Utilized For Diagnosing Breast Cancer, But Interpreting These Images Accurately Often Requires Specialized Medical Expertise, Which May Not Be Readily Available In All Clinical Environments. The Dataset Used In This Study Comprises Breast Tissue Images Labeled To Reflect The Presence Or Absence Of Cancer. A Convolutional Neural Network (CNN) Was Employed To Automatically Extract Meaningful Features From The Images, Followed By A Fully Connected Layer To Perform Classification. The Model Was Optimized By Minimizing Prediction Error Using A Suitable Loss Function And Optimization Technique. To Assess Its Effectiveness, The Model's Performance Was Measured Using Metrics Such As Accuracy. Read more Read less