Volume - 12 Issue - 1

Area of research: Management Studies

Rural Industries Play A Significant Role In Employment Generation, Yet They Face Challenges Related To Employee Welfare, Health, And Safety. This Study Analyzes The Welfare Measures, Health Facilities, And Safety Practices In Rural Industries. The Research Evaluates Employee Satisfaction, Awareness Of Personal Protective Equipment, And Effectiveness Of Health Facilities. The Findings Indicate A Need For Improved Safety Training, Better Welfare Facilities, And Enhanced Awareness Programs To Ensure Employee Well-being.

Author: M.Ashok | S.Dhanushya | C.Bhuvana

Area of research: Educational Psychology

Adolescence Is A Tender Age Where Adolescents Get Quickly Influencedfrom Celebrities, Follow The Ongoing Fashion Trend, Easily Adopt Any Wrong Habit, Get Excessively Involved In Electronic Devices. In Recent Yearsthe Rapid Advancement In Different Digital Technologies Has Led Significant Increase In The Simultaneous Use Of Devices, Such As Smartphones, Tablets, Computers, TVs, And Gaming Consoles, Particularly Among Adolescents, Either For Studies Or For Entertainment Purposes, Especially After Pandemic.With The Increasing Use Of Smartphones, Tablets, Laptops, And Televisions, Adolescents Are Frequently Engaged With Multiple Screens Simultaneously, Often Leading To Screen Addiction.This Paper Reviews The Available Literature On Multiple Screen Addiction And Explores Different Aspects Of Screen Addiction In Children And Adolescents, Using Databases Such As Google Scholar, PubMed, And Science Direct.This Paper Highlights The Impact Of Multiple Screen Addiction And Problem Faced By Adolescents On Account Of Excessive Screen Use. Further This Study Also Discusses The Strategies Helpfulfor Adolescents To Break The Screen Addiction.

Author: Dr. Gunjan Dubey | Mehrosh Aftab

Area of research: MACHINE LEARNING

Insurance Fraud Causes Significant Financial Losses And Is Difficult To Detect Due To Its Similarity To Legitimate Claims. The Growing Volume Of Data And Increasing Fraud Complexity Make Automated Detection Essential. Machine Learning Provides Effective Tools For Identifying Fraudulent Patterns In Insurance Claims.This Study Applies Logistic Regression (LR) And Support Vector Machine (SVM) For Insurance Fraud Detection. The Process Includes Data Preprocessing Such As Handling Missing Values, Feature Selection, Normalization, And Categorical Encoding. LR Estimates The Probability Of Fraud, While SVM Classifies Claims By Separating Fraudulent And Genuine Cases In A High-dimensional Space.A Comparative Analysis Using Accuracy, Precision, Recall, And F1-score Shows That Both Models Improve Fraud Detection And Reduce False Positives. The Study Highlights Their Respective Strengths And Limitations, Demonstrating How Machine Learning Enhances Fraud Detection, Reduces Losses, And Supports Better Decision-making In Insurance Systems.

Author: Shanmugapriya S | Mr.K.Mahadevan

Area of research: Civil Engineering

The Demand For Sustainable Construction Materials Has Accelerated Research Into The Utilization Of Agro-industrial Waste As Supplementary Cementitious Materials. Sugarcane Bagasse Ash (SCBA), A By-product Of Sugar Manufacturing, Exhibits Pozzolanic Characteristics That Can Reduce Cement Consumption And Environmental Impact. This Study Evaluates The Performance Of Concrete Incorporating SCBA As A Partial Replacement Of Cement At Levels Of 0%, 10%, 15%, And 20%. Mechanical Performance Was Assessed Through Compressive Strength Tests, While Non-destructive Testing Methods Were Employed To Evaluate Concrete Quality. Durability Characteristics Were Examined Using Sulphate And Chloride Content Analyses. Results Indicate That The Control Mix Achieved The Highest Compressive Strength, With Minor Strength Reductions Observed At 10% And 15% SCBA Replacement. At 20% Replacement, A Slight Improvement In Strength Was Noted Due To Enhanced Pozzolanic Activity. Cost Analysis Demonstrated A Progressive Reduction In Concrete Cost With Increased SCBA Content. The Findings Suggest That SCBA Can Be Effectively Utilized At Lower Replacement Levels As A Sustainable And Economical Cement Substitute In Concrete Production..

Author: Nirali Rajput | Anurag Pawar | Gautam Khandare | Shantanu Patil

Area of research: Civil Engineering

Traffic Congestion Is A Serious Issue In Cities Owing To The Sudden Rise In The Number Of Vehicles, Causing Increased Travel Time, Fuel Consumption, And Air Pollution. This Paper Proposes A Smart Traffic Signal Control System Using Sensors To Optimize Traffic Flow By Adjusting Traffic Signals Based On The Real-time Density Of Vehicles. Sensors Are Placed At Road Junctions To Sense The Presence And Density Of Vehicles And Send Signals To A Controller For Optimal Control Of Traffic Signals. The System Changes The Green Signal Time Based On Traffic Density On Each Road. The Proposed Method Is Beneficial In Reducing Traffic Congestion, Minimizing Waiting Time, Enhancing The Passage Of Emergency Vehicles, And Conserving Fuel. This Paper Emphasizes The Need For Sensor-based Intelligent Traffic Control Systems As A Reliable Solution For Managing Traffic In Modern Cities

Author: Mr. S.S.Rajput | Anand B. Chopade | Shaikh. Huzefa | Shaikh Huzef | Moh. Afzal

Area of research: Civil Engineering

The Intention Of The Study Is To Investigate The Possibility Of Solar Roads Around The World. Solar Road Is A Strained Project In Terms Of Constraint Used. Solar Roads Are Basically The Roads Made Of High Tensile Strength Material.Smart Highways Should Be Enhanced To Develop The Nation And Make The World Go Greener In A Way That Theconsumption Of Fossil Fuels Would Be Totally Eradicated Solar Roadways Use Solar Panels, PV Effect LED‟s And Microprocessor Chips With Circuitry Boards. It Has Already Been Implanted In Some Parts Of The World Commencing With Netherland. The Idea Is To Make Multi-functional, Self-sustainable Roads That Require In Substantial Upkeep.

Author: Ms. S. S. Ikhare | Nikhil S. Lule | Ms. N. S. Ahir | Mr. Nikhil. S. Asalkar | Mr. Vijay E Savale

Area of research: Civil Engineering

Black Cotton (B.C) Soil Is Highly Plastic Clayey Soil. In Dry State, It Is Very Stiff That Clods May Not Be Effortlessly Pulverized To Be Treated For Its Use In Road Construction. This Poses Severe Troubles When Considered In Respect To Subsequent Performance Of Road .In Addition, The Softened Subgrade Has A Tendency To Upheave Into The Upper Layers Of Pavement, Mainly When Sub-base Consists Of Stone Soling With Lot Of Voids. Regular Intrusion Of Soaked B.C Soil Perpetually Leads To Road Failure. Roads Resting On B.C Soil Base Develop Undulation On Pavement Top Because Of Strength Loss Of Subgrade Through Softening At Time Of Monsoons. In The Current Study, Specific Gravity Test, Consistency Indices (Liquid Limit (LL), Plastic Limit (PL), And Plasticity Index (PI)), Modified Proctor’s Test, And California Bearing Ratio (CBR) Tests Will Be Executed On B.C Soil (Highly Clayey Soil) First By Mixing With Altered Percentage Of Rice Husk Ash (10% , 20% ,30% ,40% ) To Stabilize Soil And Then Percent Of Rice Husk Ash At Which Maximum CBR Is Gained Is Chosen For Further Experimental Work. . The Optimal Proportion (percentage) Of Rice Husk Ash At Which Maximum CBR Is Achieved Will Be Selected And Gets Reinforced With Varying Proportion (percentage) Of Geosyntheticfibre. Along With These Altered Percentages Of Reinforcement, The Optimal Quantity Of Fibre Required To Obtain Maximum Strength Is Well-known.

Author: Milind Awalaker | Prof. Deepak Garg

Area of research: Civil Engineering

Water Quality Is An Essential Aspect Of Human Health, And Household Water Often Contains Suspended Particles, Silt, And Other Impurities That Reduce Its Clarity And Usability. This Project Focuses On The Design, Fabrication, And Evaluation Of A Home-scale Tube Settler, A Simple And Low-cost Sedimentation Unit That Improves Water Quality Before Filtration Or Use. The Tube Settler Consists Of Inclined Tubes Arranged In A Small Container, Which Accelerates The Settling Of Suspended Solids And Reduces Turbidity. Domestic Water Samples Were Collected From Sources Such As Overhead Tanks And Bore Wells, And Water Quality Analysis Was Performed Before And After Treatment. Parameters Including Turbidity, Total Suspended Solids (TSS), PH, Dissolved Oxygen (DO), Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD), And Odour Were Measured. The Results Show That The Tube Settler Effectively Reduced Turbidity From 18 NTU To 5 NTU And TSS From 42 Mg/L To 14 Mg/L. Improvements In Colour, Odour, And Overall Water Clarity Were Also Observed, While Chemical Parameters Remained Within Safe Limits. The Study Demonstrates That Tube Settlers Are A Practical, Space-efficient, And Economical Method For Pre-treatment Of Household Water, Enhancing The Performance Of Filtration Systems And Ensuring Safer Water For Domestic Use. This Project Highlights The Potential Of Small-scale Sedimentation Systems In Improving Water Quality In Homes With Minimal Cost And Maintenance.

Author: Mr. G. N. Supe | Mr. Sumit V. Wankhede | Ms. Kalyani V. Bathe | Mr. Swaraj W. Umale | Mr. Aadersh M. Tayde

Area of research: ARTIFICIAL INTELLIGENCE

This Project, Titled "Criminal Face Recognition System Using AI", Proposes An Intelligent System Designed To Identify And Track Individuals With Criminal Records Through Realtime Facial Recognition, Thereby Aiding Law Enforcement Agencies In Ensuring Public Safety. By Utilizing Deep Learning Techniques, Computer Vision, And A Robust Image-processing Pipeline, This System Can Automatically Detect Faces From Video Feeds Or Still Images And Match Them Against A Criminal Database With High Accuracy And Minimal False Positives. The Core Of This System Is Based On Convolutional Neural Networks (CNNs), Particularly Leveraging Pre-trained Models Like FaceNet, VGG-Face, Or Dlib For Efficient Facial Feature Extraction And Recognition. These Models Are Fine-tuned Using A Curated Dataset Of Criminal Mugshots To Increase Their Specificity In Recognizing Known Offenders. The Facial Recognition Pipeline Involves Four Key Stages: Face Detection, Face Alignment, Feature Extraction, And Face Matching. The System Also Incorporates OpenCV For Image Processing And TensorFlow/Keras Or PyTorch For Implementing Deep Learning Algorithms. To Make The Solution Scalable And Usable In Real-world Scenarios, A Cloud-based Database Is Integrated To Store And Manage Criminal Face Records Whenever A Face Is Detected By The Camera, The System Compares It With The Stored Records In Real-time. If A Match Is Found, Alerts Are Triggered To Notify Authorities With The Person’s Identification And Location

Author: Glarin Benisha B | DR. J .Suresh

Area of research: Computer Science And Engineering

The Identification Of Lung Cancer At The Early Stage Is Very Demanding And Difficult Task Due To Construction Of The Cell. The Cancer Grows In The Body When Cancerous Cells Start To Develop Uncontrollably. The Image Processing Plays Vital Role In The Prediction Of Lung Cancer At Early Stage Which Is Also Helpful In Treatment To Avoid The Lung Cancer. This Proposed System Is Developed To Detect Lung Cancer At Early Stage With The Help Of Image Processing Techniques And Artificial Neural Network Classifier To Design Computer Based Diagnosis System. In This System, During The Preprocessing Step, Several Image Enhancing Techniques, Masks Are Applied Using Morphological Operations And Thresholding Technique, Which Eliminates Background And Surrounding Tissue. Region Of Interest (ROI) Is Calculated Using Region Based Segmentation Algorithm. Circle Fit Algorithm Is Used To Extract The Desired Nodule. Radius, Mean Intensity, Area, Euler Number And ECD Features Are Extracted In Feature Extracting Step. Finally, Back Propagation Algorithm Is Used To Train Artificial Neural Network (ANN) In Categorization Stage.

Author: Veerender Aerranagula | Bhukya Vijay kumar | Praveen Guguloth | B.Ravindar Reddy

Area of research: Agriculture

The Agricultural Economy Of Tamil Nadu, A State In Southern India With A Wide Range Of Agroclimatic Conditions, Is Assessed In This Paper. This Paper Identifies The Main Cropping Trends And Analyses Irrigation Advancements, Technological Advancements, And Socioeconomic Factors That Influence Farmers' Livelihoods. According To The Analysis, Enduring Issues That Impede Sustainable Growth Include Land Fragmentation, Market Volatility, Water Scarcity, And Climate Variability. Additionally, It Examines Important Government Initiatives And Policy Changes Meant To Promote Sustainable Agricultural Growth, Raise Productivity, And Improve Rural Livelihoods.After Reviewing The Government Documents, The Paper Offers Long-term Solutions To Improve Systemic Resilience And Advance Equitable, Sustainable Agricultural Development In Tamil Nadu.

Author: Dr. Anshumala Chandangar

Area of research: Library & Information Science

Academic Libraries Have Always Played A Vital Role In Supporting Education, Research, And The Spread Of Knowledge. With The Rapid Development Of Artificial Intelligence (AI), Libraries Now Have New Opportunities To Improve Their Services, Operations, And Long-term Sustainability. This Paper Examines How AI Can Transform Academic Libraries By Supporting Environmental, Economic, And Social Sustainability. It Discusses Key AI Applications Such As Automated Cataloguing, Virtual Reference Services, Personalised Recommendations, Predictive Analytics, And Advanced Information Retrieval Systems. The Study Also Presents Real-world Examples From Institutions Such As The National Library Of Singapore, The University Of Huddersfield, And IIT Bombay To Demonstrate Practical Implementation. Although AI Offers Significant Benefits, Challenges Related To Ethics, Data Privacy, Algorithmic Bias, And Lack Of Technical Skills Remain Important Concerns. The Paper Concludes By Proposing A Strategic Roadmap For Adopting AI In Libraries, Focusing On Policy Development, Staff Training, Collaboration, And Continuous Assessment. Overall, The Integration Of AI Can Modernise Academic Libraries While Promoting Efficiency, Inclusivity, And Sustainability In Higher Education.

Author: Mr.Wajiskha Inuskha Pathan | Dr. Shashank S. Sonwane

Area of research: Animation

Until Now, Polygon Count, Also Known As Poly Count, Has Been A Basic Component In Both The Technical And Artistic Aspects In Terms Of Creating Models For Three-dimensional Animation And Video Games. The Role It Plays In Determining And Judging The Level Of Fidelity And Rendering Performance—not To Mention The Cost And Usability Aspects—is Imperative In Animation And Video Games. Although Huge Advances In Both Hardware And Software Technologies Capable Of Handling Geometric Complexity, This Aspect Has Played A Fundamental And Integral Part In Both Animation And Video Games. In This Academic Research, A Discussion On The Importance And Dimensions Of Ensuring And Maintaining Its Optimal Polygon Count In Models For Animation And Video Games, And Their Ideologies, Through A Comparative Analysis And Discussion On Differing Programming Processes In Both Aspects In Relation To Its Importance In Relation To Its Necessities In Both Aspects, Shall Be Explained And Defined.

Author: Abhishek Bhattacharjee

Area of research: Computer Science And Engineering

Autism Spectrum Disorder (ASD) Is A Complex Neurological Developmental Condition That Presents With A Range Of Symptoms. Early Diagnosis Along With Proper Medical Care Can Significantly Enhance The Daily Quality Of Life For Parents And Children With ASD. The Purpose Of This Study Is To Determine Whether It Is Possible To Distinguish Autistic Children From Usually Developing Kids Using Biomarkers Derived From Face Traits Retrieved From Their Images. The Study Employs Convolutional Neural Network (CNN) Models, Specifically VGG19, Densenet121, And InceptionV3, For The Extraction Of Features. Additionally, A Deep Neural Network (DNN) Model Is Utilized As A Binary Classifier To Accurately Discern Autism. The Investigation Utilizes A Publicly Accessible Dataset Comprising Facial Images Of Children Diagnosed With Autism, Alongside Control Subjects Classified Into Both Autistic And Non-autistic Categories. With A 96.66% Accuracy, 96.25% Precision, 94.75% Recall And 95.50% F1 Score, Densenet121 Performed Better Than The Other Models That Were Examined. When Applied To Groups With And Without Autism, InceptionV3 Consistently Produced Prediction Scores Of 95.33%

Author: M.Arivukarasi | Kalaiyarasan K

Area of research: Computer Science And Engineering

The Need For Cosmetic Products Has Gone Up, So There's More Focus On Being Clear About What's In Them And Making Sure They're Safe. Many Products Say They Work For Different Skin Types, But People Usually Choose Them Based On Brand And Ads Instead Of Looking At The Science Behind The Ingredients. Most Studies On Cosmetics Look At Chemicals, Safety, Or How Ingredients Are Grouped, But They Don't Often Look At How Well They Work For Specific Skin Types Like Oily, Dry, Sensitive, Or Acne-prone Skin. This Review Looks At The Methods Used To Check Cosmetic Ingredients, Including How They're Classified, Safety Tests, Assumptions About Ingredient Order, And New Data-based Techniques. It Pays Special Attention To How Different Ingredients Affect Various Skin Types. The Review Also Points Out The Main Problems With Current Studies, Like Not Having Ways To Test Products Across Many Skin Types From The User’s Point Of View. By Combining Information From Dermatology, Cosmetic Science, And Computer-based Research, This Review Shows The Need For More Flexible And Personalized Ways To Evaluate Cosmetics. The Findings Are Meant To Help Guide Future Studies And Support Better Choices For Consumers Based On Their Skin Type.

Author: Rudresh Sharma | Prof. Pankaj Raghuwanshi

Area of research: Computer Science And Engineering

In Recent Years, Educational And Corporate Institu- Tions Have Begun Transitioning To Automated Attendance Man- Agement Systems Driven By Digital Technologies. This Paper Proposes An Intelligent Online Attendance Register Integrated With A Personal Details Database And An Artificial Intelligence (AI) Module For Automated Data Analysis And Filtration. The System Is Designed To Ensure Data Accuracy, Reduce Manual Intervention, And Offer Deep Analytical Insights Regarding Attendance Patterns, Employee Performance, And Academic Discipline. This Research Emphasizes The Combination Of Database Technologies, Machine Learning Algorithms, And Data Filtration Models To Optimize Record Management And Decision-making Processes. The Pro- Posed Framework Provides A Scalable, Secure, And Adaptive System Suitable For Large Organizations, Universities, And Online Learning Platforms.

Author: Maheshwari R | Thilagan S | Dabbara Ganesh | Pradeep R

Area of research: ECE

Modern Wireless Communication Systems Demand Antennas With High Gain, Wide Bandwidth, And Low Return Loss While Maintaining Compact Size And Reduced Development Time. Conventional Electromagnetic Simulation–based Antenna Design Techniques, Although Accurate, Suffer From High Computational Cost And Prolonged Iterative Optimization Cycles. This Paper Proposes An Intelligent Hybrid Deep Learning Framework That Integrates Convolutional Neural Networks (CNNs) And Long Short-Term Memory (LSTM) Networks Enhanced With An Attention Mechanism And Physics-informed Loss Function For Efficient Antenna Performance Prediction. CNN Layers Extract Spatial Features From Antenna Geometries, While LSTM Networks Model Frequency-dependent Electromagnetic Behavior. The Attention Mechanism Prioritizes Influential Design Parameters, And Physics-informed Constraints Ensure Electromagnetic Validity.

Author: M.Hariharan | B.Gokul | M.LIshmiya | R.Makesh Boopathi

Area of research: Marketing

Consumer Preferences Is Most Important For Fast Moving Consumer Goods Because Consumers Are Very Much Interesting In Price Sensitivity, Brand Loyalty, Convenience, Shopping Channels And Ethical Product Practices Based On Reviews, The Study Also Seeks To Understand The Factors Influencing The Relationship Between Consumer Preferences And The Availability Of Products In Chemist Outlets And Also This Case Study Investigates The Potential Link Between Changing Consumer Preferences Towards Nutritional Drinks And The Stocking Behavior Of Chemist Outlets. This Case Study Employs A Quantitative Approach, Secondary Data For Sales FND Chemist Outlets In The Tiruchirappalli Region Of Tamil Nadu. The Quarter-wise Sales Data Of Functional Nutritional Drinks From The Year 2022 December Quarter To 2024 March Quarter Have Been Analyzed. The Sampling Technique Employed Is Simple Random Sampling, Drawing Data From A Chemist Outlet In Thiruchirappalli . This Case Study Investigates The Potential Link Between Changing Consumer Preferences Towards Nutritional Drinks And The Stocking Behavior Of Chemist Outlets.

Author: A. PAPPU RAJAN

Area of research: Artificial Intelligence And Data Science

Sign Language Recognition (SLR) Is An Active Research Area That Involves The Automatic Translation Of Sign Languages Into Text Or Speech Using Computational Techniques, Thereby Facilitating Communication For Deaf And Hard-of-hearing Individuals. Over The Past Two Decades, SLR Has Evolved From Traditional Handcrafted Feature-based Approaches To Modern Deep Learning-driven Methods Due To Significant Advances In Computer Vision. This Survey Reviews Approximately 15–25 Representative Studies And Categorizes Existing SLR Approaches Into Isolated And Continuous Recognition Tasks. Widely Used Datasets, Feature Extraction Techniques, Learning Models, And Evaluation Metrics Are Discussed. Classical Methods Such As Hidden Markov Models And Support Vector Machines Are Reviewed Alongside Deep Neural Architectures Including Convolutional Neural Networks, Recurrent Neural Networks, And Transformer-based Models. Key Challenges Include Data Sparsity, Signer Variability, And Difficulties In Modeling Non-manual Cues. Finally, Promising Future Research Directions Such As Multimodal Learning And Low-resource Sign Language Recognition Are Highlighted.

Author: A.BalaAyyappan | Dr.T.Gobinath | A.Sivaramakrishnan | Dr.M.Kumar | P.Srimathi | T.Selvathiyaneshwaran

Area of research: Electrical Engineering / Embedded Systems / IoT / Smart Lighting

Road Tunnels Require Continuous Illumination For Safe Transportation; However, Conventional Tunnel Lighting Systems Consume High Energy And Require Manual Monitoring, Resulting In Increased Operating Cost And Reduced Reliability. This Project Proposes An ESP32 Based Smart Tunnel Lighting And Safety System That Automatically Controls Tunnel Lights Based On Vehicle Movement And Ambient Light Conditions. The System Uses Sensors To Detect Vehicles Inside The Tunnel And Accordingly Switches ON/OFF Or Adjusts Intensity Of LEDs. Additionally, Safety Features Such As Emergency Alerts, Smoke/fire Detection Support, And Fault Indication Are Incorporated. The Proposed System Reduces Power Consumption Significantly And Increases Safety Using Automated And IoT-enabled Monitoring. This Model Is Suitable For Smart City Infrastructure, Highways, And Industrial Tunnel Applications.

Author: Sandhyarani Balasaheb Kunjir | Suraj Rajiv Jaybhaye | Pradeep Sanjay Kapse | Abhishek Sunil Adagale

Area of research: Civil Engineering

The Quality Of Drinking Water Supplied To Consumers Depends Not Only On Treatment Efficiency At The Water Treatment Plant (WTP) But Also On Conditions Within The Distribution System. In Many Indian Towns, Variations In Water Quality Are Observed During Distribution Due To Intermittent Supply And Increased Water Residence Time. This Study Evaluates The Stability Of Drinking Water Quality In The Shirpur Water Distribution System, Maharashtra. Water Samples Were Collected From The WTP Outlet And Household Taps In Selected Residential Areas And Analyzed For Key Physical And Chemical Parameters Using Standard Methods. The Results Were Compared With IS 10500 And WHO Drinking Water Standards. The Findings Indicate That Most Parameters Remain Within Permissible Limits; However, A Gradual Reduction In Residual Chlorine Was Observed Along The Distribution Network, Mainly Due To Increased Residence Time. Pipeline Leakages Were Found To Contribute Primarily To Water Losses And Reduced Supply Efficiency Rather Than Direct Deterioration Of Water Quality. The Study Highlights The Importance Of Regular Water Quality Monitoring, Effective Leakage Control To Minimize Water Loss, And Proper Maintenance Of Distribution Infrastructure To Ensure A Safe And Reliable Drinking Water Supply.

Author: Dr. Hitesh P | Chaitanya P | Umesh B | Prachi V | Shweta S | Hitesh K

Area of research: ECE

Brain-inspired Neuromorphic Computing Has Emerged As An Efficient Approach For Implementing Cognitive And Learning-based Systems With Low Power Consumption And High Parallelism. Unlike Conventional Computing Architectures, Neuromorphic Systems Emulate The Structure And Functionality Of Biological Neural Networks Using Spiking Neurons And Synaptic Connections. This Work Presents The FPGA Implementation Of A Brain-inspired Neuromorphic Computing Circuit Designed To Model Basic Neural Processing Behavior In Hardware. The Proposed Architecture Employs Neuron And Synapse Models Mapped Onto FPGA Resources To Achieve Real-time Operation And Reconfigurability. The Design Is Implemented Using Hardware Description Language And Validated Through Simulation And FPGA Synthesis. Experimental Results Demonstrate Correct Neural Signal Processing, Efficient Resource Utilization, And Suitability For Real-time Neuromorphic Applications. The Proposed FPGA-based Neuromorphic Circuit Provides A Flexible And Scalable Platform For Developing Brain-inspired Computing Systems

Author: S Vasanthiriya | M Hariharan | A Kathirvenkat | V Nithishwaran

Area of research: Computer Science And Engineering

In Many Of The Educational Institutions, Managing Attendance Of Students/candidates Is Tedious, As There Would Be Large Number Of Students In The Class And Keeping Track Of All Is Onerous. There Are Situations Where Student Act As Proxies For Their Friends Even Though They Are Not Present. The Advancement In The History Of Computer Vision Utilizing Deep Learning Approaches Especially Convolutional Neural Networks Have Accomplished To Solve Difficult Problems In Face Recognition Field. Face Recognition-based Approach Is One Amongst The Important Identification Methods Which Can Be Used As A Possible Substitution For Conventional System Of Marking Attendance Manually, Especially If A Huge Classroom Of Students Is Addressed For An Hour Session. Our Solutions Integrate AI Capabilities With Smart Analytics Features To Facilitate Transparency In Classrooms And College Campus.This Project Develops An Automatic Attendance System Using Faster R-CNN Deep Learning Based Algorithm. In This System, A Database Containing The Trained Student’s Face. A Camera Installed In The College Campus Captures The Face Of All The Student In The Classroom And Other Places Too. This Face Image Is Processed Using FRCNN Algorithms To Detect Faces And To Mark The Attendance Automatically In An Excel Sheet. The System Records The Entire Class Session And Identifies When The Students Pay Attention In The Classroom, And Then Reports To The Facilities And Also This System Can Record Violations Of Classroom, That Is Absence, Roaming Around The College Campus During The Class Hours And Send Alert Message To The H.O.D.This Dynamic Attendance System Uses Face Recognition As An Important Aspect Of Taking Attendance Which Saves Time And Proxy Attendance And Is Avoided. The System Identifies Faces Very Fast Needing Only 100 Milliseconds To One Frame And Obtaining A High Accuracy. Our Face Recognition Model Has An Accuracy Rate Of 99%.

Author: Kaviyadharshini R | Kalaiyarasan K

Area of research: Library Science

The Increasing Availability Of Electronic Resources (e-resources) Has Significantly Transformed Academic And Research Activities, Particularly In Developing Countries. This Review Paper Examines The Level Of Awareness And Utilization Patterns Of E-resources Among Students And Researchers In Asia, Africa, And Latin America. Findings From Multiple Studies Reveal That Users Generally Exhibit Moderate To High Awareness Of Common E-resources Such As Google Scholar, E-journals, And E-books; However, Awareness Of Specialized Databases Remains Limited. Utilization Is Primarily Motivated By Academic Assignments, Examinations, And Research Work, With Mobile Devices Becoming The Dominant Access Tool. Despite The Benefits Of E-resources—such As 24/7 Accessibility, Updated Information, And Improved Research Productivity—numerous Challenges Persist, Including Poor ICT Infrastructure, Low Digital Literacy, Insufficient Training, Institutional Funding Limitations, And Usability Issues. The Review Highlights The Need For Improved Information Literacy Programs, Enhanced Digital Infrastructures, Increased Participation In National Consortia, And Expanded Access To Scholarly Databases. Addressing These Challenges Will Enable Students And Researchers In Developing Countries To Fully Leverage The Potential Of E-resources For Improved Academic And Research Outcomes.

Author: Dr. Shyamsundar D. Pithore

Area of research: ECE

Quantum Dot Cellular Automata (QCA) Has Emerged As A Promising Nanotechnology Alternative To Conventional CMOS Circuits Due To Its Ultra-low Power Consumption, High Device Density, And Suitability For Nanoscale Computing. Among Fundamental Digital Components, Comparators Play A Crucial Role In Arithmetic Units, Control Logic, And Decision-making Circuits. This Work Presents The Design And Implementation Of An Efficient Multibit Full Comparator Using QCA Technology. The Proposed Design Employs Optimized QCA Logic Structures To Compare Multi-bit Binary Inputs And Generate Greater-than, Equal-to, And Less-than Outputs With Reduced Cell Count And Improved Layout Regularity. The Comparator Architecture Is Constructed Using Fundamental QCA Primitives And Extended Systematically To Support Multibit Comparison. Simulation Results Validate The Correct Functionality Of The Proposed Design And Demonstrate Improvements In Terms Of Complexity And Structural Efficiency. The Proposed Multibit Full Comparator Is Suitable For High-speed And Low-power QCA-based Digital Systems.

Author: Dr.K.Radhika | T.Dhinesh | R.Mytheeswaran | M.Naveen

Area of research: ECE

Automated Teller Machines (ATMs) Are Increasingly Vulnerable To Physical Attacks, Fraud, And Unauthorized Access Due To Limited Real-time Monitoring And Delayed Response Mechanisms. This Work Presents The Design And Implementation Of An Intelligent ATM Security System Using Artificial Intelligence (AI) And The Internet Of Things (IoT). The Proposed System Integrates Embedded Sensors, A Microcontroller-based Control Unit, And IoT Communication Modules To Continuously Monitor ATM Conditions And Detect Abnormal Activities Such As Tampering, Intrusion, Or Unauthorized Transactions. AI-based Decision Logic Enhances Threat Detection Accuracy, While IoT Connectivity Enables Real-time Alerts And Remote Monitoring Through Cloud Platforms And Mobile Interfaces. Experimental Results Demonstrate Reliable System Operation, Timely Alert Generation, And Effective Security Response. The Proposed Solution Provides A Cost-effective, Scalable, And Intelligent Security Framework Suitable For Modern ATM Environments.

Author: R Venkatesan | R Saravanan | S L Indran | G Kavin | P Keerthikaran

Area of research: Engineering & Technology

Laboratories Accredited By The National Accreditation Board For Testing And Calibration Laboratories (NABL) Handle Large Amounts Of Documentation Relating To Quality Manuals, Audit Trail Records, Standard Operating Procedures, And Compliance Documents. Traditional Systems For Managing Documents, Using Keywords For Search And Manual Navigation, Have Created Inefficiencies During Audits Or When Verifying Compliance.[4][5] As A Result, The Development Of AI Technologies, Including Retrieval-Augmented Generation (RAG), Indicates The Potential For Intelligent Ways To Retrieve Documents And Obtain Contextually Relevant Answers To Questions. In This Paper, We Will Outline Our Vision For A Laboratory Information System (LIS) Fueled By Artificial Intelligence (AI) Through The Integration Of RAG With The Current NABL Accreditation Workflow. Our Proposed LIS System Combines Semantic Document Retrieval From Vector Databases With Fact-based And Context-sensitive Responses Generated By Large-language Models. [1][3]. To This End, We Emphasize The Creation Of A Layered Architecture Based On Four Components: Document Preprocessing; Vector Embedding; Similarity Searches; And Natural-Language Generation (NLG) Components. Our Approach Significantly Enhances The Way Laboratories Audit Their Operations, Reduces The Time Required To Search For Documents, And Increases The Ability To Provide Traceable Responses Generated By The AI Component. Our Methodology Provides Laboratories With The Opportunity To Integrate RAG Technology Into Their Operations While Building A Framework For Building Trusted AI-based Accreditation Systems.[6],[10]

Author: Shivani S. Shelar | Sandeep R. Jadhav

Area of research: Civil Engineering

The Main Objective Of The Present Study Is To Find Out A Suitable, Effective And Alternative Material For Partial Replacement Of Cement And Coarse Aggregate, To Find Out Possible Utilization Of Waste Materials In Construction Industry That In Turn Considerably Minimize The Usage Of Cement And Coarse Aggregate And Ultimately Reduce Construction Cost, To Explore Possibilities Of Improving Mechanical Properties Of Concrete Using Copper Slag & Pond Ash Instead Of Fine Aggregate Partially, To Evaluate The Effect Of Using Copper Slag & Pond Ash In Concrete And To Investigate The Strength Of Replaced Concrete With That Of Conventional Concrete. This Project Is Mainly Undertaken To Study The Behavior And Performance Of Concrete Using Waste Materials Such As Copper Slag & Pond Ash. This Type Of Use Of A Waste Material Can Solve Problems Of Lack Of Aggregate In Various Construction Sites And Reduce Environmental Problems Related To Sand Mining And Waste Disposal. The Use Of Copper Slag & Pond Ash Can Also Reduce The Cost Of The Concrete Production And Increase The Workability.

Author: Amit Kumar Singh | Deepak Garg | Dr.Rakesh Patel

Area of research: Civil Engineering

Accurate Estimation Of Street-level Travel Time Is A Fundamental Requirement For Intelligent Transportation Systems (ITS), Enabling Effective Traffic Management, Route Optimization, And Real-time Traveler Information Services. With The Increasing Availability Of Urban Mobility Data, Particularly From Public Transport Systems, Data-driven Approaches Have Gained Prominence Over Traditional Rule-based Or Simulation-based Models. General Transit Feed Specification (GTFS) Data Provides A Standardized And Rich Source Of Spatio-temporal Information Related To Transit Schedules, Routes, Stops, And Frequencies, Making It Highly Suitable For Fine-grained Travel Time Estimation At The Street Level. This Paper Presents A Wavelet Tree And Support Vector Regression (SVR) Model For Forecasting Street Level Travel Time Employing GTFS Features. The WBPST Model Has Been Used For Filtration While The SVR Model Has Been Used For Pattern Recognition. The Results Show That The Proposed Model Attains An MAPE Of Just 2.44% At 68 Iterations. The Model Also Predicts The Level Of Congestion With An MAPE Of 2.14%. The Model When Compared With Existing Benchmark Models Can Be Seen To Improve Upon The Existing Results.

Author: Bhumika Patidar | Prof. Vinay W. Deulkar

Area of research: Biological Science

The Present Study Investigates The Association Between Gut-associated Bacteria Of The Freshwater Apple Snail Pila Globosa And Their Role In Lignocellulose Digestion. Cellulolytic And Hemicellulolytic Bacteria Were Isolated From The Gut Using Carboxymethyl Cellulose (CMC) Agar Medium And Subjected To Sequential Screening Based On Colony Morphology, Enzyme Activity, And Cellulose Liquefaction Ability. Two Promising Bacterial Isolates, Designated PS1 And PS2, Were Selected For Detailed Characterization. Submerged Fermentation Studies Were Carried Out To Evaluate Extracellular Enzyme Production, And Growth Dynamics Along With PH Variation Were Monitored Over Time. Molecular Identification Based On 16S RRNA Gene Sequencing Revealed That Isolate PS1 Belonged To The Genus Bacillus, While Isolate PS2 Was Affiliated With The Genus Klebsiella. Both Isolates Exhibited The Ability To Produce Cellulolytic And Hemicellulolytic Enzymes, Including CMCase, FPase, And Xylanase, With Enzyme Production Closely Associated With The Active Growth Phase. Zymogram Analysis Further Confirmed The Presence Of Extracellular Enzyme Systems Involved In Cellulose Degradation. The Results Highlight The Functional Role Of Gut-associated Microbial Consortia In Facilitating Lignocellulose Digestion In Pila Globosa And Suggest The Potential Of These Bacterial Isolates As Sources Of Industrially Relevant Enzymes

Author: Joelin Joseph | Sandeep Sreedharan

Area of research: Cyber Security

Author: Usman Mohammed | Mustapha Mukhtar Tijjani | Ridwan Salman

Area of research: Data Science

Network Security Has Become A Critical Concern For Educational Institutions Managing Complex IT Infrastructure With Hundreds Of Interconnected Devices. Manual Auditing Of Network Ports And Services Is Time-consuming, Error-prone, And Often Fails To Detect Unauthorized Services Or Outdated Software Versions That Provide Entry Points For Attackers. This Paper Presents A Comprehensive Automated Framework For Network Vulnerability Assessment Using Nmap (Network Mapper) And Its Scripting Engine (NSE). The Proposed System Performs Automated Host Discovery, Service Enumeration, Version Detection, And Vulnerability Identification Within Institutional Networks Using Stealth Scanning Techniques Combined With NSE Scripts. Our Implementation Successfully Identified Critical Vulnerabilities Including Backdoor Command Execution In Vsftpd 2.3.4 (CVE- 2011-2523), Remote Code Execution In Samba 3.0.20 (CVE-2007-2447), And Weak Authentication Mechanisms In MySQL Databases. Testing On Metasploitable 2 Environment Demonstrated 85 Percent Time Reduction Compared To Manual Assessment While Improving Accuracy And Coverage Across Large Network Segments. The Framework Generates Comprehensive XML And HTML Reports Suitable For Administrative Review And Remediation Planning. Results Validate The Effectiveness Of Automated Scanning Over Traditional Manual Methods For Continuous Security Monitoring In Educational Networks.

Author: Nupur Parihar

Area of research: Pollution

This Research Aimed To Quantify The Concentrations Of Key Heavy Metals—specifically Arsenic (As), Lead (Pb), Cadmium (Cd), Mercury (Hg), Copper (Cu), Chromium (Cr), And Zinc (Zn)—in Water Sources Utilized By Livestock. A Total Of 70 Samples Were Strategically Harvested From Diverse Geographic Locations Across The Western Region, Encompassing Urban, Semi-urban, And Rural Environments. Sampling Points Included Borewells, Open Wells, River/canal Systems, And Municipal Supplies. Analysis Was Performed Via Inductively Coupled Plasma Mass Spectroscopy (ICP-MS), With Results Benchmarked Against World Health Organization (WHO, 1998), European Union (EU, 2020), And Bureau Of Indian Standards (BIS, 2012) Guidelines. Findings Revealed That While As And Pb Concentrations Approached The Maximum Residual Limits (MRL) Defined By The WHO And EU, They Remained Within The Permissible Thresholds Of The BIS. Concentrations Of Cd, Hg, Cu, Cr, And Zn Were Consistently Below All International And Domestic Safety Standards. Notably, Urban And Semi-urban Water Sources Exhibited Significantly Higher Heavy Metal Loads Compared To Rural Sites. The Study Underscores The Necessity Of Continuous Environmental Monitoring To Mitigate Health Risks To Dairy Livestock And, By Extension, Human Consumers Through The Food Chain.

Author: Dr. Shrikant Suryakant Kekane

Area of research: Chemistry

We Present A Comprehensive Study On The Physicochemical Properties Of Water Samples From The Mula River In Pune, Maharashtra. Water Samples Are Under Investigations Were Collected From Khadkwasla Dam To Sangam Bridge During Pre-monsoon (April-May 2018), Monsoon (July-August 2018), And Post-monsoon (October-November 2018) Seasons. The Samples' Physicochemical Properties, Including PH, DO, BOD, COD, Chloride, Nitrate, Sulphate, Calcium, Magnesium, And Hardness, Were Compared To WHO Standards. The Mula-Mutha River Water In Pune Has Degraded In Quality. The Biological Oxygen Demand Has Increased To Over 30 Mg/l, Exceeding The Allowed Limits For Bathing. The Municipal Corporation Is Now Supplying Enough Water For The Planned Population.More Water Leads To More Sewage, Which Exceeds The Capacity Of Treatment Systems. As A Result, More Pollution Is Released Into The Mula-Mutha Rivers, Which Flush Out Pune's Waste. Physicochemical Parameters For Pre-monsoon, Monsoon, And Post-monsoon Seasons Are Within WHO Limits, With The Exception Of DO, BOD, COD, Chloride, Calcium, Magnesium, And Hardness.

Author: Dr. Shrikant Suryakant Kekane

Area of research: Civil Engineering

In Major Indian Cities There Is A Surge In Construction And Demolition Waste (CDW) Quantities Causing An Adverse Effect On The Environment. The Use Of Such Waste As Recycled Aggregate In Concrete Can Be Useful For Both Environmental And Economic Aspects In The Construction Industry. This Study Discusses The Possibility To Replace Natural Coarse Aggregate (NA) With Recycled Concrete Aggregate (RCA) In Structural Concrete. An Investigation Into The Properties Of RCA Is Made Using Crushing And Grading Of Concrete Rubble Collected From Different Demolition Sites And Under Construction Roads Locations Around Neelbad Bhopal. Aggregates Used In The Study Were: Natural Sand, Dolomite And Crushed Concretes Obtained From Different Sources. Groups Were Designed To Study The Effect Of Recycled Coarse Aggregates Quality/content, Cement Dosage, Use Of Superplasticizer. Tests Were Carried Out For: Compressive Strength, Flexural Strength And Workability. The Results Showed That The Cement In Concrete Can Be Replaced By Cow Dung Ash Up To 10%. Concrete Rubble Could Be Transformed Into Useful Recycled Aggregate And Used In Concrete Production With Properties Suitable For Most Structural Concrete Applications. A Significant Reduction In The Properties Of Recycled Aggregate Concrete (RAC) Made Of Higher Percentage RCA Was Seen When Compared To Natural Aggregate Concrete (NAC), While The Properties Of RAC Made Of A Blend Of 60% NA And 40% RCA Showed Not Much Significant Change In Concrete Properties.

Author: Kavish Kumar Singh | Prof. Deepak Garg | Dr.Rakesh Patel

Area of research: Microbiology

Soybean Is A Major Oilseed Crop In The State Of Maharashtra And Buldhana Is Considered As The Largest Producer District Of Soybean Crop In Maharashtra. Buldhana District Contributes To A Major Share In The Total Area Under Soybean Cultivation As Well As Total Soybean Production In Maharashtra Which Is Among The Largest Producers Of Soybean In India.This Study Investigates The Agricultural Efficiency Of Microbial Biofertilizers In Buldhana District, Maharashtra, Which Accounts For About 13.65% Of The State's Soybean Production. Field Trials In 2025-2026 Compared Traditional Chemical Recommended Dose Of Fertilisers (RDF) To Integrated Liquid Bio-consortia. The Results Reveal That Treatments Combining 75% RDF With Microbial Consortia (Rhizobium + PSB + Bacillus) Yielded Up To 24.00 Q Ha⁻¹, Matching Or Exceeding Full Chemical RDF Performance And Reducing Synthetic Input Reliance By 25%.

Author: Dr.Ashok Laxman Pawar

Area of research: Generative Adversarial Network In Deep Learning

Author: Vijay S | Naveenrajj | Kalaiyarasan K

Area of research: Civil Engg

Subgrade Soil Failure Due To Insufficient Strength, Weak Bearing Capacity, Excessive Deformation And Desiccation Cracking Of Problematic Soils Is Commonly Observed On The Road Network, And This Leads To Huge Expenditure In The Maintenance And Repair Of Highway Projects Every Year. It Is Necessary To Reduce These Engineering Problems And Economic Losses Through Environmentally And Economically Friendly Methods. Previous Studies Have Shown That Randomly Distributed Fibers Can Significantly Improve Various Soil Properties. However, There Is A Lack Of Comprehensive Study On The Engineering Properties Of Fiber Reinforced High Plastic Clay. Also, Limited Mechanical Models Have Been Proposed For Predicting The Shear Strength Behaviour Of Fiber Reinforced Clay. In Order To Investigate These Problems, A Series Of Laboratory Investigations Including Compaction, Bearing Capacity, One-dimensional Consolidation, Linear Shrinkage, Desiccation Cracking, Direct Tensile Strength, Compression Tests Should Be Conducted On Unreinforced And Coir Fiber Reinforced Clay. For This Study, The Soil Samples Were Prepared With Different Proportions Of RBI Grade-81 I.e. (2%, 4%, 6% And 8% Of Soil) Respectively. After That The Coir Fibers In Different Ratio I.e. 0.5%, 1%, 1.5% And 2% Respectively Will Be Added To The Sample Containing Suitable Content Of RBI Grade-81. Then OMC, MDD And CBR Values Evaluated For These Sample.

Author: Kumar Sabya Sachi | Prof. Deepak Garg | Dr. Rakesh Patel

Area of research: Mechanical Engineering

Conventional Thermal Engineering Systems In Industrial Applications Rely Heavily On Fossil Fuels, Resulting In Significant Greenhouse Gas Emissions, Particulate Matter Pollution, Acid Rain, And Thermal Pollution Of Water Bodies. This Paper Examines Sustainable Thermal Engineering Approaches Aimed At Mitigating These Environmental Impacts While Maintaining High System Efficiency. Key Strategies Discussed Include Waste Heat Recovery (WHR), Integration Of Renewable Energy Sources Such As Solar And Biomass, Clean Combustion Technologies, And Advanced Emission Control Devices. A MATLAB/Simulink Simulation Of A Hybrid Solar–biomass Thermal System Designed For Industrial Process Heating Demonstrates A Solar Contribution Of 40–55%, Energy Savings Of 22%, And Reductions Of 18–35% In CO₂, NOₓ, SOₓ, And Particulate Matter Emissions Compared To Conventional Systems. Furthermore, Case Studies From Cement Plants And Solar-assisted Boiler Installations Validate The Practical Feasibility Of These Approaches. The Study Aligns With Indian Regulatory Standards Set By The CPCB And MoEF And Supports The Transition Toward Low-carbon Industrial Thermal Operations.

Author: Mr. Sandeep Sansiya | Mr. Nitesh Rane

Area of research: Marketing Analytics

This Study Investigates The Effect Of Celebrity Endorsement On Luxury Brand Trust And Loyalty Among Consumers. Drawing On Source Credibility Theory And Signaling Theory, The Research Examines How Celebrity Endorser Credibility (trustworthiness, Attractiveness, Expertise) Influences Consumer Perceptions Of Brand Trust And Loyalty. Using A Structured Survey, Hypotheses Were Tested With Partial Least Squares Structural Equation Modeling (PLS-SEM). Results Indicate That Celebrity Endorsement Significantly Impacts Brand Trust And Loyalty Both Directly And Via Mediators Such As Psychological Ownership And Brand Attitude. Findings Offer Theoretical Advances And Managerial Insights For Luxury Marketing Strategies.

Author: Dr.Swapnil S. Phadtare

Area of research: Civil Engineering

With The Advent Of Intelligent Transportation Systems (ITS), Short Term Traffic Prediction Is A Major Feature To Develop The Smart Traffic System For The Smart City Framework. It Allows Real Time Data Accumulation, Analysis And Operations In Routing Traffic Through The Cities. From A Systems Perspective, The Traffic Volumes In Large Cities Often Exhibit Spatial And Temporal Coherence Which Can Be Leveraged To Predict The Short Term Traffic Volume. However, The Time Series Prediction Is Challenging Due To The Analysis Of Extremely Large And Complex Data Sets With Dependence On A Multitude Of Parameters Or Features. This Paper Presents A Machine Learning Based Approach Based On The Principal Component Analysis (PCA) And The Back Prop Algorithm To For Sales Forecasting. The Performance Of The Proposed System Is Evaluated In Terms Of The Mean Absolute Percentage Error (MAPE) And The Regression. It Is Shown That The Proposed System Outperforms The Previously Existing System Working On The Benchmark Datasets [1].

Author: Manju Chouhan | Prof. Vinay W. Deulkar

Area of research: Mathematics

The Growing Pace Of Digital Technology Has Changed The Face Of Modern Society Because Digital Technology Makes Communication, Banking, Education, And Performing Other Tasks Easier And Faster. However, Due To The Rapid Development In Digital Technology, The Threat Of Cyber Security Risks To Individuals And Organizations Has Also Escalated. Among The Numerous Cyber Security Threats, Phishing Threats Are One Of The Most Dangerous Threats On The Internet. Phishing Threats Aim At Fooling Users Into Entering Confidential Information Such As Passwords, Bank Accounts, And Other Data In The Belief That They Are Interacting With A Trusted Institution. As They Target Human Behavior Rather Than Technology, Phishing Threats Are Impressively Effective. The Purpose Of This Research Paper Is To Explore The Nature, Types, Methodologies, Effects, And Need For User Awareness When It Comes To Phishing Threats In Cyber Security. The Conclusion Drawn From This Research Work States That User Awareness Can Play An Important Part In Mitigating Threats Related To Phishing Security Along With Other Security Measures.

Author: Ishan Dhangar | Akansha Dubey

Area of research: Computer Science And Business Management

In Today's Digital Environment, Businesses Create And Engage With Previously Unheard-of Amounts Of Data Via Social Media Sites, Business Apps, Sensors, Consumer Transactions, And Linked Gadgets. Organizations Have A Lot Of Opportunity To Improve Decision-making, Streamline Operations, Create Products, And Gain A Competitive Edge Thanks To This Explosion Of Data, Often Known As Big Data. However, The Effective Use Of Big Data Is Constrained By A Number Of Organizational, Administrative, Ethical, And Technical Difficulties. Due To Significant Issues Such Data Storage And Scalability Limitations, Integration Obstacles Across Diverse Sources, Low Data Quality, Security Flaws, Privacy Concerns, And A Shortage Of Experienced Data Specialists, Organizations Are Still Unable To Fully Leverage Big Data Capabilities. The Intricacy Of The 5Vs Of Big Data—Volume, Velocity, Variety, Veracity, And Value—makes Managing Such Vast And Diverse Datasets Much More Difficult. In Order To Provide A Thorough Theoretical Understanding Appropriate For Both Academic Research And Real-world Application, This Study Attempts To Investigate, Evaluate, And Categorize The Main Big Data Difficulties Faced By Contemporary Businesses. The Abstract Emphasizes How These Issues Affect Strategic Planning, Technology Investment Choices, And Organizational Performance. The Study Also Looks At The Consequences For Long-term Sustainability, Resource Optimization, And Data Governance. The Article Summarizes Current Problems And Pinpoints Areas Where Organizations Face The Greatest Challenges Through A Methodical Study Of The Literature. To Tackle Big Data Obstacles, The Results Highlight The Necessity Of Strong Data Management Frameworks, Greater Security Measures, Improved Workforce Competencies, And More Stringent Governance Regulations. In The End, This Study Provides Fundamental Knowledge For Next Research On Organizational Data Preparedness And Digital Transformation, As Well As Insightful Information About The Changing Complexity Of Big Data Management.

Author: Kushal Sidar | Dr. Akanksha Dubey

Area of research: Mathematics

An Analysis Of Algebraic Group Structures Used In Contemporary Cryptosystems Is Presented In This Paper. The Multiplicative Group Of Integers Modulo N (used In RSA And Classical Diffie–Hellman), Cyclic Subgroups Of Finite Fields, Elliptic Curve Groups (used In ECC), Pairing-based Groups (bilinear Pairings On Elliptic Curves), And Other Algebraic Structures Closely Related To Group Theory That Support Post-quantum Schemes (e.g., Module/ring Structures In Lattice Cryptography And Isogeny-based Groups) Are All Covered. We Go Over The Mathematical Description, Cryptographic Applications, Security Presumptions, Algorithmic Complexity (for Group Operations And Principal Assaults), Efficiency And Implementation Factors, And Suggested Parameter Selections For Each Structure. The Study Ends With A Side-by-side Comparison That Highlights The Advantages, Disadvantages, And Potential Paths Forward.The Hardness Of Particular Computational Tasks Specified Over Algebraic Structures, Especially Groups, Is A Critical Component Of The Security Of Contemporary Public-key Cryptosystems. The Group Structures Underlying Modern Cryptographic Schemes, Such As Finite Cyclic Groups (used In Diffie-Hellman And DSA), Elliptic Curve Groups (ECC), And Newly Developed Post-quantum Structures Like Lattices, Isogenies, And Multivariate Polynomials, Are All Thoroughly Compared In This Paper. We Look At The Mathematical Underpinnings Of Each Group, Related Hard Problems, Trade-offs Between Security And Efficiency, Implementation Issues, And Defense Against Classical And Quantum Attacks. The Analysis Shows That The Emergence Of Quantum Computing Is Propelling A Shift Toward More Sophisticated Non-abelian And Structured Lattice-based Groups, Even If Elliptic Curve Groups Now Dominate Practical Deployments Because To Their Efficiency And Compactness. In Order To Help Choose Group Structures For Upcoming Cryptography Standards, This Paper Summarizes Important Findings.

Author: Bhavesh Kumar Sahu | Dr. Akanksha Dubey

Area of research: Applied Sciences & Humanities

This Survey-based Study Was Done To Understand Consumer Preferences For Shampoo And Conditioner Products And To Check The Physicochemical Properties Through Laboratory Testing. Consumer Responses Were Collected Using A Proper Questions Set Prepared On Google Forms, Which Focused On Brand Preference, Usage Patterns, Satisfaction Level, And The Side Effects Experienced. 70 Responses Were Collected. Also With The Survey, Laboratory Tests Such As PH Determination, Foaming Ability, Dirt Dispersion, Wetting Time, Percentage Solid Content, And Physical Appearance Were Done On Selected Popular Brands Like Dove, L’Oréal Paris, And Head & Shoulders. The Results Show That Factors Like PH Balance, Cleansing Efficiency, And Formulation Consistency Have A Noticeable Action On User Satisfaction. This Study Helps In Understanding How Scientific Properties Of Hair Care Products Affect Consumer Acceptance.

Author: Gauravi Thube | Dr. Priya Joshi | Prachi Jadhav

Area of research: AI Renewable Energy

This Growing Demand For Carbon Neutra1ity Mandates A Huge Integration Of MES Or RES. Even MES Or RES Integration Has Been Made Remarkably Difficult Because Of Their Natural Intermittency In Highly Random And Unpredictable Modes Of Operations. Today, MES Or RES Integration Has Been Made Remarkably Difficult Because Of Their Natural Intermittency In Highly Random And Unpredictable Modes Of Operations. Today, MES Or RES Integration Has Been Made Significantly Difficult Because Of Their Natural Intermittency In Highly Random And Unpredictable Modes Of Operations. This Study Aims To Describe The Critical Role Of "Artificial Intelligence (AI)" In MES, Which Has Appeared To Be A Massively Necessary Or Efficient Tool To Remove Complicated Difficulties And Problems In MES Or RES Integration. The Complexity And Critical Significance Or Importance Of "Artificial Intelligence" Have Been Widened; Thus, A Critical Role Has Been Attributed To The Activities Of Energy Forecasting, "real Time Generation, Real Time Optimization, Optimal Decision, Optimal Strategy,' Etc., Using Different Strongly Powerful And Newest Techniques. This Paper Aims To Describe The Critical Role Of Important Techniques Namely "Artificial Neural Networks (ANN), Deep Learning (DL), Fuzzy Logic Control (FLC)" Strategy, And "Heuristic Algorithms: Particle Swarm Optimization (PSO)" And "Ant Colony Optimization (ACO)" Strategy While Conveniently Ignoring Their Critical Significance Or Importance. Indeed, The Paper Will Discuss Difficulties In MES Or Conventional Operations Inconventional Grids Or Strategies Because Of Their Complexity Complexities With Critical Significance Or Importance Of "Cyber-Securities" Problems. The Subsequent Critical Phenomenon Is Joined With "Explain-ability Of AI Technology And Quantum Optimization Technology" Integration, "Digital Twinning" Strategy.

Author: Rudra Khandelwal | Priya Joshi | Devesh Bhosle | Sukhraj Girme | Krishna Sawale