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Vol 12, Issue 7 (July 2026) Electrical And Electronics Engineering

AR-IOT FRAMEWORK FOR PREVENTING VEHICLE DEPREDATION

With The Rapid Rise In Automobile Thefts Driven By Inadequate Residential Parking And A Lack Of Sophisticated Security Systems, Enhancing Vehicle Safety Has Become Paramount. This Paper Presents An Advanced, Multi-tiered Vehicle Security System That... With The Rapid Rise In Automobile Thefts Driven By Inadequate Residential Parking And A Lack Of Sophisticated Security Systems, Enhancing Vehicle Safety Has Become Paramount. This Paper Presents An Advanced, Multi-tiered Vehicle Security System That Integrates Biometric Authentication Or Real-Time One-Time Passwords (OTP). Utilizing An Arduino Uno Microcontroller Embedded Platform, The Prototype Interfaces An R307 Fingerprint Sensor Module With A Matrix Keypad. To Prevent Unauthorized Use, The System Allows A User To Turn On The Vehicle But Restricts Motion Until Both Fingerprint And OTP Verifications Are Successful. In The Event Of An Unauthorized Access Attempt, The System Instantly Triggers An Alert To The Vehicle Owner, Pairing Real-time Live Video Streaming Via An Onboard Camera Module With Exact Geographic Tracking Via GPS Technology. The Proposed System Offers A Low-cost, Fast-access, And User-friendly Solution That Significantly Improves Vehicle Security At Every Level. Read more Read less

Authors: S.NAVEEN | R.NAGARAJAN

Vol 12, Issue 7 (July 2026) PHARMACY

Andrographis Paniculata: Phytochemistry, Pharmacological Activities, Cardioprotective Potential, And Recent Advances

Traditional Herbal Medicines Remain An Important Source Of Healthcare, Particularly In Developing Countries. This Study Evaluated The Cardioprotective Effect Of An Aqueous Extract Of Andrographis Paniculata Against Dichlorvos-induced Cardiac Toxicity... Traditional Herbal Medicines Remain An Important Source Of Healthcare, Particularly In Developing Countries. This Study Evaluated The Cardioprotective Effect Of An Aqueous Extract Of Andrographis Paniculata Against Dichlorvos-induced Cardiac Toxicity In Male Wistar Rats. Dichlorvos Exposure Caused Cardiovascular Dysfunction, Increased Cardiac Injury Markers, Oxidative Stress, Inflammation, And Apoptosis. Treatment With A. Paniculata Significantly Improved Antioxidant Status, Restored Cardiac Function, Reduced Inflammatory And Apoptotic Responses, And Protected Cardiac Tissue From Damage. These Findings Suggest That A. Paniculata Possesses Promising Cardioprotective Properties Against Dichlorvos-induced Cardiotoxicity And Also Several Studies Noted That Plant Has Limited Genetic Diversity Due To Its Self-pollinating Nature, Restricting The Development Of Improved Cultivators With Enhanced Agronomic Traits And Higher Andrographolide Content. Ethyl Methanesulfonate (EMS)-induced Mutagenesis Has Been Used To Create Genetic Variation, Providing Valuable Resources For Crop Improvement, Functional Gene Studies, And The Development Of High-yielding Medicinal Varieties. Read more Read less

Authors: SIVA ANANDH P | R Madhanraj | T Harish | K P Suhaash | A Reeban Babu

Vol 12, Issue 7 (July 2026) Information Technology

A Review On Machine Learning And Deep Learning Models For Estimating Customer Churn Rates

Machine Learning Has Revolutionized Churn Prediction, Enabling Organizations To Anticipate And Mitigate Risks Effectively. By Leveraging Historical Data And Advanced Algorithms, Businesses Can Identify At-risk Customers Or Employees And Take Proactiv... Machine Learning Has Revolutionized Churn Prediction, Enabling Organizations To Anticipate And Mitigate Risks Effectively. By Leveraging Historical Data And Advanced Algorithms, Businesses Can Identify At-risk Customers Or Employees And Take Proactive Measures To Retain Them As Markets Become Increasingly Competitive, Retaining Existing Customers Has Proven To Be More Cost-effective Than Acquiring New Ones. In This Context, Machine Learning (ML) Has Emerged As A Powerful Tool For Analyzing Customer Behavior And Predicting Churn With High Accuracy. By Leveraging Vast Datasets And Sophisticated Algorithms, Businesses Can Proactively Identify At-risk Customers And Take Targeted Actions To Retain Them. The Success Of Churn Prediction Largely Depends On The Quality And Relevance Of Input Features. Important Features Include Customer Demographics, Transaction Frequency, Service Usage Patterns, Complaint Records, And Engagement Metrics. Feature Engineering, Which Involves Creating New Features Or Transforming Existing Ones, Is A Critical Step In Improving Model Performance This Paper Presents A Comprehensive Survey Of Statistical Models For Forecasting Churn Rates Along With Associated Challenges That The Sector Faces. Read more Read less

Authors: Ritu Yadav | Prof. Pradeep Sharma

Vol 12, Issue 7 (July 2026) Information Technology

A Review On Techniques For Web 3.0 Security Through Reduction Of PAPR Of Data Streams

With Increasing Number Of Cellular Network Users, Large Data Being Generated And Limited Bandwidth Available For Systems, Efficient Multiplexing Techniques Are Needed That Use The Available Bandwidth Efficiently For IoT And Web 3.0 Systems. It Is Wid... With Increasing Number Of Cellular Network Users, Large Data Being Generated And Limited Bandwidth Available For Systems, Efficient Multiplexing Techniques Are Needed That Use The Available Bandwidth Efficiently For IoT And Web 3.0 Systems. It Is Widely Used In Cellular And Internet Of Things (IoT) Based Applications. One Of The Major Challenges That IoT Suffers From Is High Value Of Peak To Average Power Ratio (PAPR), Which Reduces Its Security. High PAPR Causes High Level Of Perceptibility And Low Security. Hence It Is Necessary To Reduce The PAPR Of The OFDM Systems. Several Techniques Have Been Employed So Far For The Reduction Of PAPR In IoT Systems. This Paper Presents A Review On The Most Common PAPR Reduction Techniques For OFDM Systems. Read more Read less

Authors: Deepshikha Anijwal | Dr. Ruchika Pachori

Vol 12, Issue 7 (July 2026) Management Studies

A Comprehensive Study Of Machine Learning (ML) Algorithms And Performance Evaluation: Enhancing The Credit Card Fraud Detection

Credit Card Companies Must Be Able To Identify Fraudulent Credit Card Transactions So That Clients Are Not Charged For Items They Did Not Purchase. Previously, Many Machine Learning Approaches And Classifiers Were Used To Detect Fraudulent Transactio... Credit Card Companies Must Be Able To Identify Fraudulent Credit Card Transactions So That Clients Are Not Charged For Items They Did Not Purchase. Previously, Many Machine Learning Approaches And Classifiers Were Used To Detect Fraudulent Transactions. However, Because Fraud Patterns Are Always Changing, It Is Becoming Increasingly Vital To Investigate New Frauds And Develop The Model Based On The New Patterns. The Purpose Of This Research Is To Create A Machine Learning Classifier That Not Only Detects Fraud But Also Detects Legitimate Transactions. As A Result, The Model Should Have Excellent Accuracy, Precision, Recall, And F1-score. As A Result, We Began With A Large Dataset In This Study And Used Four Machine Learning Classifiers: Support Vector Machine (SVM), Decision Tree, Naïve Bayes, And Random Forest. The Random Forest Classifier Scored 99.96% Overall Accuracy With The Best Precision, Recall, F1- Score, And Matthew’s Correlation Coefficient In The Experiments. Read more Read less

Authors: Kabir S M