High Impact Factor : 7.883
Submit your paper here

Impact Factor

7.883


Call For Paper

Volume: 11 Issue 05 May 2025


Download Paper Format


Copyright Form


Volume - 11 Issue - 5


Volume: 11 Issue: 5 May 2025

Brain Tumor Disease Detection Using Federated Learning With FedAvg

Area of research: CSE

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.

Author: Amruta Vijayakumar kavalapure | Anusha K N | Bhuvana S Kumar | Harshitha B | Mrs. Maria Rufina P
Read More
Volume: 11 Issue: 5 May 2025

Adaptive Traffic Lights Control Using IoT And Image Processing

Area of research: CSE

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.

Author: Rishika S | Shravya S | Siri N | Sushna Subramanya K | Harshitha B
Read More
Volume: 11 Issue: 5 May 2025

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

Area of research: Computer Science And Engineering

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].

Author: Anwitha | Dhanushree BA | Monika CV | Darshini MS
Read More
Volume: 11 Issue: 5 May 2025

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

Volume: 11 Issue: 5 May 2025

Fake Product Identification

Area of research: CSE

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.

Author: Amrutha S L | Chinmaye Patel N K | Dhaarini Lokesh | Harshitha P P | Shreelakshmi C M
Read More
Volume: 11 Issue: 5 May 2025

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

Area of research: Management Studies

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.

Author: Jayaharini B S | Mr.Imayavan B
Read More
Volume: 11 Issue: 5 May 2025

Study On Capital Budgeting Techniques In Large Scale Industries

Area of research: Managerial Economics

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.

Author: Sridharshana.S.U | Dr. M. D. Chinnu, Assistant Professor
Read More
Volume: 11 Issue: 5 May 2025

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

Area of research: Computer Science And Engineering

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.

Author: Shapna Rani E | Eraiarul K | Karthick M | Darwin Shiyam B | Hariharan T
Read More
Volume: 11 Issue: 5 May 2025

HELMET DETECTION AND NUMBER PLATE USING DEEP LEARNING

Volume: 11 Issue: 5 May 2025

AN ANALYSIS OF DYNAMIC PRICING STRATEGIES IN THE DIGITAL AGE