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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
Early Detection Of Agoraphobia Using ML Algorithm
Area of research: CSE
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.
Author: Neha Premnath D | Sadhana M S | Sanjana S | Anagha H R | Lavanya S
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
Interactive Virtual Art With Hand Gestures
Area of research: Computer Science And Engineering
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.
Author: Nisha P | Pavana Rao | Sinchana C R | Souparna D S | Shyleshwari M Shetty
Desktop AI , A Virtual Assistant
Area of research: CSE
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.
Author: Disha H N | Disha H N | Kruthika H R | Spoorthi N K | Ashwini M S
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
Vehicle Insurance Fraud Detection System
Area of research: CSE
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.
Author: Pushpavalli P | Shamitha M | Aishwarya S | Shyleshwari M Shetty
Predicting Bankruptcy With Precision: Insights From Hybrid Machine Learning Models On Unbalanced Polish Financial Data
Area of research: AIML
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.
Author: Aishwarya M | Anu Shree R M | Dhanyashree T N | Meghana Shree S | Dr.Vishwesh J
Heart Disease Detection Using Logistic Regression
Area of research: CSE
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.
Author: Aishwarya R | Chitra M P | Hema L Patel | M Ishani Kuttappa | Dr. Madhu M Nayak
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
Identification Of Indian Fake Currency Using Convolutional Neural Network
Area of research: CSE
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.
Author: Manjula M | Bhagyashree Peddi | Dhanalakshmi G | Dr. Madhu M Nayak
Intelligent Attendance Monitoring System Using Deep Face Recognition With Residual Neural Network (ResNet) Analysis
Area of research: Computer Science And Engineering
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.
Author: Thinesh T | Mr. S. Chandrasekar | Sivasakthi S | Sivanthamil M | Suman Raj R
Deep Learning Approaches For Brain State Detection Under Anesthesia: A CNN-LSTM Framework
Area of research: Machine Learning
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.
Author: Ankitha D D | Harshitha M G | Manjula C | Meghana V Mathad | Rummana Firdaus
Secure Authentication Frame Work With Edge Computing For Real Time Patient Health Monitoring In Iomt
Area of research: CSE
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.
Author: Mrs. K. Ramya | Manojalex G | Mohamed Irfan J | Kathirvalavan G | Prabu A
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
Safety Companion
Area of research: CSE
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.
Author: Acharya Meghpriya Ramesh | C Poojitha | Deekshitha H | Likitha S V | Nischitha B S
A Study On Trends In Changing Oil Prices And Its Stimulate On Inflation In India
Area of research: Managerial Economics
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.
Author: Santhiya S | Dr. M. D. Chinnu, Assistant Professor
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
A Convolutional Neural Network Approach To Interview Simulation And Evaluation
Area of research: CSE
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.
Author: Manasvi H | Deeksha V | M Sanjana | Anupama C Swamy | Rummana Firdaus
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
HELMET DETECTION AND NUMBER PLATE USING DEEP LEARNING
Area of research: CSE
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.
Author: Prabhu P | Ragupriyan P | Sathish Kumar S | Sridesinguraja S | Rahul christober. F
Pneumonia Detection And Classification Using Deep Learning
Area of research: CSE
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.
Author: Amrutha K | Anusha S T | Kousar K | Maanasa Nagaraju | Asha Rani M
AN ANALYSIS OF DYNAMIC PRICING STRATEGIES IN THE DIGITAL AGE
Area of research: Economics
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.
Author: Sathveka D K | Dr.M D Chinnu
EFFECT OF SOIL STRUCTURE INTERACTION ON THE DYNAMIC BEHAVIOR OF BUILDING
Area of research: Structural Engineering
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.
Author: Dipak H. Vaidya | Mr. Girish Savai
A Study On Customer Preference For Cosmetic Brands With Reference To Coimbatore City
Area of research: Commerce
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.
Author: Dr.K.S.Nirmal Kumar | Monisha Violet. J
Breast Cancer Detection Using Hybrid Ri-Vit In Histopathalogical Images
Area of research: CSE
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.