Volume - 11 Issue - 5

Area of research: Human Resource

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

Author: R.Hariprasaath | Dr.S.Sara

Area of research: HUMAN RESOURCE MANAGEMENT

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

Author: Kavithaannadurai | A. Josphineselsia

Area of research: MBA

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

Author: M. Barath Kumar | Dr.S.Sara

Area of research: Pharmacy

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

Author: Janhavi .K. Bundele | Pratik .B. Bhanage | Dr. Megha.T. Salve

Area of research: CSA

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

Author: Dr. T. Nirmal Raj | H R Rathna Bai

Area of research: Information Technology

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

Author: Aryan Thapliyal | Royden Dixera | Daniel Thatu | Darish Dias | Balaraju Vijayalakshmi

Area of research: Information Science And Engineering

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

Author: Mohammed Gufran | Mohammed Zubairulla Khan | Mohin R Pinjar | Sona J M

Area of research: Electronics And Communication Engineering

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

Author: Mr.T.Raja | Aarthi M | Aswini P | Lavanya M | Sandhiya C

Area of research: Cybersecurity And Artificial Intelligence (AI)

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

Author: Sharon R | Janapriya S | Nishmitha R | Prof.Mrs.Ramya R

Area of research: Finance

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

Author: S.Prabhu | Dr.S.Sara

Area of research: MBA

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

Author: Gayathri K | Dr.R. Jayadurga

Area of research: Pharmacy

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

Author: Gaikwad Rutuja Rajendra | Mr. Dnyaneshwar S. Vyavahare | Dr. Megha T. Salve

Area of research: Information Science And Engineering

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

Author: N Priyanka | Sanvikha S | Shwetha Shree N | Tanushree A | Bhagyalakshmi B

Area of research: Pharmacy

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

Author: B. D. TIWARI | Mr. Madanwale N.J | FAHEEM S.S | SANDEEP S.P

Area of research: Electronics And Communication Engineering

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

Author: Mr. Balaji | Gayathri K | Dharshini M | Jayashree T | Annapoorani S

Area of research: Electronics And Communication Engineering

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

Author: Mrs. Malini P | Deepika L | Bharani Priya R | Sree B | Vijayarohini M

Area of research: Diamond Engineering Private Limited

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

Author: V. Balaji

Area of research: Commerce And Management

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

Author: E.Akilan | Dr.S.Sara

Area of research: MBA

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

Author: H. Kavipriya | Dr.R. Jayadurga

Area of research: CSA

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

Author: Dr.T.NIRMAL RAJ | G DEEPAK

Area of research: Pharmacy

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

Author: Miss. Ahiwale Pratiksha Ravindra | Prof. Chopade Babasaheb L | Dr. Megha T. Salve

Area of research: CSA

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

Author: R. Sowmiya | S. Prakasam

Area of research: ISE

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

Author: Padmaja K | Spoorthi R | Suraksha K P | Umme Hani | V Nagaveni

Area of research: CSE

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

Author: A. Padama | M.Mohamed Imran | P. Mownitharan | N. Dhanush | M. Kavinkumar

Area of research: Pharmacy

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

Author: Miss. Divya Somnath Gaikwad | Mr. Dnyaneshwar S. Vyavhare | Dr. Megha T. Salve

Area of research: Information Technology

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

Author: Nazreen Riazudeen S | Jayasundhar V.K | Sureendrababu R | Vickram S

Area of research: Information Technology

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

Author: Gowri G | Harikrishnan N | Omprakash T | Vijayalakshmi.R

Area of research: CSA

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

Author: Nandhini G | Mr. M. Krishnamoorthy

Area of research: Computer Science And Applications

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

Author: L. Rajashree | Dr. S. Prakasam

Area of research: CSA

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

Author: Dr.K.SRINIVASAN | THULASINGAM D

Area of research: Pharmacy

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

Author: Dipshikha Arun Kandekar | P.P.Khade | Dr. Megha T Salve

Area of research: MCA

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

Author: D. DINESH | S. PRAKASAM

Area of research: Artificial Intelligence / Machine Learning

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

Author: Satheesh Kumar K | Satheesh Kumar.k | B.yogeshwaran | G.v.prabakaran | G.Maheshwaran

Area of research: Computer Science

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

Author: Samruddhi Deokar | Parikshit Yedale | Aditya Ingale | Purva Nandre | Dr. Mansi Bhonsle

Area of research: Civil Engineering

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

Author: Riya V. Rajurkar | Prof. Girish Savai

Area of research: Civil Engineering

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

Author: Monika N. Gajbhiye | Prof. Girish Savai

Area of research: Computer Science And Engineering

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

Author: Hemashree B Y | N Meghana | Rabiya Afnin M S | Rashmi K S , Dr.Vishwesh J

Area of research: Electronics And Communication Engineering

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

Author: Vishalini L | Lahari Kalakata K | Dhaaranya | Dr.S.Sathiya Priya | Dr.D.Arul Kumar | Dr.V.Jeyaramya

Area of research: Community Medicine

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

Author: Dr. Malai Ammal | Dr. Arun Murugan | Dr. Ramasubramaniam | Lillian Martha Jacob | Malini S A | Manu Pranav | Mohammed Ali | Nedumaran | Shruti B | Rohit S | Poonkuzhalnathan

Area of research: CSE

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

Author: BhavanaB | BinduU | DivyaH | Eshwar | Mrs.ArathiP

Area of research: Information Technology

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

Author: Mrs.S.Fathima Chandhini | Venkatesan K | Alaguraja S | Prabanjan G

Area of research: Full Stack Development

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

Author: Dr. T. Nirmal Raj | Sri Sakthi Thulasi S

Area of research: CSE

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

Author: Likesh Kumar G Y | Preetham G | Pagadala Chethan | Chakrapani Rahul

Area of research: CSA

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

Author: Mr.R.Saravanan | B. Sharmista

Area of research: CSA

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

Author: Dr. K. Srinivasan | Monika D

Area of research: CSA

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

Author: Mr. R. Mr.R.Saravanan | Parvadhakrishnan V

Area of research: CSE

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

Author: Payyavula Harshitha | Shri Lakshmi M | Vinutha G | Yashaswini N Gowda | Darshini M S

Area of research: Electrical And Electronics Engineering

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

Author: Assist.Prof.K.Gomathy | Santhosh | Sasikumar | Abishek | Karthikeyan

Area of research: CSA

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

Author: Dr. K. Srinivasan | Sanjay Kumar A

Area of research: Electrical And Electronics Engineering

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

Author: S. Das Vignesh | S. Sathishkumar | S. Tamilarasan | L. Priyadharsan | Mr. D. Saravanan

Area of research: CSA

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

Author: Mr.R.Saravanan | S Vignesh

Area of research: ISE

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

Author: Nischitha M | Zoya Tabassum | DivyaShree S | Ms. Sandya V J | Chaya P

Area of research: MBA

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

Author: A.RAJKUMAR | DR.R.JAYADURGA

Area of research: NA

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

Author: Jeganathan C | Ghaniniyan K | Nishanth Kumar G

Area of research: MBA

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

Author: R.Renuka | Dr.S.Sara

Area of research: Pharmacy

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

Author: Shaikh Ayesha Akil | Prof.khade Poonam P | Dr.megha T.Salve

Area of research: Library And Information Science

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

Author: Dr. Hitesh Gopal Brijwasi | Dr. Tushar Malharrao Patil

Area of research: NA

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

Author: B. D. TIWARI | S. R. MASHALE | T. S. PARDESHIMATH

Area of research: NETWORKING

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

Author: Shruthi K | Chandraiah T

Area of research: Computer Science And Engineering

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

Author: Dharshini R | Jothika S | Karthika E | Mijaisha V S

Area of research: Computer Science And Engineering

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

Author: Mr. S. Chandrasekar | S. Dhatchanamoorthy | R. Nithish | P. Kishore Krishna, K. Mirresh

Area of research: CSE

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

Author: M.Lalitha | M.Abilash | S.Jeevanantham | G. Manibharathi | M.Praveenkumar

Area of research: CSE

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

Author: Bhavna Santhakumar | Kruthika S P | Monisha A M | Mythreyi Shivani M | Rajani D

Area of research: ECONOMICS

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

Author: S.Dharika | Dr.M.D.Chinnu

Area of research: NA

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

Author: B. D. Tiwari | S.S.Londhe | S.S.Kumbhar | T. P.Kulkarni

Area of research: Electrical And Electronics Engineering

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

Author: Mr. N. Mohanasundaram | S. Aravindhan | V. Krishnakanth | S. Lakshmanan | A. Chandru

Area of research: Electrical And Electronics Engineering

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

Author: Mr.R.Jeevanandh | V. Dhananjeyan | T. Dhanush kumar | S.Palanisamy | M. Srinesik

Area of research: ELECTRICAL AND ELECTRONICS ENGINEERING

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

Author: Mr.P. Gopinathan | M. Sasikaran | M. Vaishya | M. Saikrishna | D. Sakthivel

Area of research: Electronics And Communication

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

Author: Roshini H | Thigazhvazhaki B | Dr. S. Sathiya Priya

Area of research: Electronics And Communication Engineering

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

Author: Dr.S.Sathish | Ms. Gobika V | Ms. Karisni S J | Ms. Leelavathi R | Ms. Sudha P

Area of research: Management

Author: P. YOGALAKSHMI

Area of research: Civil Engineering

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

Author: Archana K | K soundhirarajan

Area of research: Electronics And Communication Engineering

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

Author: Sandya M D | Dharani Sri T G | Katharine M | Dr. D. Arul Kumar

Area of research: ECE

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

Author: Ranjith A | Venkatesan G | Vekash | Dr Sathiya Priya S

Area of research: ECE

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

Author: M.Gautham | S.Premkumar | T.Sasikumar | M.Tamil Selvan | S.Vasantha Kumar

Area of research: Electronics And Communication Engineering

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

Author: Prabakaran G | Harshini S | Shalini D | Sharumathi K | Sowmiya S

Area of research: Electronics And Communication Engineering

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

Author: Janani MN | Sivani K | Monika V | Dr. S. Sathiya Priya

Area of research: Computer Science And Technology

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

Author: Mr.Karthikeyan C | Ms. M Krithi | Mr. Pranav S | Mr. Yogesh chavan | Mr. Chandrasekar vadivelraju

Area of research: Electronics And Communication

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

Author: Keerthana.R | Kanimozhi.R | Swathi.M | Dr.S.Sathiyapriya | Dr.D. Arul Kumar | Dr.V. Jeyaramya

Area of research: Deep Learning

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

Author: Girish S C | Bhumika H N | Jayashree S | Sahana Shree T | Thejaswini L

Area of research: CSE

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

Author: Ramya K | Ragupathi R | Sivasakthi S | Thanigasalam V | Vasanthakumar S

Area of research: Information Technology

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

Author: Sri Ranjani C | Lavanya G | Deepika M | Sujitha S | Soundararajan K

Area of research: Information Technology

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

Author: S.Abdulhajees | S.Manivel | G.Kelvinjose | Mrs.K.Geetha

Area of research: Information Technology

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

Author: A Sivasankari | Madhubala V | Sivaranjini S | Keerthana I | Swetha S

Area of research: CSE

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

Author: V.Hemalatha | Thavasimani | M.Sreemahapriya | M.Rajalakshmi

Area of research: Electronics And Communication Engineering

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

Author: Anisha Banu A | Deepa Mathi R | Nutheti Likhitha Chowdary

Area of research: Civil Engineering

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

Author: Susindiran S | Roopa D

Area of research: CSE

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

Author: Mrs.V.Hemalatha | N.Rejiya Sulthana | S.Priya | A.Pavithra

Area of research: Civil Engineering

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

Author: Pooja M. Tayade | Om D. Rajput | Mahendra D. More | Kunal P. Naik | Prashik K. Ingle | Prof. Santosh P. Bhise

Area of research: Electronics And Communication Engineering

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

Author: Poojashree S | Shalini S | Monisha R | Dr.D. Arul Kumar

Area of research: Biomedical Engineering

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

Author: Dhivan T | Illayaraja R | Suresh Gopi B | Dr Mythili S

Area of research: ISE

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

Author: Chaya P | Anu Priya | Chinmayi D | Lavanya S | Waiza Fathima

Area of research: Information Technology

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

Author: Parameshwaran.T | Shamyuhi.V | Sriram P.K | Dr. S Nithya Roopa

Area of research: Mechanical Engineering

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

Author: Mrs. J. S. Tilekar | Akshay Dange | Manohar Pawar | Shivraj Rite | Vishwajeet Nimbalkar

Area of research: Computer Science And Engineering

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

Author: E. Sinega | J. Sowmiya | S. Dhulasilinkam | K. M. Anbu | K. Kavipriya

Area of research: Computer Science And Engineering

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

Author: Sanika Algude | Pratik Bevnale | Jatin Bhujbal | Roopam Wadkar | Mansi Bhonsle

Area of research: Structural Engineering

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

Author: Ahanya P

Area of research: Mechanical Engineering

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

Author: Mrs. Jyoti S.Tilekar | Mr.Shreyah Borate | Mr. Karan Jadhav | Mr.Amit Jagtap | Mr. Mohammadrehan Attar

Area of research: Artificial Intelligence / Machine Learning

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

Author: Hameed Asik K | Naveen Kumar R | Kartheeswaran V | Vimala D

Area of research: Compute Science

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

Author: S.Manishankar | K.Mathesh | P.Ramanathan | S.Renganathan

Area of research: NURSING

Author: Neha Patyal | Anamika Saini | Kajal Banyal | Jasbir Kaur | Kalpna Chauhan

Area of research: Computer Science And Technology

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

Author: R Kamal Raj | Gnanavika M | Shreyas D M | Yamanappa

Area of research: Electronics And Communication Engineering

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

Author: Gnanaprakash J | Gobichandar M | Meenatchi K | Praveena G | JOSEPH S

Area of research: Computer Science

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

Author: Ajay Singh Patel | Prof. Sunil Parihar

Area of research: Civil Engineering

Author: C Sudharsan | V.Indhumathi | A.Abitha

Area of research: Artificial Intelligence And Data Science

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

Author: Sasidaran.D | Vinithkumar.S | Siva.S | Seenivasn.P

Area of research: Computer Science

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

Author: Indhreesh.R | Sekar M | Lokesh V | Vignesh Kumar.M

Area of research: CSE

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

Author: YV Akash | V Deepak | S Gurumoorthy | S Jeeva | R Vijay

Area of research: Civil Engineering

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

Author: Prof. R. R. Sarode | Mr. shubham Gaikwad | Mr. Sarthak Raipure | Mr. Mushrauf | Sagar Patil

Area of research: CSE

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

Author: Chandana K C | Chandana N S | Hemashri H M | Kumuda L | Lavanya S

Area of research: Information Technology

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

Author: Darshini N | Dhanusree K | SriVarthini K | Swetha E

Area of research: Mechanical Engineering

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

Author: Mr. J. P. Pinjar | Mr .P. P. Mulade | Mr .N. V. Bahirgonde | Mr.P. S. Talbhandare | Mr.M. A. Puppal

Area of research: Civil Engineering

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

Author: Prof. K. G. Bhagat | Prof. K. G. Bhagat | Mr. Noor Mohd. Taslim Pinjari | Bhagat1 Mr. Noor MoMr.Tushar Malvekar | Mr.Sagar Chavan

Area of research: Civil Engineering

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

Author: Prof. A. G. Ghanmode | Prof. A. G. Ghanmode | Mr. Matim Tadvi | Rahul Kachale | Priyanka D.Sapkale

Area of research: Information Technology

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

Author: P.Komala | Agishan J | Ranjith A | Yogeshwaran K

Area of research: Artificial Intelligence And Data Science

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

Author: Vasudevan S | Sathishkumar K | Sampathkumar K | Sachin S

Area of research: Information Technology

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

Author: Dr.K.Geetha | Bhuvaneshwaran G | Aravindhan K | Syed AhamedB.A

Area of research: Electrical Engineering

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

Author: Ajay Saanjay Pawar | Ashwin Santosh Sonawane | Bhushan Hari Jadhav | Prof. Y. R. Patni

Area of research: Game Development

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

Author: Prof. (Ms). Arti Sondawale | Mrunal Wankhede

Area of research: Civil Engineering

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

Author: Mr. Shubham Babasaheb Hatte | Dr. S. A. Bhalchandra

Area of research: Information Technology

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

Author: Dr.K.Geetha | oshva S | Manikandan P | Manoj S

Area of research: Health Science

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

Author: Miss. K.Lalithavani | Deepa.T | Dhivyalakshmi.J | Sahana.R | Sindhu.K

Area of research: Artificial Intelligence And Data Science

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

Author: Nithish Kumar S | Pravin V | Killivalavan T

Area of research: Electrical And Electronics Engineering

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

Author: Mr.P.Gopinathan | Anupriya.S | Madhumitha.S | Sandhiya.A | Selvalakshmi.G

Area of research: Social Science

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

Author: Dr. Varsha Patel

Area of research: CSE

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

Author: Ms.K.Abinaya | Ms.k.jayasri | Ms.R.Akalya | Ms.D.kiruthika

Area of research: Information Technology

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

Author: Ritesh SekarAVV | VikasB | AbhinandhanPS | Dr. S Nithya Roopa

Area of research: NA

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

Author: Abhishek Jadhav | Aditya Pawar | Omkar Yerunkar | Prabhakar Shivatare

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

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

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

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

Area of research: ECE

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

Author: J.Vanaja | K.Suresh Kumar

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

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

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

Area of research: Biomedical Engineering

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

Author: Vidhya.S | Pritika.R.G | Rashmi Ifrashiya.A | Soundarya.S

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

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

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

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

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

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

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

Area of research: Computer Science And Engineering

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

Author: Sahana K | Sakshi Annasab Kamate | Srashti Kumbar | Yellamelli Sahithi | Dr. Raviraj P

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

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

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

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

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

Area of research: Mechanical Engineering

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

Author: Madhavan V | Ajith S | Karthikeyan S | Shanmuganathan P | Vinoth Kumar V

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

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

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

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

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

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

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.

Author: Mrs.T.Geetha | Lakshmi R | karthiga S | Lashiya M | Preethi B