Volume - 12 Issue - 6

Area of research: Embedded System Technologists

The Increasing Demand For Improved Fuel Efficiency And Reduced Emissions Has Driven The Development Of Advanced Automotive Electrical Systems. Conventional Vehicles Utilize Separate Starter Motors And Alternators For Engine Cranking And Battery Charging Respectively, Which Increases System Complexity And Reduces Efficiency. Integrated Starter Generator (ISG) Technology Combines These Two Functions Into A Single Electromechanical Unit, Thereby Reducing Mechanical Complexity And Improving Energy Utilization. This Paper Presents The Design And Implementation Of A Sensorless Integrated Starter Generator (ISG) Controller Using MOSFET-based Shunt Regulation. The Proposed System Employs A Three-phase Six-switch MOSFET Inverter Bridge That Performs Both Motoring And Charging Operations. During Engine Startup, The Inverter Operates In Motoring Mode To Drive The ISG Machine And Crank The Engine. Once The Engine Starts, The ISG Operates As A Generator And The Inverter Functions As An Active Rectifier To Convert AC Power Into DC For Battery Charging. Voltage Regulation Is Achieved Using MOSFET-based Shunt Control. A Sensorless Control Strategy Based On Back Electromotive Force (Back-EMF) Estimation Is Implemented To Eliminate The Need For Mechanical Position Sensors. This Reduces System Cost, Improves Reliability, And Simplifies System Integration. Experimental Results Demonstrate Stable Engine Startup, Efficient Charging Performance, And Reliable Sensorless Operation.

Author: Gokul R

Area of research: Embedded System Technologists

Modern Automotive Systems Require Efficient Electrical Power Generation And Management Due To Increasing Electrical Loads And Stringent Emission Regulations. Conventional Vehicles Utilize Separate Starter Motors And Alternators For Engine Cranking And Battery Charging Respectively. This Configuration Increases System Weight, Mechanical Complexity, And Energy Losses. Integrated Starter Generator (ISG) Technology Has Emerged As An Efficient Solution That Combines Both Functions Into A Single Electromechanical System. This Paper Presents The Design And Development Of A Series Regulated Integrated Starter Generator Controller Capable Of Operating In Both Motoring And Charging Modes. In Motoring Mode, A Three-phase MOSFET Inverter Converts DC Battery Voltage Into Three-phase AC To Drive The ISG Machine And Start The Engine. In Charging Mode, The ISG Machine Operates As A Generator And The Generated AC Power Is Rectified And Regulated Using Three SCRs Operating In Series Regulation Mode. A Microcontroller-based Control Strategy Is Implemented To Manage Inverter Switching, SCR Firing Control, And Mode Transition Between Motoring And Charging. In Addition, A Current-based Diagnostic System Is Implemented To Detect Electrical Faults Such As Phase Open Faults, Phase Short Faults, And Phase-to-ground Faults.The Proposed ISG Controller Improves System Efficiency, Reduces Mechanical Complexity, And Enhances System Reliability. Experimental Results Demonstrate Stable Engine Startup, Efficient Battery Charging, And Reliable Fault Detection Under Different Operating Conditions.

Author: Karthika A

Area of research: Computer Science

Agriculture Constantly Faces Various Challenges Including Attacks From New Pests And Insects. Often, With Large Farm Sizes And Plummeting Manpower In The Agricultural Sector, It Becomes Challenging To Continuously Monitor Crops For Pest Infestation. Precision Agriculture Has Emerged As A Promising And Much Sought After Technique For Automated And Quick Detection Of Pests In Agricultural Farms. With The Advent Of Inexpensive And Compact Drones, Image Capturing And Processing Techniques Based On Machine Learning, Automated Detection Of Pest Attacks Has Gained Prominence. In This Research Paper, A Specific Type Of Pest Attack Known As The White Fly Attack Has Been Investigated Which Affects A Variety Of Crops. This Paper Presents A Detailed Background Of Precision Agriculture Based Techniques For Automated Detection Of White Fly Attacks On Crops. A Thorough Investigation Of Image Enhancement, Segmentation, Feature Extraction And Classification Pertaining To White Fly Attacks Has Been Resented. Salient Features Of The Contemporary Techniques Used For The Purpose Have Been Cited And Evaluated.

Author: Rajendra Mandloi | Prof. Pradeep Pal

Area of research: IT

Data Centers Constitute The Foundational Infrastructure Of The Modern Digital Economy, Supporting Cloud Computing, Artificial Intelligence, And Global Telecommunications. This Paper Presents A Comprehensive Examination Of Data Center Technologies, Architectures, Operational Challenges, And Emerging Trends, With Emphasis On Sustainability And Next-generation Infrastructure Paradigms. Drawing On Peer-reviewed Literature From 2021–2024 Sourced From IEEE Xplore, Scopus, ACM Digital Library, Springer, And ScienceDirect, This Study Synthesizes Findings Across Five Data Center Typologies: Enterprise, Colocation, Cloud, Edge, And Hyperscale. The Analysis Reveals That While Data Centers Enable Unprecedented Computational Capability Supporting A Global Digital Economy Valued At Approximately USD 274 Billion In 2023, They Impose Significant Environmental Burdens, Consuming An Estimated 200–250 TWh Of Electricity Annually (roughly 1–1.5% Of Global Electricity Use). Key Challenges Include Energy Costs, Thermal Management Complexity, Cybersecurity Vulnerabilities, Water Consumption, And Regulatory Compliance. The Study Evaluates Mitigation Strategies Including AI-driven Operations, Liquid Immersion Cooling, Renewable Power Purchase Agreements, And Edge Architectures. Case Studies Of Google, Microsoft, And Equinix Demonstrate Measurable Progress In Power Usage Effectiveness (PUE) Reduction, Carbon Neutrality Commitments, And Operational Automation. The Paper Concludes With Recommendations For Researchers, Practitioners, And Policymakers, Advocating An Integrated Approach Balancing Performance, Cost, Resilience, And Environmental Responsibility.

Author: Nilesh Ramprasad Mourya

Area of research: Pharmaceutical Chemistry

Epilepsy Is A Chronic Neurological Disorder Characterized By Recurrent, Unprovoked Seizures Resulting From Abnormal Electrical Activity In The Brain. Despite The Availability Of Conventional Antiepileptic Drugs, Treatment Limitations Such As Adverse Effects, Drug Resistance, And Drug Interactions Have Prompted The Development Of Newer Anticonvulsant Agents. Recent Advances In Medicinal Chemistry Have Led To The Discovery Of Several Effective Antiepileptic Drugs Including Gabapentin, Lamotrigine, Levetiracetam, Oxcarbazepine, Tiagabine, Topiramate, And Zonisamide. These Agents Exhibit Diverse Mechanisms Of Action And Improved Safety Profiles. This Review Summarizes The Structure–activity Relationship (SAR), Mechanisms Of Action, And Beneficial Therapeutic Effects Of Selected Newer Antiepileptic Drugs. Understanding The Relationship Between Chemical Structure And Pharmacological Activity May Facilitate The Development Of Novel Anticonvulsants With Enhanced Efficacy And Reduced Adverse Effects.

Author: Mrs. P. Keerthana.,M.Pharm., Assistant Professor, | K Kaviyarasan | B. Bhavani | J.S. Kanishka | T. Poovizhi

Area of research: Computer Engineering

EXPOAIR (Environmental EXposure Prediction & Observation For Air Intelligence And Reporting) Is An Intelligent Air Quality Monitoring And Prediction Platform Developed To Provide Real-time Environmental Awareness And Personalized Exposure Insights. The System Integrates An ESP32-based IoT Sensing Unit With Environmental APIs To Collect Localized And Regional Air Quality Data. Sensor Observations Are Transmitted Through MQTT, Processed Using A FastAPI Backend, And Stored In A PostgreSQL Database For Analysis And Visualization. A React-based Web Application Presents Live AQI Monitoring, Weather Conditions, Historical Trends, Prediction Results, And User-oriented Environmental Recommendations Through An Interactive Dashboard. The Modular Architecture Supports Scalable Deployment And Future Integration Of Advanced Machine Learning Models For Improved Air Quality Forecasting. By Combining IoT Sensing, Cloud Communication, And Intelligent Analytics, EXPOAIR Offers A Practical And Cost-effective Solution For Environmental Monitoring, Promoting Informed Decision-making And Healthier Communities.

Author: Vaishnavi Shinde | Harshada Patil | Sayali Adsul | Abhiruchi Kotlapure

Area of research: Civil Engineering

Pervious The Rapid Pace Of Urbanization Has Significantly Increased Impermeable Surface Areas, Leading To Severe Storm Water Runoff And Depleting Groundwater Reserves. Pervious Concrete Offers A Viable Solution For Sustainable Storm Water Management; However, Its Widespread Structural Application Is Often Hindered By Inherently Low Tensile Strength And Brittleness. This Study Investigates A Cleaner Production Approach By Evaluating The Mechanical And Hydrological Performance Of Eco-friendly Pervious Concrete Incorporating Two Distinct Industrial And Municipal Waste Streams: Waste Marble Powder (WMP) And Recycled Polyethylene Terephthalate (PET) Fibers. In This Investigation, WMP Was Used As A Partial Replacement For Ordinary Portland Cement (OPC) At Varying Percentages, While Recycled PET Bottles Were Processed Into Fibers And Introduced As Discrete Reinforcement. A Comprehensive Laboratory Program Was Executed To Analyze The Fresh And Hardened Properties Of The Modified Mixes. The Hydrological Performance Was Assessed Through Total Porosity And Falling-head Permeability Tests, While The Mechanical Behavior Was Evaluated Via Compressive Strength, Split Tensile Strength, And Flexural Strength Testing. The Results Indicate That While PET Fibers Bridge Micro-cracks And Substantially Enhance The Tensile And Flexural Capacity Of The Porous Matrix, The Extreme Fineness Of WMP Acts As A Micro-filler, Densifying The Interfacial Transition Zone Around The Smooth Plastic Fibers. This Synergistic Interaction Helps Mitigate The Typical Strength Loss Associated With Plastic Inclusions Without Significantly Compromising The Required Hydraulic Conductivity. The Findings Demonstrate That Optimizing WMP And PET Fiber Content Yields A Highly Sustainable Construction Material Suitable For Low-volume Traffic Pavements, Pedestrian Walkways, And Parking Lots

Author: P GOWTHAM | P SASIKALA

Area of research: STRUCTURAL ENGINEEERING

- The The Depletion Of Natural Resources And The High Carbon Footprint Associated With Conventional Portland Cement Production Have Intensified The Search For Sustainable Alternatives In The Construction Industry. This Study Investigates The Synergistic Effect Of Incorporating Binary Agricultural Ash Blends Specifically Palm Oil Fuel Ash (POFA) And Sugarcane Bagasse Ash (SCBA) Along With Natural NYLON Fiber Reinforcement To Produce An Eco-efficient, High-performance Concrete. A Series Of Concrete Mixtures Were Prepared Where Ordinary Portland Cement Was Partially Replaced By Binary Blends Of POFA And SCBA At Varying Percentages While NYLON Fibers Were Introduced At Localized Volume Fractions (e.g., 0.5% To 1.5%) To Mitigate The Inherent Brittleness Of The Ash-modified Matrix. The Present Project Involves A Comprehensive Laboratory Experimentation Program Aimed At Validating The Application Of These Recycled Waste Materials. The Primary Objectives Of This Investigation Is To Systematically Analyze The Fresh, Mechanical, And Tensile Behavior Of The Resulting Concrete Mixes, Establishing The Structural And Environmental Performance Of The Combined Binary Ash And Natural Fiber System And To Evaluate The Mechanical And Strength Properties Of Concrete Developed By Replacing Varying Percentages Of Ordinary Portland Cement With Binary Blends Of POFA And SCBA. To Analyze The Tensile And Post-cracking Behavior Of The Matrix Upon The Addition Of Natural NYLON Fibers (ranging From 0.5% To 1.5% By Volume Fraction).

Author: A CHIRU PAVAN KUMAR | K. SURENDRA BABU

Area of research: Engineering

The Present Study Deals With The Analysis Of A Framed Structure With And Without Knee Bracing Under Lateral Loading. Lateral Loads Due To Earthquake And Wind Action Produce Storey Displacement, Inter-storey Drift, Base Shear, Overturning Effect, And Additional Forces In Structural Members. In Ordinary Moment-resisting Frames, Lateral Stiffness Is Comparatively Low, Which May Lead To Excessive Deformation And Serviceability Problems. To Improve The Lateral Load Resistance, Knee Bracing Is Introduced As An Effective Structural System. In A Knee-braced Frame, The Main Diagonal Brace Is Connected To A Short Knee Element Near The Beam-column Joint. The Diagonal Member Provides Lateral Stiffness, While The Knee Element Acts As An Energy-dissipating Component During Seismic Excitation. In This Study, The Behaviour Of Braced And Unbraced Frame Systems Is Compared By Considering Important Parameters Such As Displacement, Drift, Stiffness, Member Stress, Velocity, Acceleration, And Overall Stability. The Structural Modelling And Analysis Are Carried Out Using Structural Analysis Software Under Lateral Load Conditions. The Results Indicate That The Provision Of Bracing Improves The Lateral Performance Of The Frame By Reducing Displacement And Controlling Dynamic Response. Therefore, Knee Bracing Can Be Considered An Efficient, Economical, And Practical System For Improving The Safety And Serviceability Of Framed Structures Under Lateral Loads.

Author: Sanket Suryakant Kamale | Prof. Abhijeet Undre | Dr. Atul Pujari

Area of research: Engineering

Structural Damage Identification Is An Important Part Of Structural Health Monitoring Because Damage In Beams, Columns, Joints And Lateral-load-resisting Members Reduces Stiffness And Changes The Dynamic Behaviour Of Framed Structures. In Reinforced Concrete High-rise Buildings, Damage May Occur Due To Cracking, Corrosion, Overloading, Seismic Action, Material Deterioration Or Loss Of Member Stiffness. Such Damage May Not Always Be Visible During Routine Inspection, But It Can Be Identified Through Changes In Modal Parameters Such As Natural Time Period And Natural Frequency. The Present Study Deals With Damage Identification In A Framed Structure Using Natural Frequencies Obtained From ETABS Analysis. A G+15 Reinforced Concrete High-rise Building With Moment-resisting Frame And Shear Wall System Was Modelled In ETABS. The Plan Dimension Of The Structure Is 40 M × 20 M, With A Ground Storey Height Of 4.0 M, Typical Storey Height Of 3.0 M And Total Height Of 49 M. M25 Concrete And Fe500 Steel Were Used For Modelling. The Structure Was Analysed For Three Conditions: Undamaged Model, 25% Damaged Model And 50% Damaged Model. Damage Was Simulated By Reducing Selected Property Modifiers Of Beams And Columns While Keeping Mass And Weight Modifiers Constant. Modal Analysis Was Performed To Obtain The First Twelve Mode Shapes, Natural Time Periods And Frequencies, While Response Spectrum Analysis Was Used To Evaluate Displacement, Storey Drift And Base Shear Under EQX Loading. The Results Show That The Fundamental Time Period Increased From 2.1299 S In The Undamaged Model To 2.4381 S And 2.9458 S In The 25% And 50% Damaged Models Respectively. Correspondingly, The Natural Frequency Reduced From 0.4695 Hz To 0.4102 Hz And 0.3395 Hz. This Confirms That Reduction In Stiffness Increases Flexibility And Reduces Natural Frequency. Therefore, Natural Frequency Variation Can Be Effectively Used As A Simple And Practical Indicator For Identifying Damage In Framed Structures.

Author: Shrenika Sheshgiri Pai | Prof. Abhijeet Undre | Dr. Atul Pujari

Area of research: Computer Science And Engineering

Customer Churn Prediction Is An Essential Task For Telecommunication Companies To Reduce Customer Loss And Improve Retention.A Machine Learning System Is Proposed In This Paper To Predict Customer Churn By Using Customer Usage History. Front End Is Configured Using HTML, CSS And JavaScript, While The Back End Of The System Is Based On Python With Flask Framework. Data Preprocessing Techniques Such As Removal Of Irrelevant Attributes, Encoding Of Categorical Variables, And Normalization Of Numerical Data Are Applied To Enhance Model Performance.Two Machine Learning Algorithms, Support Vector Machine (SVM) And Extreme Gradient Boosting (XGBoost), Are Used To Build Classification Models. These Models Analyze Historical Data To Models Is Evaluated Using Accuracy, And A Comparison Is Made To Identify The Better-performing Algorithm.These Results Indicate That The Proposed Model Indicates A Comparatively Good Prediction For Customer Churn. This Enables Organizations To Implement Strategies Like Enhanced Service, Customized Plans, Etc. That Could Lead To Improved Customer Retention And Business Continuity.

Author: Sowndarya .M | MS.G.P Angeline Pearl

Area of research: STRUCTURAL ENGINEEERING

Cement The Heavy Reliance On Portland Cement In Infrastructure Development Poses Severe Environmental Threats Due To Massive Carbon Dioxide Emissions, Alongside Escalating Economic Costs. To Address These Challenges, This Study Evaluates The Strength Assessment Of Fiber-Matrix Composite Enhanced With Dual Mineral Admixtures, Utilizing Nano-Silica And Metakaolin As Eco-friendly, Partial Cement Replacements In M35 Grade Structural Concrete. To Counter The Inherent Brittleness Of Conventional Concrete, Nylon Fibers The First Commercialized Synthetic Polymer Are Integrated As Micro-reinforcement. An Extensive Laboratory Investigation Was Conducted To Analyze The Individual And Combined Effects Of These Materials. Cement Was Substituted With Metakaolin At Replacement Levels Of 10%, 20%, 30%, And 40% By Weight, Alongside A Constant Optimized Addition Of Nano-Silica To Refine The Binder Matrix Through Accelerated Pozzolanic Activity. Concurrently, Synthetic Nylon Fibers Were Introduced Into The Matrix At Volume Fractions Of 0%, 1%, 1.5%, And 2% To Study Their Crack-bridging Mechanics. The Experimental Program Specifically Focuses On Evaluating The Composite's Mechanical Performance, Measuring Compressive Strength, Tensile Behavior, And Impact Resistance. The Findings Aim To Establish An Optimal Binary Mix Design That Minimizes Cement Consumption, Successfully Repurposes Industrial Pozzolanic Byproducts, And Yields A Fiber-reinforced Composite With Enhanced Structural Integrity And Impact Energy Absorption.

Author: B TEJESH | K URMILA DEVI

Area of research: Mechanical Engineering

Energy Scarcity And Depletion Of Non-renewable Fossil Fuels Are Two Of The Most Pressing Challenges Facing Modern Civilization. This Paper Proposes And Analyses A Novel, Low-cost Mechanism For Harvesting Electrical Energy From The Kinetic Energy Dissipated By Vehicles Crossing Road Speed Breakers. The Proposed System — The Power Hump — Integrates A Rack-and-pinion Mechanism Beneath A Spring-loaded Speed Breaker Plate. Vehicular Pressure Drives The Rack Downward, Rotating A Pinion Gear. Through A Compound Gear Train (1:5 Speed Ratio) And Belt Drive, Rotational Motion Is Transmitted To A DC Generator Capable Of Generating Up To 70 V. Electricity Is Stored In A 12 V Rechargeable Battery For Road And Street Lighting. A CATIA V5 Three-dimensional CAD Model Has Been Developed To Validate The Mechanical Design. Experimental Results Demonstrate Stable Voltage And Current Output Across Varying Vehicle Loads. A Single Power Hump Unit Can Generate Approximately 3.5 KWh Per Day At A Moderately Busy Intersection, Sufficient To Power 8–10 LED Street Lamps Throughout The Night.

Author: Omkar Balasaheb Chavan | Prathamesh Dangmali | Deep Dhande | Rohan Godbole | Prof. S. K. Bidgar

Area of research: Electrical Engineering

This Paper Introduces An Automated, Internet Of Things (IoT)-enabled Load-sharing Framework Designed To Optimize Power Distribution And Protect Distribution Transformers From Operational Degradation Caused By Overloading. Conventional Electrical Distribution Networks Typically Employ Static Load Configurations That Fail To Adapt Dynamically During Peak Demand Periods, Resulting In Thermal Stress, Reduced Efficiency, And Sudden Component Failures. To Mitigate These Challenges, The Developed System Utilizes An ESP32 Microcontroller To Monitor Grid Conditions And Autonomously Redistribute Electrical Loads Between Parallel-connected Transformers. In Contrast To Hardware-intensive Legacy Designs, This System Minimizes Deployment Complexity By Relying Primarily On Potential Sensing Modules, While Calculating Transformer Operating Temperature Via A Software-defined Mathematical Estimation Model. Based On These Monitored And Calculated Parameters, The Microcontroller Executes Real-time Load Switching Via Relays When Safe Thresholds Are Breached. Furthermore, Integration With A Cloud-based Firebase Realtime Database Enables Instantaneous Data Synchronization, Providing A Remote Web-based Graphical User Interface Dashboard For Telemetry Visualization And Manual Override Capabilities. Local Validation Is Maintained Through A 16×2 Liquid Crystal Display Using The I2C Protocol, Ensuring Operational Continuity And Status Visibility During Network Offline States. The Prototype Offers A Highly Scalable, Robust, And Cost-effective Methodology For Enhancing Power Distribution Reliability And Smart Grid Automation.

Author: Vaibhav P. Mule | Shrey Parag Bhangale | Rajas Manoj Attarde | Jay Vishwambhar Aswale | Harshal Subhash Deshmukh

Area of research: Computer Engineering

Limited Real-time Feedback Or Lack Of Keeping Young Learners Engaged Are Some Of The Shortcomings Of Most Traditional Education Systems. In Response, We Have Created SmartPlay, An Interactive Learning Platform That Utilizes Artificial Intelligence Technology, Specifically For Students Aged 5-12 Years Old. It Is A Cloud-based System, That Brings Together Gamified Modules, Video Explanations, Automated Question Generation And On-the-spot Assessment Through AI. It Does So, Making It Easier For The Teacher And Providing Students With A Clearer Understanding Of Concepts, And Keeping Them More Engaged. This Is Supported By Our Experiments With The System, Where We Observed An 18 Per Cent Increase In Accuracy And 93 Per Cent Engagement Even When The System Was Used Concurrently.

Author: Prof.S. S. Shinde | SURAJ SURESH NANGARE | AYUSH ASHOK JAGTAP | NIKHIL PANDIT KEDARI | VEDANT SUDHAKAR RAKSHE

Area of research: Artificial Intelligence And Machine Learning, Agricultural E-Commerce, Food Waste Reduction

A Major Challenge That The Agriculture Supply Chain Is Facing In India Is The Lack Of Technology. Fragmented With Large Losses In Food Post Harvest, Lowering Farmers' Income, Poor Food Availability For Low-income Groups. This Paper Outlines A Web Based Platform That Creates A Direct Market Between Farmers, Thereby Not Only Catering To The Needs Of Consumers But Also Enabling The Distribution Of Near-expiry Produce In Agriculture To Registered Non Governmental Organizations (NGOs). They Combine A System That Integrates A Random Forest Ensemble Classifier For Analysing Uploaded Images To Predict The Freshness Score Of Fruits And Vegetables Using OFV Estimated Shelf Life. If The Forecasted Expiry Period Falls Below A Certain Threshold (default: 2 Days), The System Automatically Sends Email Alerts To Farmers And All Registered NGOs, So As To Sell/donate Them On Time Before It Becomes A Waste Produce. The Platform Is Implemented Developing The Front-end Using HTML, CSS, And JavaScript And The Backend Using PHP; MySQL As The Relational Database; And Python For The Machine Learning Module. Experimental Evaluation On A Labelled Data Set Of 4200 Fresh And Near-expiry Produce Images Across 12 Different Categories Of Common Indian Agricultural Commodities Demonstrates That The Random Forest Classifier Has An Accuracy Of 93.2%, A Precision Of 92.7%, A Recall Of 0.934, And An F1-score Of 0.932. A 45-day Pilot Deployment Resulted In A 27% Rise In Farmer Profitability, A 19% Reduction In Consumer Prices, The Prevention Of 340 Kg Of Food Wastage, And An Estimated 2,800 Meals Served To NGO Beneficiaries.

Author: Pranav Ashokrao Agone | Anuj Rawat | Pratham Mangesh Patil | Om Ashok Jadhav | Prof.S.B.Nimbekar

Area of research: Computer Engineering

The Physical Violence Detection Using Key Framing App Is An AI-based Android Application Designed To Detect Violent Activities From Video Footage Using Intelligent Frame Analysis. The System Focuses On Identifying Suspicious Physical Actions Such As Fighting, Hitting, Kicking, And Aggressive Movement Patterns By Extracting Important Key Frames From Live Or Recorded Video. These Selected Frames Are Analyzed Using A Convolutional Neural Network (CNN) Or A Hybrid CNN-LSTM Model, Which Helps The System Recognize Violence-related Patterns With Better Accuracy And Reduced Processing Load. The Application Is Developed Using Android Java/XML And Integrates Firebase Authentication, Firebase Realtime Database, And Firebase Cloud Storage For Secure User Access, Real-time Data Handling, And Cloud-based Evidence Storage. When Violent Activity Is Detected, The System Automatically Saves The Detected Frames Along With Important Details Such As Timestamp And GPS Location. It Also Sends Instant Alerts To Authorized Users, Administrators, Or Security Personnel So That Quick Action Can Be Taken. Traditional Surveillance Systems Depend Heavily On Manual Monitoring, Which Can Lead To Delayed Response, Human Error, And Missed Incidents. This Project Solves That Problem By Adding Artificial Intelligence And Automation To The Surveillance Process. The Use Of Key-frame Extraction Makes The System More Efficient Because It Avoids Analyzing Every Video Frame And Instead Focuses Only On Meaningful Frames That May Contain Suspicious Activity.

Author: Kaustubh Santosh Abhangkar | Kaustubh Santosh Abhangkar | Vinayak Sachin Gadekar | Shantanu Bhausaheb Kohok | Devendra Santosh Pingat

Area of research: Computer Engineering

Cloud Computing Offers Flexible And Scalable Re-sources, But Uncontrolled Consumption Often Leads To Escalating Costs. Organizations Frequently Face Unexpected Cloud Bills Due To Forgotten Development Instances, Oversized VM Types, Unattached Storage Volumes, And Inefficient Autoscaling Settings. This Paper Presents Cloud Cost Optimizer, A Comprehensive Web-based Platform Designed To Analyze Cloud Usage And Billing Data, Detect Inefficiencies And Idle Resources, And Automatically Recommend Cost-saving Actions While Preserving Performance. The System Integrates Data Collection From Cloud Provider APIs (AWS, Azure, GCP), Preprocessing Modules, Analytical Models Using Rule-based Heuristics And Machine Learning, And A Recommendation Engine With An Interactive Dashboard For Visualization. The Platform Supports Three Primary User Roles: Administrators, Cloud Operators, And Finance Managers. Key Functionalities Include Idle Resource Detection, Rightsizing Recommendations, Storage Cleanup Alerts, And Reserved Instance Suggestions. The System Architecture Incorporates Secure API Integration, Role-based Access Control, And Real-time Data Processing. Expected Outcomes Include Ac-tionable Recommendations With Estimated Savings Of 20–30%. This Work Demonstrates How Modern Web Technologies And Data Analytics Can Effectively Optimize Cloud Expenditure While Maintaining Service Quality.

Author: Atharv Kulkarni | Atharv Kulkarni | Sushant Gaikwad | Vaishnavi Kale | Tushar Patil

Area of research: Artificial Intelligence And Machine Learning

This Paper Presents The Design, Development, And Evaluation Of AI Closet, An Intelligent Full-stack Web Application Built To Modernize Personal Wardrobe Management Through The Integration Of Artificial Intelligence, Cloud Computing, And Real-time Data Services. The System Addresses Six Common User Pain Points Including Wardrobe Disorganization, Inefficient Outfit Selection, Lack Of Sustainability Awareness, And Poor Shopping Decisions By Combining A React 18 Frontend With A Node.js And Express.js Backend, MongoDB For Persistent Storage, And Mongoose As The Object Document Mapper. Clothing Items Are Analyzed Automatically Using Google Cloud Vision API For Label Detection, Color Recognition, And Occasion Inference, While Images Are Stored And Delivered Through Cloudinary's Content Delivery Network. An Eight-mode Outfit Recommendation Engine Scores Wardrobe Items Using A Weighted Algorithm Incorporating Mood Profiling, Live Weather Conditions Fetched From A Real-time Weather API, Color Harmony Rules, And Learned User Preferences. Security Is Enforced Through Bcryptjs Password Hashing, JSON Web Token Based Stateless Authentication, And Role-based Access Control Distinguishing Regular Users From Administrators. Additional Features Include A Sustainability Impact Tracker Quantifying Water Saved And Carbon Emissions Prevented Through Clothing Donations, A Color Harmony Matcher Built On A Thirteen-color Rule Database, A Smart Wardrobe Gap Analyzer With E-commerce Integration, And A Calendar-based Outfit History System. Performance Evaluation Recorded An Average API Response Time Of 350 Milliseconds, A Page Load Time Of 2.4 Seconds, And A Mobile Accessibility Score Of 92 Out Of 100, Confirming That The Platform Is Production-ready And Scalable.

Author: Prof S.B.Nimbekar | Siddharth Ovhal | Sandesh Rasal | Aditya Ghule | Pratik Deshmukh

Area of research: Technology

This Paper Presents A Real-time Vehicle Localization And Velocity Monitoring System Using Smartphone Sensors. The System Utilizes GPS, Accelerometer, And Gyroscope Data To Estimate Vehicle Position And Speed Without Requiring Dedicated Hardware. By Applying Sensor Fusion Techniques, Accurate Results Are Achieved Even In Noisy Environments. The Proposed System Is Cost-effective, Scalable, And Suitable For Modern Intelligent Transportation Systems.

Author: B.Yugesh | Mr.R.Pushpanathan

Area of research: Civil Engineering

The Global Demand For Cement Is Rising Exponentially To Meet The Rapid Development Of Infrastructure. However, The Production Of Ordinary Portland Cement (OPC) Is Highly Carbon-intensive, Contributing Significantly To Global Greenhouse Gas Emissions And Environmental Degradation. Consequently, Researchers Are Under Substantial Pressure To Find Sustainable, Alternative Binders That Can Minimize This Carbon Footprint. Geopolymers Have Gained Significant Traction As Novel, Eco-friendly Binders Emerging As A Viable Alternative To OPC. The Superior Performance Of Geopolymer Binders Particularly Regarding Fire And Corrosion Resistance—is Largely Attributed To The Distinct Absence Of Water And Calcium-rich Phases Within Their Cross-linked Network Compared To Traditional Cement Hydrates. To Further Enhance Sustainability, Industrial By-products Can Be Upcycled As Supplementary Cementitious Materials. Metakaolin (MK), Metakaolin Is A Highly Reactive Supplementary Cementitious Material (SCM), Primarily Used In Concrete Technology As A Partial Replacement For Ordinary Portland Cement (OPC).. Its High Silica And Alumina Content Plays A Crucial Role In Achieving Superior Mechanical Strength And Durability In Concrete. Similarly, Copper Slag Is A Massive By-product Generated During The Smelting And Refining Of Copper. The Current Disposal Methods For Both Metakaolin And Copper Slag Pose Severe Environmental And Spatial Challenges Around Industrial Sectors. Incorporating These Industrial Wastes Into Geopolymer Concrete Serves A Dual Purpose: It Mitigates The Environmental Impact Of Cement Production And Provides An Effective Waste-management Solution. While Many Geopolymer Systems Require Elevated Heat Curing To Achieve Optimal Properties, This Study Focuses On Ambient-cured Systems To Maximize Practical Field Applicability. This Paper Presents An Experimental Investigation Into The Acid Resistance Of Ambient-cured Alkali-activated Metakaolin And Copper Slag Concrete. Additionally, It Provides A Comprehensive Review Of Existing Literature Evaluating The Performance And Degradation Mechanisms Of Geopolymer Concrete When Exposed To Various Acidic Media Over Extended Periods.

Author: B V S AYYAPPA | K URMILA DEVI

Area of research: Artificial Intelligence, Deep Learning, And Computer Vision

Skincare Has Become An Important Part Of Personal Grooming And Health Awareness, Yet Choosing The Right Prod Ucts And Routine Remains Difficult Because Skin Type, Envi Ronmental Exposure, And Product Ingredients Vary So Much From Person To Person. Most People Fall Back On Generic Ad Vice, Advertisements, Or A Slow And Costly Trial-and-error Pro Cess. This Paper Presents Fiora.AI, An AI-powered Web Ap Plication That Analyzes A User-uploaded Facial Image With A MobileNetV2-based Convolutional Neural Network To Classify Skin Type, Then Feeds That Prediction Into A Rule-based Rec Ommendation Engine That Generates A Personalized Morning And Night Skincare Routine Along With Specific Product Sug Gestions. The System Was Implemented Using Python, Flask, OpenCV, TensorFlow/Keras, And SQLite, With A Responsive HTML/CSS/JavaScript Front End. The Completed Prototype Was Evaluated Through Unit, Integration, System, Functional, Performance, And Security Testing, All Of Which Passed, And In Formal Classification Trials Placed Accuracy In The Low-to-mid Nineties. The Result Is A Fast, Low-cost, And Reasonably Accurate Digital Skincare Assistant That Can Be Extended Toward Concern Level Detection, Mobile Deployment, And Dermatologist Collab Oration In Future Iterations

Author: Adesh Dasharath Ghodekar | Atharv Satyawan Gholap | Omkar Pandurang Kangane | Bhumika Shambhu Pardeshi | Dr. Shubhangi R. Patil

Area of research: Civil Engineering

This Experimental Study Investigates The Fresh And Hardened Properties Of M30-grade Self-compacting Concrete (SCC) Prepared Using Magnetic Field-treated Water (MFTW) At A Field Intensity Of 0.8 Tesla. To Enhance The Sustainability And Economic Feasibility Of The Mix, Ordinary Portland Cement (OPC 53 Grade) Was Partially Replaced With Silica Fume At Intervals Of 0%, 10%, 20%, 30%, 40%, And 50%. Simultaneously, The Fine Aggregate Component Was Partially Replaced With Industrial Waste By-products, Utilizing Sintered Fly Ash Aggregate (0%–50%) And Foundry Sand (0%–25%). The Fresh-state Characteristics Of The Concrete—specifically Filling Ability, Passing Ability, And Segregation Resistance—were Systematically Evaluated Using Standard Slump Flow, J-ring, L-box, And U-box Testing Methods. To Determine The Mechanical Behavior Of The Modified Concrete Matrix In Its Hardened State, Destructive Testing Was Conducted To Evaluate Compressive And Split Tensile Strength. All Specimens Were Subjected To Standard Wet Curing And Tested At Intervals Of 7, 14, And 28 Days. The Primary Objective Of This Research Is To Establish A Comprehensive Comparative Analysis Between The Performance Profiles Of These Multi-component, Magnetically Conditioned Concrete Mixes And Conventional Self-compacting Concrete Configurations.

Author: A MANIKANTA | B Ganesh

Area of research: Electrical Engineering

T-WATCH (Transformer Winding Alert Temperature Control Hub) Is An IoT-based Transformer Monitoring And Control System Designed To Improve Reliability, Safety, And Operational Efficiency. The System Uses A NodeMCU ESP32s To Continuously Monitor Key Transformer Parameters Such As Temperature, Voltage, And Current Through Dedicated Sensors. Real-time Processing Enables Power Calculation, Fault De-tection, And Automatic Relay Tripping When Predefined Thresholds Are Exceeded, Ensuring Fail-safe Operation. The System Employs MQTT-based Bidirectional Communication Through HiveMQ Cloud, Enabling Real-time Data Transmission And Remote Control Functions Such As Transformer Trip And Reset. A Cross-platform React Native Application Provides Live Monitoring, Data Visual-ization, Diagnostic Analysis, And System Health Assessment. The Application Also Supports Offline Data Storage, Cloud Synchroniza-tion Via Firebase, And PDF Report Generation For Maintenance Purposes. By Integrating Real-time Sensing, Cloud Communication, And Intelligent Diagnostics, T-WATCH Offers A Scalable And Cost-effective Solution For Modern Transformer Management. The System Enhances Fault Detection, Enables Predictive Maintenance, And Contributes To Improved Asset Performance And Lifecycle Management.

Author: Prof. A. V. Tamhane | Namancris Stephen | Ayush Kumar | Srishti Sinha | Aditya Shirbhate

Area of research: Vlsi Design

The Rapid Growth Of IoT Applications Requires Secure, Energy-efficient, And Reliable Communication For Resource-constrained Devices. UART Is Widely Used In IoT Systems Due To Its Simplicity And Low Hardware Cost, But Conventional Secure UART Designs Typically Employ A Single Lightweight Cryptographic Algorithm, Limiting Adaptability To Changing Power, Security, And Performance Requirements. This Paper Proposes An Adaptive Lightweight Encryption-Based Secure UART Architecture That Dynamically Selects Among Three Lightweight Block Ciphers—GIFT, PRESENT, And LBlock-S—based On Real-time System Conditions. A Cipher Selection Controller Monitors Power Level, Security Needs, And Speed Requirements To Activate The Most Suitable Algorithm: GIFT For Low-power Operation, PRESENT For Higher Security, And LBlock-S For High-speed Communication. The Design Is Implemented In Verilog HDL And Validated Using Xilinx Vivado FPGA Simulations. Results Demonstrate Accurate Cipher Selection, Seamless Encryption Switching, And Reliable UART Communication Without Data Loss. The Proposed Architecture Provides A Flexible, Hardware-efficient, And Context-aware Secure Communication Solution For IoT Devices.

Author: AJAS AHAMED F | Mohamed Rafeek K | Nihal F | Ms.Menaga,Assiatant Professor

Area of research: Civil Engineering

The Rapid Growth Of The Construction Sector Has Intensified The Demand For Sustainable Alternatives To Natural Aggregates And Traditional Concrete Additives. This Experimental Study Evaluates The Structural Feasibility Of Incorporating Iron Ore Slag Waste As Partial Replacements For Natural Coarse Aggregates In M25 And M40 Grade Concrete. Furthermore, The Study Explores The Synergistic Effect Of Natural Sisal Fiber Integration On The Mechanical Performance Of The Optimized Mix. Coarse Aggregates Were Replaced With Iron Ore Slag At Substitution Levels Of 20%, 40%, And 60% By Weight. Mix Proportions Were Designed Following The IS 10262:2019 Standard. The Fresh And Hardened Properties Of The Concrete, Including Workability, Compressive Strength, And Split Tensile Strength, Were Rigorously Tested At 7 And 28-day Curing Intervals. The Investigation Identified The Optimum Replacement Ratio That Balances Structural Integrity With Workability. To Address The Inherent Brittleness Of The Concrete Mixes, Sisal Fibers Were Subsequently Integrated Into The Identified Optimum Mix At 0.5% Increments (up To 2% By Volume). The Results Demonstrate The Comparative Performance Of The Slag-replaced Concrete Against The Control Mix, Highlighting The Influence Of Fiber Reinforcement On Tensile Strength And Crack Resistance. This Research Provides A Critical Framework For The Utilization Of Industrial By-products In Civil Engineering, Demonstrating That While Aggregate Replacement Affects Workability, Strategic Fiber Integration Can Effectively Mitigate Strength Degradation, Leading To More Sustainable And Cost-effective Construction Materials.

Author: D.V KONDAIAH | K URMILA DEVI

Area of research: Computer Science And Engineering

N Infrastructure Build In The Edge Computing Platform Which Is Reliable To Challenge The Commercial And Non-commercial IT Development Communities Of Data Streams In High Dimensional Data Cluster Modeling. This Project Research Is Mainly Focuses The Effective Medical Disease Prediction And Data Sharing Scheme In The Cloud Storage Is Presented. In Our Security System, We Remove The Attribute Matching Function, Where Attributes Will Be Hidden Into The Anonymous Access Structure. In The AKE(Authentication Key Exchange) Algorithm, The Collected Information For Encrypted And Then Stored On A Cloud Server Such That Only Authorized Users, Such As The Data Owner And The Doctors, Can Access. However, Smart Terminals Are Usually Limited In Computing Power And Users’ Privacy Issues Remain.

Author: G.Pavithra | Mrs.Shankari | Dr.N.Purushothaman

Area of research: Computer Science And Engineering

The Growing Challenges Of Social Awareness, Sustainability, And Community Engagement Demand Innovative Approaches To Education And Behavioral Change. Traditional Awareness Programs Often Fail To Sustain Interest And Long-term Participation, Especially Among Younger Generations. This Project, Titled “Development Of A Gamified Learning Platform For Social Transformation,” Presents A Technology-driven Solution That Integrates Game Design Principles With Digital Learning Methodologies To Promote Positive Social Behavior And Collective Responsibility. The Proposed System Leverages Gamification Elements Such As Points, Badges, Leaderboards, Challenges, And Rewards To Motivate Users To Learn And Adopt Socially Beneficial Practices. The Platform Is Designed To Educate Users On Key Societal Issues—such As Waste Reduction, Energy Conservation, Responsible Consumption, And Community Service—through Interactive Modules And Real-world Activity Tracking. A User-friendly Interface And Data-driven Feedback Mechanisms Are Incorporated To Enhance Engagement And Measure Individual Progress.

Author: MANOJ.D | Dr.N.Purushothaman | P.Revathi

Area of research: VLSI Design

This Paper Presents A Modified Design Of RISC-V RV32I 32-bit Microprocessor. The RISC-V Processor Consists Of A Flexible 5 Stage Pipelined Processor With Certain Techniques For Hazard Management. The Processor Consists Of 6 Blocks- Fetch Block, Decode Block, Control Logic Block, Memory Block, Register Block And The ALU Block. The Processor Is Then Pipelined Into 5 Stages – The Fetch Stage, The Decode Stage, The Execute Stage, The Memory Stage And The Write Backstage. After Pipelining, The Pipeline Hazards Such As Data Hazards And Control Hazards Are Managed By Using Data Forwarding From Previous Stages Of The Pipeline And By Introducing Delay Slots For Control Transfer Instructions. After That, The Schematic For The Processor Is Obtained By Feeding The Verilog Code In Cadence Software. Also, There Is A Future Scope Of Implementing New Instructions That Can Combine The Functions Of Two Separate Instructions Into One Single Instruction, Which Are Considered Extensions To The Existing ISA Of RV32I.

Author: Abinaya G | Dr S. Lakshmi

Area of research: Civil Engineering

The Dramatic Escalation In Global Urbanization And Infrastructural Modernization Has Placed An Unsustainable Burden On Natural Geological Reserves. Concrete, Being The Structural Backbone Of Modern Civilization, Requires Massive Volumes Of Fine Aggregate To Compose Its Matrix. Traditionally, River Sand Has Been Preferred Due To Its Rounded Morphology, Smooth Grain Texture, And Favorable Geological Grading Curves. However, Unchecked Sand Dredging From Aquatic Zones Has Triggered Severe River Bank Degradation, Ecological Collapse Of River Beds, Drops In Groundwater Tables, And Structural Stability Risks For Adjacent Bridges. In Response, Environmental Protection Boards Globally Have Instituted Aggressive Bans Or Stringent Quota Constraints On Natural Sand Collection, Inducing A Critical Supply Deficit In The Construction Industry. Concurrently, Manufacturing, Metallurgical, And Municipal Entities Face Severe Waste-management Crises Due To Accumulating Non-hazardous By-products. This Project Investigates A Sustainable Concrete By Evaluating The Potential Of Upcycling Three Distinct Heterogeneous Industrial And Municipal Waste Streams—Spent Refractory Ceramics (SRC), Metallurgical Slag (MET), And Crushed Waste Glass (CWG)—as Sustainable Fine Aggregate Sand Substitutes In M30 Structural Concrete. Controlled Replacement Levels Of These Materials Are (0% For The Control Mix, And 15% And 30% For Each Alternative Material). The Present Project Involves A Comprehensive Laboratory Experimentation On Fresh Concrete Workability Profiles And Strength Development Kinetics Like Compressive Strength, Split Tensile Strength And Flexural Strength To Study For The Application Of New Waste Materials In The Preparation Of Concrete.

Author: R ALEKHYA NANDAN | K. SURENDRA BABU

Area of research: Information Technology

- The Live Auction System For Farm Fresh Products Is A Web-based Platform Developed To Modernize Agricultural Trading Through Real-time Online Bidding. Traditional Agricultural Markets Are Often Inefficient, Geographically Restricted, And Dependent On Intermediaries, Resulting In Reduced Profit Margins For Farmers And Limited Transparency For Buyers. The Proposed System Introduces A Localized Digital Auction Environment Where Farmers Can List Products And Buyers Can Participate In Live Bidding Within Their Geographic Proximity.The Platform Is Implemented Using HTML5, CSS3, Bootstrap, JavaScript, PHP, And MySQL. It Provides Functionalities Such As Secure User Authentication, Auction Management, Real-time Bid Updates, Automatic Winner Declaration, And Centralized Database Management. The System Follows A Three-tier Architecture Con- Sisting Of Presentation, Application, And Database Layers, Ensuring Scalability, Maintainability, And Reliability. Experimental Evaluation Demonstrates Improved Accessibility, Transparent Price Discovery, Reduced Operational Overhead, And Efficient Auction Handling Under Concurrent User Interactions. The Proposed Solution Bridges The Gap Between Agriculture And Digital Commerce By Providing A Lightweight, Secure, And Afford- Able Platform Suitable For Rural And Semi-urban Environments. Index Terms—Online Auction, E-Agriculture, Real-Time Bid- Ding, Web Application, PHP, MySQL, Digital Marketplace, Localize Trading

Author: Prof K.S Mulani | Manali Walse | Siddhi Phatak | Harsh Naik | Shweta Shejwal

Area of research: Mechanical Engineering

Flexible, Miniaturized, And Lead-free Piezoelectric MEMS Accelerometers Are Increasingly Required In Aerospace, Mechatronics, And Structural Health Monitoring Applications. In This Work, A Flexural-type MEMS Piezoelectric Accelerometer Is Designed And Numerically Investigated Using Aluminium Nitride (AlN) As The Piezoelectric Sensing Material. The Proposed Accelerometer Consists Of A Triangular Seismic Mass Supported By Three Symmetrically Arranged Suspension Beams To Enhance Strain Concentration At The Beam Junctions While Maintaining Structural Stiffness. Numerical Simulations Carried Out Via COMSOL Multiphysics Implement Coupled Solid Mechanics And Piezoelectric Physics. Modal Analysis Revealed A High Fundamental Resonant Frequency Of Approximately 26.5 KHz. Static Analysis Conducted Under A 1\text{ G} Acceleration Load Demonstrated That Von Mises Stress And Displacement Remain Safely Within Elastic Limits. Electro-mechanical Analysis Confirms Effective Strain-induced Voltage Generation In The AlN Layer, Yielding An Estimated Sensitivity Of Approximately 0.33 MV/g With Stable Linearity Up To 15,000 G. A Comparative Study Highlights That This Lead-free Design Offers A Significantly Higher Resonant Frequency And Acceleration Handling Capability Than Traditional Configurations.

Author: Ahamed Suhaib A | Dr. Prasanth | Prasatth

Area of research: Design And Development Of An IoT-Based

This Project Presents A Modified And Improved Version Of A Wearable Communication Glove Designed For Speech-impaired Individuals. An Existing Gesture-glove Concept Was Analyzed And Redesigned To Improve Reliability, Usability, And Healthcare Applicability. Initially, The System Used Flex Sensors For Gesture Detection. During Testing, The Flex Sensors Produced Unstable Readings And Inconsistent Outputs, Making The System Difficult To Use Reliably. To Solve This Issue, The Sensing Method Was Redesigned Using Capacitive Touch Sensors (TTP223 Modules). A Thumb-touch Interaction Method Was Introduced, Allowing The User To Easily Select Communication Commands. The Modified System Uses An Arduino Nano Microcontroller, I2C LCD Display, DFP Layer Mini Audio Module, Speaker, And Capacitive Touch Sensors. When The User Touches A Sensor, Predefined Messages Are Displayed On The LCD And Spoken Through The Speaker. The Project Focuses On Healthcare Assistance For Speech-impaired Individuals By Providing A Simple, Low-cost, Reliable, And User-friendly Communication System.

Author: Mohamed Jaffar | MOHAMED NADEEM

Area of research: Computer Science And Applications

Student Performance Prediction Is An Important Application Of Educational Data Mining That Helps Institutions Identify Students Who May Require Academic Support At An Early Stage. This Study Proposes A Classification-based Approach To Predict Student Performance Using Historical Academic And Demographic Data. Various Factors Such As Attendance, Internal Assessment Marks, Study Habits, Previous Academic Records, And Participation In Extracurricular Activities Are Considered As Input Attributes. Classification Algorithms Such As Decision Tree, Random Forest, Naive Bayes, And Support Vector Machine (SVM) Are Employed To Categorize Students Into Performance Classes Such As Excellent, Good, Average, And Poor. The Dataset Is Preprocessed Through Data Cleaning, Feature Selection, And Normalization To Improve Prediction Accuracy. Experimental Results Demonstrate That Machine Learning Classification Techniques Can Effectively Predict Student Outcomes And Assist Educators In Making Informed Decisions. The Proposed Model Enables Timely Intervention, Improves Academic Planning, And Contributes To Enhancing Overall Student Success Rates In Educational Institutions.

Author: Saraswathi P

Area of research: Computer Science And Information Technology

Speech Emotion Recognition Is An Essential Component For Applications Like Education And Human-computer Interaction [1]. While Deep Neural Networks (DNNs) Have Advanced The Field, Many Studies Ignore The Semantic Information Present Within The Speech Signal [2]. This Paper Proposes A Novel Framework Designed To Capture Both Semantic And Paralinguistic Information [5]. The Model Consists Of A Semantic Feature Extractor And A Paralinguistic Feature Extractor, Which Are Fused Together Using A Novel Attention Mechanism Into A Unified Representation. This Representation Is Then Processed By A Long Short-Term Memory (LSTM) Network To Model Temporal Dynamics [23]. Evaluated On The SEWA Dataset From The AVEC Challenge [16], The Model Achieves State-of-the-art Results In The Valence And Liking Dimensions.

Author: Amit Somnath Dombe | Dr. Vaijanath V. Yerigeri

Area of research: Computer Science And Information Technology

Stress Profoundly Alters Human Cognitive And Physiological States, Making Early And Automated Detection A Critical Technological Goal [1]. This Study Presents A Streamlined Speech Emotion Recognition (SER) System Engineered For Accurate Stress Classification [2]. The Methodology Operates Across Two Major Domains: A Manual Feature Architecture Integrating Gammatone Wavelet Cepstral Coefficients (GWCC), Mel Frequency Cepstral Coefficients (MFCC), Pitch, Vocal Tract Frequency, And Spectral Energy; And An Artificial Neural Network (ANN) Classifier Optimized Using A Bio-inspired Hybrid Framework Of The Bat Algorithm And Particle Swarm Optimization (BAT+PSO) [4], [3]. Extensively Evaluated On The Benchmark SUSAS Dataset And A Custom Marathi Speech Database, The Proposed Framework Completely Bypasses Localized Gradient Trapping To Deliver An Outstanding Overall Stress Classification Accuracy Of 84.2% With A Minimal Mean Square Error (MSE) Of 0.0170 [5].

Author: Sakshi Suresh Birajdar | Dr. Vaijanath V. Yerigeri

Area of research: Electrical & Electronics Engineering

Smart Healthcare Is Important For People Who Need Continuous Monitoring Which Cannot Be Provided Outside Hospitals. It Is Also Important At Rural Areas Or Villages Where Nearby Clinics Can Be In Touch With City Hospitals About Their Patient’s Health Condition. This Work Presents A Smart Health Monitoring System That Uses Biomedical Sensors To Check Patient’s Condition And Uses Internet To Inform The Concerned. The Biomedical Sensors Here Are Connected To Arduino UNO Controller To Read The Data Which Is In Turn Interfaced To An LCD Display/serial Monitor To See The Output. Data Is Uploaded To The Server To Store And Converted It Into JSON Link For Visualizing It On A Smartphone. An Android Application Has Been Designed In Order To Easily See The Patient's Information By Their Doctors And Family Members.

Author: MOHAMED SUHAIL | UMAR FARUK | MOHAMED SUBAIR | Bharathidasan

Area of research: Computer Applications

Co-occurring Chronic Conditions And Complex Multi-disease Pathologies Represent An Escalating Global Public Health And Socioeconomic Crisis. In Rapidly Growing Urban Patient Populations, Overlaps Between Metabolic, Cardiovascular, And Neurological Syndromes Contribute Heavily To Prolonged Diagnostic Latencies, High Multi-clinic Tracking Overheads, Secondary Clinical Omissions, And Therapeutic Cross-remediations. Accurate Tracking, Early Risk Stratification, And Concurrent Non-invasive Classification Of Heterogeneous Patient Clinical Profiles Are Therefore Essential For Modern Preventive Medicine, Automated Out-patient Triage, And Smart City Health System Asset Management. Conventional Clinical Diagnostic Strategies, Including Isolated Domain-specific Laboratory Assessments, Paper-based Single-disease Tracking Indices, And Disjointed Diagnostic Pipelines, Are Often Time-consuming To Execute, Heavily Subjective To Observer Visual Fatigue Or Missing Metadata Segments, And Difficult To Apply Efficiently Across High-throughput Health Networks. To Overcome These Operational And Computational Limitations, This Study Proposes An Automated, Computer-aided Multi-disease Screening Framework Using Multi-parametric Clinical Data Fusion And A Hybrid Machine Learning Ensemble Architecture. Heterogeneous Clinical Records Capturing Signs Of Cardiovascular, Metabolic, And Neurological Anomalies Are Aggregated Into A Single Integrated Schema And Preprocessed Via Automated Median Data Imputation, Structural Outlier Filtering, And Min-max Feature Normalization To Resolve Calibration Variances Across Different Clinical Measurement Devices. Discriminative Physiological Indicators —including Continuous-time Blood Parameters, Resting Electrocardiographic Metrics, Serum Insulin Distributions, Gray-level Texturing Variables, And Patient Demographic Indicators—are Mapped To Establish A Unified High-dimensional Physiological Feature Matrix. Predictive Modeling Is Executed Through A Optimized Hybrid Ensemble Model Combining Three Distinct Baseline Algorithms: Random Forest, Support Vector Machine (SVM), And Extreme Gradient Boosting (XGBoost). The Individual Probabilistic Predictions Are Consolidated Via A Weighted Soft-voting Ensemble Consensus Layer Optimized To Recognize Multi-label Classification Objectives Simultaneously. Model Execution Is Measured Comprehensively Using Standard Validation Metrics, Evaluating Multi-class Classification Accuracy, Precision, Recall, F1-score, And Structural Confusion Matrix Tracking To Verify Diagnostic Consistency. Experimental Results Suggest That The Proposed Hybrid Framework Achieves A Total Predictive Accuracy Of 96.44%, Completely Flattening Cross-domain Validation Errors While Maintaining Execution Runtimes Suitable For Modern Edge-device Implementation. The Complete Framework Is Deployed As An Interactive Software Application Via A Streamlit Web Interface, Integrating Responsive Data Submission Grids, Interactive Risk-distribution Plots, Multi-disease Prediction Charts, And Real-time Critical Health Warnings For Municipal Healthcare Ecosystems.

Author: Tharun C | Dr.K.Annalakshmi

Area of research: COMPUTER APPLICATION

The Valuation Of Used Consumer Assets, Particularly Pre-owned Two-wheeled Vehicles, Remains A Major Secondary-market And Socioeconomic Challenge. In Rapidly Developing Urban Transport Ecosystems, Frequent Fluctuations In Marketplace Demand, Regional Brand Preferences, Seasonal Consumer Choices, And Macroeconomic Inflationary Changes Contribute Heavily To Unstable Price Valuations, Significant Financial Losses For Individual Traders, And Extended Structural Negotiation Delays. Accurate Estimation And Real-time Management Of Vehicle Depreciations Across Transactional Digital Platforms Are Therefore Essential For Contemporary Asset Lifecycle Management, Transparent Consumer Electronics And Automotive Commerce, And Automated Municipal E-marketplace Quality Control. Conventional Valuation Methodologies, Including Manual Inspections By Local Technicians, Subjective Dealer-broker Assessments, And Static Depreciation Lookup Tables, Are Often Time-consuming To Execute, Heavily Vulnerable To Human Bias Or Incomplete Vehicle Information, And Difficult To Scale Effectively Across High Throughput, Cloud-integrated Consumer-to-consumer (C2C) Transaction Pipelines. To Overcome These Financial And Computational Limitations, This Study Proposes An Automated, Data-driven Bike Valuation And Asset Screening Framework Using Multi-parametric Vehicle Feature Engineering And Advanced Ensemble Machine Learning Techniques. Tabular Historical Sales Records Capturing Extensive Transaction Instances From Online Marketplaces And Kaggle Repositories Are Ingested And Preprocessed Through Automated Missing-value Handling, Categorical Encoding, And Min-max Feature Normalization To Resolve Baseline Input Variances Across Disparate Digital Platform Entries. Discriminative Physical And Administrative Parameters—specifically Focusing On Brand Equity Indexes, Model Classifications, Manufacture Years, Cumulative Kilometers Driven, Engine Displacement Capacity, Fuel Type Variants, Ownership Histories, Insurance Validities, And Geographical Sales Locations—are Extracted And Engineered To Establish A Structured Asset Feature Matrix. Predictive Modeling Is Executed Through A Comparative Optimization Of Three Distinct Mathematical Architectures: Linear Regression, Random Forest Regressor, And Extreme Gradient Boosting (XGBoost) Regressor, Which Are Trained To Solve The Continuous Target Optimization Task Of Estimating Accurate Market Prices. Model Execution Is Measured Comprehensively Using Standardized Validation Criteria, Evaluating Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), And The R-squared (R²) Coefficient To Ensure Prediction And Evaluation Stability. Experimental Observations Suggest That The Integrated XGBoost Framework Yields A Predictive R² Score Of 0.9512, Providing An Objective, Scalable, And Non-invasive Decision Support Pipeline. The System Is Deployed As An Interactive Application Via A Streamlit Web Interface, Integrating Responsive Data Entry Fields, Correlation Charts, Feature Importance Modules, And Real-time Price Prediction Alerts For Public Vehicle Marketplaces.

Author: BALAVIGNESH T | S.Anushalakshmi

Area of research: COMPUTER APPLICATION

The Diabetes Mellitus Is One Of The Fastest-growing Chronic Health Conditions Globally, Affecting Hundreds Of Millions Of Individuals And Imposing Significant Burdens On Healthcare Systems Worldwide. Early Identification Of At-risk Individuals Is Essential For Timely Intervention, Lifestyle Modification, And Prevention Of Severe Complications Such As Nephropathy, Neuropathy, Cardiovascular Disease, And Vision Impairment. This Paper Presents DiaPredict, An Intelligent, Web-based Clinical Dashboard Built Using React, Tailwind CSS, And Framer Motion, Designed To Deliver Real-time Diabetes Risk Prediction Through A Multi-parameter AI Model. The System Accepts Fourteen Clinically Validated Input Parameters Including Age, Gender, BMI, Fasting Or Post-meal Glucose Level, HbA1c Percentage, Blood Pressure, Insulin Level, Skin Thickness, Diabetes Pedigree Function, Physical Activity, Smoking, Alcohol Consumption, And Pregnancy History.

Author: AKASH J

Area of research: Civil Engineering

The Rapidly Increasing Volume Of Industrial Waste Material Poses A Severe Threat To Environmental Sustainability And Ecological Balance. Furthermore, The Commercial Production Of Synthetic Fibers Contributes Significantly To Global Warming Through Greenhouse Gas Emissions. In Structural Applications, Conventional Concrete Exhibits High Compressive Strength But Remains Inherently Weak In Tension. This Vulnerability Is Primarily Caused By The Presence Of Micro-cracks At The Mortar-aggregate Interface. To Mitigate This Weakness, Control Micro-crack Propagation Into Macroscopic Failures, And Enhance Tensile Properties, Fiber-reinforced Concrete (FRC) Is Widely Utilized. This Study Investigates The Integration Of Polypropylene Fiber (PPF)—a Synthetic Hydrocarbon Polymer—into Cement Concrete Mixes. A Comprehensive Laboratory Investigation Was Conducted To Evaluate The Structural Behavior Of This Material In FRC Applications. The Primary Objective Of This Research Is To Analyze The Influence Of Varying Polypropylene Fiber Lengths And Densities On The Fundamental Strength Characteristics Of Concrete, Specifically Its Compressive Strength, Split Tensile Strength, Flexural Strength, And Impact Resistance.

Author: CH RAMESH | K URMILA DEVI

Area of research: Electronics Engineering

As The Production Of Electric Vehicles (EVs) Has Significantly Increased In Recent Years, Driven By The Need To Reduce Carbon Emissions And Promote Environmentally Friendly Transportation. This Transition From Traditional Internal Combustion Engine Vehicles To Battery-powered Electric Vehicles Has Been Further Accelerated By The Rise In Petroleum And Diesel Prices, Resulting In A Growing Market Share For EVs. Consequently, The Rapid Increase In EV Adoption Has Highlighted The Need For Widespread Charging Infrastructure. However, Establishing Conventional Charging Stations Can Be Challenging, Particularly In Densely Populated Urban Areas Where Space Is Limited And The Initial Investment Cost Is High. To Address This Issue, This Project Proposes An Innovative AI-based Mobile Wireless Charging System For Electric Vehicles. The System Provides A Cost-effective And Reliable Charging Solution, Especially Suitable For High-traffic Locations Such As Parking Facilities, Shopping Malls, Parks, Theaters, And Other Public Areas. To Enhance System Effectiveness And Adaptability, A Wireless Sensor Network (WSN) Is Integrated With The Charging Infrastructure. The AI-enabled Control System Continuously Monitors Charging Requirements And Optimizes Charging Operations, Thereby Improving Accuracy, Efficiency, Reliability, And Modularity. This Approach Contributes To The Development Of A Smarter And More Sustainable EV Charging Ecosystem.

Author: Mohamed Halith | Mrs. J. Stephy Angelin | Mohamed Haris | Harish Adithya

Area of research: Mech. Engg

Smart Manufacturing Systems Represent The Modern Evolution Of Industrial Automation Where Machines, Sensors, Communication Technologies, And Intelligent Algorithms Work Together To Improve Productivity And Operational Efficiency. Industries Are Increasingly Adopting Predictive Maintenance Techniques To Reduce Machine Downtime, Improve Reliability, And Minimize Maintenance Costs. Traditional Maintenance Approaches Such As Corrective Maintenance And Preventive Maintenance Often Fail To Provide Accurate Predictions Regarding Machine Failures. Corrective Maintenance Acts Only After A Failure Occurs, While Preventive Maintenance Follows Fixed Schedules That May Lead To Unnecessary Servicing. To Overcome These Limitations, Intelligent Predictive Maintenance Systems Based On Data Analytics And Machine Learning. This Paper Presents A Hybrid Neuro Fuzzy Inference Systems (ANFIS) Model For Automated Fault Prediction For Smart Manufacturing Systems Which Aim Predictive Maintenance. The Proposed Model Improves Upon The Error Performance Of Existing Work In The Domain

Author: Krishna Bhayal | Prof.Manish Soni

Area of research: Computer Applications

Urban Traffic Congestion Imposes Significant Economic, Environmental, And Social Burdens On Modern Cities. Traditional Fixed-timing Traffic Control Systems Lack The Adaptability To Handle Dynamic Traffic Flows, Resulting In Inefficiencies, Elevated Emissions, And Delayed Emergency Response. This Paper Presents The Design And Implementation Of A Smart Traffic Management System (STMS) That Integrates Internet Of Things (IoT) Sensor Networks, Artificial Intelligence (AI), And Machine Learning (ML) Algorithms To Enable Real-time, Adaptive Traffic Control. The Proposed System Employs A Distributed Sensor Architecture To Collect Vehicle Density, Speed, And Weather Data, Which Is Processed By A Cloud-based AI Engine Employing Random Forest Regression And Reinforcement Learning For Signal Optimization And Congestion Prediction. Vehicle-to-Infrastructure (V2I) Communication Relays Updates To Connected Vehicles. Algorithms Including Dijkstra's Shortest Path, A* Search, Genetic Algorithm, And K-Means Clustering Underpin Route Planning And Traffic Pattern Analysis. Simulation-based Evaluation Demonstrates Significant Reductions In Average Vehicle Wait Times And Improved Intersection Throughput Compared To Conventional Systems. The STMS Also Incorporates An Incident Detection Module Capable Of Rapid Anomaly Identification And Emergency Rerouting. Results Confirm The Viability Of This Integrated Approach For Scalable, Sustainable Urban Mobility Management.

Author: Vignesh S | Ms. S. Anusha Lakshmi

Area of research: Civil Engineering

Structural Failure In Pertaining To Bridges Is Extremely Challenging To Assess.. Stochastic And Statistical Computing Is Being Explored To Derive Conclusive Decisions Where Human Intervention Is Difficult In Time And Resource Constrained Situations. One Such Situation Is Bridge Failures In Cases Of Seismic Impacts. In Case Of Earthquakes, It Is Necessary To Immediately Evaluate The Possibility Of Damage To Bridges As They Are Critically Important To Carry Out Relief Operations While Carrying Population And Essential Goods. However, Human Inspection In Earthquake Stricken Areas May Take A Lot Of Time Increasing The Risk Of Using Bridges Which Are Severely Damaged Thereby Risking Human Life. Hence, Quick Automated Tools Are Required Which Can Predict Bridge Damages Quickly And Based On Less Number Of Parameters With Relatively High Accuracy. This Work Presents A Back Propagation Based Neural Network Architecture For Bridge Failure Prediction. The Data Set Used Is The Stanford Earthquake Dataset (STEAD). It Has Been Shown That The Proposed Work Attains High Classification Accuracy And Low Computation Complexity Making The Model Effective For Quick Evaluation Of Bridges From Seismic Impacts

Author: Raksha Patel | Prof. Sumit Pahwa | Prof. Salma Patel

Area of research: Information Technology Engineering

Smart City Traveller–TripTogether Is An Intelligent Travel Assistance Platform Designed To Improve Urban Travel Through GPS Navigation, IoT-enabled Infrastructure, Real-time Traffic Monitoring, Public Transport Information, Weather Updates, And Personalized Recommendations. The System Assists Travelers In Route Planning, Safety Management, Emergency Support, And City Exploration While Supporting Sustainable Urban Mobility.

Author: Aditya Masurkar | Akshada Pathare | Rajshri Bhosale | Anuradha Pawar | Prof. Umesh Palaskar

Area of research: COMPUTER APPLICATION

QuickFix Is A Web-based Local Service Finder Application Developed To Connect Users With Nearby Service Providers Efficiently And Conveniently. In Today's Fast-paced World, Finding Reliable Professionals For Services Such As Plumbing, Electrical Work, Home Cleaning, Appliance Repair, And Other Household Needs Can Be Time-consuming And Challenging. The Proposed System Provides A Centralized Platform Where Customers Can Easily Search, Compare, And Book Service Providers Based On Their Requirements. The Application Supports Features Such As User Registration, Service Provider Registration, Service Browsing, Location-based Search, Booking Management, Customer Reviews And Ratings, Secure Authentication, And Real-time Service Status Updates. The System Is Developed Using Modern Web Technologies With A Responsive User Interface And A Robust Backend Database For Secure Data Storage And Management The QuickFix Platform Improves Accessibility, Reduces Service Search Time, Enhances Customer Satisfaction, And Promotes Trust Between Customers And Service Providers Through A Transparent Rating And Review Mechanism.

Author: AJAY KUMAR K

Area of research: Computer Application

Vision-threatening Ocular Diseases Such As Diabetic Retinopathy, Glaucoma, Cataract, Age-related Macular Degeneration (AMD), Hypertensive Retinopathy And Pathological Myopia Are Leading Causes Of Preventable Blindness Worldwide. Conventional Diagnosis Depends On Manual Examination By Ophthalmologists, Which Is Time-consuming, Costly And Constrained By The Limited Availability Of Specialists, Particularly In Rural Areas. This Paper Presents An AI-powered Diagnostic Framework That Applies Clinical Data Analytics And Machine Learning To Classify Ocular Conditions Using Patient Parameters Such As Intraocular Pressure, Visual Acuity, Blood Pressure, Diabetes Status, Family History And Symptom Duration. Algorithms Including Random Forest, Support Vector Machine (SVM), Decision Tree, Bagging Classifier And XGBoost Were Implemented And Integrated Into A Django-based Web Application That Provides Registration, Login, Data Entry, Prediction And Report Generation Modules. Experimental Evaluation Achieved An Accuracy Of 96%, Precision Of 95%, Recall Of 94% And F1-score Of 95%, Demonstrating That The Proposed System Can Assist Ophthalmologists In Early Detection, Reduce Diagnostic Time And Improve Healthcare Accessibility.

Author: VIJAY.S

Area of research: Computer Application

The Increasing Use Of Digital Documents Has Created A Demand For Efficient Text Extraction And Accessibility Solutions. Traditional Methods Of Extracting Text From Images And PDF Files Are Time-consuming And Often Inaccessible To Visually Impaired Users. This Project Proposes A Web-based System That Integrates Optical Character Recognition (OCR) And Text-to-Speech (TTS) Technologies To Extract Text From PDF Documents And Images And Convert It Into Speech. The System Provides Features Such As Text Customization, Copy/download Functionality, And Theme Management. Developed Using HTML, CSS, And JavaScript, The Solution Improves Accessibility, Reduces Manual Effort, And Enhances Productivity For Students, Researchers, Professionals, And Visually Impaired Users.

Author: Santhosh D

Area of research: Computer Application

R Two-wheeler Accidents Often Prove Fatal Because Victims Cannot Call For Help After A Crash. Existing Approaches Such As Manual Reporting, Mobile-based Crash Apps, GPS Trackers, And Basic Helmet Sensors Are Unreliable When The Rider Is Unconscious, The Phone Is Damaged, Or The Application Is Not Activated. This Paper Proposes An IoT-enabled Smart Safety System That Combines An Accident-detecting Bike Unit With An Interactive Smart Helmet. The Bike Module Uses An Accelerometer, Gyroscope, And Vibration Sensor To Detect Sudden Impacts And Falls. When A Possible Accident Is Identified, The Helmet Initiates A Short Voice Or Button-based Confirmation With The Rider. If No Response Is Received Within A Set Time, The System Retrieves The GPS Location And Uses A GSM Module To Send An Emergency SMS To Family Members, Guardians, Or The Nearest Police Station. A Web-based Monitoring Dashboard Built Using PHP (Laravel), MySQL, And RESTful APIs Supports Administration, Rider Registration, And Accident-log Analysis. The Proposed Threshold-based Decision Algorithm Reduces False Alarms While Ensuring Rapid Emergency Response, Offering A Cost-effective And Scalable Solution To Improve Two-wheeler Road Safety.

Author: Ezhilarasan K

Area of research: HUMAN RESOURCES

The Indian Eyewear Retail Sector Has Witnessed Rapid Transformation Driven By Digital Innovation, Shifting Consumer Lifestyles, And The Emergence Of Omnichannel Retail Models. This Paper Investigates Customer Preferences In The Indian Eyewear Market With Specific Reference To An Omnichannel Eyewear Retailer, Examining The Factors That Shape Purchasing Decisions, Satisfaction Levels, And Brand Loyalty. A Descriptive Research Design Was Employed, And Primary Data Were Collected From 120 Respondents In Salem City, Tamil Nadu, Through A Structured Likert-scale Questionnaire Encompassing 25 Items. Percentage Analysis, Correlation Analysis, And Simple Regression Analysis Were Applied To Interpret The Data. Key Findings Reveal That Product Range Diversity, Competitive Pricing, Digital Platform Usability, Home Try-On And 3D Virtual Try-on Features, Timely Delivery, And Brand Trust Are The Primary Determinants Of Customer Preference. A Strong Positive Correlation (r = 0.763) Between Customer Preference And Satisfaction Was Established, With Regression Analysis Confirming That Customer Preference Explains 58.2% Of The Variance In Satisfaction Scores (R² = 0.582). The Study Concludes With Strategic Recommendations Aimed At Enhancing Customer Experience, Deepening Brand Loyalty, And Sustaining Competitive Advantage In The Evolving Eyewear Market.

Author: PAVITHRA V | Dharani A

Area of research: Machine Learning

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

Author: Rudresh Sharma | Prof. Pankaj Raghuwanshi

Area of research: Computer Applications

Predictive Maintenance Has Emerged As A Cornerstone For Maximizing Operational Efficiency And Minimizing Unexpected Machine Breakdowns In Industrial Environments. This Paper Presents An Integrated End-to-end Machine Learning Framework For Forecasting Machine Failure Status Using Ensemble Classification Algorithms—Random Forest, Gradient Boosting, And Naïve Bayes. The System Addresses Critical Limitations Of Legacy Threshold-based Monitoring And Uncalibrated Deep Learning Models, Specifically Targeting Overconfidence And Poor Confidence Separation Between Correctly Classified And Misclassified Samples. A Structured Preprocessing Pipeline Handles Missing Values, Duplicates, And Class Imbalance Via RandomOverSampler. Models Are Validated On An 80:20 Training-testing Split With Accuracy, Precision, Recall, And F1-score Benchmarks. The Best-performing Model Is Deployed Through A Django-based Web Application Enabling Real-time Sensor Input And Failure Prediction For Non-technical Operators. Experimental Results Demonstrate High Classification Accuracy With A Widened Confidence Gap, Making The System A Reliable Solution For Manufacturing, Energy, And Transportation Sectors.

Author: BALAJI R | Dr. K. Annalakshmi

Area of research: Information Technology

This Paper Presents The Design, Architecture, And Full-stack Implementation Of ContribuX, A Web-based Creator Support And Funding Platform That Enables Project Creators To Raise Funds From Contributors Using Secure Digital Payment Infrastructure. The System Integrates Razorpay As A Multi-instrument Payment Gateway Supporting UPI, Credit Cards, Debit Cards, Net Banking, And Digital Wallets, A Next.js 15 App Router Frontend For Server-side Rendered Campaign Pages, A MongoDB Persistence Layer Managed Through The Mongoose ODM, And NextAuth V5 For Session-based Authentication With Temporary Identity Management. The Platform Supports Campaign Creation And Management, Secure Contributor Checkout With HMAC-SHA256 Payment Verification, Transactional Email Delivery Via Nodemailer, And Real-time Toast Notifications Powered By React-Toastify. The Proposed Architecture Is Evaluated Against Established Security Frameworks For Electronic Payment Systems And Is Shown To Satisfy The Core Requirements Of Confidentiality, Integrity, Non-repudiation, Anonymity, Authentication, And Authorisation. The System Further Extends Prior Art By Supporting Multiple Payment Instruments And An Embedded Crowdfunding Model Within A Single Unified Platform. The Proposed Design Provides A Reproducible And Open-source Reference Implementation For Secure Digital Payment Integration In Modern Creator-economy Web Applications

Author: Pratik Andhare | Jayesh Punde | Arya Kompalwar | Tejas Patekar | Prof. Umesh Palaskar

Area of research: OPERATIONS

This Study Examines Reverse Logistics And Product Returns Management Practices In Modern Organizations. The Research Focuses On Inventory Management, Transportation Efficiency, Warehousing, Technology Adoption, Customer Satisfaction, And Operational Performance. Primary Data Were Collected Through A Structured Questionnaire From 70 Respondents. Statistical Tools Such As Percentage Analysis, Chi-square Test, And Correlation Analysis Were Used. The Findings Indicate That Effective Reverse Logistics Practices Contribute To Operational Efficiency, Cost Reduction, Customer Satisfaction, Sustainability, And Improved Organizational Performance.

Author: KIRUBAGARAN G | DHARANI A

Area of research: Computer Applications

Water Is A Vital Natural Resource, And Its Quality Directly Impacts Human Health, Agriculture, And Ecosystem Sustainability. Traditional Water Quality Monitoring Methods Are Often Time-consuming, Expensive, And Limited In Spatial And Temporal Coverage. To Overcome These Challenges, This Paper Presents An Automated Internet Of Things (IoT)-based Water Quality Monitoring System (WQMS) That Integrates Smart Sensor Arrays, Embedded Processing, Wireless Telemetry, And Cloud Computing For Real-time Analysis. The System Employs Multiple Submersed And Environmental Sensors To Continuously Measure Critical Physical And Chemical Parameters, Including Potential Of Hydrogen (pH), Total Dissolved Solids (TDS), Dissolved Oxygen (DO), Electrical Conductivity, Turbidity, Temperature, And Chemical/biochemical Oxygen Demand (COD/BOD). These Sensors Interface Directly With An ESP32 Microcontroller Unit (MCU) Leveraging Its Native Successive Approximation Register (SAR) Analog-to-Digital Converter (ADC). The Aggregated Parameters Are Processed Locally And Securely Transmitted Via Integrated Wi-Fi Or GSM Modules Utilizing Lightweight Message Queuing Telemetry Transport (MQTT) And HTTP Communication Protocols To A Dedicated Cloud Environment (Adafruit IO / AWS Backend). Cloud Infrastructures Facilitate Automated Database Logging, Historical Trend Analytics, And Crossplatform Visualization Through Graphical Dashboards. Integrated Automated Emergency Mechanisms Trigger Localized Piezoelectric Acoustic Buzzer Warnings And Send Immediate Remote Alert Notifications Via SMS And Email Channels Upon Any Parametric Cross-contamination Or Extreme Deviation From Safe Drinking Standards. Empirical Results Validate High System Stability, Minimal Calibration Errors, And Immediate Sub-3-second Warning Propagation, Proving The Solution's Extreme Viability Across Residential Water Frameworks, Aquaculture, And Industrial Effluent Tracking.

Author: Amulraj M | Dr. K. Annalakshmi

Area of research: Artificial Intelligence (AI) In Healthcare

Cervical Cancer Remains One Of The Leading Causes Of Cancer-related Deaths Among Women Worldwide. Early Identification Of High-risk Individuals Is Essential For Timely Intervention And Improved Survival Rates. Traditional Machine Learning And Deep Learning Approaches Often Fail To Capture Complex Feature Relationships, Temporal Dependencies, And Provide Adequate Interpretability. This Study Proposes An Explainable Hybrid Graph Attention Network–Temporal Deep Learning (GAT-TDL) Framework For Cervical Cancer Risk Prediction Using Structured Clinical Data. The Framework Integrates Graph Attention Networks (GAT) For Modeling Feature Dependencies, Bidirectional Gated Recurrent Units (BiGRU) For Temporal Learning, And A Lightweight 1D Residual CNN For Feature Refinement. To Enhance Transparency And Clinician Trust, Explainable AI (XAI) Techniques Including SHAP And Integrated Gradients Are Incorporated. The Model Was Evaluated Using The UCI Cervical Cancer Dataset And Achieved An Accuracy Of 96.58%, Sensitivity Of 95.65%, Specificity Of 97.52%, And AUC-ROC Of 98.64%. Experimental Results Demonstrate That The Proposed Framework Outperforms Conventional Machine Learning Models While Providing Interpretable And Clinically Meaningful Predictions.

Author: Aashifa Banu | Mohan Kumar

Area of research: Civil Engineering

In This Study, An Experimental Investigation Was Carried Out On The Flexural Behaviour Of Glass Fibre Reinforced Polymer (GFRP) Reinforced Concrete Beams Incorporating Polypropylene Fibres And Waste Granite Aggregate. The Primary Objective Of The Research Was To Evaluate The Structural Performance, Strength Characteristics, And Crack Behaviour Of Sustainable Concrete Beams Reinforced With GFRP Bars. Waste Granite Aggregate Was Used As A Partial Replacement For Conventional Coarse Aggregate To Promote Sustainable Waste Management And Reduce Environmental Impact. Polypropylene Fibres Were Added To Improve The Ductility, Crack Resistance, And Overall Performance Of Concrete. GFRP Bars Were Adopted As Reinforcement Due To Their High Corrosion Resistance, Lightweight Nature, And Superior Tensile Strength Compared To Conventional Steel Reinforcement. Concrete Specimens Such As Cubesand Prisms Were Cast Added At Varying Percentages (0%, 0.5%, 0.75%) To Improve Ductility And Minimize Shrinkage Cracks. Waste Granite Aggregate, Obtained As A Byproduct Of Stone Cutting Industries, Was Partially Replaced With Natural Coarse Aggregates At 0%, 10%&20% By Volume To Improve Sustainability And Reduce Natural Resource Consumption.tested To Determine Compressive Strengthand Flexural Strength.This Study Presents An Experimental Investigation On The Flexural Behaviour Of Reinforced Concrete Beams Incorporating Glass Fiber Reinforced Polymer (GFRP) Bars, Polypropylene (PP) Fiber, And Partial Replacement Of Natural Coarse Aggregate With Waste Granite Aggregate. The Primary Objective Is To Evaluate The Improvement In Structural Performance, Particularly In Terms Of Load-carrying Capacity, Crack Resistance, Ductility, And Energy Absorption Characteristics Under Flexural Loading. A Series Of Beam Specimens Were Cast And Tested, Including Control Beams And Modified Beams With Varying Percentages Of Polypropylene Fiber And Waste Granite Aggregate Replacement. All Specimens Were Subjected To Two-point Loading To Simulate Flexural Conditions, And Their Structural Responses Were Recorded In Terms Of Load–deflection Behaviour, Crack Development, And Ultimate Failure Patterns. The Experimental Results Indicate That The Inclusion Of GFRP Reinforcement Significantly Enhances Corrosion Resistance And Increases Tensile Performance, While Polypropylene Fiber Contribute To Improved Crack Control And Post-cracking Behaviour. Furthermore, The Partial Replacement Of Natural Aggregates With Waste Granite Improves Sustainability Without Severely Compromising Mechanical Strength. The Combined Effect Of These Materials Leads To Enhanced Flexural Strength, Delayed Crack Propagation, And Improved Overall Ductility Of The Beam Specimens. The Study Concludes That The Synergistic Use Of GFRP Bars, Polypropylene Fiber, And Waste Granite Aggregate Can Be Effectively Adopted In Concrete Structures To Achieve Sustainable And High-performance Construction Materials With Improved Structural Efficiency Under Flexural Loading.

Author: SHARATH R R | Prof. DEEPIKA K C

Area of research: MBA

The Study Titled “A Role Of HR Practices On Employee Satisfaction At TVS Vehicle Mobility Solutions Ltd.” Aims To Examine The Impact Of Various Human Resource (HR) Practices On Employee Satisfaction Within The Organization. HR Practices Such As Recruitment And Selection, Training And Development, Performance Appraisal, Compensation, Rewards, And Employee Welfare Play A Crucial Role In Influencing Employee Motivation, Commitment, And Productivity. The Study Adopts A Descriptive Research Design And Uses Both Primary And Secondary Data Sources. Primary Data Were Collected Through A Structured Questionnaire Administered To Employees, While Secondary Data Were Gathered From Books, Journals, Company Records, And Websites. The Findings Reveal That Effective HR Practices Significantly Enhance Employee Satisfaction, Skill Development, And Organizational Performance. The Study Also Highlights The Importance Of Fair Compensation, Transparent Appraisal Systems, Career Growth Opportunities, And Employee Welfare Measures In Maintaining A Motivated Workforce. It Concludes That Continuous Improvement In HR Policies And Practices Is Essential For Increasing Employee Satisfaction, Reducing Turnover, And Achieving Long-term Organizational Success.

Author: VENGATESHAN A | Mr.K.J.ARANGANATHAN

Area of research: Information Technology

For New Batches Of Students Who Are Often Con-fused With Their Respective Admissions And Placements, Existing Digital Platforms Do Not Always Relate To Relevance And Often Appear Vague. Legacy Campus Search Engines Are Built On Simple Keyword-based Algorithms That Are Cumbersome And Not User-friendly. We Propose A Centralized Knowledge Base Powered By Artificial Intelligence (AI) Techniques That Enable More Efficient Search Through Semantic Retrieval, Large Language Models (LLMs), And Personalized Ranking. This System Includes Structured Q&A, Resource Sharing, And Context-based Ranking On The Basis Of User Profiles Such As Branch, Year, And Interests. The Proposed System Was Evaluated Through User Testing Followed By Performance Evaluation. Results Demonstrate That The Platform Can Lower Search Time By 30%–40%, Improve Result Relevancy By 25%–35%, And Yield Greater User Satisfaction Compared To Keyword-based Platforms. Findings Also Indicate That Users Had A Positive Experience With Interview Experience Summaries And Personalization Filters, Which Improved Information Accessibility And Encouraged Continued Platform Engagement. The Findings Confirm That Applying AI-based Ranking Alongside Semantic Search Significantly Increases Information Discovery And Peer Interaction Among College Stu-dents.

Author: Manasi Ahire | Yadnesh Bhoomkar | Savim Meshram | Prashik Ramteke | A. P. Kulkarni

Area of research: NA

This Paper Describes The Design And Development Of A Web-based Job Portal System Aimed At Making The Recruitment Process Faster And Less Cumbersome. Built With Java, Servlets, JSP, And MySQL, The Platform Brings Job Seekers And Employers Together In One Place. Candidates Can Register, Build Profiles, Upload Their Resumes, And Submit Applications, While Employers Get Tools To Post Openings And Track Incoming Applicants. Rather Than Relying On AI-driven Matching, The System Uses Skill-, Experience-, And Category-based Database Filtering To Connect The Right Candidates With The Right Roles. Security, Clean Data Flow, And Ease Of Use Were Central Priorities Throughout Development. Evaluation Results Confirm That The System Meaningfully Cuts Down Recruitment Time, Lowers Manual Workload, And Makes The Hiring Process More Accessible For Everyone Involved.

Author: Nalawade Ishwari Atul | Awari Kanchan Santosh | Mane Shradha Sunil | Sawant Uddhav Chandu | V P Tonde

Area of research: CSE

Agriculture Is Very Important In The Economic Development And Food Security Of Most Nations, However, It Has Been Experiencing Major Threats Like Fluctuating Weather, Crop Infections And Lack Of Accessibility To Professional Agricultural Advice. The High Pace Of Developing Digital Technologies And Artificial Intelligence Has Presented New Possibilities To Overcome These Challenges Successfully. GRINOVA Application Is An Intelligent And Combined Smart Agriculture Support System Which Will Help Farmers Make Better Decisions By Integrating Weather Forecasting, Predicting Crop Diseases, And AI-assisted Chatbot Into One Platform. On The Whole, The AGRINOVA Application Is An Example Of How Artificial Intelligence And Data-driven Technologies Can Be Successfully Used To Transform Agriculture, Enhance Its Productivity, Minimize Risks, And Aid Sustainable Farming And, Thus, Is A Useful Instrument In The Context Of Smart Agricultural Management.

Author: K.NAGAMANI | N.SANDEEP KUMAR

Area of research: Computer Applications

This Paper Proposes An AI-powered Cybersecurity Framework Integrating Machine Learning, Deep Neural Networks, Real-time Network Monitoring, Automated Alerting, And Dashboard-driven Response For Enterprise Threat Detection And Prevention. This Implementation-oriented Paper Discusses Architecture Design, Module Interactions, Workflow, Deployment Considerations, Performance Evaluation, And Practical Enterprise Applicability. The Framework Supports Dashboard Visualization, Automated SOC Notification, Anomaly Classification, And Response Orchestration In Operational Environments.

Author: Gokul G | Ms. Mahalakshimi

Area of research: Management

This Research Investigates Logistics And Supply Chain Management Practices Within A Technology Manufacturing Organization And Assesses Their Influence On Operational Efficiency And Customer Satisfaction Outcomes. Effective Supply Chain Operations Require The Coordinated Management Of Procurement Activities, Transportation Systems, Inventory Handling, Warehousing Facilities, And Distribution Networks. The Central Objectives Of This Study Are To Evaluate The Logistics Practices Adopted By The Selected Organization, Determine Their Effectiveness In Sustaining Service Levels, And Identify Operational Challenges That Limit Supply Chain Performance. Primary Data Were Gathered Through A Structured Questionnaire Administered To 70 Employees Working Across Logistics And Operations Departments. Secondary Data Were Derived From Academic Journals, Company Records, And Relevant Industry Publications. Statistical Analysis Using Percentage Analysis, Chi-square Test, And Correlation Analysis Was Employed To Interpret The Collected Data. The Findings Reveal That Effective Inventory Management, Transportation Planning, Digital Tracking, And ERP System Adoption Significantly Enhance Supply Chain Visibility And Operational Coordination. Key Challenges Identified Include Supply Chain Delays, Inadequate Automation, Communication Gaps Across Departments, And The Need For Greater Technology Investment. The Study Concludes That Efficient Logistics And Supply Chain Management Practices Lead To Reduced Operational Costs, Improved Delivery Performance, And Elevated Customer Satisfaction. Recommendations Include Increased Investment In Advanced Technologies, Enhanced Interdepartmental Communication, Strengthened Supplier Relationships, And Continuous Performance Monitoring To Optimize Logistics Operations.

Author: D.SRIRAM | SUGANTHI A

Area of research: Management

This Research Investigates Time Management Strategies Within Logistics Operations And Their Influence On Service-level Outcomes In A Manufacturing Environment. Logistics Efficiency Depends Heavily On How Organizations Coordinate Transportation, Inventory Handling, Warehousing, Order Processing, And Delivery Scheduling. The Primary Objectives Of This Study Are To Examine The Time Management Approaches Adopted By The Selected Organization, Evaluate Their Effectiveness In Sustaining Optimal Service Levels, And Identify The Key Operational Challenges Hindering Logistics Performance. Both Primary And Secondary Data Were Utilized. Primary Data Were Gathered Through Structured Questionnaires Administered To Logistics And Operations Staff, While Secondary Data Were Sourced From Organizational Records, Academic Journals, Company Websites, And Related Industry Publications. The Analysis Reveals That The Organization Maintains Structured Logistics Procedures Encompassing Transportation Planning, Warehouse Coordination, Inventory Control, And Dispatch Scheduling. Key Challenges Identified Include Transportation Delays, Inadequate Scheduling Frameworks, Interdepartmental Communication Gaps, And Insufficient Technological Infrastructure. Despite These Hurdles, The Organization Demonstrates Commendable Operational Discipline That Positively Impacts Overall Performance. The Study Concludes That Robust Time Management In Logistics Operations Leads To Improved Productivity, Reduced Delays, And Elevated Customer Satisfaction. Recommendations Include The Adoption Of Advanced Technologies, Enhanced Cross-departmental Coordination, And Strengthened Monitoring Systems To Optimize Service Delivery.

Author: SATHEESH B | PRADHAP B

Area of research: Civil Engineering

Metro Railway Systems Are Among The Most Important Modes Of Urban Transportation, Providing Safe, Reliable, And Efficient Mobility For Millions Of Passengers Every Day. One Of The Most Critical Components Of A Metro Railway Network Is The Turnout Or Switch System, Which Enables Trains To Change Tracks And Facilitates Smooth Traffic Management. Since Turnouts Are Subjected To Continuous Mechanical Stress And Environmental Variations, They Are Vulnerable To Wear, Degradation, And Unexpected Failures. Therefore, Timely Fault Detection In Metro Turnout Data Is Essential To Ensure Operational Safety, Minimize Service Disruptions, And Reduce Maintenance Costs.A Turnout System Consists Of Several Mechanical And Electrical Components, Including Switch Rails, Point Machines, Locking Mechanisms, Motors, And Sensors. During Operation, These Components Generate Various Forms Of Data Such As Current Signals, Voltage Measurements, Vibration Signatures, Temperature Readings, And Switching Times. The Analysis Of These Data Enables Maintenance Engineers To Assess The Health Condition Of The Turnout And Identify Abnormalities That May Indicate The Onset Of Faults. Effective Fault Detection Systems Can Provide Early Warnings And Prevent Catastrophic Failures. This Paper Presents A Review Of Existing Statistical Models In The Domain Of Research.

Author: Ramavtar Singh Sikarwar | Dr. Sunil Sugandhi

Area of research: Transportation Engineering

With Rapid Advances In Statistical Modelling And Data Analysis, Analysis Of Bridge Failure And Damage Is Also Being Considered Under The Purview Of Statistical Models. It Is Ubiquitous That Manual Inspection Of Bridge Damage And Estimations Are Time Consuming And May Not Be Practically Feasible In Cases Where The Experts Are Not Readily Available And/or Sites Of Huge Mass Calamities Such As Earthquake Prone Cites. Thus A Quick Estimation And Evaluation Of The Damages Is Necessary So As To Immediately Stop The Cascading Effect Of The Possible Failure And Casualties. In This Paper, A Comprehensive Review On The Various Statistical Techniques Being Employed For The Evaluation Of Bridge Failure Have Been Discussed Along With The Salient Points. Previous Work In The Domain Has Also Been Cited So As To Garner Attention Towards The Recent Trends In The Domain. The Evaluation Metrics Commonly Employed For The Analysis Of The Statistical Systems Have Also Been Defined. It Is Expected That This Comprehensive Review Would Pave The Path For Further Research Domains In The Field.

Author: Rajesh Kumar | Dr. Sunil Sugandhi

Area of research: Computer Engineering

The Rapid Proliferation Of Highly Realistic Synthetic Media, Commonly Known As Deepfakes, Poses A Severe Threat To Digital Identity Verification And Media Authenticity. Current Deepfake Detection Methodologies Predominantly Rely On Single-modality Neural Networks Or Computationally Prohibitive Feature-level Fusion, Rendering Them Inefficient For Real-time Web Deployment. This Paper Surveys Existing Unimodal And Multimodal Deepfake Detection Frameworks And Proposes A Novel, Highly Scalable Alternative: A Decoupled, Tri-Modal Late-Fusion Architecture. The Proposed System Evaluates Media Through Three Parallel, Asynchronous Pipelines: A Spatial Engine Utilizing Error Level Analysis (ELA) Paired With A Convolutional Neural Network (CNN) For Compression Artifact Detection; A Biometric Engine Employing A ResNeXt-50 And LSTM Network For Temporal Facial Tracking; And An Auditory Engine Converting 1D Waveforms Into 2D Mel-Spectrograms For Synthetic Frequency Classification. By Intercepting Live WebRTC Streams Via A Zero-dependency DOM Injection Protocol, The Architecture Bypasses Traditional File-download Bottlenecks. Utilizing A Weighted Confidence Algorithm For Decision-level Fusion, The System Achieves A 97.8% Ensemble Accuracy And Gracefully Degrades In The Absence Of Specific Data Streams, Analyzing 5-second Media Buffers With A Maximum Latency Of 2.1 Seconds. This Survey Demonstrates That Decoupled, Parallel Modality Processing Offers A Vastly Superior, Fault-tolerant Framework For Commercial Deepfake Interception Compared To Traditional Synchronous Models.

Author: Harsh Rathod | Aryan Pardeshi | Apurva Shinde | Prajwal Pansare | Ashvini Kheole

Area of research: Computer Applications

There Has Been A Significant Shift In Clothing Shopping Habits Over The Past Decade, With Most Purchases Now Made Online. A Major Challenge Remains Consumers' Inability To Try On Products Before Purchase, Leading To Return Rates Of 30–40% Due To Incorrect Sizing. This Paper Presents Vision Fit, A System That Resolves This Problem Using An Ordinary Laptop Webcam. Vision Fit Employs A Dual-Anchor Anthropometric Calibration Pipeline Using Blaze Pose To Detect 33 Body Landmarks, Then Converts Pixel Coordinates Into Centimetres Without Physical Reference Objects. Two Anatomical Proportionality Ratios (Nose-Hip ≈ 48% And Nose-Ankle ≈ 82% Of Standing Height) Are Fused With A 0.6:0.4 Weight To Derive A Robust Scale Factor. A 30-frame Temporal Stabilization Pipeline With Jitter Rejection Reduces RMSE From 2.15 Cm To 0.71 Cm — A 67% Improvement. The Brand Advisory Module Achieves 94% Size-label Accuracy Across 10 Major Apparel Brands With No Missed Recommendations Over 50 Subjects. The System Operates Fully Offline Via FastAPI And PyWebView.

Author: VINODH VS | Anusha Lakshmi

Area of research: Human Resource

This Study Examines The Recruitment Policies And Employee Retention Strategies Followed By Linux Life Sciences Private Limited, Puducherry. The Research Analyzes Factors Such As Work Environment, Management Support, Compensation, Career Growth, Employee Engagement, And Training Programs That Influence Employee Retention And Job Satisfaction. Primary Data Were Collected From 100 Employees Through A Structured Questionnaire, And The Data Were Analyzed Using Percentage Analysis And Karl Pearson’s Correlation Coefficient. The Findings Reveal That A Supportive Work Environment, Effective HR Practices, Recognition, Rewards, And Career Development Opportunities Significantly Improve Employee Retention And Organizational Performance. A Strong Positive Correlation (r = 0.946) Was Found Between Work Environment And Job Satisfaction. The Study Concludes That Strategic Recruitment And Retention Practices Play A Vital Role In Improving Employee Loyalty, Productivity, And Overall Organizational Effectiveness.

Author: M. Harish Raj | N. Indumathi

Area of research: Computer Applications

The Rapid Growth Of Digital Technologies Has Substantially Increased Exposure To Cybersecurity Threats Including Malware, Phishing, Ransomware, And Unauthorized Network Intrusions. Traditional Signature-based Security Systems Are Inherently Reactive And Fail Against Zero-day Exploits And Polymorphic Attacks. This Paper Presents ThreatGuardian, An AI-powered Cybersecurity Threat Detection And Prevention Framework That Leverages Machine Learning Algorithms — Random Forest (RF), AdaBoost Classifier (ADC), And Bernoulli Naive Bayes Classifier (BNC) — To Identify And Classify Malicious Network Activities In Real Time. The System Integrates Data Preprocessing, Exploratory Data Analysis, Feature Extraction, And Model Evaluation Pipelines With A Django-based Web Interface For Practical Deployment. Trained And Evaluated On A Publicly Available Network Intrusion Dataset From Kaggle, The Best-performing Model Is Serialized And Deployed For Real-time Inference. Performance Evaluation Using Accuracy, Precision, Recall, And F1-score Demonstrates That The Proposed Framework Significantly Outperforms Traditional Rule-based Methods, Providing An Adaptive, Scalable, And User-accessible Solution For Modern Cyber Threat Management.

Author: Giritharan R | Dr. K. Annalakshmi

Area of research: HUMAN RESOURCES

This Study Examines The Role Of Strategic Onboarding In Influencing Employee Retention At Skylark Information Technologies Private Limited, A Leading IT Services Company Headquartered In Chennai, India. The Study Employs A Descriptive Research Design With Primary Data Collected From 56 Respondents Who Had Undergone The Company's Onboarding Programme. The Survey Instrument Covered Five Key Dimensions: Pre-joining Preparation, Role And Goal Clarity, Cultural And Team Integration, Training And Learning Effectiveness, And Employee Wellbeing And Retention Intent. Statistical Analysis Using Percentage Analysis, Chi-Square Test, And Pearson Correlation Revealed That The Onboarding Programme Is Largely Effective Across All Dimensions. The Chi-Square Test Confirmed A Statistically Significant Association Between Training Programme Effectiveness And Retention Intention (χ² = 28.743, Df = 16, P = 0.026 < 0.05). The Pearson Correlation Analysis Established A Moderate Positive And Significant Relationship Between The Overall Onboarding Experience And Retention Intention (r = 0.312, P = 0.019). The Study Concludes That Strategic Onboarding Is A Critical Driver Of Early-stage Employee Retention In IT Organisations And Provides Actionable Recommendations For Further Strengthening Skylark's Onboarding And Talent Retention Framework.

Author: JOEL PRITHIV RAJ P | INDUMATHI N

Area of research: Machine Learning

Cardiovascular Disease (CVD) Continues To Pose A Significant Global Health Challenge, Demanding Innovative Approaches For Early Detection And Prevention. This Paper Presents An AI Cardiologist System That Leverages Supervised Machine Learning Techniques To Predict Heart Disease With High Accuracy. The Proposed System Integrates A Bagging Classifier Ensemble Method Alongside A LeNet Convolutional Neural Network Architecture To Analyse Multi-dimensional Patient Data—including Demographics, Clinical History, Laboratory Results, And ECG Readings. The System Is Deployed As A Full-stack Web Application Using The Django Framework, Enabling Clinicians To Receive Real-time, Personalised Risk Assessments. Experiments Conducted On The UCI Heart Disease (CARDIO) Dataset Demonstrate Competitive Accuracy. Future Directions Include Integration Of Explainable AI (XAI) And Federated Learning To Enhance Transparency And Privacy Preservation.

Author: Vinoth M | Dr.K.Annalakshmi

Area of research: Management Studies

Conflict Is A Natural And Unavoidable Phenomenon In Any Organization Where Individuals With Diverse Backgrounds, Skills, And Opinions Interact. This Study Examines The Impact Of Group Conflict Management On Employee Productivity Improvement At A.S Aqua Farm, Vikkiravandi, A Water Purification And Supply Organization. The Study Identifies The Major Causes Of Workplace Conflict, Evaluates The Existing Conflict Management Practices, And Analyzes Their Influence On Employee Productivity. Data Was Collected From 100 Employees Through A Structured Questionnaire Using A Five-point Likert Scale, And Was Analyzed Using Percentage Analysis, Chi-square Test, And Correlation Analysis. The Findings Reveal That Communication Gaps, Work Pressure, Role Ambiguity, And Personality Differences Are The Major Causes Of Conflict, And That Effective Conflict Management Practices Significantly Improve Teamwork, Employee Morale, And Overall Productivity. The Chi-square Test Confirmed A Significant Association Between Conflict Management And Productivity Improvement, While Correlation Analysis Revealed A Strong Positive Relationship (r = 0.782) Between The Two Variables. The Study Concludes With Suggestions For Improving Communication, Leadership, And Conflict Resolution Mechanisms To Enhance Organizational Productivity.

Author: Abitha. C

Area of research: MBA

Employee Retention Has Become One Of The Major Concerns For Organizations In Today’s Competitive Business Environment. Compensation And Benefits Play A Vital Role In Improving Employee Satisfaction And Reducing Employee Turnover. The Present Study Aims To Analyse The Impact Of Compensation And Benefits On Employee Retention In Swashthik Using PlasconPvt. Ltd. The Study Was Conducted Among Employees Working In The Organization A Structured Questionnaire. Both Primary And Secondary Data Were Used For The Research. The Collected Data Were Analysed Using Percentage Analysis, Correlation Analysis, And Chi-square Analysis. The Findings Reveal That Salary Structure, Incentives, Overtime Payment, Medical Benefits, And Leave Policies Significantly Influence Employee Retention. The Study Concludes That Effective Compensation And Benefit Policies Improve Employee Motivation, Job Satisfaction, And Loyalty Toward The Organization.

Author: HARIHARAN P | A Dharani

Area of research: OPERATIONS MANAGEMENT

Operational Efficiency Plays A Significant Role In Determining The Growth And Sustainability Of Organizations In Today's Competitive Business Environment. Efficient Utilization Of Resources Such As Manpower, Machinery, Materials, Technology, And Time Contributes To Improved Productivity, Reduced Operational Costs, Enhanced Product Quality, And Increased Customer Satisfaction. The Purpose Of This Study Is To Examine The Relationship Between Operational Efficiency And Company Growth. The Study Adopts A Descriptive Research Design And Utilizes Both Primary And Secondary Data Sources. Data Were Collected From 140 Respondents Using A Structured Questionnaire. Statistical Tools Such As Percentage Analysis, Correlation Analysis, And Chi-square Tests Were Used For Data Interpretation. The Findings Indicate That Operational Efficiency Has A Positive Impact On Production Output, Revenue Growth, Competitive Advantage, And Overall Organizational Performance. The Study Concludes That Organizations That Continuously Improve Operational Processes And Optimize Resource Utilization Are More Likely To Achieve Sustainable Growth And Long-term Success.

Author: SATHISH C | A DHARANI

Area of research: Computer Applications

Traditional Street Lighting Operates On Fixed Schedules With No Fault Detection, Causing Energy Waste, Delayed Maintenance, And Safety Risks. This Paper Proposes An IoT-Based Smart Street Light Fault Detection And Adaptive Brightness Control Framework Leveraging ESP32 Microcontrollers, LDR, ACS712 Current Sensors, Voltage Sensors, And PIR Motion Detectors. The Blynk Cloud Platform Provides Remote Monitoring Via Mobile Apps, While MQTT Enables Lightweight Device-to-cloud Communication. Threshold-based Anomaly Detection Identifies Lamp Failures, Voltage Irregularities, And Connectivity Disruptions. Results Show ~45% Energy Reduction And Fault Detection Within 15 Seconds. The Modular Architecture Supports Future AI-driven Predictive Maintenance, Solar Integration, And Smart City Interoperability.

Author: EMIL NITHISH RANI M D

Area of research: Human Resource Management

Recruitment Process Outsourcing (RPO) Has Emerged As A Widely Adopted Strategic Approach In Human Resource Management (HRM), Enabling Organizations To Delegate Hiring Functions To Specialized External Agencies. This Study Investigates The Impact Of Outsourcing On Recruitment Effectiveness, With A Specific Focus On Cost Efficiency, Time-to-hire, Candidate Quality, Cultural Alignment, And HR Strategic Reorientation. Using A Quantitative Research Design, Primary Data Were Collected From 123 HR Professionals And Employees In The Training And Services Sector Through Structured Questionnaires. Statistical Tools Including Percentage Analysis, Chi-square Tests, One-way ANOVA, Pearson Correlation, And Linear Regression Were Employed For Data Analysis. Findings Reveal That Outsourcing Significantly Reduces Time-to-hire And Improves Candidate Quality, While Also Enhancing HR Capacity For Strategic Activities. However, Challenges Related To Cultural Mismatch, Communication Gaps During Bulk Hiring, And Vendor Dependency Were Identified. The Study Concludes That RPO, When Managed Strategically, Delivers Measurable Benefits In Recruitment Efficiency And Quality, And Provides Actionable Recommendations For Optimizing Outsourcing Partnerships.

Author: Vershan K | Indumathi N

Area of research: MANAGEMENT STUDIES

Work-life Balance Has Emerged As A Critical Concern In Modern Manufacturing Organizations Due To Its Impact On Employee Well-being, Job Satisfaction, And Organizational Productivity. Factory Workers Often Face Challenges Such As Shift Work, Overtime Demands, Physical Strain, And Limited Flexibility, Which May Negatively Affect Their Personal And Family Lives. This Study Examines The Role Of Management Policies In Improving The Work-life Balance Of Factory Workers. The Research Adopted A Descriptive Research Design And Collected Primary Data From 100 Factory Workers Through A Structured Questionnaire. Statistical Tools Such As Percentage Analysis, Chi-square Test, And Correlation Analysis Were Used To Interpret The Data. The Findings Indicate That Effective Management Policies, Including Leave Provisions, Flexible Shift Arrangements, Transport Facilities, Health And Wellness Programs, And Employee Support Initiatives, Positively Influence Employees’ Work-life Balance. However, Overtime Management And Mental Health Support Require Further Improvement. The Study Concludes That Well-designed And Effectively Implemented Management Policies Contribute Significantly To Employee Satisfaction, Retention, And Organizational Performance.

Author: RAJKUMAR K | Dharani A

Area of research: Marketing

Business-to-Business (B2B) Purchase Decisions Play A Significant Role In Industrial Engineering Services, Where Organizations Select Suppliers And Service Providers To Improve Operational Efficiency And Achieve Organizational Goals. The Purchasing Process Involves Evaluating Several Factors Such As Service Quality, Technical Capability, Supplier Reliability, Pricing, Delivery Performance, And Customer Support. This Study Aims To Identify The Major Factors Influencing B2B Purchase Decisions In Industrial Engineering Services And Examine Their Impact On Customer Satisfaction. The Research Is Descriptive In Nature And Is Based On Primary And Secondary Data. Primary Data Were Collected Through A Structured Questionnaire From 140 Respondents. Statistical Tools Such As Percentage Analysis, Chi-Square Test, And Correlation Analysis Were Used For Data Analysis. The Findings Reveal That Service Quality, Technical Expertise, Timely Delivery, And Supplier Reliability Are The Most Influential Factors Affecting Purchase Decisions. The Study Concludes That Effective Supplier Selection Contributes Significantly To Customer Satisfaction And Organizational Performance.

Author: SRIDEVI V | PRADHAP.B

Area of research: Human Resource Management

Equal Employment Opportunity (EEO) Is A Fundamental Principle Of Human Resource Management That Ensures Fairness, Equality, And Non-discrimination In Recruitment And Selection Practices. This Study Examines Employee Perceptions Regarding The Implementation Of EEO Practices Within Organizational Recruitment Processes. The Research Focuses On Fairness, Transparency, Policy Awareness, Ethical Hiring Practices, And Employee Satisfaction. A Quantitative Research Design Was Adopted, And Data Were Collected From 106 Respondents Using A Structured Questionnaire. Statistical Techniques Such As Percentage Analysis, Independent T-test, ANOVA, Correlation Analysis, And Friedman Ranking Test Were Used For Data Analysis. The Findings Reveal That Employees Generally Perceive Recruitment And Selection Practices As Fair, Transparent, And Inclusive. Strong Positive Relationships Were Identified Between EEO Practices, Recruitment Processes, Recruitment Policies, And Employee Satisfaction. The Study Concludes That Effective Implementation Of Equal Employment Opportunity Principles Contributes Significantly To Employee Satisfaction And Organizational Credibility.

Author: Dhilip J | Mrs. A. DHARANI

Area of research: MANAGEMENT

This Study Analyses The Role Of Operations Executive In Improving Operational Efficiency At Power Mech Projects Limited, A Leading Engineering, Procurement, And Construction (EPC) Company Headquartered In Hyderabad, India. Operations Management Is Critical In Engineering And Infrastructure Industries Where Project Timelines, Manpower, And Resource Utilization Directly Impact Organizational Success. The Study Adopts A Descriptive Research Design With Primary Data Collected From 100 Employees Using A Structured Questionnaire. Statistical Tools Including Percentage Analysis, Chi-square Test, Correlation, And Weighted Average Method Were Used For Data Analysis. Findings Reveal That Operations Executives Play A Significant Role In Improving Workflow Management, Employee Coordination, Productivity, And Operational Performance. The Study Concludes That Effective Operational Coordination, Communication, And Supervision Contribute Positively To Achieving Organizational Efficiency And Project Success.

Author: LAVANYA L | SUGANTHI A

Area of research: Operations

This Research Investigates The Scrap Management And Utilization Strategies Adopted In An Automobile Component Manufacturing Plant, Focusing On Identifying Systemic Inefficiencies, Evaluating Employee Perceptions, And Validating The Financial Impact Of Optimization Interventions. A Structured Questionnaire Was Administered To 139 Employees Across Multiple Departments. Descriptive Percentage Analysis, Pareto Analysis, And A Paired-Samples T-Test Using IBM SPSS Were Employed To Analyze The Data. Findings Reveal That Poorly Segregated Mixed Scrap And Oversized Loose Scrap Collectively Account For Approximately 99.9% Of Total Scrap Tonnage. The Implementation Of Optimized Nesting Layouts And High-density Baling Workflows Produced A Statistically Significant Reduction In Monthly Scrap Costs (mean Reduction: ₹28.98 Lakhs Per Production Line; T(4) = 10.130, P = 0.00053). The Study Provides Actionable Recommendations Including Source-level Segregation, IoT-based Tracking, Structured Employee Incentive Programs, And Design-for-manufacturability Initiatives.

Author: BALAJI VARMAN E V | Indumathi N

Area of research: Marketing

This Study Entitled “A Comparative Study Of Fiberglass Products Vs Plastic Products In Marketing” Aims To Compare Fiberglass And Plastic Products Based On Customer Perception, Quality, Durability, Pricing, And Marketing Effectiveness. The Study Was Conducted To Understand Consumer Preferences And Identify The Factors Influencing Their Purchasing Decisions. A Descriptive Research Design Was Adopted, And Primary Data Were Collected From 100 Respondents Through A Structured Questionnaire Using The Convenience Sampling Method. The Collected Data Were Analyzed Using Percentage Analysis, Mean Score Analysis, Comparative Analysis, And The Chi-Square Test. The Findings Reveal That Fiberglass Products Are Highly Preferred For Their Durability, Strength, Performance, And Long-term Value, Whereas Plastic Products Are Preferred For Their Affordability And Ease Of Use. The Study Also Indicates That Environmental Concerns And Product Quality Significantly Influence Consumer Buying Behavior. Furthermore, The Results Show That Customers Have A Positive Perception Of Fiberglass Products And Recognize Them As A Reliable Alternative To Plastic Products. The Study Concludes That Effective Marketing Strategies, Increased Consumer Awareness, And Continuous Product Improvement Can Help Strengthen Its Competitive Position And Achieve Sustainable Growth In The Market

Author: PAVITHRA MK

Area of research: HUMAN RESOURCE

Performance Appraisal Is A Systematic Process Used By Organizations To Evaluate Employee Performance And Determine Their Contribution Towards Achieving Organizational Objectives. It Serves As A Valuable Tool For Improving Employee Productivity, Identifying Training Needs, Motivating Employees, And Supporting Managerial Decisions Regarding Promotions, Transfers, Rewards, And Career Development. In Today's Competitive Business Environment, An Effective Performance Appraisal System Is Essential For Maintaining Employee Satisfaction And Enhancing Organizational Performance. The Present Study Focuses On Evaluating The Employee Performance Appraisal System At Vell Biscuits Private Limited. The Study Aims To Analyze Employee Perceptions Regarding The Appraisal Process, Examine Its Effectiveness In Motivating Employees, And Assess Its Impact On Organizational Growth. The Research Is Descriptive In Nature And Is Based On Both Primary And Secondary Data. Primary Data Were Collected From 120 Employees Through A Structured Questionnaire. Statistical Tools Such As Percentage Analysis, Chi-Square Test, And Correlation Analysis Were Used To Analyze The Collected Data. The Findings Indicate That Employees Generally Have A Positive Opinion Regarding The Performance Appraisal System. Most Employees Believe That The Appraisal System Helps In Identifying Strengths And Weaknesses, Supports Promotion Decisions, Improves Motivation, And Contributes To Personal Growth. The Study Concludes That The Performance Appraisal System Adopted By Vell Biscuits Private Limited Plays A Significant Role In Enhancing Employee Performance And Organizational Effectiveness.

Author: VIJAYASANTHI V | DHARANI.A

Area of research: FINANCE MANAGEMENT

Finance And Accounting Practices Play A Vital Role In Ensuring Transparency, Accountability, And Effective Utilization Of Financial Resources Within Organizations. Among Various Financial Activities, Stipend Management Is A Critical Function In Institutions That Provide Training, Internships, Or Apprenticeship Programs. This Study Examines The Effectiveness Of Finance And Accounting Practices In Stipend Administration, Focusing On Areas Such As Calculation Accuracy, Payment Timeliness, Internal Controls, Budgeting, Financial Reporting, And Reconciliation Procedures. A Descriptive Research Design Was Adopted, And Primary Data Were Collected Through A Structured Questionnaire Administered To 102 Respondents Involved In Finance And Administrative Activities. Secondary Data Were Obtained From Financial Records, Reports, Journals, And Related Documents. Statistical Tools Including Chi-Square, Correlation, Regression, One-Way ANOVA, And Mann–Whitney U Test Were Used For Analysis. The Findings Reveal That Although Finance And Accounting Practices Provide A Structured Framework For Stipend Administration, Respondents Exhibit Mixed Perceptions Regarding Their Effectiveness. Correlation Results Indicate A Significant Positive Relationship Between Accounting Practices And Stipend Management Effectiveness, While Regression Analysis Demonstrates Limited Predictive Influence. The Study Concludes That Financial Systems Alone Are Insufficient To Ensure Efficient Stipend Administration And Recommends Greater Automation, Stronger Internal Controls, Enhanced Coordination, And Improved Reporting Mechanisms. Future Research May Explore Technological And Organizational Factors Affecting Stipend Management Effectiveness.

Author: GOKUL B | INDUMATHI N

Area of research: Marketing

This Paper Evaluates The Competitive Dynamics Of The Indian Biscuit Industry, Comparing The Market Leader, Britannia Industries, With Its Major Challenger, ITC Limited’s Sunfeast Brand. The Empirical Investigation Is Anchored In Vell Biscuits Private Limited (Thirubuvanai, Puducherry), A Key Contract Manufacturing Unit That Dedicates Its Full Production Capacity To ITC's Biscuit Lines. Utilizing A Descriptive Research Design, Primary Quantitative Data Was Gathered From 150/156 Respondents Across The Local Consumer Ecosystem Using Structured Questionnaires. The Empirical Framework Subjects Consumer Evaluations Across 21 Product, Marketing, And Logistical Parameters To Percentage Analysis, Chi-Square Test Of Independence, And Weighted Average Score Analysis Aggregated Into Four Strategic Pillars. The Statistical Results Reveal A Highly Significant Association (\chi^2 = 83.537, Df = 16, P < 0.001) Between Consumer Satisfaction Ratings, Demonstrating That Brand Perceptions Are Dynamically Benchmarked Against Each Other Rather Than Evaluated In Isolation. The Weighted Average Score Matrix Confirms That Britannia Maintains A Consistent Competitive Advantage Across All 21 Parameters, Achieving Its Most Substantial Margins In Distribution Channel Availability, Supply Chain Regularity, And Pricing Satisfaction. Conversely, Sunfeast Demonstrates Notable Strength And Market-share Expansion Through High-end Product Innovation, Attractive Packaging, And Premium Flavor Variants. Strategic Recommendations Are Provided For Britannia To Accelerate Its Premium Innovation Cycle, For Sunfeast To Consolidate Mass-market Quality Consistency, And For Vell Biscuits To Align Manufacturing Logistics With Regional Demand Variations.

Author: PUNITHA P | PRADHAP B | Dr. N. Indumath

Area of research: Human Resources

Employee Satisfaction And Retention Are Critical Components Of Organizational Success In Today’s Competitive Business Environment. Satisfied Employees Are More Productive, Committed, And Likely To Remain With An Organization, Thereby Reducing Turnover Costs And Enhancing Overall Performance. This Study Explores The Key Factors Influencing Employee Satisfaction, Including Work Environment, Compensation, Recognition, CareerDevelopment Opportunities, And Leadership Support. It Also Examines Various Retention Strategies Employed By Organizations To Maintain A Stable And Motivated Workforce. Through A Review Of Relevant Literature And Practical Case Analysis, The Study Highlights The Strong Correlation Between Employee Satisfaction And Retention Rates. The Findings Emphasize The Need For Organizations To Adopt A Proactive Approach By Fostering A Positive Organizational Culture, Offering Competitive Benefits, And Ensuring Continuous Employee Engagement. The Study Concludes That An Integrated Strategy Focusing On Employee Needs And Expectations Is Essential For Long-term Retention And Organizational Growth.

Author: V.Madhumitha | Aranganathan. K.J

Area of research: Computer Applications

Widespread Smartphone Adoption And Ubiquitous High-speed Connectivity Have Collectively Reshaped How Consumers Discover And Purchase Goods, Driving Sustained Migration Toward Digital Retail Channels Across All Product Categories. The Grocery Segment, Long Anchored To In-person Shopping, Is Now Experiencing Accelerating Structural Change As Customers Increasingly Prioritise Doorstep Delivery, Broad Product Assortment, And Faster Fulfilment Over The Traditional In-store Experience. This Paper Presents Grozo, A Production-ready, AI-powered Online Grocery Delivery Platform Built On The MERN (MongoDB, Express.js, React, Node.js) Technology Stack. Grozo Integrates A Suite Of Intelligent Subsystems — Including A Multi-signal Product Recommendation Engine, A Real-time Dynamic Pricing Model, An Inventory Forecasting Module, A Haversine-based Route Optimisation Algorithm, And A Gemini-powered Conversational AI Chatbot — To Deliver A Seamless And Personalised Shopping Experience. The System Supports Over 8,000 Product Listings Sourced From BigBasket, Multi-modal Payment Processing Via Razorpay, Live Order Tracking Through Leaflet.js/OpenStreetMap, A Four-tier Loyalty Rewards Programme, And A Voice-command Interface Built On The Web Speech API. Experimental Evaluation Demonstrates That Grozo Achieves Sub-second API Response Times, A Recommendation Click-through Rate Improvement Of 34% Over Static Listings, And A Dynamic Pricing Engine Accuracy Within 8% Of Market Fluctuations. The Architecture Described Herein Offers A Scalable Blueprint For Next-generation AI-driven Grocery Retail Platforms.

Author: M. Sadhish | V. Udhayakumar

Area of research: Management

The Hybrid Work Model Has Become One Of The Most Significant Workplace Transformations In Recent Years. This Study Examines The Impact Of The Hybrid Work Model On Employee Productivity And Well-being At Fives India Engineering And Projects Private Limited. The Findings Reveal That Hybrid Work Positively Influences Productivity, Flexibility, And Employee Satisfaction While Also Presenting Communication And Coordination Challenges.

Author: Saya Devi | K.J.Aranganathan | Saya Devi

Area of research: MANAGEMENT STUDIES

The Efficient Management Of Inventory Plays A Vital Role In Improving Operational Performance And Maintaining Smooth Warehouse Activities In Manufacturing And Industrial Organizations. Inventory Management Techniques Such As FIFO (First In First Out) And FEFO (First Expired First Out) Help Organizations Reduce Wastage, Improve Stock Control, And Ensure Timely Availability Of Materials. This Study Focuses On Analysing The Effectiveness Of Inventory Management Practices With Special Reference To Hindustan Unilever Limited. The Research Aims To Examine The Inventory Methods Adopted By The Organization, Evaluate The Efficiency Of Warehouse Operations, And Understand Employee Perception Regarding Inventory Control Systems. Primary Data For The Study Were Collected Through Structured Questionnaires From Employees Working In Warehouse And Inventory-related Departments. Secondary Data Were Collected From Company Reports, Websites, Journals, And Other Relevant Sources. Statistical Tools Such As Percentage Analysis, Descriptive Statistics, And Chi-square Analysis Were Used To Interpret The Collected Data. The Findings Of The Study Indicate That Effective Inventory Management Practices Help Improve Stock Accuracy, Reduce Operational Delays, And Increase Overall Warehouse Efficiency. The Study Also Highlights The Importance Of Adopting Systematic Inventory Control Techniques For Better Organizational Performance And Customer Satisfaction.

Author: ABISHEK I

Area of research: Villupuram

Author: JANARTHANAN.T | SUGANTHI

Area of research: Civil Engineering

Concrete Is The Most Widely Used Construction Material; However, Its Low Tensile Strength, Brittleness, And Susceptibility To Cracking Limit Its Performance In Structural Applications. The Incorporation Of Natural And Cellulose-based Fibers Offers A Sustainable And Eco-friendly Solution To Improve The Mechanical And Durability Properties Of Concrete. This Study Investigates The Influence Of Various Natural Fibers (cotton, Jute, And Sisal) And Cellulose-derived Fibers (viscose, Tencel, And Cellulose Acetate) On The Performance Of M25 Grade Concrete. Concrete Mixes Were Prepared With Fiber Contents Ranging From 0.1% To 2.0% By Weight Of Cement. The Specimens Were Tested For Compressive Strength, Flexural Strength, Water Absorption, And Ultrasonic Pulse Velocity (UPV) At Different Curing Ages. The Experimental Results Indicated That The Incorporation Of Fibers Significantly Enhanced The Mechanical Performance Of Concrete Up To An Optimum Dosage Of Approximately 1.25%. Beyond This Level, Strength Gradually Decreased Due To Reduced Workability, Fiber Agglomeration, And Increased Porosity. Among All The Fibers Investigated, Tencel Fiber Exhibited The Best Overall Performance, Achieving A Maximum 28-day Compressive Strength Of 37.2 MPa And Flexural Strength Of 7.2 MPa At 1.25% Fiber Content. Sisal And Cellulose Acetate Fibers Also Demonstrated Excellent Performance, With Compressive Strengths Of 36.3 MPa And 36.0 MPa, Respectively. The Addition Of Fibers Improved Crack Resistance, Toughness, And Post-cracking Behavior Of Concrete By Bridging Microcracks And Delaying Crack Propagation. Water Absorption Increased With Increasing Fiber Content Due To The Hydrophilic Nature Of The Fibers; However, The Increase Remained Within Acceptable Limits At Optimum Dosages. UPV Results Confirmed Good Internal Quality And Homogeneity Of The Fiber-reinforced Concrete, With Maximum Values Of Approximately 4.7 Km/s Observed For Tencel, Sisal, And Cellulose Acetate Fiber Mixes. The Study Concludes That Natural And Cellulose-based Fibers Can Be Effectively Utilized As Sustainable Reinforcing Materials In Concrete. Their Incorporation Enhances Mechanical Strength, Crack Resistance, And Durability While Promoting Environmentally Friendly Construction Practices Through The Use Of Renewable And Biodegradable Resources. Among The Fibers Studied, Tencel Fiber Was Found To Be The Most Effective In Improving The Overall Performance Of Concrete.

Author: Mohammad Hamza | Rakesh Sakale | Hirendra Pratap Singh

Area of research: Human Resources

Employee Turnover And Absenteeism Are Among The Most Consequential Challenges Confronting Manufacturing Organisations. High Turnover Elevates Recruitment And Training Expenditures, Depletes Institutional Knowledge, And Disrupts Operational Continuity. Absenteeism Simultaneously Depresses Productivity And Burdens Present Employees. This Study Investigates The Causes And Effects Of Both Phenomena Within A Precision Fastener Manufacturing Plant. Primary Data Were Gathered Through A Structured Questionnaire Administered To 146 Employees From Operator, Quality, Broaching, Oil Cleaning, And Supervisor Departments Using Simple Random Sampling. A Descriptive Research Design Was Employed. Statistical Tools Comprising Percentage Analysis, Chi-square Test, And Pearson's Correlation Were Applied To The Collected Data. Findings Indicate That Poor Working Conditions, Low Salary, And Lack Of Career Growth Are The Predominant Drivers Of Voluntary Turnover, While Heavy Workload And An Unsupportive Work Environment Are The Leading Causes Of Absenteeism. Job Satisfaction Is Critically Low, With Dissatisfaction The Most Frequently Reported Level. Both Hypothesis Tests Accepted The Null Hypotheses, Confirming The Absence Of Statistically Significant Associations At The 5% Level Between The Specific Variable Pairs Examined. The Study Concludes With Practical, Data-driven Recommendations Encompassing Competitive Compensation, Improved Working Conditions, Structured Career Pathways, Employee Recognition, Supervisor Development, And Transparent Absenteeism Management.

Author: Gurunathan M | Pradhap B

Area of research: MANAGEMENT

Employee Relationship Management (ERM) Has Emerged As One Of The Most Vital Aspects Of Human Resource Management In Contemporary Organisations. Effective Management Of Employer-employee Relationships Contributes Significantly To Organisational Productivity, Employee Retention, And Workplace Harmony. This Study Examines The Nature And Effectiveness Of ERM Practices At Quess Corp Ltd., One Of India's Leading Business Services Companies. The Research Evaluates Communication Quality, Employee Engagement, Grievance Handling, Recognition, Training Opportunities, And Career Development Support. Primary Data Were Collected Through A Structured Questionnaire Administered To 110 Employees Across Various Designations. A Descriptive Research Design With Purposive Sampling Was Adopted. Statistical Tools Including Percentage Analysis, Chi-Square Test, And Pearson Correlation Were Applied. The Findings Reveal That A Majority Of Employees Perceive The Organisation Positively In Terms Of Management Support, Teamwork, And Job Satisfaction. However, Improvement Is Needed In Conflict Resolution, Work-life Balance, And Career Growth Support. The Study Concludes With Actionable Recommendations For Strengthening Employee Relationships Within The Organisation.

Author: HARIHARAN J | Suganthi A

Area of research: HUMAN RESOURECE

Workforce Capability Development Has Emerged As A Critical Lever For Competitive Advantage In Knowledge-intensive Service Industries. This Paper Investigates Structured Training And Development (T&D) Frameworks Within The US Staffing And Recruitment Sector, With Particular Emphasis On How Systematic Learning Interventions Affect Recruiter Performance, Process Compliance, And Client Satisfaction Outcomes. Using A Descriptive Research Design, Primary Data Were Gathered From Twelve Recruitment Professionals Through Structured Questionnaires And Direct Interaction, Supplemented By Secondary Literature. Quantitative Findings Are Interpreted Through Weighted Average, Correlation, And Simple Regression Analyses. Results Confirm That Practical And Technology-oriented Training Modalities Are Most Favoured, That All Participants Experienced Measurable Gains In Confidence And Process Comprehension Following Structured Programmes, And That A Statistically Meaningful Positive Relationship Exists Between Training Satisfaction And Reported Performance Improvement (r = 0.78). A Regression Coefficient Of 0.85 Further Validates The Predictive Strength Of Training Effectiveness On Performance. The Study Identifies Gaps In Programme Duration And Advanced-tool Coverage, And Concludes With Evidence-based Recommendations For Designing Future-ready T&D Curricula In Offshore US Recruitment Operations.

Author: Mr. Sugisivam S | PRADHAP B

Area of research: BUSINESS ADMINISTRATION

Financial Statement Analysis Is An Essential Tool For Evaluating The Financial Performance, Stability, And Operational Efficiency Of An Organization. The Present Study Focuses On The Financial Statement Analysis Of Integra Software Services Pvt. Ltd., Puducherry, Using Common Size Statements, Ratio Analysis, And Trend Analysis. The Study Aims To Assess The Company's Profitability, Solvency, Operational Efficiency, And Financial Position Over A Period Of Five Years. Secondary Data Were Collected From Audited Financial Statements, Annual Reports, And Company Records. Various Analytical Tools Such As Operating Profit Margin, Earnings Per Share, Trend Analysis, And Common Size Balance Sheet Analysis Were Used To Interpret The Financial Performance Of The Company. The Findings Reveal That The Company Recovered From Earlier Losses And Achieved Positive Reserves In Recent Years. However, Declining Profitability, Increasing Borrowings, And Rising Operational Liabilities Indicate The Need For Effective Financial Management. The Study Concludes That Proper Cost Control, Efficient Resource Utilization, And Strategic Financial Planning Are Necessary For Ensuring Sustainable Growth And Long-term Financial Stability. The Research Concludes That Organizations That Invest In Strengthening Their EVP Are More Likely To Develop A Strong Employer Brand And Maintain A Competitive Advantage In Attracting And Retaining Skilled Employees.

Author: PARTHASARATHI T | GANGALAKSHMI R

Area of research: BUSINESS ADMINISTRATION

The Dairy Cooperative Sector Plays A Significant Role In Supporting Rural Livelihoods And Ensuring The Supply Of Quality Milk And Milk Products To Consumers. Aavin Cooperative Milk Producers Union Ltd., Villupuram, Is One Of The Leading Dairy Cooperatives In Tamil Nadu. In Recent Years, The Organization Has Experienced Financial Losses Due To Increasing Milk Procurement Costs, Operational Expenses, Government-controlled Pricing Policies, And Limited Subsidy Support. The Present Study Aims To Identify The Major Factors Contributing To Financial Loss And Suggest Effective Strategies For Improving Profitability. The Study Adopted Descriptive And Analytical Research Designs. Primary Data Were Collected From 45 Employees Through A Structured Questionnaire, And Secondary Data Were Collected From Financial Statements, Annual Reports, And Company Records. Statistical Tools Such As Percentage Analysis, Comparative Analysis, Trend Analysis, And Ratio Analysis Were Used For Interpretation. The Findings Reveal That High Procurement Cost, Government Price Control, Lack Of Subsidy Support, Inadequate Expense Monitoring, And Insufficient Employee Training Significantly Affect Profitability. The Study Concludes That Effective Cost Control, Improved Inventory Management, Increased Focus On Value-added Products, And Operational Efficiency Can Improve The Financial Performance Of The Organization.

Author: MANIKANDAN S | GANGALAKSHMI R

Area of research: Human Resorces

Understanding The Psychological Determinants Of Workforce Performance Has Become Increasingly Critical In Today's Competitive Organisational Landscape. This Study Examines The Relationship Of Emotional Intelligence On Employee Productivity, Incorporating Job Satisfaction As A Mediating Framework. A Structured Questionnaire Was Administered To 160 Employees Across Seven Functional Departments. The Instrument Measured Six Dimensions Of Emotional Intelligence Self-awareness, Self-regulation, Self-motivation, Empathy, Social Skills, And Adaptability Along With Ten Indicators Of Job Satisfaction And Eleven Parameters Of Employee Productivity, Using A Five-point Likert Scale. Statistical Tools Employed Include Percentage Analysis, Chi-Square Test, And Correlation. Chi-Square Test Results Confirmed Significant Associations Between Emotional Intelligence And Employee Productivity (p = .012), Emotional Intelligence And Job Satisfaction (p = .006), And Job Satisfaction And Employee Productivity (p = .021). Correlation Revealed Moderate Positive Relationships: Emotional Intelligence And Employee Productivity (r = .412), Emotional Intelligence And Job Satisfaction (r = .387), And Job Satisfaction And Employee Productivity (r = .356). The Findings Confirm That Emotionally Competent Employees Tend To Be More Satisfied With Their Work And Consequently More Productive. Practical Recommendations Are Drawn For Organisations Seeking To Improve Workforce Performance Through Emotional Competency Development And Job Satisfaction Enhancement.

Author: KEERTHIVASAN A | Prof. N. INDUMATHI

Area of research: Computer Applications

The Rapid Advancement Of Artificial Intelligence Has Made It Possible To Generate Highly Convincing Manipulated Media, Commonly Referred To As Deepfakes. These Synthetic Videos And Audio Recordings Pose A Serious Threat To Information Integrity, Personal Reputation, And Public Trust. TruthLens Is An AI-powered Deepfake Detection System Developed To Help Users Verify The Authenticity Of Uploaded Video And Audio Files. The System Is Built Using A MERN Stack Web Application Integrated With A Python Flask Backend Responsible For Running Deep Learning Inference. For Video Analysis, The System Employs MTCNN For Face Detection And A Transformer-based Deepfake Classification Model To Analyse Sampled Frames. Audio Analysis Uses Spectral Feature Extraction Techniques To Identify Patterns Associated With Synthetic Speech. Experimental Results Show That The System Accurately Classifies Media As Real Or Fake With A Meaningful Confidence Score. This Work Demonstrates How Machine Learning And Neural Network Techniques Can Be Deployed In Full-stack Applications To Address The Growing Challenge Of Digital Misinformation.

Author: Dr.T. Amalraj Victoire | Aakila Nifaha A H

Area of research: Computer Applications

Small And Medium Retail Businesses Frequently Face Operational Difficulties Related To Inventory Management, Stock Monitoring, Demand Estimation, And Financial Reporting. Most Shop Owners Rely On Manual Records Or Entry-level Billing Software That Can Generate Invoices But Offer No Analytical Insight Into Business Performance. BI-Mart Is Proposed As A Web-based Business Intelligence Platform Developed To Address These Challenges. The System Is Built Using React.js For The Frontend, PHP REST APIs For Backend Services, And MySQL For Data Storage. It Incorporates Over Twenty Functional Modules Including A Performance Dashboard, Demand Forecasting, Risk Alert Generation, Expiry Tracking, GST Report Automation, And Customer Segmentation. The Demand Forecasting Module Processes The Past Ninety Days Of Sales Records And Applies A Day-of-week Multiplier To Estimate Product Demand For The Coming Thirteen Weeks. A Risk Scoring Mechanism Evaluates Stock Levels, Expiry Timelines, And Profit Margins To Classify Products Into Risk Categories. Results Confirm That BI-Mart Provides Small Retailers With Clear And Actionable Business Insights To Support Efficient And Informed Decision-making.

Author: Dr.T. Amalraj Victoire | Srinivasan S

Area of research: Finance

The Project Report Was Related To “A Study On Asset Liability Management With Special Reference To Chengalrayan Co-Operative Sugar Mills Ltd.” The First Chapter Explains The Concepts Of Asset Liability Management, Industry Profile, Company Profile, Company Products, Objectives, Scope, Need, And Limitations Of The Study. The Second Chapter Presents The Review Of Literature Related To Asset Liability Management And Financial Performance. The Third Chapter Covers Research Methodology, Data Collection, Research Design, And Financial Tools Used For The Study. The Fourth Chapter Deals With Data Analysis And Interpretation Using Ratio Analysis, Comparative Balance Sheet, And Common Size Balance Sheet To Evaluate The Financial Position Of The Company. The Findings Revealed The Company's Liquidity Position, Working Capital Efficiency, Asset Utilization, And Debt Management Practices. The Findings, Suggestions, And Conclusion Of The Study Are Presented In The Final Chapter.

Author: Kavitha A | N.indumathin

Area of research: Computer Engineering

The Growing Interest In Healthy Eating And Nutritional Tracking Has Led To A Need For Smart Systems That Can Automatically Identify Food Items And Estimate Their Calorie Content. Traditional Calorie Tracking Apps Rely Heavily On Manual User Input, Which Can Be Inaccurate And Time-consuming. To Address These Issues, Food-vision Is Introduced As An AI-based Food Detection And Calorie Estimation Platform That Uses Advanced Deep Learning Techniques. The System Uses The YOLOv8 Object Detection Model To Identify Multiple Indian Food Items From Uploaded Images And Estimate Calorie Values From A Structured Nutritional Database. Unlike Standard Image Classification Systems That Can Only Recognize One Food Item At A Time, This Solution Detects Multiple Food Categories Simultaneously, Including Biryani, Dosa, Idli, Rice, Roti, Dal, Chloe, , Jalebi, Shahi Paneer, Palak Paneer, Pooh, And Samosa. The System Runs Through A Flask-based Web Application That Lets Users Upload Food Images And Get Instant Detection Results Along With Calorie Information. A Custom Annotated Dataset Of Thousands Of Food Images Was Created For Training And Validation. The YOLOv8 Architecture Delivers High Detection Accuracy While Ensuring Efficient Processing Speed. Experimental Results Show That This Approach Effectively Combines Computer Vision, Deep Learning, And Nutritional Analysis To Support Users In Dietary Monitoring And Health Management.

Author: Sanchita Gadkari | Vaishnavi Khedkar | Sayali Ahire | Vanita Thite | Prof.Vikram Chavan

Area of research: MBA

Human Resource (HR) Practices Play A Decisive Role In Shaping Employee Performance And Organisational Success. This Study Examines The Influence Of HR Practices – Encompassing Recruitment And Selection, Training And Development, Performance Appraisal, Compensation, Motivation, And Work Environment – On The Performance Of Employees At Network Diesel Sales And Service Company Pvt. Ltd. (NDSS), Puducherry. A Descriptive Research Design Was Adopted, And Data Were Collected From 100 Respondents Through A Structured Questionnaire. Statistical Tools Including Percentage Analysis, Chi-square Test, Pearson Correlation, And The Weighted Average Method Were Employed For Analysis. The Results Reveal A Significant Positive Correlation (r = 0.633, P = 0.043) Between HR Practices And Employee Performance. The Weighted Average Score Of 3.98 Indicates An Overall Positive Employee Perception Of HR Practices. Findings Highlight That Training, Work Environment, And Recruitment Fairness Receive The Highest Positive Ratings, While Performance Appraisal Transparency And Career Development Opportunities Require Improvement. The Study Concludes That Effective HR Practices Are Essential For Improving Employee Motivation, Productivity, And Overall Organisational Performance.

Author: Padmavathi R | Suganthi A

Area of research: Marketing Management

This Study Aims To Analyze The Level Of Internal Customer Satisfaction Among L&T Construction Employees Regarding The Services Provided By The Formwork Shop. Data Were Collected From 120 Respondents And Analyzed Using Percentage Analysis, Chi‑Square Test, And Correlation Analysis.

Author: Sivaranjani

Area of research: Marketing

Author: K SENTHAMARAI | Suganthi A

Area of research: Computer Science And Engineering

The Online Event Management System Is A Web-based Application Developed To Automate And Simplify The Process Of Managing Events In Educational Institutions. Traditional Event Management Methods Using Notices, Spreadsheets, And Manual Communication Often Lead To Scheduling Conflicts, Poor Coordination, And Inefficient Record Maintenance. The Proposed System Provides A Centralized Platform Where Admins, Staff, Approvers, And Students Can Manage Event-related Activities Efficiently. The System Supports Functionalities Such As Event Creation, Approval Workflow, Participant Registration, QR Code-based Attendance Tracking, Feedback Collection, And Automatic Certificate Generation. The Application Is Developed Using Flutter For Frontend Development And REST APIs With MySQL Database Integration For Backend Processing And Secure Data Management. The System Improves Communication, Reduces Manual Workload, Enhances Data Accuracy, And Provides A Scalable Solution For Modern Event Management In Educational Institutions.

Author: Muthu Raja

Area of research: MBA

Recruitment Effectiveness Plays A Vital Role In Improving Employee Performance And Organizational Productivity In Modern Business Organizations. Effective Recruitment And Selection Practices Help Organizations Attract Qualified And Skilled Employees Who Contribute To Operational Efficiency, Quality Improvement, And Achievement Of Organizational Goals. Recruitment Practices Such As Proper Job Analysis, Employee Selection, Interviews, And Training Support Organizations In Maintaining A Productive Workforce And Improving Overall Organizational Performance. This Study Focuses On Analyzing The Effectiveness Of Recruitment Practices. The Research Aims To Examine The Recruitment And Selection Practices Adopted By The Organization, Evaluate Their Impact On Employee Performance, And Understand Employee Perception Regarding Productivity And Work Efficiency. Primary Data For The Study Were Collected Through Structured Questionnaires From Employees Working In Different Departments Of The Organization. Secondary Data Were Collected From Company Reports, Websites, Journals, And Other Relevant Sources. Statistical Tools Such As Percentage Analysis, Descriptive Statistics, Correlation, And Chi-square Analysis Were Used To Interpret The Collected Data. The Findings Of The Study Indicate That Effective Recruitment Practices Improve Employee Performance, Reduce Operational Errors, Increase Productivity, And Enhance Organizational Efficiency. The Study Also Highlights The Importance Of Training, Teamwork, Employee Motivation, And Performance Evaluation Systems In Achieving Better Organizational Performance And Employee Satisfaction.

Author: VENKATESH M | K.J.Aranganathan

Area of research: MBA

The Gold Loan Segment Has Emerged As One Of The Most Significant And Rapidly Growing Components Of The Non-Banking Financial Company (NBFC) Sector In India. This Study Examines The Priority Given To Gold Loans Within The Overall Loan Portfolio Of Manappuram Finance Ltd., One Of India's Leading Gold Loan NBFCs Headquartered In Kerala. The Research Aims To Evaluate Customer Preferences, Satisfaction Levels, Processing Efficiency, Interest Rate Perceptions, And The Overall Contribution Of Gold Loans To Manappuram's Lending Portfolio In Comparison With Other Loan Products. Primary Data Were Collected Through A Structured Questionnaire From 117 Respondents Comprising Customers And Employees Associated With Gold Loan Services At Manappuram Finance Ltd. Statistical Tools Such As Percentage Analysis, Chi-square Test, And Pearson Correlation Were Applied To Analyse The Data. The Study Finds That The Majority Of Customers Prefer Gold Loans Due To Quick Processing, Minimal Documentation, And Easy Availability. The Findings Indicate That Gold Loans Hold A Dominant And Strategic Position In Manappuram's Loan Portfolio, And Customer Satisfaction With Gold Loan Services Is Considerably High. Recommendations Are Provided For Further Strengthening The Gold Loan Business And Expanding The Customer Base

Author: Mr. D. MANICKARAJ | Mr. K. J. ARANGANATHAN

Area of research: IT

This Study Examines The Recruitment And Selection Process In The Banking Sector With Special Reference To Quess Corp Limited, A Leading Workforce Management Company In India. The Study Analyses The Effectiveness Of Existing Recruitment Practices, Identifies Key Strengths And Challenges In The Hiring Process, And Evaluates Their Impact On Organizational Performance. Using A Structured Questionnaire Administered To 108 Respondents Across Departments — Banking, Payroll, Recruitment, And Staffing — The Study Employs Percentage Analysis, Chi-Square Test, And Pearson's Correlation Coefficient As Statistical Tools. Findings Indicate That Recruitment Efficiency (74%), Communication Effectiveness (77%), And Timeliness (70%) Are Strong Areas, While Talent Attraction (53%) And Selection Accuracy (59%) Require Improvement. The Study Recommends Adopting AI-powered Applicant Tracking Systems, Structured Competency-based Interviews, And Diversified Sourcing Channels To Enhance Recruitment Effectiveness.

Author: YUDESH T | SUGANTHI A

Area of research: Human Resource

In Today's Competitive Business Environment, Organizations Are Increasingly Focusing On Creating A Positive Work Experience To Attract And Retain Talented Employees. Employee Value Proposition (EVP) Represents The Overall Value And Benefits That Employee Receive In Exchange For Their Contributions To An Organization. It Includes Elements Such As Compensation, Career Development Opportunities, Recognition, Work-life Balance, And Organizational Culture. A Strong EVP Not Only Enhances Employee Satisfaction But Also Contributes Significantly To Building A Positive Employer Image. The Present Study Examines The Impact Of Employee Value Proposition On Employer Branding. The Research Was Conducted Using A Descriptive Research Design And Data Were Collected From Employees Through A Structured Questionnaire. Various Statistical Tools Such As Percentage Analysis, Weighted Average Analysis, Correlation, And Regression Analysis Were Used To Interpret The Collected Data. The Findings Indicate That Employees Place Considerable Importance On Career Growth, Recognition, Fair Compensation, And A Supportive Work Environment. The Study Further Reveals That An Effective EVP Positively Influences Employer Branding By Improving Employee Engagement, Commitment, And Retention. The Research Concludes That Organizations That Invest In Strengthening Their EVP Are More Likely To Develop A Strong Employer Brand And Maintain A Competitive Advantage In Attracting And Retaining Skilled Employees.

Author: Ms. B. Meena | Mrs. R. Gangalakshmi

Area of research: Human Resources

Recruitment, Onboarding, And Performance Management Are Three Interconnected And Critical Functions Of Human Resource Management (HRM) That Collectively Determine The Quality Of An Organization's Workforce And Its Overall Effectiveness. In The Rapidly Evolving Information Technology (IT) Sector, Where Talent Acquisition And Retention Are Central To Sustaining Competitive Advantage, These HR Functions Assume Greater Strategic Importance. This Study Examines The Effectiveness Of Recruitment, Onboarding, And Performance Management Practices In An IT Organization And Evaluates Employee Perceptions Regarding Each Of These HR Functions. A Descriptive Research Design Was Adopted. Primary Data Were Collected From 108 Respondents Through A Structured Questionnaire. Statistical Tools Including Percentage Analysis, Chi-Square Test, And Pearson Correlation Analysis Were Used. The Findings Indicate That The Organization Has Established Strong Foundations In All Three HR Functional Areas. The Study Concludes With Recommendations To Strengthen Transparency, Structural Consistency, And Continuous Feedback Mechanisms.

Author: LOKESH K | Pradhap.b

Area of research: Computer Engineering

The Proliferation Of E-commerce Has Fundamentally Altered Consumer Purchasing Behaviour, Yet A Persistent Challenge Remains: The Inability To Physically Try On Garments Before Purchase. This Paper Presents A Virtual Clothing Try-On (VCTO) System That Bridges This Gap By Combining A Custom Conditional Generative Adversarial Network (cGAN) With A Geometric Matching Module (GMM) Embedded In A Full-stack MERN (MongoDB, Express.js, React.js, Node.js) Web Application. The Proposed System Accepts A Reference Person Image And A Desired Garment Image As Inputs And Synthesises A Photorealistic Composite Image By (i) Estimating Human Body Pose Using A Lightweight Keypoint Detector, (ii) Warping The Garment Via Thin-Plate Spline (TPS) Transformation, And (iii) Generating The Final Try-on Image Through An Adversarial Training Scheme. Experimental Evaluation On The VITON-HD Benchmark Dataset Yields A Structural Similarity Index (SSIM) Of 0.873, Fréchet Inception Distance (FID) Of 8.34, And Learned Perceptual Image Patch Similarity (LPIPS) Of 0.072, Outperforming Several Baseline GAN-based Methods. The System Achieves An Average Inference Latency Of 320 Ms On A Single NVIDIA RTX 3060 GPU, Making It Suitable For Near-real-time Web Deployment.

Author: Saurabh Brahmankar | Sagar Arjun Kharat | Vishal Raghunath Dipake | Aditya Arvind Kawade | Prof. S. B. Nimbekar

Area of research: Computer Engineering

The Increasing Prevalence Of Cardiovascular And Respiratory Conditions Demands Continuous, Reliable, And Intel-ligent Health Monitoring Systems Capable Of Operating Beyond Clinical Settings. Existing Threshold-based Monitoring Systems Suffer From A High Rate Of False Alarms Triggered By Single Anoma-lous Readings, Significantly Undermining Trust And Operational Reliability. This Paper Presents CardioGuard, A Real-time Health Monitoring And Emergency Alert System That Continuously Tracks Vital Physiological Parameters — Heart Rate (HR) And Blood Oxygen Saturation (SpO2) — And Applies A Persistence-based Anomaly Detection Mechanism To Validate Abnormal Conditions Across Multiple Consecutive Readings Before Generating Alerts. The System Leverages WebSocket Communication (Socket.IO) For Low-latency, Bidirectional Data Transmission Between A React.js Frontend Simulator And A Node.js Backend. Upon Confirming A Critical Condition, The System Dynamically Classifies Alert Severity, Identifies The Geographically Nearest Hospital Using The Haversine Formula, And Asynchronously Dispatches Notifications Via Email And Push Notification Services. A Selective Data Storage Strategy Persists Only Significant Alert Events To A MySQL Database, Ensuring Efficient Storage And Scalability. Experimental Evalu-ation Demonstrates Accurate Alert Generation With A Significantly Reduced False Positive Rate, Real-time Processing With Minimal End-to-end Latency, And Reliable Emergency Response, Making CardioGuard A Practical And Scalable Solution For Remote Patient Monitoring.

Author: Suraj Ghogare | Prof. Shreyas S. Shinde | Dr. Shubhangi R. Patil | Ibrahim Kapadwanchwala | Abhishek Kolekar | Sashwat Tare

Area of research: Department Of Psychology

The Purpose Of The Present Study Was To Compare The Mental Health Of Government And Non-government Secondary Teachers In The Chhatrapati Sambhaji Nagar District. In The Present Study The Data Was Collected From Government And Non-government School Teacher. For The Present Investigation, The Revised Employees Mental Health Inventory, Consisting Of 24 Items, Developed By Dr. Jaddish, Was Used As The Main Tool For Data Collection. The Sample Included 60 Secondary School Teachers, Of Whom 30 Were Government Teachers And 30 Were Non-government Teachers. Respondents Were Selected Using A Random Sampling Method. The Descriptive Survey Method Was Used In This Study. Statistical Techniques Such As Mean, Standard Deviation, And T-test Were Applied To Analyze The Collected Data. The Results Of The Present Study Showed A Significant Difference In Mental Health Between Government And Non-government Secondary School Teachers. Government Teachers Were Found To Have Better Mental Health Scores Than Non-government Teachers. The Study Concluded That Factors Other Than Non-government Teachers. The Study Concludes That Factors Such As Job Security, Supportive Work Environment, And Institutional Stability Strongly Influence Teachers´ Mental Health.

Author: Mailare Sachinkumar Sopanrao

Area of research: Information Technology Engineering

Agricultural Product Quality Assessment Plays A Critical Role In The Global Food Supply Chain, Directly Impacting Market Pricing, Consumer Safety, And Post-harvest Optimization. Traditional Methods Rely Heavily On Human Vision And Manual Sorting, Which Are Inherently Subjective, Slow, Prone To Error, And Difficult To Scale. To Overcome These Deep-seated Inefficiencies, This Research Presents A Comprehensive, End-to-end Multi-modular Artificial Intelligence And Machine Learning (AIML) Quality Assurance Platform Leveraging The TensorFlow Deep Learning Engine. For Seed Grading, High-resolution Spatial Feature Vectors Are Extracted From Digital RGB Images And Processed Through A Custom Multi-layer Convolutional Neural Network (CNN) Alongside Parallelized Support Vector Machines (SVM) To Classify Essential Staple Grains, Including Maize And Corn Samples, Into Definitive, Industry-standard Quality Tiers (Grade A, B, And C). Concurrently, The Architectural Framework Implements An Interactive Smart Analytical Engine Using Pythonic Data Mining Structures To Process Regional Context Parameters—such As Soil PH, Micro-climate Averages, Kahrif/rabi Seasonality, And Localized Rain Statistics—enabling Precise, Site-specific Chemical And Organic Fertilizer Recommendations (e.g., Target Phosphorus Formulations). Benchmarked Against Standard Datasets, The Deep Feature Extraction Model Achieves Cross-validated Training Accuracies Exceeding 90%, Outperforming Archaic Manual Pipelines. The Complete Infrastructure Is Integrated Within An Optimized, Cross-platform Architecture Deployable Across High-availability Web Clients And Responsive Standalone Desktop Wrappers, Running Model Inferences Smoothly In Less Than 0.65 Seconds Without Absolute Remote Server Dependencies. This Technical Ecosystem Offers An Immediate, Production-ready, Grassroots Solution To Reinforce Precision Agriculture, Limit Harvest Wastage, And Augment Supply Chain Valuation Metrics.

Author: Aditya Kundlik Hande | Aditya Hande | OMKAR PALAVE | VISHWAJEET JAGTAP | VINIT CHAKANE

Area of research: Computer Applications

Digital Identity Verification Is A Critical Requirement In Modern E-governance And Passport Management Systems. Traditional Passport Verification Methods Suffer From Manual Delays, Identity Fraud, And Unauthorized Access Vulnerabilities. This Paper Presents A Secure, Multi-layer Digital Identity Verification System That Integrates Aadhaar QR Code Scanning, One Time Password (OTP) Authentication, And FaceNet-based Facial Recognition To Ensure Tamper-resistant Passport Verification. The System Is Built Using The Spring Boot Framework For Backend Processing, MySQL For Database Management, And Python For FaceNet-based Facial Comparison. The Proposed Approach Significantly Reduces Fake Identity Submissions, Minimizes Manual Intervention, And Improves Authentication Accuracy. Experimental Results Demonstrate That All Core Modules — Including QR Verification, OTP Validation, And Face Matching — Achieved 100% Pass Rates In Functional Testing. The System Represents A Scalable, Secure Framework Suitable For E-governance And National Identity Management Applications.

Author: TharunKumaar K

Area of research: Human Resources Management

Recruitment Plays A Vital Role In Ensuring That Organizations Acquire Competent Employees Capable Of Meeting Business Objectives. In The Information Technology (IT) Industry, Project-based Hiring Has Become Increasingly Important Due To The Dynamic Nature Of Client Requirements, Technological Advancements, And Project Deadlines. Organizations Face Several Challenges In Identifying, Attracting, And Selecting Candidates Who Possess The Required Technical Skills, Experience, Adaptability, And Project Readiness. This Study Focuses On Analyzing The Recruitment Challenges And Effectiveness Of IT Project-based Hiring At TVM Infotech Private Limited. The Objectives Of The Study Are To Identify The Major Challenges Faced During Recruitment, Evaluate The Effectiveness Of Hiring Practices, Examine Candidate Selection Methods, And Assess The Contribution Of Recruitment Strategies Toward Project Success. The Study Also Investigates Employee Perceptions Regarding Recruitment Processes And Their Impact On Workforce Quality. A Descriptive Research Design Was Adopted For The Study. Primary Data Were Collected From 100 Employees Through A Structured Questionnaire, While Secondary Data Were Gathered From Journals, Books, Company Records, Websites, And Previous Research Studies. Statistical Tools Such As Percentage Analysis, Chi-Square Test, And Correlation Analysis Were Used For Data Analysis And Interpretation. The Findings Reveal That Skill Shortages, Competition For Talent, Time Constraints, And Candidate Retention Are Major Recruitment Challenges In Project-based Hiring. However, Effective Recruitment Planning, Structured Selection Procedures, Technical Assessments, And Employee Referral Programs Significantly Improve Hiring Outcomes. The Study Concludes That An Efficient Recruitment Process Contributes To Project Success, Employee Performance, And Organizational Growth.

Author: Ms.E. Jemimah Cynthiya

Area of research: English

The Poetry Of Robert Browning Displays The Elegance Of Philosophical Ideas, Artistic Fictional Characters, And Excellent, Fit, And Amazing Creations And Creatures On The Flawless Techniques That Gave It A Lifelike Experience Of The Good And Terrible Persona. A Monk, A Priest, A Painter, A Doctor, A Duke, A Scholar, A Musician—all Of These Characters Are Found With The Belonging To The Form Of Suffering, Painful, And Bad Into The Bad Situations And Conditions With The Common Subjects Aesthetically In His Most Poems Where The Poet Leads One Thing To Another By His Own Mental Association—were Chosen By Browning From The Different Lands, From The Different Periods Of Time, And From The Different Spheres Of Activity. His Poetry Serve As Some Of The Best Illustrations Of How Best Practises In Literary Philosophy And Theory May Be Used To Create Fiction That Is True To Life. All Of The Aforementioned Arguments Serve To Demonstrate That This Essay Is A Thoroughly Researched Analysis Of The Philosophy Of Pain, Suffering, And Evil As They Relate To The Recurrent Themes In Robert Browning's Poetry.

Author: Dr. Shital Vipulkumar Chandak

Area of research: ECE

In The Ultra Large Scale Integration (ULSI) Field, With Widespread Growing Demand For Portable And Battery-operated Electronic Devices, Power Consumption Has Become A Major Concern. Common Arithmetic Circuits Are Adders And Subtractors Which Are Important Components Of Digital Systems And Form A Significant Part Of The Total Power Dissipation Of The Circuit. Traditional CMOS Realization Of These Circuits Consumes Excessively Large Number Of Transistors, Leading To High Power Consumption, Long Propagation Delay And Silicon Silicon Area. To Solve These Problems, In This Paper, Low-power NAND-based Arithmetic Circuits Have Been Developed By 45nm CMOS Technology And Combined With The Idea Of SAPON (Self-Adaptive Power Optimization Network). To Reduce The Complexity Of The Hardware, Half Adder, Full Adder And Full Subtractor Circuits Are Achieved By Using NAND-gate-based Architectures As A Universal Logic Gate. The Technique Called 'SAPON' (sequential Active Only Power Optimum) Is Used To Optimize Switching Activity And Thus Also The Power Consumption With Correct Logical Function. The Proposed Circuits Are Designed And Simulated In Cadence Virtuoso Tool, Keeping Technology 45nm CMOS, And Performance Analysis Is Performed Based On Three Metric Parameters Of The Circuits Namely, The Number Of MOS Transistors, Power Dissipation And Propagation Delay. Through Simulation, It Has Been Found That The Arithmetic Circuits Proposed To Use SAPON Achieves Significant Power Saving With Respect To Conventional CMOS Circuits While Maintaining The Same Delay. Hence, The Proposed Approach Will Be Suitable For Low Power VLSI Applications, Portable Electronics And The Low Power Digital System Applications.

Author: K.J.Poornima | Gogula Chandana

Area of research: Agricultural Engineering

Groundnut Shelling Is An Essential Post-harvest Operation That Converts Dried Pods Into Marketable Kernels, But Small Farmers Commonly Depend On Hand Shelling Or Costly Powered Machines. This Study Presents The Design, Fabrication, Structural Validation And Experimental Evaluation Of A Revised Pedal-operated Groundnut Shelling Machine Developed As A Low-cost Intermediate Technology For Rural And Small-scale Users. The Machine Consists Of A Hopper-fed Rotary Drum, Stationary Perforated Concave, Chain-and-sprocket Transmission, 25 Mm Main Shaft, Welded Mild-steel Frame, Pedal Crank, Fan-assisted Separation Unit And Collection Arrangement. Human Power Is Transmitted Through The Pedal Crank To The Shelling Shaft; The Drum-concave Pair Cracks Pods By Rubbing And Compression, While The Outlet And Fan Arrangement Separate Lighter Shells From Heavier Kernels. Design Calculations Were Performed For Pedal Torque, Power, Chain Drive Ratio, Shaft Stress And Drum Peripheral Speed. Structural Safety Was Verified Through Finite-element/static Simulation Of The Frame, Shaft, Shelling Drum, Pedal Crank, Bearing Bracket And Complete Assembly. The Maximum Equivalent Stress In The Full Assembly Was 93.6 MPa, Which Is Below The 250 MPa Yield Reference, And The Corresponding Factor Of Safety Was 2.67. Experimental Trials For 1-10 Kg Batches Showed Shelling Efficiency Between 89.2% And 91.3%, Cleaning Efficiency Between 84.5% And 86.2%, And Mechanical Damage Between 4.3% And 5.4%. The Results Confirm That The Pedal-operated Configuration Can Reduce Drudgery, Avoid Electricity Dependence And Deliver Acceptable Shelling Performance When Speed, Feed Rate And Concave Clearance Are Controlled.

Author: Nayan Sandeep Bora1 | Ajaykumar Ugale

Area of research: Computer Applications

The Expiry Product Management And Alert System Is Designed To Assist Retail Shop Owners In Effectively Managing Inventory With A Focus On Product Expiry Tracking And Prevention Of Expired Product Sales. In Many Small And Medium Retail Businesses, Manual Monitoring Of Expiry Dates Is Inefficient And Prone To Human Error, Which Can Lead To Financial Losses And Potential Health Risks For Consumers. This Project Provides A Digital Solution To Automate And Streamline The Entire Process. The System Allows A Shop Owner To Register And Log In To A Secure Platform, Where They Can Access A Centralized Dashboard For Managing Inventory, Purchases, And Sales. During The Purchase Process, The Shop Owner Records Detailed Information About Each Product, Including Its Expiry Date. Each Product Item Within A Purchase Invoice Is Assigned A Unique QR Code, Enabling Precise Identification And Tracking. This Ensures That Even When Multiple Items Are Purchased Under A Single Invoice, Each Unit Can Be Monitored Individually. At The Point Of Sale, Products Are Scanned Using Their QR Codes. The System Automatically Verifies The Expiry Status Of The Product Before Allowing The Transaction. If The Product Has Exceeded Its Expiry Date, The System Blocks The Sale And Notifies The User, Thereby Preventing The Distribution Of Expired Goods. Additionally, The Dashboard Continuously Monitors Inventory And Provides Alerts For Products That Are Nearing Their Expiry Dates, Allowing The Shop Owner To Take Timely Action Such As Promotions Or Stock Clearance. The System Also Generates Reports On Expired And Soon-to-expire Products, Helping In Better Decision-making And Inventory Control. By Integrating QR-based Tracking, Automated Expiry Validation, And Real-time Alerts, This Project Enhances Operational Efficiency, Reduces Losses, And Ensures Customer Safety. Overall, The Proposed System Offers A Reliable And Scalable Solution For Modern Retail Inventory Management.

Author: Dhanush H | Mahalakshimi G

Area of research: Computer Engineering

Preparing For Technical Employment Interviews Is A High-stakes Endeavor That Demands Both Domain Expertise And Practiced Verbal Communication. Conventional Preparation Strategies—textbook Study, Static Question Banks, And Peer Mock Sessions—suffer From Well-documented Limitations: They Are Non-personalised, Require Scheduling Coordination, And Provide No Systematic Feedback On Performance. This Paper Presents The AI Interview Assistant (AIIA), A Full-stack, Multi-modal Web Platform That Automates The Entire Interview Simulation Life-cycle. AIIA Integrates Three Distinct AI Services: (1) Google Gemini, A Large Language Model (LLM) Responsible For Context-aware Question Generation, Adaptive Conversational Follow-up, Code Evaluation, And Structured Feedback Synthesis; (2) Assem-blyAI Universal-2, A State-of-the-art Automatic Speech Recognition (ASR) Engine For Real-time Candidate Voice Transcription; And (3) Murf AI FALCON, A Neural Text-to-speech (TTS) Synthesiser That Voices The AI Interviewer Natalie. The System Supports Eight Technical Roles, Three Difficulty Tiers, And Three Code Chal-lenge Formats—write, Fix, And Explain—across Four Program-ming Languages. Interview Sessions Are Stored In A MongoDB Document Database, Enabling Longitudinal Progress Tracking. A Five-category, LLM-generated Feedback Report Is Delivered Upon Session Completion. Empirical Observations Demonstrate That The Five-prompt LLM Orchestration Architecture Produces Contextually Coherent Question Sets And Qualitatively Discrimi-native Performance Assessments. The AIIA System Establishes A Replicable Architectural Template For Deploying Conversational AI Agents In High-stakes Educational Assessment Contexts.

Author: Manohar Chaudhari | Atharv Kulkarni | Siddhedh Shelar | Sanika Dhanve | Sanmesh Satpute

Area of research: CSE

The Rapid Growth Of Android Applications Has Increased Concerns Regarding Excessive And Unjustified Permission Usage, Which Can Lead To Privacy Breaches, Data Leakage, And Unauthorized Access To Sensitive Resources. Existing Android Security Solutions Often Rely Solely On Static Or Dynamic Analysis, Which Limits Their Accuracy And Fails To Provide Comprehensive Insights Into Real-world Permission Misuse. To Address These Limitations, This Work Proposes PermissionShield, A Hybrid Static–dynamic And Forensic Analysis Framework Designed To Detect, Predict, And Visualize Suspicious Permission Behaviors In Android Applications. The System Integrates Multi-stage Analysis, Beginning With Static Extraction Of Declared Permissions, Followed By Dynamic Evaluation Of Runtime Behavior To Identify Inconsistencies Between Requested And Actual Usage. A Machine Learning–based Prediction Model Further Enhances Detection Accuracy By Classifying Potentially Malicious Permission Patterns Using Historical Datasets And Feature Encoding. The Framework Also Incorporates A Feature-extraction Engine To Quantify Risk Levels And Generates Detailed Forensic Reports Along With Severity Visualizations To Assist Developers, Analysts, And End Users In Understanding The Threat Landscape. Experimental Results Demonstrate That PermissionShield Effectively Identifies High-risk Permissions, Reduces False Positives, And Provides A Scalable And Interpretable Solution For Android Permission Misuse Detection. This Research Contributes Toward Strengthening Mobile Security, Improving Transparency In Permission Handling, And Enabling Proactive Protection Of User Privacy.

Author: Dr. Shubhangi Patil | Prof. Shreyas Shinde | Shreyas Kadam | Kishor Khandagle | Shivam Shinde , Jayraj Yadav

Area of research: Electronics And Telecommunication Engineering

As The Demands For Higher Clock Frequencies In Modern VLSI Systems Continue To Rise, Achieving Timing Closure Has Become A Significant Hurdle In Digital Design. Many RTL Designs Struggle To Meet Setup And Hold Time Requirements After Synthesis, Often Because Timing Constraints Weren't Adequately Considered In The Early Stages Of Design. This Paper Introduces The Design And Implementation Of A Straightforward RISC Processor Using Verilog HDL, Employing A Timing-aware RTL Methodology. The Architecture Of The Processor Features A Program Counter, Instruction Memory, Register File, Arithmetic Logic Unit (ALU), Control Unit, And Data Memory. We Conduct Static Timing Analysis (STA) Using Xilinx Vivado To Assess Critical Path Delay, Worst Negative Slack (WNS), And Maximum Operating Frequency. Initially, We Analyze A Baseline Design To Pinpoint Timing Violations, Followed By Restructuring The RTL And Applying Optimization Techniques Like Logic Balancing And Inserting Pipeline Registers. The Experimental Results Show A Notable Reduction In Critical Path Delay And An Increase In The Maximum Achievable Clock Frequency, Confirming The Benefits Of Integrating Timing Awareness Into The RTL Design Process. This Proposed Method Enhances Timing Reliability While Minimizing The Number Of Design Iterations Needed After Synthesis.

Author: Prathamesh Pakhale | Rahul Ingale

Area of research: Information Technology

Effective Disaster Management Requires Rapid, Coordinated Communication Among Citizens, Relief Organisations, And Administrative Authorities. Existing Systems Are Fragmented, Lack Real-time Geographic Awareness, And Fail To Provide Emergency Navigation Assistance To Affected Individuals. This Paper Presents DisasterAlert, A Progressive Web Application (PWA) Built On A Fully Serverless Amazon Web Services (AWS) Architecture, Designed To Address These Critical Deficiencies. The System Implements Role-based Access Control Via Amazon Cognito, Supporting Three User Roles: Citizen, NGO, And Administrator. A Real-time Disaster Map Powered By Leaflet.js And OpenStreetMap Visualises Geotagged Incident Reports Submitted By Citizens. An Emergency Navigation Module Integrates The OpenStreetMap Overpass API For Hospital Discovery And The Open Source Routing Machine (OSRM) For Turn-by-turn Navigation, Enabling Citizens In Disaster Zones To Locate And Route To The Nearest Medical Facility. The Backend Comprises AWS Lambda Functions, Amazon API Gateway, And Amazon DynamoDB, Deployed Via Amazon S3 And Amazon CloudFront. The System Was Fully Developed And Deployed Within A Seven-day Period. Evaluation Results Confirm All Functional Requirements Are Satisfied, With Lighthouse Audit Scores Exceeding 87/100 And Zero Operational Cost Under AWS Free Tier. DisasterAlert Demonstrates The Viability Of Rapid, Cost-effective, Cloud-native Disaster Management Solutions.

Author: Prof. Vandana Tonde | Sneha Shetfale | Abhijeet Pandit | Samiksha Jadhav | Ashwini Pahune

Area of research: Computer Engineering

Food Safety Is An Important Element In Public Health, Economic Stability, And Sustainable Development Since Foodborne Disease Continues To Affect Humans Around The World. Estimates Show That Almost 600 Million Individuals Contract Foodborne Diseases Every Year, Resulting In Substantial Death, Medical Expenses, And Reduced Efficiency. Rapid Globalization Of Food Supply Chains, Urbanization, And Changing Consumer Behaviors Have Made Food Safety An Even More Challenging Task To Accomplish Due To High Risks Of Contamination Throughout The Food Chain. The Current Research Paper Contains A Thorough Review Of Food Safety Standards And Measures Implemented To Reduce The Possibility Of Contamination Of Food From Its Initial Stage Until It Reaches Consumers. Specifically, The Study Analyzes The Most Common Forms Of Contamination, Biological, Chemical, And Physical Ones, Along With The Factors Contributing To Food Contamination And Food Safety Issues. Hygiene And Food Safety Management Failures Are Among These Factors, Along With Cross-contamination, Poor Food Handling Practices, And Inappropriate Storage Conditions. Environmental Factors, Such As Contaminated Soils, Can Also Be Considered To Be Among Food Safety Hazards Indirectly. This Paper Also Reviews Some Important Food Safety Approaches Nationally And Internationally, Which Include HACCP (Hazard Analysis And Critical Control Points), ISO 22000, Codex Alimentarius, And Regulations Set Forth By The Regulatory Agencies. The Focus Of These Frameworks Is To Adopt A Preventive And Risk-based Approach For Managing Food Safety In Identifying Food Hazards At Critical Control Points And Taking Action Against Them. Nevertheless, Even With The Presence Of Such Structured Frameworks, There Is Inconsistency In Applying These Methods On The Ground, Especially In Underdeveloped Areas And Unorganized Food Sector. This Research Applies A Mixed Methodology Involving Extensive Literature Search, Process Mapping Of Food Safety Practices, And Theoretical Models To Understand The Pathway Of Contamination And Means Of Controlling It. Moreover, A Descriptive Survey Is Included In This Study To Explore The Level Of Awareness, Attitude, And Behavior Of Consumers Regarding Food Safety Practices. It Is Evident From The Results Obtained Through The Research That Some Level Of Awareness About Food Safety Practices Is Present Among People, But There Is Still A Substantial Gap Between Awareness And Practice. The Paper Finds That For Effective Food Safety Management, It Is Imperative To Adopt A Holistic And Multidisciplinary Strategy That Encompasses Stringent Regulation, Constant Training, Incorporation Of Sophisticated Technology Such As Internet Of Things (IoT) Systems, And Engagement Of All Parties Concerned Such As Governments, Food Industries, And Consumers. This Would Greatly Minimize The Likelihood Of Food Contamination Incidences And Enhance Public Health Impacts.

Author: Dnyanesh Paygude | Atharv Jagtap | Rutuja Gadge | Sachin Dhavale

Area of research: Information Technology

The Manual Preparation Of Examination Question Papers In Higher Educational Institutions Is A Labor-intensive, Error-prone, And Time-consuming Activity. Faculty Members Often Spend Between Two And Four Hours Assembling A Single Question Paper, Ensuring Appropriate Difficulty Distribution, Compliance With Examination Templates, And Alignment With Course Outcomes Defined Under Outcome-Based Education (OBE) Frameworks. This Paper Presents QPGS—a Question Paper Generation System—a Cloud-native, Offline-capable, And Role-based Web Application Designed To Automate And Streamline Examination Paper Creation For Institutions Affiliated With The Savitribai Phule Pune University (SPPU) Pattern. The System Employs A React 19-based Single-page Application (SPA) With Firebase Firestore As A NoSQL Backend, Firebase Authentication For Secure Role-based Access, And An IndexedDB-based Offline Queue For Uninterrupted Operation In Low-connectivity Environments. At The Core Of Paper Generation Lies A Configurable OR-group Template Engine Combined With The Fisher-Yates Shuffle Algorithm, Ensuring Randomized Yet Difficulty-balanced Question Selection. Additionally, QPGS Integrates An AI-powered Question Generation Pipeline Capable Of Extracting Syllabus Content From Uploaded PDF And DOCX Files And Generating Pedagogically Appropriate Questions. Evaluation Of The System In A Pilot Institutional Setting Demonstrated An 80% Reduction In Paper Preparation Time, With Successful Generation Of Well-balanced Papers In Under Two Minutes. The System's Approval Workflow, LaTeX Rendering Support, And Analytics Dashboard Further Contribute To Its Academic Value. A System Usability Scale Score Of 82.4 Confirms Excellent Usability Across Non-technical Faculty Users

Author: Balaji Uplanchwar | Harsh Murkewar | Sanket Bhapkar | Vinayak Godse

Area of research: Information Technology

Farmers Who Sell Directly To Consumers Through Digital Platforms Often Have No Idea What Price To Charge For Their Produce. They Rely On Heuristic Judgment And Informal Market Signals, Or Whatever Their Neighbor Charged Last Week. This Leaves Money On The Table During High-demand Periods And Causes Unnecessary Losses When The Market Is Already Saturated. On The Other Hand, Consumers Have No Way To Tell Whether A Listed Price Is Fair Or Inflated. This Paper Tackles That Specific Problem By Building A Price Prediction System Called AIPPS (Agricultural Intelligent Price Prediction System) That Is Designed From The Ground Up For Farmer-to-consumer (F2C) Digital Markets, Not The Wholesale Exchanges That Most Existing Research Focuses On. AIPPS Combines A Bidirectional LSTM Network With An XGBoost Ensemble In A Two-stage Architecture. The Bi-LSTM Handles The Sequential Price History And Weather Patterns, While XGBoost Cleans Up Residual Errors By Incorporating Structured Features Like Supply Volumes, Transport Costs, And Seasonal Indicators. We Trained And Tested The Model On 24 Months Of Transaction Data From 120 Micro Markets Covering Five Commodity Groups. The System Achieves A MAPE Of 1.84%, RMSE Of 1.84, And R² Of 0.963, Which Is Substantially Better Than ARIMA, SVR, Random Forest, And Standalone LSTM Baselines. We Also Built A Mobile Dashboard For Farmers Showing A Simple Sell/hold Recommendation And A Consumer-facing Page That Shows Predicted Fair Price Ranges. Ablation Experiments Confirm That Each Component Of The Architecture Genuinely Contributes To The Final Accuracy.

Author: Rahil Mulani | Muskan Bajaj | Vansh Gondane | Shubham Adkar | Vinayak Shinde

Area of research: Computer Engineering

This Study Presents A Short-term, High-resolution Assessment Of Ambient Air Quality In Pune, Maharashtra, India, Using 30 Consecutive Days Of Continuous Monitoring Data (February 16 To March 17, 2026) From Government-operated Continuous Ambient Air Quality Monitoring Stations (CAAQMS). Six Pollutants Were Analysed: PM2.5, PM10, CO, SO2, NO2, And O3. The Period-mean Air Quality Index (AQI) Was 106.0 ± 44.8, With Daily Values Ranging From 30 To 188. PM2.5 Exceeded The Central Pollution Control Board (CPCB) 24-h National Ambient Air Quality Standard (NAAQS) Of 60 μg/m³ On 9 Of 30 Days (30 %). PM2.5 And PM10 Co-varied Near-perfectly (r = 0.9997), With A Mean PM2.5/PM10 Mass Ratio Of 0.84 ± 0.01, A Pattern Consistent With Combustion-dominated Aerosol. Three Distinct Temporal Phases Were Identified And Provisionally Attributed To Winter-inversion-enhanced Traffic Emissions, A Synoptic Flushing Event, And A CO-elevated Pre-monsoon Transition. The Study Is Explicitly Limited In Scope: It Covers A Single Meteorological Transition Season, Lacks Co-located Meteorological Measurements And Source-apportionment Analysis, And Cannot Support Causal Attribution. On The Basis Of The Observed Exceedances And Phase Structure, Evidence-anchored Recommendations For Vehicular Emission Control, Industrial Monitoring, And Network Expansion Are Proposed.

Author: Vaibhav Shivaji Gaikwad | Nikhil Gaikwad | Vishal Kutaphale | Apeksha Bagade

Area of research: Computer Engineering

An Increase In The World’s Demand For Renewable/sustainable Sources Of Energy Has Created Significant Interest In The Environmental Benefits Of Producing Biogas From Organic Waste For Both Energy Production And Managing Organic Waste On A Distributed Basis. The Traditional Biogas Plants Exhibit A Number Of Problems – Including Low Concentrations Of Methane, Instability In The Digestion Process, Poor Feedstock Management, And Lack Of Effective Systems To Purify Gas Produced – All Of Which Lower The Combustion Efficiency And Energy Output Of The Plant. In This Article, The Authors Provide A Conceptual Framework For Designing And Optimising A Smart Biogas Plant To Improve Yields And Quality Of Biogas Produced. The Proposed System Integrates Multiple Optimisation Strategies, Including Feedstock Balancing Through Controlled Carbon-to-nitrogen (C:N) Ratio Management, Mechanical And Thermal Pretreatment Of Biomass, Mesophilic Temperature Regulation Using Insulation And Solar-assisted Heating, Controlled Mixing Mechanisms, And Low-cost Biogas Purification Techniques For The Removal Of Carbon Dioxide, Hydrogen Sulfide, And Moisture. In Addition, The Incorporation Of Temperature Sensors And Automated Control Systems Is Proposed To Maintain Stable Anaerobic Digestion Conditions And Improve Process Efficiency. Theoretical Analysis And Findings From Existing Literature Indicate That The Integrated Optimisation Approach Can Significantly Improve Digestion Stability, Methane Concentration, Calorific Value, And Biogas Purity Compared To Conventional Biogas Systems. Furthermore, The Proposed Model Offers Potential Benefits In Terms Of Renewable Energy Generation, Waste Utilisation, Environmental Sustainability, And Reduced Greenhouse Gas Emissions. The Study Provides A Comprehensive Conceptual Foundation For Future Experimental Validation And The Development Of Intelligent High-efficiency Biogas Systems For Rural And Industrial Applications.

Author: Yadnyesh Surange | Sachin Tidke | Shubham Sanap | Prachi Hivarale | Vishwajit Hajare

Area of research: Computer Engineering

With Abundant Availability Of Solar Energy Year-round, The Country Has Ample Scope For Using Solar Power To Meet Its Growing Energy Demands. Solar Panels Are Thus Gaining Popularity In India And Playing A Crucial Role In Its Energy Transition. This Paper Studies The Usage Of Solar Panels In Rural And Urban Areas, Highlighting Their Application And Overall Impact. Rural Communities Make Use Of Solar Panels For Basic Lighting Purposes, As Well As Irrigation Purposes. On The Other Hand, The Primary Drivers Of Adoption In Urban Communities Include Rising Electricity Prices And Increased Environmental Awareness. The Paper Will Look Into Various Factors Associated With This Issue, Including Benefits And Constraints. Among Several Benefits, Reduction In Dependence On Fossil Fuels, Decreased Carbon Footprint, And Financial Savings Are Mentioned As Some Of The Key Benefits. Constraints Include Initial Installation Costs, Lack Of Awareness Among Communities In Certain Locations, As Well As Maintenance Issues. The Paper Will Analyze The Financial Feasibility Of Solar Panel Adoption In India, Demonstrating That The Cost Of Installation Would Be Recovered Within A Relatively Short Period Of Time. It Becomes Clear That Further Adoption In This Regard Requires Raising Awareness Levels Among Communities.

Author: Uday Jaybhaye | Onkar Suryawanshi | Sanika Jadhav | Nandini Tribhuvan | Priyanka Shivalkar

Area of research: Computer Engineering

Solar Energy Is A Great Option For Cooking In An Eco-friendly And Sustainable Way, Especially In Areas With Plenty Of Sunlight. This Paper Talks About An Experiment That Tested How Well A Box-type Solar Cooker Can Collect And Use Solar Energy Under Different Conditions. The Study Looked At How Things Like The Angle Of The Reflector, The Type Of Material Used To Absorb Heat, And Whether The Cooker Has A Clear Cover Affect How Efficiently It Works. A Model Of A Box Solar Cooker Was Made Using Materials That Are Easy To Find Locally. To Understand How Different Factors Influence Heat Collection, Tests Were Done At Different Times. It Was Found That While A Black Surface Is Good At Absorbing Heat, Using Reflective Surfaces Helps Gather More Solar Energy. A Clear Cover Also Helps By Trapping Heat Inside, Like A Greenhouse, Which Makes The Cooker Work Better. Other Things, Like How Hot The Outside Is And How Strong The Sunlight Is, Also Play A Big Role In How Well The Cooker Works.From This Study, It Is Clear That There Is A Great Potential In The Utilization Of Solar Energy As An Alternative Energy Source For Cooking. Experimental Evaluation Based On Various Parameters Is Essential In Improving Cooker Designs. By Comparing Results, It Was Seen That Setting The Reflector At The Right Angle And Improving Insulation Makes The Cooker More Efficient. The Findings From This Study Can Help Improve The Design Of Solar Cookers. Overall, This Study Shows That Solar Energy Has A Lot Of Potential As A Clean And Renewable Energy Source For Cooking. Testing And Looking At Different Factors Is Important For Making Solar Cookers Work Better.

Author: Kiran Kawale | Trupti Shinde | Sankalp Barapatre | Tanishka Kedari | Vaishnavi Gite

Area of research: Information Technology

As The World Moves Towards Renewable Sources Of Energy, Solar Photovoltaic (PV) Energy Systems Have Been Identified As One Of The Main Options For Reducing Greenhouse Gas Emissions And Sustainable Development. Although They Generate Clean Energy During Operation, There Are Certain Environmental Aspects Relating To These Solar Energy Systems Throughout Their Lifetimes. Life Cycle Assessment (LCA) Method Has Been Employed To Undertake An Elaborate Analysis Of These Environmental Considerations. Environmental Factors Such As Energy Payback Time, Carbon Emission, Resource Usage, Land Use, And Post-use Disposal Of The PV System, Among Others, Have Been Considered In The Study. Environmental Impacts Associated With The Use Of This Solar Energy Source Such As Habitat Destruction, Extraction Of Raw Materials, And Generation Of Waste Due To Decommissioning Of Old Panels Have Also Been Looked Into. By Contrasting The Environmental Impacts Of Renewable Energy Sources And Non-renewable Sources Of Energy, The Benefits Of Solar Energy Are Identified. Further, The Impacts Of Emerging Technologies Like IoT-based Monitoring Systems And Energy Storage Systems Have Also Been Investigated By The Study. Various Mitigation Methods Including Sustainable Site Selection, Better Recycling Procedures, And Regulatory Support, Especially In Developing Nations Like India, Have Been Reviewed. Finally, It Has Been Found Out That Despite Being A Sustainable Energy Option, The Overall Efficiency Of The Energy Source Lies In Appropriate Policy Support And Technology Developments.

Author: Shweta Kedari | Omkar Raut | Dipak Kale | Preksha Raut | Diksha Budhnavar

Area of research: ENTC

Urban Plastic Waste Has Become One Of The Most Visible Environmental Stresses Associated With Rapid Urbanization, Changing Consumption Patterns, And Inadequate Waste Management Systems [5], [6], [7]. This Paper Examines How Plastic Waste Generated In Cities Can Be Transformed Into Value-added Products Rather Than Being Treated Only As Residual Waste. The Study Synthesizes Recent Literature On Urban Plastic Waste Management, Circular Economy Strategies, Plastic-sand Composite Construction Materials, Recycled Polymer Use In Textiles, Consumer Goods Manufacturing, And Pyrolysis-based Fuel Recovery [2], [6], [11], [12]. It Also Proposes An Integrated Urban Plastic Resource Conversion Framework And Includes An Illustrative Survey-based Analysis To Understand Public Awareness And Acceptance Of Recycled Plastic Applications. The Review Indicates That Weak Source Segregation, Low Recovery Efficiency, Contamination Of Recyclables, Limited Infrastructure, And Poor Market Linkages Remain Major Barriers In Urban Settings. At The Same Time, Plastic Roads, Paver Blocks, Composite Bricks, Recycled PET-based Textiles, Household Products, And Carefully Regulated Pyrolysis Systems Show Practical Promise When Deployed In The Correct Context. Literature On Plastic-sand Pavers Reports Strong Performance At Around A 30:70 LDPE-to-sand Ratio, While Pyrolysis Studies Commonly Investigate Temperature Windows Around 450–600 °C For Liquid-fuel-oriented Recovery [1], [2], [4] . The Paper Concludes That Cities Need A Combined Strategy Of Segregation, Decentralized Preprocessing, Standards-based Product Manufacturing, Producer Responsibility, And Demand Creation For Recycled Products. The Aim Is To Present An Original, Publication-ready Synthesis That Frames Plastic Waste As A Recoverable Urban Resource Within A Circular Economy.

Author: Tanmay Dharmik | Tanmay Kalal | Diksha Ausarmal | Atharv Phadake | Sonali Virkar, Prerana Pawar

Area of research: Computer Engineering

Many College Students Are Drawn To The Growing Trend Of Pursuing Higher Education Overseas, But A Number Of Obstacles Affect Their Decision-making. The Purpose Of This Study Is To Examine How College Students Perceive Possible Challenges When Studying Overseas. Financial Limitations, Immigration And Visa Procedures, Language Barriers, Housing Costs, And Racism Or Discrimination Were Identified As The Five Main Problems. Existing Research Papers Pertaining To Each Of These Issues Were Analyzed As Part Of A Literature Review. Additionally, 40 College Students Participated In A Survey That Was Conducted Using A Structured Questionnaire Via Google Forms. The Findings Were Examined To Identify The Students' Top Concerns. The Results Show That Language Barriers And Financial Limitations Are Viewed As The Most Important Obstacles, Followed By Housing Costs And Language Visas/immigration. Existing Solutions Were Also Looked At, But They Are Frequently Incomplete And Fragmented. Based On The Analysis, The Study Emphasizes The Necessity Of More Comprehensive Support Systems To Successfully Handle The Various Difficulties Encountered By Students Who Intend To Study Overseas.

Author: Asmita Paygude | Nilam Patil | Rajeshwari Gadilkar | Deepak Surnar | Bhagyashree Sonkamble

Area of research: Electronics & Telecommunication

Menstrual Hygiene Management (MHM) Remains A Critical Yet Under-addressed Public Health Challenge, Particularly In Rural And Peri-urban Communities Of Developing Nations. Inadequate Access To Safe, Affordable, And Sustainable Menstrual Hygiene Products Continues To Affect The Health, Dignity, Education, And Socioeconomic Participation Of Millions Of Women And Girls. The Menstrual Cup, A Reusable Silicone-based Intravaginal Device Designed To Collect Menstrual Fluid, Has Emerged As A Promising Alternative To Conventional Disposable Products Such As Pads And Tampons. This Paper Presents A Comprehensive Research Study Examining The Menstrual Cup As A Sustainable Solution For Menstrual Hygiene Management. Through A Mixed-method Approach Encompassing A Structured Survey Of 200 Participants (comprising Rural And Urban Women Aged 15–45), Field Observations, And Literature Synthesis, The Study Evaluates Awareness Levels, Acceptability, Usage Patterns, Advantages, And Socio-cultural Barriers Associated With Menstrual Cup Adoption. Findings Indicate That While Awareness Of Menstrual Cups Remains Significantly Low In Rural Areas (approximately 18%), Urban Acceptance Is Comparatively Higher (approximately 62%). Key Advantages Identified Include Cost-effectiveness Over Long-term Use, Environmental Sustainability (reduction Of Menstrual Waste), Health Benefits (lower Risk Of Toxic Shock Syndrome And Infections), And Enhanced Convenience. Major Challenges Include Cultural Taboos, Lack Of Awareness, Initial Discomfort During Insertion/removal, And Limited Access To Clean Water For Maintenance. The Study Concludes With Evidence-based Recommendations For Policy Interventions, Community Outreach Programs, And Educational Campaigns To Foster Wider Adoption Of Menstrual Cups, Especially Among Underserved Populations.

Author: Tanusha Mahakal | Ganesh Patil | Princy Kumari | Sarthak Pawar

Area of research: Information Technology

Climate Change Has Become A Major Global Concern. Due To Nonstop Large- Scale Deforestation, Artificial Growth, Urbanization And Expanding Industrialization, The Natural Balance Of The Earth’sbiosphere Is Decreasing. This Has Led To Serious Ecological Challenges Affecting Sustainability And Biodiversity. Afforestation And Reforestation Aretwo Of The Most Effective Natural Solutions To Climate Change.This Study Aims To Analyze The Contribution Of Tree Plantation In Mitigating Carbon Dioxide Concentrations And Balancing Environmental Conditions. The Exploration Is Grounded On Findings, Research, Case Studies, Graphical Analysis, Community Initiatives,and Relative Compliances Of Areas With And Without Foliage. The Results Indicate That Trees Significantly Contribute To Climate Regulation, Maintaining Ecological Balance, Improving Air Quality,carbon Sequestration, And Temperature Reduction. Trees Planted On A Small Scale In Urban Areas Significantly Contributes In Temperature Reduction And Pollution Control. The Findings Punctuate That Both Small- Scale And Large- Scale Tree Plantations Play An Important Role In Combating Climate Change And Promoting Sustainable Development.

Author: Sneha Dnyaneshwar Harne | Vaishnavi Sunil Purigosavi | Aditya Chhagan Suryavanshi | Aryan Tanaji Waghmare

Area of research: Electrical Engineering

- India’s Transition Toward Sustainable Energy Systems Necessitates The Effective Utilization Of Its Abundant Renewable Resources. Among These, Wind Energy Presents A Significant Yet Underutilized Opportunity, With An Estimated Potential Of Approximately 695 GW At Higher Hub Heights, Compared To An Installed Capacity Of Just Over 50 GW As Of 2025. This Study Investigates The Disparity Between Resource Availability And Actual Deployment Through An Integrated Technical And Economic Perspective. The Analysis Combines Recent Literature, National Datasets, And Graphical Evaluations To Examine Capacity Trends, Regional Distribution, And Cost Dynamics. The Findings Indicate That The Primary Barriers To Wind Energy Deployment Are Not Resource-related But Stem From Systemic Challenges, Including Grid Limitations, Financial Constraints, And Policy Inconsistencies. Furthermore, The Study Highlights The Economic Dominance Of Onshore Wind And The Emerging Potential Of Offshore Systems Despite Their Higher Costs. The Paper Concludes That Addressing These Interconnected Challenges Through Coordinated Infrastruc-ture Development, Policy Stability, And Technological Advancement Is Essential For Unlocking India’s Wind Energy Potential.

Author: Gaurav Abasaheb Shirsath | Shruti Sanjay Sutar | Sagar Kiran Kulkarni | Trupti Nilesh Shinde | Pooja Rajendra Bondge, Kuldeep Devidas Thorat

Area of research: Computer Engineering

Rapid Urbanization Has Placed Tremendous Pressure On Available Agricultural Land, Making It Increasingly Difficult To Meet The Food Demands Of Growing City Populations. Traditional Farming Methods Require Large Tracts Of Land, Which Are Simply Unavailable In Densely Built Urban Environments. This Paper Examines Vertical Farming As A Practical And Sustainable Solution To Address Land Scarcity In Cities. The Study Explores Three Core Technologies—hydroponics, Aeroponics, And Aquaponics—and Evaluates Their Technical Working Principles, Components, Benefits, And Limitations. A Comparative Analysis Of Global Case Studies From Singapore, Japan, The United States, The Netherlands, And The UAE Has Been Conducted. The Methodology Relies On Secondary Data Gathered From Published Research Papers, FAO Reports, And Government Agriculture Studies. Results Indicate That Vertical Farming Can Reduce Water Usage By Up To 95% Compared To Conventional Agriculture, Increase Crop Yield Per Unit Area By 5 To 10 Times, And Enable Year-round Production Independent Of Weather Conditions. While Challenges Such As High Energy Costs And Initial Capital Investment Remain, The Technology Shows Significant Feasibility As Part Of Smart City Planning. This Paper Concludes That With Appropriate Policy Support And Technological Improvements, Vertical Farming Can Play A Meaningful Role In Achieving Urban Food Security.

Author: Mohil Patil | Kavy Changela | Janhavi Patharavat | Mahesh Manolikar | Megha Khanderao

Area of research: Computer Dept

Organic Farming Has Become A Sustainable Farming Method That Focuses On Maintaining Soil Health, Supporting Biodiversity, And Protecting The Environment. However, Moving To Organic Agriculture Comes With Several Challenges, Including High Start-up Costs, Lower Yields, Policy Issues, And Difficulties In Managing Crops After Harvest. This Study Looks At How Sustainable Organic Farming Is By Exploring Its Role In Adapting To Climate Change And Identifying Key Barriers To Its Adoption. The Research Uses Secondary Data From Published Studies, Government Reports, And Case Studies From India. The Findings Show That Organic Farming Improves Soil Fertility, Lowers Chemical Pollution, And Supports Long-term Sustainability, But Farmers Encounter Financial And Technical Obstacles During The Transition. Strengthening Policy Support, Improving Training Programs, And Creating Better Marketing Infrastructure Can Help More Farmers Switch To Organic Methods.

Author: Aman Gajage | Saba Shaikh Rauf | Chetan Datre | Dhanashree More | Ujjwal Katre

Area of research: IT

This Research Investigates The Impact Of Performance Appraisal Systems On Employee Motivation At EEE Infra Equipments Pvt. Ltd., Chennai. The Study Evaluates The Effectiveness Of The Appraisal Process And Its Influence On Employee Performance. Through A Structured Questionnaire Distributed To 110 Respondents And Statistical Analysis Using Correlation And Chi-square Tests, The Research Explores Employee Perceptions Of The Appraisal System And Its Motivational Effect. The Study Reveals That A Majority Of Employees Are Moderately To Strongly Motivated By The Appraisal System, With Significant Positive Correlations Found Between Promotion-linked Salary Increments And Expected Benefits. The Study Concludes With Recommendations To Enhance Transparency, Feedback Mechanisms, And Career Development Opportunities To Foster A More Motivated And Productive Workforce.

Author: Swathi P. | DR.S.PRAKASH

Area of research: Biomedical Engineering

Face Recognition Systems Have Become A Fundamental Component Of Modern Biometric Authentication. However, These Systems Remain Vulnerable To Presentation Attacks Such As Printed Photographs, Replay Videos, Masks, And AI-generated Deepfakes. Existing Face Anti-spoofing (FAS) Methods Often Exhibit Limited Generalization When Exposed To Unseen Attack Types And Cross-domain Variations. This Paper Presents A Robust FAS Framework That Combines Token-level Feature Learning, Contrastive Representation Learning, And Angular Margin Optimization To Improve Liveness Detection Performance. The Proposed Framework Learns Discriminative Feature Representations By Encouraging Compact Live-face Embeddings And Enhanced Separation From Spoof-face Embeddings. Localized Facial Analysis Enables The Extraction Of Fine-grained Liveness Cues, Including Texture Inconsistencies, Illumination Artifacts, And Reflectance Variations. Experimental Evaluation Demonstrates An Accuracy Of 90.91%, An ACER Of 9.9%, And A ROC-AUC Of 0.9903, Indicating Strong Discriminative Capability. The Proposed System Provides An Effective And Practical Solution For Enhancing Biometric Security Against Presentation Attacks.

Author: Libinsha E | M. K. Dwaraka

Area of research: Computer Engineering

Mental Health Has Become A Big Worry For Young People Today, Especially Because Of Fast Changes In Technology, Tough School Competition, High Expectations From Society, And New Ways Of Living. Teenage Years And The Early Years Of Adulthood Are Times When People Go Through A Lot Of Changes In Their Emotions, Thoughts, And How They Relate With Others. These Changes Can Make Young People More Likely To Face Mental Health Problems Like Anxiety, Depression, And Stress. Even Though More People Are Talking About Mental Health Now, There Isn't Enough Awareness Or Support Systems, Especially In Places Like India. This Research Paper Looks At How Aware Young People Are About Mental Health, What Problems They Face, And How Well The Support Systems Work. It Also Looks At How Young People Seek Help And How Society's View On Mental Health Affects Open Discussions. Data Was Collected Through A Questionnaire Given To Students And Young People Aged 16 To 30. The Results Show That Most People Know What Mental Health Is, But They Don’t Always Take Action When They Need Help. Another Key Point From This Study Is How Digital Platforms Are Changing The Way People Think About Mental Health. Although Social Media Is Often Linked To Stress, Comparison, And Anxiety, It Also Plays A Big Role In Raising Awareness, Sharing Personal Stories, And Reducing The Stigma Around Mental Health Issues. Many Young People Come Across Mental Health Information Online, Which Helps Them Learn Basic Facts, But This Information Isn't Always Accurate Or Helpful For Taking Action. This Shows That Digital Platforms Have Both Good And Bad Effects, Which Means There's A Need For Trusted, Reliable, And Easy-to-use Mental Health Resources That Are Designed For Young People. The Study Also Points Out How Important It Is To Spot Mental Health Problems Early And Take Action To Stop Them From Getting Worse. Without Timely Help, Mental Health Issues Can Lead To Long-term Effects On School Performance, Future Career Opportunities, And Relationships With Others. By Recognizing Early Signs And Common Stress Factors, Schools And Government Leaders Can Create Focused Solutions That Deal With The Real Causes Of Mental Health Struggles, Not Just The Symptoms .The Study Ends By Suggesting Practical And Easy-to-spread Solutions Like Teaching Mental Health Topics In Schools, Improving Counseling Services, And Using Digital Tools To Offer Support That's Easier To Reach. It Also Highlights The Value Of Early Help, Involving The Community, And Creating Policies That Support Mental Health For Young People. By Combining Survey Results With Existing Research, This Paper Adds To The Discussion On Youth Mental Health By Offering Useful Observations And Clear Steps That Can Help Increase Awareness, Lower Stigma, And Improve Support For Young Individuals. [1], [3], [4] Reasons Like Social Judgment, Not Having Easy Access To Help, Money Problems, And Poor Support From Schools Or Institutions Stop Them From Getting Help. The Paper Suggests Ways To Improve Things, Like Teaching Mental Health In Schools, Making Better Counselling Services, And Using Technology To Reach More People. This Study Adds Valuable Information To The Conversation About Mental Health Among Young People And Gives Ideas That Can Be Used In Real Life.

Author: Sanika Dhanve | Ishwari Nalawade | Durvanka Kolapate | Shruti Singh | Ketan Rathod

Area of research: Computer Engineering

The Growing Population Is The Biggest Challenge Will Be Facing For The Entire World And Feeding This Entire Range Seems More Challenging. As The Rapid Growth In Industrialization More And The Rapid Conversion Of Agricultural Land To Non-agricultural Transforming Into Planning Well Designed Emerging Futuristic Human Society And Establishing This Led To Unavailability Of Well-maintained Fertile Land. And The Byproduct Of This From Past And From Now To Upcoming Decade Will Definitely Decrease The Fertility And Nutrition In The Cultivable Agricultural Land. So, To Maintain The Balance Between The Growing Population, Growing Industrialization And Balance Between The Food Chain The Importance Of Fulfilling Them With Help Of Hydroponic For Their Understanding The Importance Of Hydroponic In Installation At Appropriate Is One Of The Key Factor Responsible Successful Acceptance Rates For This Technique. The Benefit Against The Traditional Farming Is Which Can Be The Water And Space Utilization For Applying In Apartments And Their Individual Balcony The Design By Modifying The Current Model The System Is Design Over By Us For Space Utilization And Most Important The Highlighting The Fulfilment Of Food Resources With The Organic Essentials. The Initial Investment For This Technique Is Quite High As Comparative To Traditional But The Productivity Marine That Is Gain Is More As Compared To Traditional This Can Be The Source For Fulfilling The Requirement In The Factor Of Food Chain. This Technique Gives The Opportunity For The People Which Want To Cultivate For The But Lack In The Availability Of Cultivable Nutrition Rich Land. Thus, It's Obvious For Fulfilling The Need Variety Of Options Have Been Adopted By The Human And A Every Step Taken Have Its Contribution For The Development Even That If It Is A Small Portion. And Thus, Hydroponic Farming Plays A Vital Role In Future Food Security In Context Over The Variety Of Type Of Society And Humankind That May Range From Urban To Rural, Rich To Poor.

Author: Parth Gaikwad | Shreya Ghatge | Aditi Singh | Aditya Shinde | Sakshi Kute

Area of research: ENTC

The Rapid Evolution Of Digital Learning Has Trans-formed The Educational Landscape, Yet Profound Disparities Remain Between Urban And Rural Sectors. This Paper Ana-lyzes The Efficacy Of Digital Platforms In Bridging The Educa-tional Gap, Specifically In Resource-constrained Environments. By Synthesizing Empirical Data From Diverse Regions, Includ-ing India And Myanmar, We Identify A “triumvirate Of Bar-riers”—infrastructural, Socioeconomic, And Pedagogical. Find-ings Indicate That While Digital Platforms Enable Individualized Learning, 63% Of Rural Learners Face Network Instability That Leads To Disengagement. We Conclude By Recommending Localized Infrastructure Solutions To Foster Equitable Digital Inclusion.

Author: Nakshatra Kale | Shivaji Mohan Mane | Sakshi Sampat Bhutada | Aryan Subhashrao Waghmare | Suhas Daphal, Jayesh Baban Hirve

Area of research: Information Technology

Alcohol And Tobacco Use Is A Problem Among Young People Nowadays. It Is Happening Everywhere Not In One Place And India Is No Exception. Many Students Are Drinking Alcohol Using Tobacco And Sometimes Other Substances Too. There Are Reasons Why This Is Happening. It Could Be Because Of The Pressure To Do Well In School.. It Could Be Because Of Changes In Lifestyle. Sometimes It's Because Of The Kind Of People They Hang Out With, Like Friends Or Family.. Sometimes Young People Just Start Using These Things Without Thinking Much. In Our Study We Looked At People In The Pune Region. We Found That Many Students Have Tried Alcohol And Tobacco At Once. Some Are Using It Regularly. Alcohol Seems To Be The Substance That Young People Use The Most. It's Worrying That Many Students Start Using Alcohol And Tobacco When They Are Still, In School Or Early College. We Found That Many Factors Contribute To People Using Alcohol And Tobacco. Friends Influence Plays A Role. Family Situation Is Also Important. It's Easy To Get Alcohol And Tobacco. The Way Young People Use These Substances Varies. It Depends On Their Age, Family Background And Financial Condition. Substance Abuse Is An Issue That We Need To Pay Attention To. If We Ignore Itit May Become A Problem Later. We Can Take Steps To Prevent It. *We Can Give Awareness About The Dangers Of Substance Abuse. We Can Provide Guidance And Support From Family And College. It Is Better To Act Instead Of Waiting Until It Becomes Serious. Substance Abuse, Alcohol And Tobacco Use Needs To Be Addressed Properly. Otherwise It Can Affect Young People In The Future.

Author: Balaji Uplanchwar | Sneha Shetfale | Aditi Biradar | Anish Jadhav | Paurnima Kawalekar

Area of research: Electronics & Telecommunication Engineering

The Textile Industry Is A Well-established And Growing Sector. It Has Both Positive And Negative Impacts, As It Provides Livelihoods For Thousands Of Families But Also Harms Many. Large Quantities Of Water Are Used In The Process, Significantly Affecting Local Ecosystems And Communities. The Water Is Contaminated With Hazardous Chemicals From Dyeing And Other Procedures, Which Are Discharged Into Nearby Water Bodies, Disrupting The Ecological Balance And Causing Toxicity. Water Sources Used For Farming, Fishing, And Drinking Become Contaminated, Gradually Harming Local Populations. While Previous Research Has Addressed These Issues, They Continue To Persist. This Study Aims To Consolidate Existing Efforts And Focus On Processes That Can Achieve Better Outcomes. The Paper Seeks To Propose Solutions, Emphasize The Enforcement Of Regulations, And Promote Proper Treatment Of Wastewater Discharged By The Textile Industry. The Paper References Various Studies Conducted Over Time, Ensuring That Meaningful Results Can Be Drawn. These References Include Research From Regions Where The Industry Is Most Active.

Author: Sabyasachi Mohapatra | Shreya Boda | Utkarsha Kulkarni | Aditya Jangam | Anjali Kharade

Area of research: Computer Engineering

Digital Addiction Has Become A Major Worldwide Public Health Problem Which Mainly Affects Teenagers And Young People Between 15 And 25 Years Old. The Mass Adoption Of Smart- Phones Along With Social Media Platforms And Online Gaming Platforms Has Generated An Unprecedented Amount Of Digital Interaction Which Leads People To Develop Obsessive Digital Usage Patterns That Harm Their Physical And Mental Health. The Research Paper Conducts An All-encompassing Data-based Examination Of Digital Addiction Through A Multi-dataset Approach Which Combines Original Survey Information With Extensive Secondary Data Collections. The Research Findings Demonstrate That Spending Too Much Time In Front Of Screens Leads To Negative Effects On Vital Health Markers Which Include Sleep Quality, Mental Health Scores, Cognitive Performance, And Physical Activity Levels. Young People Between 15 And 25 Years Old Spend The Most Time Watching Screens While They Primarily Use Their Devices For Social Media And Entertainment Purposes. The Research Presents A Multi- Level Intervention System Which Combines Personal Behavioral Approaches With Family-based Initiatives, Institutional Support, Policy-based Directives, And A Smart Monitoring System. This System Uses Data For Tracking Purposes. The Research Paper Adds To The Growing Evidence Which Supports That Digital Addiction Requires Specific Evidence-based Treatment Approaches For The Current Digital Age.

Author: Atharv Kulkarni | Viraj Bramhadev Jadhav | Deepak Kakasaheb Thakar | Shrinivas Balaji Bakare | Shrutika Rupak Shete

Area of research: Computer Applications

This Paper Presents The Design And Implementation Of The Online Educational Management System (OEMS), A Comprehensive Web-based Platform Developed To Address The Limitations Of Conventional, Manual-driven Educational Administration. The System Integrates Static Content Delivery, Dynamic Processing, And E-commerce Functionalities Into A Unified Hybrid Architecture, Serving Distinct User Roles — Administrator And Student. Developed Using HTML5, CSS3, JavaScript, Bootstrap 5 On The Frontend And Node.js/Express On The Backend, With MySQL As The Relational Database, The Platform Automates Course Management, Content Delivery, Student Interaction, And Payment Processing. The System Employs A Three-tier Client-server Architecture And Enforces Role-based Access Control (RBAC) To Ensure Data Security And Operational Integrity. Testing Results Confirm That The System Meets Functional And Non-functional Requirements, Achieving High Performance, Scalability, And Usability. Future Work Includes AI-driven Personalization, Multi-factor Authentication, And Virtual Classroom Integration.

Author: Murugu Santhosh