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Volume: 12 Issue 03 March 2026
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A STUDY ON IMPACT OF RECENT TECHNOLOGY AMONG THE MODERN SOCIETY WITH REFERENCE TO COIMBATORE CITY
Area of research: B.com With Professional Accounting
This Study Investigates The Profound Socio-economic Shifts In Coimbatore City Driven By Recent Technological Integrations, Specifically Focusing On Industry 4.0, Artificial Intelligence And Smart City Initiatives. Traditionally Recognized As An Industrial And Textile Hub, Coimbatore Is Currently Undergoing A Rapid Digital Metamorphosis Into A Tier-2 IT Powerhouse. The Primary Objective Of This Research Is To Evaluate How These Technologies Have Altered Urban Living Standards, Industrial Productivity Among MSMEs, And Social Behavior Among The City’s Residents.
Author: Dr. Saranya W | Mr.AadithyaK
Read MoreTouchless ATM Authentication System Using Haar Cascade, LBPH And MediaPipe
Area of research: Information Technology
Automated Teller Machines (ATMs) Are Among The Most Common Banking Tools. They Mostly Rely On Physical Interaction Through Keypads And Touchscreens. This Can Raise Hygiene Concerns And Create Security Risks Like Shoulder Surfing And Keypad Tampering. This Paper Offers A Touchless ATM Access System That Combines Facial Recognition, Gesture-based Navigation, And A Virtual Keyboard For Secure Authentication And Transaction Processing. The System Applies The Haar Cascade Algorithm Along With Local Binary Pattern Histogram (LBPH) For User Identification, Followed By Password Confirmation Via A Gesture-based Virtual Keyboard. Hand Gesture Recognition, Powered By Computer Vision, Allows For Cursor Control And Menu Navigation Without Physical Contact. The Proposed Model Boosts Security With Multi-factor Authentication While Improving Accessibility And Hygiene By Removing Physical Touchpoints. Experiments Show Reliable User Authentication And Smooth Transaction Interaction With A Standard Webcam Setup. This Approach Provides A Practical And Scalable Framework For Future Contactless Banking.
Author: Harshitha D | Supraja M B | Thipirishetty Kavya | Dr. G. Ragu
Read MoreAI-Based Smart Traffic Signal Control System For Emergency Vehicle Prioritization
Area of research: Artificial Intelligence And Data Science
The Rapid Growth Of Urbanization Has Significantly Increased Traffic Congestion, Posing Critical Challenges For Emergency Medical Services, Particularly Ambulances. Delays Caused By Conventional Fixed-time Traffic Signal Systems Can Lead To Increased Response Times And Adverse Outcomes For Patients. This Situation Highlights The Need For Intelligent, Automated, And Real-time Traffic Management Solutions Capable Of Prioritizing Emergency Vehicles. This Paper Presents An AI-Based Smart Traffic Control System For Emergency Vehicle Prioritization Using Deep Learning And Computer Vision Techniques. The Proposed System Employs A Custom-trained YOLOv8 Model To Accurately Detect Ambulances From Real-time Or Recorded Traffic Camera Feeds. The Detection Model Is Trained Using A Labeled Dataset Prepared With Roboflow, Ensuring High Precision For Ambulance-specific Classification. Upon Detecting An Ambulance With Sufficient Confidence, The Traffic Signal Dynamically Switches To A Green Phase, Allowing Uninterrupted Passage Through The Intersection. To Ensure System Stability And Prevent False Triggering, A Timeout-based Control Mechanism Is Incorporated, Enabling The Signal To Revert Safely To Normal Operation Once The Ambulance Exits The Camera’s Field Of View. A Graphical User Interface Is Developed To Visually Represent Traffic Signal States And Emergency Conditions, Providing Real-time Monitoring And Transparency. The System Is Implemented Using Python, OpenCV, And A Multithreaded Architecture To Maintain Real-time Performance Without Blocking The User Interface. Experimental Evaluation Demonstrates Reliable Ambulance Detection, Smooth Signal Transitions, And Effective Prioritization Under Varying Traffic Conditions. The Proposed Solution Enhances Emergency Response Efficiency, Reduces Manual Intervention, And Offers A Scalable Framework That Can Be Integrated Into Smart City Traffic Infrastructure. This Work Contributes Toward Improving Urban Emergency Mobility Through Intelligent Automation And AI-driven Traffic Management.
Author: Aishwarya
Read MoreWildGuard AI: A Spatial Machine Learning Framework For Poaching Risk Assessment
Area of research: Artificial Intelligence And Machine Learning For Wildlife Conservation
Illegal Wildlife Poaching Remains One Of The Most Critical Threats To Biodiversity Conservation, Leading To Ecological Imbalance, Species Extinction, And Economic Loss In Protected Reserves. Traditional Anti-poaching Strategies Rely Heavily On Manual Patrolling And Reactive Response Mechanisms, Which Are Inefficient In Large And Geographically Complex Forest Areas. There Is A Pressing Need For Intelligent, Data-driven Systems Capable Of Predicting High-risk Zones Before Poaching Incidents Occur. This Paper Presents WildGuard AI, A Wildlife Vulnerability Intelligence Engine Designed To Predict Poaching Risk Levels Across Protected Forest Zones Using Machine Learning Techniques. The Proposed System Utilizes Spatial, Environmental, And Historical Incident Data To Classify Forest Grids Into Low, Medium, And High-risk Categories. By Dividing Protected Reserves Into Structured 1 Km² Grids And Applying Supervised Learning Algorithms Such As Random Forest And Gradient Boosting, The System Generates Predictive Risk Heatmaps To Assist Forest Authorities In Strategic Patrol Deployment. The System Is Implemented Using Python, Flask Framework, And A Relational Database For Structured Storage Of Grid-level Intelligence Data. A Web-based Dashboard Visualizes Predicted Risk Zones, Patrol Allocation Data, And Vulnerability Metrics. Experimental Evaluation Demonstrates Reliable Classification Performance And Operational Feasibility. WildGuard AI Contributes Toward Proactive Conservation Strategies, Optimized Resource Allocation, And Technology-driven Wildlife Protection.
Author: Aadhikesavan D | John jabez A | Shanjay GS
Read MoreA STUDY ON CONSUMER LOYALTY: WHY CONSUMERS STICK TO THE APPLE BRAND
Area of research: MARKETING
This Study Focuses On Understanding The Strategic Objectives Aimed At Building A Strong And Sustainable Business Model. The Primary Objective Is To Deliver A Seamless Ecosystem Experience That Integrates Products, Services, And Customer Interactions Into A Unified And Convenient Platform. The Study Also Emphasizes Maintaining High Product Quality And Continuous Innovation To Meet Evolving Consumer Expectations And Remain Competitive In The Market. Furthermore, It Highlights The Importance Of Creating A Strong Brand Identity And Building Customer Trust, Which Are Essential For Long-term Loyalty And Business Growth. By Analyzing These Objectives, The Study Aims To Explore How An Organization Can Enhance Customer Satisfaction, Strengthen Market Positioning, And Achieve Sustainable Development Through Quality, Innovation, And Trust-based Relationships.
Author: Dr.Vadivel M | Thiruselvam M
Read MoreNYKAA’S MARKETING MIX AND ITS EFFECT ON CONSUMER CHOICES
Area of research: MARKETING
This Study Examines Nykaa’s Marketing Mix And Its Effect On Consumer Choices In The Competitive Beauty And Personal Care Industry. Nykaa Has Emerged As One Of India’s Leading Online And Offline Beauty Retailers By Effectively Implementing The 4Ps Of Marketing—Product, Price, Place, And Promotion. The Research Analyzes How Nykaa’s Wide Product Range, Competitive Pricing Strategies, Strong Online Presence, And Influencer-driven Promotional Campaigns Influence Consumer Purchasing Decisions. The Study Also Explores Factors Such As Brand Trust, Product Authenticity, Convenience, Discounts, And Personalized Recommendations. Data Collected Through Surveys And Secondary Sources Interpretation Primary Data Indicate That Digital Marketing, Social Media Engagement, And Customer-centric Strategies Significantly Impact Consumer Preferences And Brand Loyalty. The Findings Suggest That Nykaa’s Well-integrated Marketing Mix Plays A Crucial Role In Shaping Consumer Perceptions And Driving Repeat Purchases. This Study Provides Insights Into How Strategic Marketing Decisions Influence Buying Behavior In The Growing Online Skincare And Cosmetics Market.
Author: Dr.Vadivel M | S.Sowmiya
Read MoreCuriosity To Comfort: Cultural Adaptation And Consumer Trust In KFC
Area of research: MARKETING
This Research Investigates How Cultural Adaptation Influences Consumer Trust Toward KFC In India. While Initial Interactions With The Brand Are Largely Driven By Curiosity And Global Appeal, Sustained Acceptance Depends On Cultural Comfort And Perceived Reliability. The Study Analyses How Localized Menus, Culturally Sensitive Practices, And Consistent Service Quality Contribute To Trust-building Among Consumers. Primary Data Were Collected From 120 Respondents Through A Structured Questionnaire And Analysed Using Percentage Analysis, Ranking Analysis, And Likert Scale–based Weighted Averages. The Results Demonstrate That Cultural Familiarity And Hygiene Perception Significantly Enhance Consumer Confidence And Repeat Patronage. The Study Concludes That KFC’s Success Lies In Its Ability To Integrate Global Branding With Local Cultural Expectations, Transforming Curiosity-driven Trials Into Long-term Consumer Comfort.
Author: Harish V | Dr D Sharon
Read MoreA Study On The Impact Of Lifestyle And Awareness On Food Choices Of College Students
Area of research: MARKETING
Food Habits Among College Students Are Undergoing A Rapid Transformation, Shaped By Academic Pressure, Urban Lifestyles, Technological Influence, And Increasing Independence In Food Choices. This Transitional Phase Of Life Marks A Critical Period Where Young Adults Move Away From Home-cooked Meals Toward Convenience-based And Commercially Prepared Foods. The Present Study Explores The Evolving Dietary Patterns Of College Students, Focusing On Meal Regularity, Food Preferences, Nutritional Awareness, And The Impact Of Social And Environmental Factors. It Highlights How Factors Such As Time Constraints, Peer Influence, Digital Food Delivery Platforms, And Stress Contribute To Irregular Eating Habits And Nutritional Imbalance. While Students Increasingly Prioritize Taste, Affordability, And Convenience, Health Considerations Often Take A Secondary Role. The Study Emphasizes The Long-term Implications Of These Habits On Physical Health, Mental Well-being, And Academic Performance. By Examining The Changing Relationship Between Young Minds And Their Food Choices, This Research Aims To Draw Attention To The Need For Nutritional Awareness And Institutional Support To Encourage Healthier Eating Practices Among College Students.
Author: Dr W Saranya | Ms R Arbutha
Read MoreZonevo: Low-Cost Collaborative Smartphone-Based Urban Vehicle Collision Detection And Navigation System
Area of research: Intelligent Transportation Systems
Urbanization Has Led To A Rapid Increase In Vehicle Density, Resulting In Frequent Road Congestion And Accidents. According To Global Traffic Studies, A Significant Percentage Of Urban Accidents Occur Due To Delayed Reaction Time, Blind Spots, And Lack Of Situational Awareness. Advanced Driver Assistance Systems (ADAS) Have Been Introduced To Reduce Accident Rates. These Systems Use Hardware Sensors Such As LiDAR And Radar To Detect Obstacles And Nearby Vehicles. Although Effective, These Systems Are Costly And Mostly Limited To High-end Vehicles. In Contrast, Smartphones Have Become Ubiquitous And Contain Advanced Sensing And Communication Capabilities. These Devices Can Continuously Monitor Location, Velocity, Orientation, And Motion Patterns. Leveraging These Built-in Sensors For Collaborative Vehicle Safety Presents An Affordable Alternative To Traditional Hardware-dependent Systems. The Primary Objective Of This Research Is To Design And Implement A Low-cost Smartphone-based Collaborative System That Enhances Urban Vehicle Safety Without Requiring Additional Hardware Installations.
Author: Srikumaran S | Sam Stephen S | Sanjai G | Ranjith Kumar S | PRADEEP K (Mentor)
Read MoreSmart Healthcare Appointment And Disease Prediction System
Area of research: Artificial Intelligence And Machine Learning In Healthcare
The Rapid Advancement Of Artificial Intelligence (AI) And Machine Learning (ML) Technologies Has Significantly Transformed Various Domains, Particularly The Healthcare Sector. Early Disease Detection And Timely Medical Consultation Play A Crucial Role In Reducing Mortality Rates, Treatment Costs, And Healthcare Burden. However, Many Individuals Delay Hospital Visits Due To Lack Of Awareness, Limited Accessibility, Long Waiting Times, Or Uncertainty Regarding The Severity Of Symptoms. This Gap Highlights The Need For Intelligent, Accessible, And Integrated Healthcare Assistance Systems. This Paper Presents A Smart Healthcare Disease Prediction And Appointment System Using Machine Learning, A Web-based Application Designed To Provide Preliminary Medical Diagnosis Support And Seamless Doctor Appointment Scheduling Within A Unified Platform. The Proposed System Leverages Supervised Machine Learning Algorithms Trained On Structured Symptom-disease Datasets To Predict Possible Diseases Based On User-input Symptoms And Demographic Attributes Such As Age And Gender. The Prediction Engine Computes Probability Scores For Multiple Disease Classes And Identifies The Most Probable Condition Along With A Confidence Percentage. Additionally, The System Displays The Top Three Predicted Diseases To Enhance User Awareness And Informed Decision-making. To Further Enhance Reliability, The System Incorporates A Risk Classification Mechanism That Categorizes Predicted Diseases Into Low, Medium, And High Risk Levels Based On Model Confidence And Disease Severity. Unlike Traditional Symptom Checker Applications That Provide Only Textual Suggestions, The Proposed Platform Integrates A Complete Appointment Management Module. Users Can View Available Doctors Categorized By Specialization, Book Appointments Directly After Prediction, And Maintain Structured Appointment History Records. The System Is Implemented Using Python, Flask Framework, And A Relational Database For Secure Data Storage And Session Management. The Modular Architecture Ensures Scalability And Extensibility For Future Integration With Hospital Management Systems, Chatbot Assistance, And Mobile Applications. Experimental Evaluation Demonstrates Reliable Prediction Performance And Smooth System Functionality. The Proposed Solution Contributes Toward Enhancing Digital Healthcare Accessibility, Reducing Unnecessary Hospital Visits, And Supporting Early-stage Medical Decision-making Through Intelligent Automation.
Author: JanarthananV | JananiPriyaV | ShamlinThisha J | Abirami R
Read MoreAgri-Guard: An AI-Based Smart Agricultural Surveillance System Using YOLOv8 For Real-Time Object Detection
Area of research: Artificial Intelligence And Smart Agriculture
Crop Damage Caused By Wild Animals, Birds, And Unauthorized Human Intrusion Remains A Major Challenge In Modern Agriculture. Traditional Monitoring Approaches Such As Manual Supervision, Fencing, And Static Alarm Systems Are Often Inefficient, Labor-intensive, And Unable To Provide Continuous Protection. This Paper Presents Agri-Guard, An AI-powered Smart Agricultural Surveillance System That Leverages Real-time Computer Vision And Deep Learning For Automated Detection And Deterrence Of Threats In Farmland Environments. The Proposed System Utilizes The YOLOv8 Object Detection Model For Identifying Humans, Animals, And Birds From Live Camera Feeds. Video Streams Are Processed Using OpenCV, And Detections Are Handled Through A FastAPI-based Backend Architecture. Upon Detecting A Threat, The System Triggers Intelligent Alert Mechanisms, Records Evidence, And Logs Detection Data Into A Structured Database For Monitoring And Analysis. Experimental Evaluation Demonstrates That The System Achieves High Detection Accuracy With Low Latency, Making It Suitable For Real-time Agricultural Deployment. The Modular Architecture Ensures Scalability, Hardware Extensibility, And Integration With IoT-based Deterrent Mechanisms Such As Lights, Alarms, And Laser Systems.
Author: DHARUN A | AKASH SARAVANA SINGH R | REDONE RAJ S | AKASH G | Dr P Shenbagavalli (GUIDE)
Read MoreLUNG TUMOR SEGMENTATION USING VISUAL GEOMETRY GROUP NETWORKS IN MRI IMAGES
Area of research: Electronics And Communication Engineering
Lung Cancer Is One Of The Most Life-threatening Diseases Worldwide, And Early Diagnosis Significantly Increases The Chances Of Survival. Accurate Detection And Classification Of Lung Tumors Remain Challenging Due To Variations In Tumor Size, Shape, And Texture In MRI Images. This Study Proposes An Automated Lung Tumor Segmentation And Classification Framework Using Convolutional Neural Networks (CNN) And The Visual Geometry Group (VGG) Network Architecture. The Proposed System Utilizes MRI Images To Analyze Textural And Spatial Features Of Lung Tissues For Distinguishing Between Normal And Malignant Cases. A Multi-scale Feature Extraction Approach Is Incorporated To Improve Detection Performance And Enhance Classification Accuracy. The VGG Network Serves As The Base Model For Deep Feature Learning, While CNN Layers Refine Segmentation And Classification Tasks. The Developed Database Includes Multiple MRI Views To Ensure Robust Training And Validation. Experimental Results Demonstrate That The Proposed Model Achieves High Precision And Overall Classification Accuracy Of Up To 98%, As Evaluated Using Confusion Matrix Metrics. The System Reduces Manual Interpretation Errors And Provides An Efficient Computer-aided Diagnostic Tool For Early Lung Tumor Prediction.
Author: Mrs.M.Geethalakshmi, Nithish S | Nithish S | Gokul K
Read MoreA Study On Effects Of Television Advertisement On Consumer Buying Behaviour
Area of research: Marketing
Television Advertising Plays A Significant Role In Shaping Consumer Buying Behavior By Influencing Awareness, Attitudes, And Purchase Decisions. This Study Examines The Effect Of Television Advertisements On Consumers By Analyzing How Visual Appeal, Message Content, Repetition, Celebrity Endorsements, And Emotional Elements Impact Buying Intentions. Television Advertisements Act As A Powerful Communication Tool That Not Only Informs Consumers About Products And Services But Also Persuades Them By Creating Brand Recall And Positive Perceptions. The Study Highlights That Frequent Exposure To Television Advertisements Increases Product Familiarity And Trust, Which In Turn Affects Consumers’ Preferences And Choice Of Brands. Moreover, Advertisements Targeting Emotions And Lifestyles Are Found To Be More Effective In Motivating Impulse Buying And Brand Loyalty. The Findings Suggest That Television Advertising Continues To Be An Influential Factor In Consumer Decision-making, Despite The Growth Of Digital Media, And Remains A Crucial Strategy For Marketers To Attract, Influence, And Retain Consumers In Competitive Markets.
Author: Dr W Saranya | Mr. B L Santosh
Read MoreOpaline Attachment Defense System For Proactive Detection And Sanitization Of Malicious Email Files
Area of research: Information Technology
Email Attachments Remain A Primary Vector For Phishing And Malware Attacks, With Traditional Signature-based Defences Struggling Against Zero-day Threats. This Paper Introduces Opaline, A Proactive Defence System That Leverages Deep Learning Models Like RoBERTa To Analyse Textual Semantics And Structural Features In Attachments Such As PDFs And Documents, Achieving Early Detection Before User Interaction. By Integrating Sandbox Isolation And Automated Sanitization—converting Suspicious Files Into Safe Static Previews—Opaline Minimizes Risks While Preserving Workflow Usability, Offering A Practical Advancement Over Resource-heavy Existing Solutions.
Author: Mrs. M.Radhika | G.Ashika | Shreya Agrawal | T.Sruthik
Read MoreCustomer Churn Prediction In B2B Software As A Service
Area of research: Machine Learning
Customer Retention Has Become A Major Concern For Subscription-based B2B Software As A Service (SaaS) Companies Because Long-term Contracts Directly Influence Revenue Stability. Even A Small Increase In Churn Rate Can Result In Noticeable Financial Loss For Service Providers. In This Study, Machine Learning Techniques Are Applied To Analyze And Predict Customer Churn Using Structured Enterprise Data. The Proposed Framework Includes Data Preprocessing, Handling Class Imbalance Using The Synthetic Minority Oversampling Technique (SMOTE), And Evaluating Several Ensemble Learning Models Including Random Forest, AdaBoost, CatBoost, And LightGBM. The Models Are Assessed Using Commonly Used Classification Metrics Such As Accuracy, Precision, Recall, F1-score, And ROC-AUC. Special Attention Is Given To Recall Since Identifying Potential Churn Customers Is Particularly Important For Business Decision-making. Among The Evaluated Approaches, LightGBM Demonstrated Stable And Comparatively Better Performance Across Multiple Metrics. To Improve Transparency, SHAP Analysis Is Used To Interpret Feature Contributions And Identify Factors Influencing Churn Behavior. The Trained Model Is Further Integrated Into A Streamlit-based Dashboard That Allows Real-time Predictions And Batch Processing Of Customer Data, Helping Organizations Monitor Churn Risk And Plan Proactive Retention Strategies.