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VOLUME 13, ISSUE 4, APRIL 2025
5G AND AI IN WEARABLE MEDICAL DEVICES: A NEW ERA OF PREVENTIVE HEALTHCARE
Zeeshan Ahmed Mohammed, Muneeruddin Mohammed, Rahmat Ali, Nasar Mohammed
SMART VOICE-CONTROLLED APPLIANCE MANAGEMENT SYSTEM USING LABVIEW
Dr. Shanmugavalli M, Mohammed Abdullah N, Prasanna V, Koushik T
ADVANCED TRAFFIC CONTROL THROUGH IOT INTEGRATION
Varshini. V. G, Dr. R. Praba
Animal Detection system using OpenCV
V. Ajay Priyadharrsan, Dr. K. Santhi
AI-Based Fake News Detection System
Mr.Arun Prasanth S, Dr. K. Banuroopa, M.Sc., M.Phil., Ph.D.
SOLAR POWER MONITORING AND MANAGEMENT USING IOT
Srimanya.S, Dr.Adhiselvam.A
CROP YIELD PREDICTION USING DEEP XG BOOST ALGORITHM
MATHIVADHANI.M, Dr. K. THENMOZHI M.Sc., M.Phil., Ph. D
PC BASED MONITORING OF HUMAN BIOLOGICAL SIGNALS USING LABVIEW
Dr. M. Shanmugavalli V*, Mathangini M, Praveena M, Kaavya N R
DIABETES EARLY DIAGNOSIS AND HEALTH MONITORING SYSTEM
Vignesh.R, DR J.Savith
DETECTION AND IFENTIFICATION OF PILLS USING MACHINE LEARNING
VARUN PRADHAP G, ARUMUGAM N, Dr. K. THENMOZHI
EARLY DISEASE DETECTION AND BANANA TREE HEALTH MONITERING SYSTEM USING MACHINE LEARNING
GOKULPRIYAN V, Dr. A. NIRMALA
Interactive AI-Based Communication Assessment: Overview and Significance
JASWIN B, Dr. A. NIRMALA
SMART MEDICAL ASSISTANCE AND LIFE SAVING ALERT SYSTEM
MANO DEEPAN. G, Dr. A. NIRMALA
CUSTOMISABLE RESTAURANT FOOD ORDERING AND MANAGEMENT SYSTEM
SHANMATHI R, Mrs. SOWNDHARYA M
IOT BASED – SMART CAR PARKING SYSTEM
SENTHIL S A, Dr. P. SAKTHI MURUGAN
LOGISTICS AND CARGO AUTOMATION SYSTEM
Ms. KEERTHANA S, Dr.A.NIRMALA
DIABETES DISEASE PREDICTION USING MACHINE LEARNING
SATHISH.M, P. SAKTHI MURUGAN M.Sc., M.Phil., NET., Ph.D.,
The Virtual Reality Dining & Social Food Hub concept
Nivedita R, Tanmay Das, Mahalakshmi Keruthika C, Aalok KR Pandit, Surya S, Shravana Khodanpur, Dr. Gopalakrishnan Chinnasamy
The impact of Integrated Silent Work Pods and Café Services on Productivity and Employee Well-being.
Shreevalli K M, Shreeya M, Yerrachowdu Lakshmi Narasimhulu, A N Nishaanth, Badri Narayanan KS, Adith MP, Dr. Gopalakrishnan Chinnasamy
DECENTRALIZED VOTING SYSTEM USING BLOCKCHAIN TECHNOLOGY
Negin Chandru.A, Dr. K. Banuroopa
MEDICINE REMINDER SYSTEM WITH AVAILABILITY NOTIFICATION
PAVITHRA.S, Mrs. P. MENAKA
A METHOD OF SKIN DISEASE DETECTION USING IMAGE PROCESSING AND MACHINE LEARNING
Narayana Moorthi. A, Shanthini. S
ENHANCED MACHINE LEARNING ALGORITHM TO PREDICT LUNG CANCER
NANDHINI.S, Dr. R. SENTHIL KUMAR
VEHICLE DETECTION AND TRACKING SYSTEM
K. ARUNTHATHI, Dr. R. SENTHIL KUMAR
DATA SECURITY AND PRIVACY MANAGEMENT IN HEALTHCARE
U. Ranjith, Dr S Shanthini
GREENARY ACRES SALES AND FARMER HIRING PORTAL
Vishal N, Mrs. Vishnu Priya S
INTEGRATED HOME SERVICES PROVIDER APPLICATION
Mukil.P, Dr. A.Adhiselvam
NGO Fund management Web Application
B. Kavin Prabhu, Dr. A.Adhiselvam
EMPLOYEE LEAVE MANAGEMENT SYSTEM
Ms. SRINITHI R, Dr. P. SAKTHI MURUGAN M.Sc., M.Phil., NET., Ph.D.,
AI POWERED EMAIL AUTOMATION FOR CUSTOMER QUERY HANDLING IN UIPATH
Mr. Karpagavinayagam S, Mrs. Sowndharya M
Blockchain Based Logistics Protocol Using Hyperledger Fabric
Rakshun Selvaa S.K., Mrs. A. Sathiya Priya
IMAGE READING AND INTERPRETATION USING OCR
M.SABARI, Dr.R.SENTHIL KUMAR
DEVELOPMENT OF AN ENHANCED FLAPPY BIRD GAME WITH DYNAMIC ENVIRONMENTAL EFFECTS USING PYTHON AND PYGAME
Gnana Deepak R, Dr. A. Nirmala
A photovoltaic solar system with multiple tasks
Priyanka Malyal, Bhargavi Konda, Maheshwari Kota, S. A. Malvekar
BITCOIN PRICE PREDICTION USING MACHINE LEARNING AND DEEP LEARNING MODELS
R.Mugil Vishnu, Dr.K.Santhi
BLOOD BANK AND DONOR MANAGEMENT SYSTEM
Mr. Rubanraj S, Mrs. Vishnu Priya R
Health and Fitness Chatbot with Diet and Workout Recommendations
Ms. Monika C, Dr. A. NIRMALA MCA., M.Phil., Ph.D.
IoT-Based Anti-Poaching Alarm System for Forest Conservation
Sudharshan Srinivasan. S.L, Shanthini. S
PERSONALIZED FINANCE MANAGEMENT
Ms. Vinothini S, Mrs. Sowndharya, M.Sc, M.Phil, Ph.D,
NUMBER PLATE RECOGNITION
PRAVIN V, Dr K BANUROOPA MCA., M.Phil., Ph.D.
SOCIAL MEDIA SENTIMENT ANALYSIS FOR COLLEGE BRANDING
V. Gayathri, Dr. A. Nirmala
LEVERAGING ARTIFICIAL INTELLIGENCE TO TRANSFORM E- COMMERCE PLATFORMS
RAHUL K.M, SABARI.T, RUTHRAPRASATH.D, GOKUL.N
A WEB PORTAL FOR PLANT NURSERY MANAGEMENT
N. Rithanya, Dr.A.Adhiselvam
Cyber threat detection using machine learning
SIVAKUMAR.V, Dr. R. PRABHA
CHATBOT FOR DOCTOR APPOINMENT SCHEDULING
Ms. GEETHA N, DR. P. SAKTHI MURUGAN M.SC., M.PHIL., NET., PH.D.
Accessible Job Portal Website For Disabled Individuals
Mr. S. Abishek, Mr. A. ADHISELVAM, M.Sc., P.B.D.C.A., M.C.A., M.Phil., Ph.D., SET.
QUALITY CONTROL SYSTEM FOR GARMENT FACTORY
S.Deepalakshmi, Dr. A.Adhiselvam
DIGITAL BUS FARE MANAGEMENT SYSTEM
MEGAVARSHNA V, Dr. P. SAKTHI MURUGAN
Why Women Need Exclusive Gyms for a Healthier Future
Hemanth ZB, Ankita Ghosh, Anjali Phukan, Dev Aadhisesh S K, C Khiruthic, G Vedant, Dr. Kalavathy K S
Hostel Gate Pass Management System
Amirthavarshini S, Mrs. L. Ajitha
STUDENT STAFF INTRA CONNECT WEB APP
Mr. Akash G, Dr. P. Sakthi Murugan
A Descriptive Study on Consumer Preferences for Natural Moisturizers for Bangalore’s Climate
DharaniLakshmi, Ansita Pattnaik, Shradha Murthy, Dakshatha G, Md Faiz Ansari, Gowtham G, Sayon Paul, Boddepalli Pranay Kumar, Dr Malani V Nair
INDUSTRY ENERGY CONSUMPTION PREDICTION USING MACHINE LEARNING ALGORITHM
M.Sabarishan, Dr. K. Santhi
EEG BASED MENTAL STABILITY ANALYSIS FOR CORPORATE EMPLOYEES USING TRANSFORMER NEURAL NETWORK
Ms. Nivethitha.G, Dr. K. Banuroopa, MCA., M.Phil., Ph.D.,
A STUDY ON DEVELOPING AN EXPANDABLE SPOON FOR THE USER-FRIENDLY CONSUMERS- AN ECO-FRIENDLY INITIATIVE
Dr. L Sudershan Reddy, Samruddhi Pattanashetti, Tejasvani D, Saravana Kumar, Varun N, Shirisha Suresh, Shridhar Srinivas Nagarhalli, Suraj Ranjan Nayak
FOOD CALORIES ESTIMATION
Sabarish M, Mrs. M. Sowndharya M.Sc., M.Phil., (Ph. D.)
PROJECT REPORT ON ANALYZING THE MARKET POTENTIAL FOR NICHE FOOD DELIVERY SERVICES TARGETING PG ACCOMMODATOR SEGMENTS
Aditya Kedlaya H, Aishwarya Prasad B G, Akshar R, Aneesh S Poojary, Anirudh Santhosh K, Dr. Umesh Chandra
RESEARCH REPORT ON ANALYZING THE MARKET POTENTIAL FOR NICHE FOOD DELIVERY SERVICES TARGETING PG ACCOMMODATOR SEGMENTS
Aditya Kedlaya H, Aishwarya Prasad B G, Akshar R, Aneesh S Poojary, Anirudh Santhosh K, Dr. Umesh Chandra
CNC BASED WRITING AND DRAWING MACHINE
Ashvini Shrikrushna Hirve, Rohit Dashrath ade, om prahlad kanse, rashmi rahul rohane, ayush nitin bhagwate, Sanjana ravidas barve, sanidh kamble, umesh kaware
ROLE OF AI CHATBOTS IN ENHANCING CUSTOMER ENGAGEMENT ON E-COMMERCE PLATFORMS
Duraiarasu N, Rajlaxmi Narayan Relekar, Rakshitha D Desai, Rishabh Roonwal M, Rohan R, R Sekhar, P K Thomas
Orchid Fixie Flora: The Eco Bloom Initiative
N Roshan Choudhury, Kiruthika G, Kavya Ganji, Arjun R, Murari Jagadeesh, Kanimozhi A
RESEARCH REPORT ON ANALYZING THE MARKET POTENTIAL FOR ECO FRIENDLY PACKAGING SOLUTION
Sayan Banik, Srujan P S, Sriram V, Thashwin, Vishnu P, Yajat Sureka, Dr. Umesh Chandra
THE STUDY ON CUSTOMER SATISFACTION TOWARDS GOOGLE PAY WITH SPECIAL REFERENCE TO TIRUPUR CITY
Rahul. V, Malarvizhi. D
“LONG-TERM MENTAL HEALTH EFFECTS OF SEPARATION FROM LOVED ONES: THE ROLE OF EMPATHY AND RELATIONSHIP ATTACHMENT STYLES”
Dr. Kumar Mukul, Mohamed Shihaab Ibrahim M, Noorah Sachora, S Gayan Gowda, Tendulkar Shivam Kishor, Rithikanand Kedanuri, Rupesh M
Turning Plastic into Rewards: A Smart Recycling Solution
Kamalesh S, Brunda B V, Diksha Singh, Dilip Marwal, Bhavish D Kotian, Darshan H, Dr. Uma C Swadimath
FAMILY FINANCIAL LITERACY IN THE DIGITAL ERA: A SURVEY OF ONLINE INVESTMENT APPLICATION AWARENESS AND USAGE IN COIMBATORE
Dr. S. Kamalaveni, Mr. S. Abhishek, Ms. G. Aarthi
DESIGN AND FABRICATION OF A LONG-RANGE SPY ROBOT WITH NIGHT VISION USING ULTRASONIC SENSOR
A JHON PRESIN KUMAR, ABHIJEET MONDAL, B YESHWANTH, C S MUGIL
TREADMILL EXERCISE: EFFECTS ON PSYCHOLOGICAL DISTRESS IN SEDENTARY STUDENTS
Dr. Sinku Kumar Singh, Dr. Shivaji Suryawanshi
AWARENESS OF STOCK MARKET INVESTMENT USING AI
Dr. P. KANNAN M.Com., M.Phil., Ph.D. PGDCA, GOKULA KRISHNAN .R
IMPACT OF CORPORATE SOCIAL RESPONSIBILITY ON BRAND REPUTATION AMOUNG TATA GROUPS
Dr. P. KANNAN M.Com., M.Phil., Ph.D. PGDCA, GANESAN.R
A STUDY ON MOBILE APP DEVELOPMENT FOR E-COMMERCE IN COIMBATORE CITY
Dr. S. Kamalaveni, Ms. A.Anish Fathim
ANALYSING THE IMPACT OF EMPLOYEE ENGAGEMENT ON PRODUCTIVITY IN COTTON CODE GARMENTS TIRUPPUR
Dr. S. Kamalaveni, Ms. S. Aiswariya, Ms. M.T Padmapriya, Mr. S. Abhishek
Leveraging Sustainable Urban Development through Smart Waste Management in Bangalore
AISWARYA V B, ALKA SINGH, DILSHAN M, EAPEN JINIX THARAKAN PEREKATT, ADITYA JEETENDRA PORWAL, HARSHITHA L, RUDRA S, KAKULAPATI NAVEEN
IMPULSE BUYING BEHAVIOUR OF CONSUMERS TOWARDS FMCG PRODUCTS
Mr. S. Muruganantham, Mr. S. Kaleeswaran, Mr. R. Rohinth, Mr. S. Abhishek
A STUDY ON COMPARING COMPENSATION STRATEGIES FOR STARTUPS VS ESTABLISHED COMPANY
Dr. P. Kannan, Mr. N. Harish Nithin, Mr. S. Abhishek
A STUDY ON DATA SECURITY ON MOBILE APPLICATION
Dr. P. Kannan, Ms. T. Ilakkya, Mr. S. Abhishek
A STUDY ON ANALYSIS OF CYBERSECURITY THREATS IN BIG BASKET
Mrs. R. Anitha, D. Udhaya
A STUDY ON COMPARING COMPENSATION STRATEGIES FOR STARTUPS VS ESTABLISHED COMPANY
Dr. P. Kannan, Mr. V. K. Hareesh Raja, Ms. U. Heena Sri, Mr. S. Abhishek
A STUDY ON CONSUMER PERCEPTION TOWARDS SMART HOME TECHNOLOGY
Kannan P, Heena Sri U, Hareesh Raja V K, Abhishek S
SMART NEST REALITY HUB
Dr. N. Deepa, Mr. Manoj kumar, Mr. S. Abhishek
A STUDY ON CONSUMER ADOPTION ON DIGITAL WALLET PROJECT REPORT
Mr. R. Surya, Mr. G. Sethuraman
THE IMPACT OF DIGITAL TRANSFORMATION ON RETAIL MANAGEMENT AND CONSUMER BEHAVIOR
M.KUMARAN, Dr. N. DEEPA
CONSUMER BEHAVIOUR TOWARDS PERSONALIZED DIGITAL RECOMMENDER ENGINE – A STUDY IN COIMBATORE CITY
G. RAJESHWARI, M.COM., MBA., M.PHIL., PH.D., S. MANOJKAVIN
SECURED CLOUD-BASED FILE STORAGE SYSTEM
G. RAJESHWARI, M.COM., MBA., M.PHIL., PH.D., K. NAVEEN
Exploring Virtualization Techniques for Enhancing Business IT
Kamesh M, Dr. D. Suresh Kumar
ARTIFICIAL INTELLIGENCE IN FINANCIAL SERVICES
Mrs. R. Anitha, P. Vinoth Kumar
IoT Based Crop Monitoring Drone Using Mobile App
Mrs. M. U. Phutane, Kaushaly Dadaso Jadhav, Shubham Nagoji Kasalkar, Vaibhav Shrikant Chavan
"User-Friendliness of the New Income Tax Portal: A Study on Individual Taxpayers’ Experience"
Alka Singh, Amal Fatema Hussain, Patnala Akanksha, Dr. Kiran Kumar M
IMPACT OF SOCIAL MEDIA INFLUENCERS ON CONSUMER PURCHASING DECISIONS
Mr. s. Muruganantham, G. sujitha
GUI FOR MESS MANAGEMENT SYSTEMUSING GSM
Mrs. M. U. Phutane, Daksha Nikam, Revati Halingale, Sanika Bodhale
A Comprehensive Review of Robo Navigator: A Smart, Mobile-Controlled Educational Robot
Dr.P.D.Pawar, Sakshi Yadav, Amit Kasambe, Roshan Jadhao, Chetana Rathod, Anjali Yewale, Aditi Ingole, Bharti Naidu
AUTOMATED CONTENT GENERATION IN NLP
Ms. TRISHA. A, Dr. K. BANUROOPA, MCA., M.Phil., Ph.D.
The Interplay of Mathematics and Artificial Intelligence: Foundations and Future Directions
Laxmi Bhavani Cheekatimalla
A FACE RECOGNITION SECURITY SYSTEM FOR DISABLED PEOPLE
HARIPRIYA R, Dr. PRABA R
PREDICTION OF CHRONIC KINDNEY DISEASE USING MACHINE LEARNING
PARAMESWARI.N, Mrs. P. MENAKA
RAILWAY ACCIDENT PREVENTION SYSTEM
Prof. Dr. M. B. Bhilavade., Omkar Arun Tibile, Sumit Shivaji Hajare, Rushikesh Sunil Davari
IOT- Smart Parking System
Mandar Molawade, Swapnil Kante, Tejas Patil, Shrajal Dwivedi, Vishal Jaiswal, Mr. Rahul S Desai, Dr. Mayuri H Molawade
DESIGN OF A MINIATURIZED ULTRA WIDE BAND MIMO ANTENNA WITH MULTI-BAND CHARACTERISTICS
Sri. A. Rajasekhar, K. Jahnavi, K. Pratheesh, CH.V. Sujith, I. Kranthi babu
“IoT BASED SMART MONITORING & CONTROL SYSTEM FOR GREENHOUSE POWERED BY DUAL AXIS SOLAR TRACKED PV SYSTEM”
Prof. Dr. T. H. Mohite, Sayali Dilip Powar, Siddhi Shital Yelappure, Shraddha Niranjan Tathe
Tax Planning Strategies for Small and Medium Enterprises (SMEs): Optimizing Tax Liability and Enhancing Growth
Dr. Kiran Kumar, Nandhini M, Bhavana Varsha Kothapalli, Dhriti Kumari, AnjaliBharti
BRAIN-COMPUTER INTERFACES: CAN WE CONTROL MACHINES WITH OUR MINDS?– EXPLORING NEURALINK AND THOUGHT-POWERED DEVICES.
ABIRAMI S, CHITTAL N, DR. K. THENMOZHI
A DECENTRALIZED FRAMEWORK FOR STUDENT-OWNED EDUCATIONAL DATA BASED ON HASH
Pagadala Likith Reddy (21WJ1A05P1), Racharla Anil (21WJ1A05R0), Madasu Indu (22WJ5A0518), K Akshitha
|DOI: 10.17148/IJIREEICE.2025.134113
BLOCKCHAIN-BASED LOGGING TO DEFEAT MALICIOUS INSIDERS: THE CASE OF REMOTE HEALTH MONITORING SYSTEMS
Kasi Sailaja, Mudavath Chandi Priya (22WJ5A0522), Md Kareemoddin (22WJ5A0519), Sai Pratham Reddy (21WJ1A05T1), Dr. Mahesh Kotha
Vertical Axis Wind Turbine
Rohit Rajuke, Pratik Wankhede, Pallavi Imane, Karishma Satpudkar, Akash Girghuse, Sakshi Kotambe, Prof. R.M Bhombe
Abstract
5G AND AI IN WEARABLE MEDICAL DEVICES: A NEW ERA OF PREVENTIVE HEALTHCARE
Zeeshan Ahmed Mohammed, Muneeruddin Mohammed, Rahmat Ali, Nasar Mohammed
DOI: 10.17148/IJIREEICE.2025.13401
Abstract: The integration of 5G and artificial intelligence (AI) in wearable medical devices is revolutionizing healthcare by enabling continuous, cost-effective monitoring for patients. 5G connectivity enhances the healthcare industry by offering reduced latency, increased energy efficiency, and speeds up to 100 times faster than its predecessor, 4G LTE. The wearable medical device market, currently valued at over $40 billion, continues to experience significant growth and shows promise for the future. However, as more companies adopt 5G technology, several challenges must be addressed to realize its full potential. Key issues include improving battery life, investing in data security and privacy, and reducing infrastructure acquisition costs, particularly in developing and underdeveloped countries.
Keywords: Artificial intelligence, Wearable medical devices, machine learning (ML), predictive analytics and deep learning
Keywords: Artificial intelligence, Wearable medical devices, machine learning (ML), predictive analytics and deep learning
Abstract
SMART VOICE-CONTROLLED APPLIANCE MANAGEMENT SYSTEM USING LABVIEW
Dr. Shanmugavalli M, Mohammed Abdullah N, Prasanna V, Koushik T
DOI: 10.17148/IJIREEICE.2025.13402
Abstract: Home automation has transformed the way electrical appliances are managed, improving convenience, energy efficiency, and accessibility. This research presents a smart voice-controlled appliance management system that allows users to operate household appliances using voice commands transmitted via Bluetooth to myRIO. The system is particularly designed to assist individuals by providing a hands-free and intuitive method for controlling electrical devices. Using LabVIEW, the system processes and interprets voice commands, subsequently triggering the activation or deactivation of appliances through relay control circuits. The methodology includes voice recognition, wireless data transmission via Bluetooth, real-time processing in myRIO, and execution of commands through relay switching. Experimental validation demonstrates high command accuracy, minimal latency, and reliable Bluetooth communication. This paper discusses the system’s design, working principles, testing outcomes, and future improvements. The proposed system is cost-effective, scalable, and practical for integration into smart homes and assistive technology applications.
Keywords: Home Automation, IoT, MyRIO, Bluetooth communication, LabVIEW.
Keywords: Home Automation, IoT, MyRIO, Bluetooth communication, LabVIEW.
Abstract
ADVANCED TRAFFIC CONTROL THROUGH IOT INTEGRATION
Varshini. V. G, Dr. R. Praba
DOI: 10.17148/IJIREEICE.2025.13403
Abstract: Advanced traffic control through IoT (Internet of Things) integration represents a transformative approach to urban mobility management, enabling smarter, more efficient, and responsive transportation systems. By leveraging IoT- enabled sensors, devices, and communication networks, real-time data collection and analysis are made possible, providing a holistic view of traffic conditions and allowing for dynamic decision-making. IoT devices, such as connected traffic signals, smart cameras, and vehicle-to-infrastructure (V2I) communication systems, can continuously monitor traffic flow, vehicle speeds, pedestrian movements, and environmental factors like weather or air quality. This data is then processed using advanced analytics, machine learning algorithms, and cloud computing, which can predict traffic congestion, optimize signal timings, reduce delays, and enhance safety measures. Furthermore, IoT integration enables the coordination of various transportation modes, such as public transit, ride-sharing services, and autonomous vehicles, leading to seamless intermodal connectivity. This connected infrastructure also allows for real-time updates and alerts to drivers, helping them navigate efficiently and avoid potential hazards. Additionally, IoT-enabled traffic systems can contribute to sustainable urban planning by reducing carbon emissions, improving energy efficiency, and minimizing traffic- related noise pollution. Ultimately, the integration of IoT in traffic control systems represents a crucial step towards the development of smart cities, where transportation infrastructure is not only automated but also intelligently adaptive to changing conditions, enhancing the overall quality of life for residents and visitors alike.
Keywords: Advanced traffic control through IoT integration uses smart sensors and real- time data to manage traffic better and reduce congestion. It connects vehicles and traffic lights to improve traffic flow and make roads safer.
Keywords: Advanced traffic control through IoT integration uses smart sensors and real- time data to manage traffic better and reduce congestion. It connects vehicles and traffic lights to improve traffic flow and make roads safer.
Abstract
Animal Detection system using OpenCV
V. Ajay Priyadharrsan, Dr. K. Santhi
DOI: 10.17148/IJIREEICE.2025.13404
Abstract: The project is aimed at developing an animal detection system using OpenCV, a very commonly used computer vision tool, without involving any sophisticated machine learning models. The system detects and classifies animals instantaneously in real-time using simple image processing techniques based on images and video feeds. With some of the techniques such as edge detection, background subtraction, contour finding, and motion tracking, this system can detect animals in diverse settings such as towns, farms, and forests with a high degree of accuracy. This method enables accurate detection of animals without the need for pre-trained AI models, thus simplifying the device and rendering it weightless and facilitating quicker deployment. It suits applications such as wildlife monitoring, security, and agricultural management, where effective and real-time tracking of animals is absolutely important. The strong image processing capability of OpenCV allows the system to run effectively.
Abstract
AI-Based Fake News Detection System
Mr.Arun Prasanth S, Dr. K. Banuroopa, M.Sc., M.Phil., Ph.D.
DOI: 10.17148/IJIREEICE.2025.13405
Abstract: The rapid spread of false information in the digital age poses a serious threat to society, influencing how people think and make decisions. As a result, identifying fake news has become essential to ensuring the reliability of information found online. Traditional fact-checking methods often rely on slow, labor-intensive manual processes that are increasingly ineffective given the volume and speed of misinformation. This has led to growing interest in machine learning-based solutions for automating fake news detection. In this study, we propose a fake news classification model that uses Logistic Regression for classification and TF-IDF (Term Frequency-Inverse Document Frequency) vectorization for feature extraction, helping to distinguish between real and fake news articles more efficiently.However, many existing fake news detection systems face significant challenges. Traditional models often struggle to adapt to evolving misinformation patterns, leading to outdated or inaccurate results. Additionally, class imbalances in datasets — where one type of news (real or fake) heavily outweighs the other — can create biased predictions. Feature extraction techniques commonly used in older models also fail to capture the deeper, semantic meaning of text, resulting in subpar classification performance. Moreover, many models lack the ability to generalize across diverse datasets, which limits their effectiveness in real-world applications. These challenges highlight the need for a more reliable and adaptable system for fake news detection.
To address these issues, we propose an enhanced machine learning-based detection system. Our approach incorporates Logistic Regression alongside TF-IDF vectorization for effective feature extraction. We also introduce stratified train- test splitting to maintain class distribution during training and use RandomOverSampler to combat class imbalances by generating synthetic samples for underrepresented classes. To thoroughly evaluate performance, we measure accuracy, precision, recall, and visualize results using a confusion matrix, providing a clearer picture of how well the model performs.In addition to these core techniques, our system introduces several novel improvements. We implement automatic dataset validation to identify and handle missing or imbalanced labels, ensuring data is ready for training without manual intervention. If one class is significantly underrepresented or missing altogether, our model performs class augmentation, generating synthetic data to restore balance. We also introduce an interactive user prediction feature, allowing users to input custom news articles for real-time classification. This interactive component enhances the model’s practicality, making it a valuable tool for everyday use. These improvements collectively enhance model reliability, resulting in a more robust, accurate, and adaptable fake news detection system capable of keeping up with the ever-changing landscape of misinformation.
To address these issues, we propose an enhanced machine learning-based detection system. Our approach incorporates Logistic Regression alongside TF-IDF vectorization for effective feature extraction. We also introduce stratified train- test splitting to maintain class distribution during training and use RandomOverSampler to combat class imbalances by generating synthetic samples for underrepresented classes. To thoroughly evaluate performance, we measure accuracy, precision, recall, and visualize results using a confusion matrix, providing a clearer picture of how well the model performs.In addition to these core techniques, our system introduces several novel improvements. We implement automatic dataset validation to identify and handle missing or imbalanced labels, ensuring data is ready for training without manual intervention. If one class is significantly underrepresented or missing altogether, our model performs class augmentation, generating synthetic data to restore balance. We also introduce an interactive user prediction feature, allowing users to input custom news articles for real-time classification. This interactive component enhances the model’s practicality, making it a valuable tool for everyday use. These improvements collectively enhance model reliability, resulting in a more robust, accurate, and adaptable fake news detection system capable of keeping up with the ever-changing landscape of misinformation.
Abstract
SOLAR POWER MONITORING AND MANAGEMENT USING IOT
Srimanya.S, Dr.Adhiselvam.A
DOI: 10.17148/IJIREEICE.2025.13406
Abstract: The goal of this project is to improve efficiency and dependability by managing and monitoring solar energy characteristics in real time. The system monitors important solar metrics including light intensity, voltage, and current using the ESP32 microcontroller, which has integrated WiFi and energy efficiency. To ensure accurate data gathering, a voltage sensor keeps an eye on the solar panels' output voltage while the ACS712 current sensor measures the current produced by the panels. Through the processing and wireless transmission of this data by the ESP32, customers can utilize a mobile application to track solar performance in real time. One noteworthy aspect is the automated light control system, which maintains constant illumination by turning on an artificial light source when the solar voltage drops below a predetermined threshold. Even in a variety of environmental circumstances, its feature guarantees dependable operation. Applications such as real-time solar power monitoring, renewable energy management, and smart lighting systems are ideal for the suggested system. This project optimizes energy consumption and supports consistent illumination by providing a cost-effective, efficient, and sustainable method of controlling solar power. This helps to improve the use of renewable resources and make smarter energy consumption decisions.
Keyword: Green energy, IOT, Remote storage, Solar panels, Real-time monitoring, solar panel
Keyword: Green energy, IOT, Remote storage, Solar panels, Real-time monitoring, solar panel
Abstract
CROP YIELD PREDICTION USING DEEP XG BOOST ALGORITHM
MATHIVADHANI.M, Dr. K. THENMOZHI M.Sc., M.Phil., Ph. D
DOI: 10.17148/IJIREEICE.2025.13407
Abstract: Because crop yield is dependent on a number of variables, it is a difficult task to predict. Even though a lot of models have been created so far in the literature, they still need to be improved because their performance is inadequate. In order to assess the performance of the underlying algorithms in relation to various performance criteria, we created deep learning-based models for this study. The XGBoost machine learning (ML) algorithm, convolutional neural networks (CNN), XGBoost, and recurrent neural networks (RNN) are the algorithms that were assessed in this study. According to the environmental, soil, silt, nitrogen, clay, ocd, ocs, pHH2O, sand, soc, ceo, water, and crop parameters, we estimated crop yield for the case study.
Keywords: XGBOOST,Crop yield learning algorithms.
Keywords: XGBOOST,Crop yield learning algorithms.
Abstract
PC BASED MONITORING OF HUMAN BIOLOGICAL SIGNALS USING LABVIEW
Dr. M. Shanmugavalli V*, Mathangini M, Praveena M, Kaavya N R
DOI: 10.17148/IJIREEICE.2025.13407
Abstract: PC-based monitoring of human biological signals using LabVIEW is an indirect method for measuring human biological parameters, which helps determine whether a person is in a normal or abnormal condition. Human body parameters such as body temperature, pulse rate, blood pressure, and respiration rate are commonly used to assess health. These parameters are measured from the external surface of the body, which characterizes the indirect method of measurement. Sensors like LM 35, XD 58C, and 6820 are used to measure temperature, pulse, and blood pressure signals. These signals are then interfaced with the LabVIEW simulator through myRIO. The obtained signals are compared with normal parameter values, and if any abnormalities are detected, the system provides an output indicating the issue.
Keywords: LabVIEW, myRIO, Temperature sensors, Pulse sensor, Pressure sensor, Pulse rate, Blood Pressure.
Keywords: LabVIEW, myRIO, Temperature sensors, Pulse sensor, Pressure sensor, Pulse rate, Blood Pressure.
Abstract
DIABETES EARLY DIAGNOSIS AND HEALTH MONITORING SYSTEM
Vignesh.R, DR J.Savith
DOI: 10.17148/IJIREEICE.2025.13409
Abstract: The "AI-Powered Diabetes Early Diagnosis and Health Monitoring Platform" project introduces an innovative method for the early detection and management of diabetes, a growing global health concern. Utilizing advanced machine learning algorithms, this initiative focuses on analyzing and interpreting complex medical data to accurately forecast the likelihood of diabetes onset in individuals. The predictive model integrates a thorough examination of multiple factors, including genetic predispositions, lifestyle habits, environmental influences, and pre-existing medical conditions, all of which play a crucial role in shaping an individual’s risk profile for developing diabetes.
A standout feature of this system is its emphasis on personalized medicine. By considering each individual's unique health profile and risk factors, the model delivers customized risk evaluations, enabling more precise and effective prevention strategies. This tailored approach not only enhances patient outcomes but also supports the efficient use of healthcare resources
Keywords: Artificial intelligence, Machine learning, Diabetes, Disease prediction.
A standout feature of this system is its emphasis on personalized medicine. By considering each individual's unique health profile and risk factors, the model delivers customized risk evaluations, enabling more precise and effective prevention strategies. This tailored approach not only enhances patient outcomes but also supports the efficient use of healthcare resources
Keywords: Artificial intelligence, Machine learning, Diabetes, Disease prediction.
Abstract
DETECTION AND IFENTIFICATION OF PILLS USING MACHINE LEARNING
VARUN PRADHAP G, ARUMUGAM N, Dr. K. THENMOZHI
DOI: 10.17148/IJIREEICE.2025.13410
Abstract: In the medical field, precise pill identification and detection are essential for avoiding prescription mistakes and guaranteeing patient safety. Machine learning must be used to automate the process because traditional manual approaches are tedious and susceptible to human mistake. Due to changes in illumination, background noise, and picture quality, conventional rule-based image processing methods—which depend on texture, colour, and shape—frequently have accuracy and robustness issues. Using Convolutional neural networks, more commonly trained on a dataset of pill pictures with data augmentation for improved generalisation, this study suggests a deep learning-based method to overcome these drawbacks. Multiple convolutional, maximal pooling, and layer dropouts are included into the model to improve feature extraction and lessen overfitting. Accuracy under various circumstances is ensured by validating performance on a distinct dataset.
Abstract
EARLY DISEASE DETECTION AND BANANA TREE HEALTH MONITERING SYSTEM USING MACHINE LEARNING
GOKULPRIYAN V, Dr. A. NIRMALA
DOI: 10.17148/IJIREEICE.2025.13411
Abstract: In many tropical and subtropical areas, banana farming is an essential part of the agricultural economy. However, crop yield and quality are impacted by a number of diseases and environmental factors. In order to maintain plant health and increase productivity, early disease detection is essential. An AI and ML-based system for early disease detection and banana tree health monitoring is presented in this paper. To identify diseases and give farmers real-time feedback, the system makes use of deep learning and image processing techniques. To extract features and classify various banana leaf diseases, a convolutional neural network (CNN) model is used. The effectiveness of the suggested system in precisely identifying diseases and supporting decision-making for improved crop management is demonstrated by experimental results.
Keywords: Convolutional Neural Networks, AI, Machine Learning, Disease Detection, Banana Tree.
Keywords: Convolutional Neural Networks, AI, Machine Learning, Disease Detection, Banana Tree.
Abstract
Interactive AI-Based Communication Assessment: Overview and Significance
JASWIN B, Dr. A. NIRMALA
DOI: 10.17148/IJIREEICE.2025.13412
Abstract: Communication skills are central to professional success, and the need for an automated assessment system has gained importance. In this paper, an interactive AI-driven communication assessment system is proposed that examines verbal and non-verbal communication skill through real-time video and voice analysis. Machine learning-based speech recognition, sentiment analysis, facial expression recognition, and linguistic analysis are used in the system to provide whole-system feedback. Experimental results indicate the effectiveness of the proposed system in effectively evaluating communication competency. The study adds value to the research community by enhancing automated interview and training processes using AI-driven analytics.
Keywords: AI, Communication Assessment, Machine Learning, Speech Recognition, Sentiment Analysis, Facial Expression Detection, Natural Language Processing, Deep Learning, Interview Coaching.
Keywords: AI, Communication Assessment, Machine Learning, Speech Recognition, Sentiment Analysis, Facial Expression Detection, Natural Language Processing, Deep Learning, Interview Coaching.
Abstract
AGRICULTURAL YIELD FORECASTING SYSTEM
DARSHINI R, Dr. A. NIRMALA
DOI: 10.17148/IJIREEICE.2025.13413
Abstract: Agriculture is the hub of international food security and economic stability. Farmers are, however, faced with unpredictable climatic patterns, soil quality variations, and inadequate access to accurate predictions of yield. The traditional forecasting techniques rely on manual data and complex interpretations and, hence, are less accessible to farmers. This paper suggests an Agricultural Yield Forecasting System with machine learning concepts to provide accurate predictions of yield with fewer input parameters. The system employs Lasso Regression, Elastic Net (ENet), and Kernel Ridge Regression, with stacking procedures to achieve maximum accuracy. Our method demonstrates improved efficiency, accuracy, and accessibility and provides a user-friendly web-based solution, which can be further developed as a mobile app with regional languages.
Keywords: Agricultural Forecasting, Machine Learning, Crop Yield Prediction, Regression Models, Data-Driven Farming
Keywords: Agricultural Forecasting, Machine Learning, Crop Yield Prediction, Regression Models, Data-Driven Farming
Abstract
SMART MEDICAL ASSISTANCE AND LIFE SAVING ALERT SYSTEM
MANO DEEPAN. G, Dr. A. NIRMALA
DOI: 10.17148/IJIREEICE.2025.13414
Abstract: In recent times, many individuals seek financial aid for medical treatment by posting fund requests on social media platforms such as WhatsApp and Facebook. However, scammers exploit this situation by fabricating fake medical bills to deceive donors. Identifying fraudulent medical fund requests is a challenging task. The proposed application utilizes Artificial Intelligence (AI) to detect and block fake medical fund requests by verifying uploaded medical treatment documents against a verified hospital dataset. This system ensures that only legitimate patients receive financial assistance. The project consists of two roles: patient and donor. Patients can upload their medical bills, which are then validated using AI-based pattern matching. Donors can confidently contribute, knowing that the system filters out fraudulent requests.
Keywords: Artificial Intelligence, Medical Fund Requests, Fraud Detection, Pattern Matching, Online Donation, Healthcare Support.
Keywords: Artificial Intelligence, Medical Fund Requests, Fraud Detection, Pattern Matching, Online Donation, Healthcare Support.
Abstract
IOT BASED – SMART CAR PARKING SYSTEM
SENTHIL S A, Dr. P. SAKTHI MURUGAN
DOI: 10.17148/IJIREEICE.2025.13416
Abstract: The Smart Car Parking System is an IoT-based solution designed to automate the process of parking and gate control using advanced technologies such as an ESP32 microcontroller, infrared (IR) sensors, RFID modules, and servo motors. The system detects the presence of vehicles at the entrance and within parking spaces, enabling seamless entry and exit while ensuring optimal utilization of available parking spots. By leveraging three IR sensors, the system determines if a parking spot is vacant or occupied and triggers the servo motor to control the opening and closing of the parking gate. Additionally, an RFID-based access control mechanism ensures that only authorized vehicles are allowed entry. Through IoT integration, the system provides real-time monitoring of parking status via a mobile or web interface, allowing users to remotely track space availability and manage the parking process. This automated and secure car parking solution optimizes parking management, reduces human intervention, and enhances user experience.
Keywords: Smart Parking, IoT, Vehicle Management, Automated Detection, Traffic Control.
Keywords: Smart Parking, IoT, Vehicle Management, Automated Detection, Traffic Control.
Abstract
LOGISTICS AND CARGO AUTOMATION SYSTEM
Ms. KEERTHANA S, Dr.A.NIRMALA
DOI: 10.17148/IJIREEICE.2025.13417
Abstract: The "Logistics and Cargo Automation System" is a web application developed using PHP for the front-end and MySQL for the back-end. It aims to streamline the logistics business by managing employee and customer details, bookings, transport, and deliveries. Employee information, including contact details, is stored in the system. Customers can book shipments, providing details like product type, quantity, weight, total amount, and delivery address, all of which are saved for future reference. Based on this information, trips are assigned, and transport details are allocated to employees for delivery. Employees can track deliveries, generate bills for pending payments, and update delivery statuses. Admins can monitor outgoing shipments, ongoing trips, and generate reports for system management.
Keywords: Cargo Automation ,Employee Management ,Customer Management, Product Details, Pending Payments, Transport Allocation.
Keywords: Cargo Automation ,Employee Management ,Customer Management, Product Details, Pending Payments, Transport Allocation.
Abstract
DIABETES DISEASE PREDICTION USING MACHINE LEARNING
SATHISH.M, P. SAKTHI MURUGAN M.Sc., M.Phil., NET., Ph.D.,
DOI: 10.17148/IJIREEICE.2025.13418
Abstract: Diabetes is a long-term metabolic condition characterized by consistently high blood sugar levels. If not properly controlled, it can lead to serious health complications such as cardiovascular diseases, kidney failure, nerve damage, and vision impairment. The rising global incidence of diabetes highlights the urgent need for effective diagnostic methods that enable early detection and proactive management.
Machine Learning (ML) is revolutionizing predictive healthcare by offering a fast, non-invasive, and highly precise approach to assessing diabetes risk. Traditional diagnostic procedures, including fasting blood tests and glucose tolerance tests, often require significant time, clinical visits, and invasive sampling. In contrast, ML-based models can process health data efficiently, recognizing patterns that contribute to a more accurate and timely diabetes risk assessment.
This project aims to develop an ML-powered Diabetes Prediction System that employs various classification algorithms to evaluate an individual's likelihood of having diabetes. Key health factors such as age, glucose concentration, insulin levels, body mass index (BMI), blood pressure, and family medical history are used as predictive features. To ensure high accuracy, the system utilizes multiple machine learning models, including Logistic Regression, Decision Trees, Random Forest, and Support Vector Machines (SVM), allowing for a comparative analysis to determine the most effective approach.
By integrating machine learning into diabetes prediction, this system facilitates early diagnosis, reduces dependence on conventional diagnostic techniques, and supports data-driven medical decision-making. Future improvements may incorporate deep learning models, continuous health monitoring, and wearable device integration to enhance prediction accuracy and improve patient outcomes.
Keywords: Random Forest, Machine learning, support vector machine, Logistic Regression, Logistic Regression
Machine Learning (ML) is revolutionizing predictive healthcare by offering a fast, non-invasive, and highly precise approach to assessing diabetes risk. Traditional diagnostic procedures, including fasting blood tests and glucose tolerance tests, often require significant time, clinical visits, and invasive sampling. In contrast, ML-based models can process health data efficiently, recognizing patterns that contribute to a more accurate and timely diabetes risk assessment.
This project aims to develop an ML-powered Diabetes Prediction System that employs various classification algorithms to evaluate an individual's likelihood of having diabetes. Key health factors such as age, glucose concentration, insulin levels, body mass index (BMI), blood pressure, and family medical history are used as predictive features. To ensure high accuracy, the system utilizes multiple machine learning models, including Logistic Regression, Decision Trees, Random Forest, and Support Vector Machines (SVM), allowing for a comparative analysis to determine the most effective approach.
By integrating machine learning into diabetes prediction, this system facilitates early diagnosis, reduces dependence on conventional diagnostic techniques, and supports data-driven medical decision-making. Future improvements may incorporate deep learning models, continuous health monitoring, and wearable device integration to enhance prediction accuracy and improve patient outcomes.
Keywords: Random Forest, Machine learning, support vector machine, Logistic Regression, Logistic Regression
Abstract
The Virtual Reality Dining & Social Food Hub concept
Nivedita R, Tanmay Das, Mahalakshmi Keruthika C, Aalok KR Pandit, Surya S, Shravana Khodanpur, Dr. Gopalakrishnan Chinnasamy
DOI: 10.17148/IJIREEICE.2025.13420
Abstract: The application of Virtual Reality (VR) technology in the food industry is revolutionizing dining by fusing immersion with social interaction and instant food delivery. The concept of Virtual Reality Dining & Social Food Hub (VRDSFH) offers a new solution where users explore virtual restaurants, interact with chefs, and socialize with friends before ordering food cooked in cloud kitchens and delivered in real life afterwards. The present study examines the usability, feasibility, and potential limitations of VR dining based on qualitative and quantitative research. Empirical data indicate potential consumer interest in experience-based dining by tech-savvy consumers. Greater accessibility, usability, and immediate availability of VR-based dining are, however, needed to make mass consumption a reality. The present study presents insights into potential future development of VR-based dining and implications for the foodservice industry on the basis of technology and consumer trends.
Keywords: Virtual Reality Dining, Cloud Kitchen, Metaverse Restaurants, Social Food Hub, AI-driven Dining, VR Food Delivery, Consumer Behaviour in VR, Smart Restaurant Technology, Digital Dining Trends
Keywords: Virtual Reality Dining, Cloud Kitchen, Metaverse Restaurants, Social Food Hub, AI-driven Dining, VR Food Delivery, Consumer Behaviour in VR, Smart Restaurant Technology, Digital Dining Trends
Abstract
The impact of Integrated Silent Work Pods and Café Services on Productivity and Employee Well-being.
Shreevalli K M, Shreeya M, Yerrachowdu Lakshmi Narasimhulu, A N Nishaanth, Badri Narayanan KS, Adith MP, Dr. Gopalakrishnan Chinnasamy
DOI: 10.17148/IJIREEICE.2025.13421
Abstract: Integrating café services within silent work pods, a solution designed to address the evolving needs of the modern workforce. The escalating trend of remote and hybrid work environments, evidenced by numerous studies highlighting the shifting landscape of professional practices, has underscored the critical demand for adaptable and conducive workspaces. Concurrent research focusing on the pervasive issue of distractions within both traditional open- plan offices and home-based settings emphasizes the detrimental impact of noise and interruptions on individual productivity. In response, this paper examines the efficacy of silent work pods, drawing upon scholarly investigations that establish a clear correlation between quiet environments and enhanced cognitive function, thereby promoting heightened concentration and improved output. Furthermore, recognizing the psychological and social benefits inherent in café environments, this research explores the strategic integration of café services within these pods, supported by analyses that demonstrate the positive influence of café culture on creativity and overall well-being. Finally, the study scrutinizes the technological infrastructure required for seamless service integration, analyzing the optimization of app- based ordering and delivery systems to ensure a user-friendly and efficient experience. By synthesizing these diverse research streams, this paper aims to provide a comprehensive understanding of how the synergistic combination of silent work pods and café services can cultivate a highly productive and satisfying work environment, catering to the demands of today's dynamic professional landscape.
Abstract
DECENTRALIZED VOTING SYSTEM USING BLOCKCHAIN TECHNOLOGY
Negin Chandru.A, Dr. K. Banuroopa
DOI: 10.17148/IJIREEICE.2025.13422
Abstract: In democratic societies, the integrity of electoral processes is critical. Traditional voting methods, whether paper-based or electronic, often face challenges including tampering, lack of transparency, centralization, and inefficiency. This paper explores a decentralized voting system leveraging blockchain technology, particularly smart contracts, to establish a secure, transparent, and tamper-proof voting infrastructure. We propose a model built on the Ethereum blockchain to ensure immutability, voter anonymity, and verifiability. The system addresses double voting, real-time result publication, and user authentication through cryptographic means. Results demonstrate the system's resilience, scalability, and potential to revolutionize modern electoral practices. The findings highlight the transformative impact of distributed ledger technology in reshaping the foundations of democratic engagement.
Keywords: Blockchain, E-voting, Smart Contracts, Ethereum, Cryptography, Decentralization, Democracy, Immutability
Keywords: Blockchain, E-voting, Smart Contracts, Ethereum, Cryptography, Decentralization, Democracy, Immutability
Abstract
MEDICINE REMINDER SYSTEM WITH AVAILABILITY NOTIFICATION
PAVITHRA.S, Mrs. P. MENAKA
DOI: 10.17148/IJIREEICE.2025.13423
Abstract: The Medicine Reminder System with Availability Notification is an Android-based application designed to assist individuals in managing their medication schedules efficiently. This system allows users to set personalized reminders for their medications, ensuring timely intake and adherence to prescribed schedules. The application sends notifications through the mobile app and SMS alerts as an alternative, reducing the chances of missed doses, especially for elderly individuals or those with busy lifestyles. A key feature of this system is its ability to notify users about the availability of medicines in nearby pharmacies or online stores, helping them restock essential medications without delays. By integrating an intuitive user interface, customizable reminder settings, and real-time medication availability updates, this solution enhances medication adherence and reduces health risks associated with skipped doses. The system caters to individuals managing chronic illnesses, complex medication regimens, or memory impairments. Additionally, it leverages SMS technology to ensure accessibility even in areas with limited internet connectivity. By addressing challenges such as forgetfulness, stock shortages, and accessibility issues, the Medicine Reminder System with Availability Notification contributes to improved health outcomes, better medication compliance, and a seamless healthcare experience for users.
Keywords: Medicine, reminder system, user interface, health risks, memory impairments
Keywords: Medicine, reminder system, user interface, health risks, memory impairments
Abstract
A METHOD OF SKIN DISEASE DETECTION USING IMAGE PROCESSING AND MACHINE LEARNING
Narayana Moorthi. A, Shanthini. S
DOI: 10.17148/IJIREEICE.2025.13424
Abstract: Skin diseases are a growing health concern worldwide, and early detection is crucial for effective treatment and management. This paper presents a novel method for skin disease detection using image processing and machine learning techniques. The proposed system utilizes high-quality images of the skin, which are pre- processed to enhance features and reduce noise. Image processing techniques such as color normalization, edge detection, and segmentation are employed to extract relevant features from the images. These features are then used as inputs for machine learning models, specifically Convolutional Neural Networks (CNN), which have demonstrated high performance in image classification tasks. The CNN model is trained on a large dataset of labelled skin disease images to distinguish between various skin conditions, including melanoma, eczema, psoriasis, and acne. The system is designed to automate the diagnostic process, reducing the reliance on manual examination by dermatologists and minimizing the risk of human error. Performance metrics, such as accuracy, precision, recall, and F1 score, are evaluated to assess the efficiency and reliability of the system. The results demonstrate the potential of combining image processing and machine learning to provide a robust tool for early skin disease detection, offering a significant advantage in terms of speed and accessibility. The method’s ability to provide accurate, real-time analysis can greatly aid healthcare professionals in diagnosing skin diseases promptly and can be used in both clinical and remote settings, facilitating broader access to dermatological care. This approach offers promising implications for improving the accuracy and efficiency of skin disease diagnosis, ultimately contributing to better patient outcomes and more accessible healthcare solutions.
Keywords: Skin disease detection, image processing, machine learning, classification, medical imaging, Automation.
Keywords: Skin disease detection, image processing, machine learning, classification, medical imaging, Automation.
Abstract
ENHANCED MACHINE LEARNING ALGORITHM TO PREDICT LUNG CANCER
NANDHINI.S, Dr. R. SENTHIL KUMAR
DOI: 10.17148/IJIREEICE.2025.13425
Abstract: Lung cancer ranks among the top causes of cancer-related fatalities globally and is frequently detected at later stages when therapeutic options are scarce. Timely diagnosis is vital for enhancing the survival rates of patients. This paper examines the use of the Support Vector Machine (SVM) algorithm for lung cancer prediction. SVM is a supervised machine learning method known for its efficacy in classification tasks, particularly in high-dimensional contexts. In this research, the dataset is evaluated using various attributes, including age, gender, smoking background, and imaging results, to train the SVM model. The study highlights the promise of machine learning methods, particularly SVM, in aiding healthcare professionals with early detection and improving patient outcomes.
Keywords: Lung cancer prediction, SVM, Patient Dataset, model training, Early detection.
Keywords: Lung cancer prediction, SVM, Patient Dataset, model training, Early detection.
Abstract
DATA SECURITY AND PRIVACY MANAGEMENT IN HEALTHCARE
U. Ranjith, Dr S Shanthini
DOI: 10.17148/IJIREEICE.2025.13427
Abstract: Health information is the most sensitive information attached to an individual. Although several appropriate policies, guidelines, and compliance regulations are established to protect health information, privacy and security breaches are central concerns for electronic healthcare systems. Here in this paper, we discuss these concerns and present a model of security and privacy deployed in the Methodist Environment for Translational and Outcomes Research (METEOR). METEOR was designed at Houston Methodist. Hospital and is comprised of two parts: the enterprise data warehouse (EDW) and an SIA software intelligence and analytics layer. This model signifies that patient confidentiality is most effectively maintained by employing a systematic combination of technologies and best practices like technical deidentification of information, restrictive data access, and security solutions in the base technical platforms. Our findings propose that the presented security model makes data security compromise and unauthorized access of safeguarded patient health information highly unlikely.
Keywords: Data Security, Privacy Management, Healthcare Information Security, Health Data Protection, Cybersecurity in Healthcare, Confidentiality.
Keywords: Data Security, Privacy Management, Healthcare Information Security, Health Data Protection, Cybersecurity in Healthcare, Confidentiality.
Abstract
GREENARY ACRES SALES AND FARMER HIRING PORTAL
Vishal N, Mrs. Vishnu Priya S
DOI: 10.17148/IJIREEICE.2025.13428
Abstract: The agricultural real estate sector is also evolving with the increasing reliance on digital platforms. This paper presents "Greenary Acres Sales and Farmer Hiring Portal" a web-based system designed to enhance farm property transactions, workforce hiring, and agricultural business management. The platform streamlines real estate activities such as property listing, buying, renting, and workforce recruitment, ensuring transparency and efficiency. The system addresses existing challenges in real estate platforms by integrating farmer-specific requirements, secure transactions, and digital hiring processes.
Abstract
INTEGRATED HOME SERVICES PROVIDER APPLICATION
Mukil.P, Dr. A.Adhiselvam
DOI: 10.17148/IJIREEICE.2025.13429
Abstract: The inequalities that existed in affordable service provision and access to technology is fast disappearing as integrated home services applications are becoming more and more advanced due to growth in technology and demand for convenience in everyday activities. Thanks to the application, users no longer have difficulty contacting trustworthy professionals for cleaning, plumbing, electricals, and even general maintenance. This journal examines the conception, evolution, challenges, and prospects of these apps and how they impact the management of households.
Keywords: Integrated, Conception, AI, Multithreaded OS.
Keywords: Integrated, Conception, AI, Multithreaded OS.
Abstract
NGO Fund management Web Application
B. Kavin Prabhu, Dr. A.Adhiselvam
DOI: 10.17148/IJIREEICE.2025.13430
Abstract: Fundraising is an essential element in the supporting causes for various activities of a non-governmental organization (NGO). The current project is with respect to developing a web-based donation application for an NGO called Kousika Neerkarangal, meant for better donation management and efficient donor engagement through donations. In actual sense, the application was developed with HTML, CSS, and JavaScript as the frontend technology, thereby allowing easy user navigation and Node.js with Express.js for the backend, thereby helping the application in server-side functioning, whereby MongoDB was also used to store the user's details regarding their donations and campaign activities.
This is where the user can sign up, start his or her fundraising campaign, donate money, and track the progress of the fundraiser. There is an administrative panel where the authorized persons can administer the campaigns, view the donations being made, and main tain the user records. Real-time tracking of donations and alerts is also integrated into WebSockets. It also includes security in terms of authentication, that is, donor and admin.
The application provides an effective and highly reliable user-friendly solution for NGO-harnessing fundraising activities. The solution is very transparent and greatly impacts the performance of an individual.
This is where the user can sign up, start his or her fundraising campaign, donate money, and track the progress of the fundraiser. There is an administrative panel where the authorized persons can administer the campaigns, view the donations being made, and main tain the user records. Real-time tracking of donations and alerts is also integrated into WebSockets. It also includes security in terms of authentication, that is, donor and admin.
The application provides an effective and highly reliable user-friendly solution for NGO-harnessing fundraising activities. The solution is very transparent and greatly impacts the performance of an individual.
Abstract
PDF TO AUDIO CONVERTER
Mr. Tamilselvam M, Mrs. Vishnu Priya S
DOI: 10.17148/IJIREEICE.2025.13431
Abstract: This project aims to create a user-friendly tool that helps people by turning written content into speech while also offering translation into different languages. The idea is to make documents more accessible for everyone, especially for people who have difficulty reading or those who speak different languages. By combining translation technology with text-to-speech, this tool can take a PDF, translate it into the user’s preferred language, and then read it aloud in a natural- sounding voice. Whether for individuals with visual impairments, language learners, or anyone who needs content in a more accessible form, this project has the potential to make information more inclusive and easier to understand. The result will be a practical tool that breaks down language barriers and helps people engage with content in a new way.
Keywords: Text-to-Speech (TTS), Translation, Multilingual Accessibility, PDF to Audio, Natural Language Processing (NLP), Assistive Technology, Language Translation, Document Accessibility, Voice Synthesis, Speech Conversion, Inclusive Technology, AI-powered Tools, Cross-language Communication, Speech Synthesis, Accessibility Tools, Language Barriers.
Keywords: Text-to-Speech (TTS), Translation, Multilingual Accessibility, PDF to Audio, Natural Language Processing (NLP), Assistive Technology, Language Translation, Document Accessibility, Voice Synthesis, Speech Conversion, Inclusive Technology, AI-powered Tools, Cross-language Communication, Speech Synthesis, Accessibility Tools, Language Barriers.
Abstract
EMPLOYEE LEAVE MANAGEMENT SYSTEM
Ms. SRINITHI R, Dr. P. SAKTHI MURUGAN M.Sc., M.Phil., NET., Ph.D.,
DOI: 10.17148/IJIREEICE.2025.13432
Abstract: The Employee Leave Management System is a web-based application developed to automate and simplify the management of employee leave requests within an organization. Traditionally, managing employee leaves has done manually or through spreadsheets, leading to inefficiencies, errors, and a lack of transparency. This system designed to address these issues by providing an easy-to-use, automated solution for both employees and administrators. Built using PHP for the backend and MySQL as the database management system, it allows for the efficient tracking and approval of leave requests in a centralized, real-time platform.
The system enables employees to request leaves, check their leave balances, and track the status of their requests from a user-friendly dashboard. Employees can apply for different type of leaves, including vacation, sick leave, and other forms of absence, all through an intuitive leave request form. Once a request is submitted, the system sends it to the administrator or manager, who can approve or reject the request based on organizational policies and employee leave balances. This approval or rejection process is managed directly from the admin panel, where managers can also view detailed reports on employee leave data.
Keywords: The Employee Leave Management System automates leave requests using PHP and MySQL, enhancing efficiency and transparency for employees and administrators.
The system enables employees to request leaves, check their leave balances, and track the status of their requests from a user-friendly dashboard. Employees can apply for different type of leaves, including vacation, sick leave, and other forms of absence, all through an intuitive leave request form. Once a request is submitted, the system sends it to the administrator or manager, who can approve or reject the request based on organizational policies and employee leave balances. This approval or rejection process is managed directly from the admin panel, where managers can also view detailed reports on employee leave data.
Keywords: The Employee Leave Management System automates leave requests using PHP and MySQL, enhancing efficiency and transparency for employees and administrators.
Abstract
AI POWERED EMAIL AUTOMATION FOR CUSTOMER QUERY HANDLING IN UIPATH
Mr. Karpagavinayagam S, Mrs. Sowndharya M
DOI: 10.17148/IJIREEICE.2025.13433
Abstract: AI-powered email automation for customer query handling in UiPath leverages Robotic Process Automation (RPA) and Artificial Intelligence (AI) to enhance customer support efficiency. The system automates the entire email response workflow by integrating UiPath for email extraction and automation, and OpenAI’s GPT model for intelligent query processing and response generation. Incoming emails are retrieved, analyzed, and processed using Natural Language Processing (NLP) to understand customer intent. The AI-generated responses are then validated, formatted, and sent automatically, reducing manual effort and response time. Additionally, the system includes logging and error handling mechanisms to track processed queries and ensure seamless operation. This solution significantly improves response accuracy, reduces human workload, and enhances customer satisfaction. Future enhancements include sentiment analysis, multilingual support, and custom AI training to further optimize the automation process.
Keywords: AI-powered automation, UiPath, customer query handling, email automation, OpenAI GPT, robotic process automation (RPA), natural language processing (NLP), intelligent response generation, sentiment analysis, workflow automation, unattended bot, machine learning, customer support automation.
Keywords: AI-powered automation, UiPath, customer query handling, email automation, OpenAI GPT, robotic process automation (RPA), natural language processing (NLP), intelligent response generation, sentiment analysis, workflow automation, unattended bot, machine learning, customer support automation.
Abstract
Blockchain Based Logistics Protocol Using Hyperledger Fabric
Rakshun Selvaa S.K., Mrs. A. Sathiya Priya
DOI: 10.17148/IJIREEICE.2025.13434
Abstract: The logistics sector is a cornerstone of global trade and faces challenges such as inefficiencies lack of transparency and vulnerability to fraud. Blockchain technology offers a promising alternative with its decentralized secure and transparent ledger system. This paper presents a blockchain based logistics protocol using Hyperledger Fabric. Our work demonstrates how the proposed system can improve traceability reduce costs and build trust among supply chain stakeholders. We include technical implementation details practical examples and diagrams that clearly explain the data flow and overall protocol.
Keywords: Blockchain; Logistics; Hyperledger Fabric; Supply Chain Management; Transparency; Security; Efficiency; Traceability; Decentralized Systems
Keywords: Blockchain; Logistics; Hyperledger Fabric; Supply Chain Management; Transparency; Security; Efficiency; Traceability; Decentralized Systems
Abstract
IMAGE READING AND INTERPRETATION USING OCR
M.SABARI, Dr.R.SENTHIL KUMAR
DOI: 10.17148/IJIREEICE.2025.13435
Abstract: Optical Character Recognition (OCR) is a predominant aspect to transmute scanned images and other visuals into text. Computer vision technology is extrapolated onto the system to enhance the text inside the digitized image. This preliminary provisional setup holds the invoice's information and converts it into JSON and CSV configurations. This model can be helpful in divination based on knowledge engineering and qualitative analysis in the nearing future. The existing system contains data extraction and nothing more. In a paramount manner, image pre-processing techniques like black and white, inverted, noise removal, grayscale, thick font, and canny are applied to escalate the quality of the picture.In the very next step, three different OCRs are used: Keras OCR, Easy OCR, and Tesseract OCR, out of which Tesseract OCR gives the precise result.
Keywords: Optical Character Recognition (OCR), Computer Vision,Image Pre-processing,Data Extraction,Invoice Processing
Keywords: Optical Character Recognition (OCR), Computer Vision,Image Pre-processing,Data Extraction,Invoice Processing
Abstract
DEVELOPMENT OF AN ENHANCED FLAPPY BIRD GAME WITH DYNAMIC ENVIRONMENTAL EFFECTS USING PYTHON AND PYGAME
Gnana Deepak R, Dr. A. Nirmala
DOI: 10.17148/IJIREEICE.2025.13436
Abstract: This project is an improved version of the popular Flappy Bird game created through the use of Python and Py game library. The game is based on the original mechanics where the players guide a bird navigating it through barriers by tapping to ensure it stays in flight while ensuring that it does not collide with any obstacles. The game has advanced features such as changing weather and backgrounds depending on real-time weather conditions, thus making the gaming experience more realistic.
The game features sprite-based graphics, physics-based motion, and real-time event processing to maintain a smooth gaming experience and active user interaction. Realistic sound effects and seamless animations are also implemented to complement the experience. The system has been designed using modular pieces such as a Bird class for player navigation, a Pipe class for managing obstacles, and a Weather module for aesthetic improvement.
Thorough testing techniques like unit testing, integration testing, and performance testing have been used to verify well- oiled and lag-free gameplay. The game is simple to install and only needs Python and Pygame to execute.
Future development can include multiplayer support, AI-driven difficulty levels, and other environmental conditions like thunderstorms and fog. This project is both an entertainment program and a demonstration of game development concepts using Python, and thus it is a fun and technically enhanced gaming experience.
Keywords: Flappy Bird, Pygame, Python Game Development, 2D Side-Scrolling Game, Game Physics, Dynamic Weather Effects, Real-Time Background Change, Collision Detection, Game Animation, Procedural Generation, Game Optimization.
The game features sprite-based graphics, physics-based motion, and real-time event processing to maintain a smooth gaming experience and active user interaction. Realistic sound effects and seamless animations are also implemented to complement the experience. The system has been designed using modular pieces such as a Bird class for player navigation, a Pipe class for managing obstacles, and a Weather module for aesthetic improvement.
Thorough testing techniques like unit testing, integration testing, and performance testing have been used to verify well- oiled and lag-free gameplay. The game is simple to install and only needs Python and Pygame to execute.
Future development can include multiplayer support, AI-driven difficulty levels, and other environmental conditions like thunderstorms and fog. This project is both an entertainment program and a demonstration of game development concepts using Python, and thus it is a fun and technically enhanced gaming experience.
Keywords: Flappy Bird, Pygame, Python Game Development, 2D Side-Scrolling Game, Game Physics, Dynamic Weather Effects, Real-Time Background Change, Collision Detection, Game Animation, Procedural Generation, Game Optimization.
Abstract
A photovoltaic solar system with multiple tasks
Priyanka Malyal, Bhargavi Konda, Maheshwari Kota, S. A. Malvekar
DOI: 10.17148/IJIREEICE.2025.13437
Abstract: Solar panels require full bright sunlight in order to produce optimal power. Shadow as well as prolonged cloudy weather can affect the panel's performance. When there is a lack of a bright sunlight, the rate of voltage output is reduced. So, to make sure that the panels are producing energy optimally, you must install them on mounts that are angled towards the sun. You should install solar tracking systems so solar panels can change their position towards the sun. Fortunately, rainfall does help when it comes to washing off dust, leaves, pollen, and any other lost debris collected on the panels during the rainy season. However, when these environmental elements are accumulated during dry season, you will need to clean the panels. Accumulated dirt can prevent enough sunlight from reaching the panels, hence causing reduced power output. To prevent this, you will need to perform regular inspections to see if the panel cleaning is required. Offers should contact a firm, professional solar panel cleaning company for the best results. You should insert a solar cleaning system by using a new technology. The performance of a photovoltaic solar panel can be determined by measuring the relationship between the panel's voltage current. The amount of solar radiation on the earth's surface can be instrumentally measured, and precise measurements are important for providing background solar data for solar energy conversion applications. These all things are to be included in the single system, so that the solar system will work effectively and give the high efficiency, due to this production of electrical energy will increases and demand of electrical power will be fulfilled.
Keywords: Solar, Multitasking, Arduino and Energy.
Keywords: Solar, Multitasking, Arduino and Energy.
Abstract
AI WEBSITE GENERATOR
HARISH RAGAVENDRA.M, Dr. P. SAKTHI MURUGAN
DOI: 10.17148/IJIREEICE.2025.13438
Abstract: With the growing demand for website, companies need efficient and user -friendly tools to create professional websites. In this paper, a AI-driven website generator is presented with which user can adapt your website, in particular the "About us" page, via an interactive, gradual interface. With the system, users can enter business details, set design settings and upload the media and facilitate the quick creation of personalized websites.
Keywords: AI -Website -Generator, web adjustment, business automation, user interface, HTML, CSS, JavaScript
Keywords: AI -Website -Generator, web adjustment, business automation, user interface, HTML, CSS, JavaScript
Abstract
BITCOIN PRICE PREDICTION USING MACHINE LEARNING AND DEEP LEARNING MODELS
R.Mugil Vishnu, Dr.K.Santhi
DOI: 10.17148/IJIREEICE.2025.13439
Abstract: Bitcoin, the most widely used cryptocurrency, is characterized by extreme price fluctuations, making its prediction is a complex and crucial task in the financial domain. This research paper presents a comparative analysis of various machine learning and deep learning models for Bitcoin price forecasting. Traditional approaches such as Linear Regression, Decision Trees, and Support Vector Machines are compared with advanced deep learning architectures like Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), and Transformer-based models. The study evaluates these models using key performance metrics, including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R²-score. The results demonstrate that Transformer-based models outperform other techniques due to their ability to capture long-term dependencies and complex patterns in sequential data. This paper further discusses hyperparameter tuning, trading strategy implications, and limitations of each model, providing a comprehensive perspective on Bitcoin price forecasting.
Abstract
BLOOD BANK AND DONOR MANAGEMENT SYSTEM
Mr. Rubanraj S, Mrs. Vishnu Priya R
DOI: 10.17148/IJIREEICE.2025.13440
Abstract: The Blood Bank and Donor Management System is designed to efficiently manage blood donations, storage, and distribution. It helps in registering donors, tracking blood inventory, and handling blood requests from hospitals and patients. The system ensures that blood is available when needed by keeping records of donors, their blood groups, and donation history. It also sends reminders to donors for their next eligible donation and alerts hospitals about available stocks. By automating the process, the system saves time, reduces errors, and improves the availability of blood for emergencies, ultimately helping to save lives.
Keywords: Real-time Blood Availability, Secure Donor Authentication, Emergency Blood Requests, Community Engagement, Location-based Services, Firebase Integration, Cross-platform Connectivity.
Keywords: Real-time Blood Availability, Secure Donor Authentication, Emergency Blood Requests, Community Engagement, Location-based Services, Firebase Integration, Cross-platform Connectivity.
Abstract
Health and Fitness Chatbot with Diet and Workout Recommendations
Ms. Monika C, Dr. A. NIRMALA MCA., M.Phil., Ph.D.
DOI: 10.17148/IJIREEICE.2025.13441
Abstract: Health and Fitness Chatbot with Diet and workout recommendations is an online tool that aims to assist users in attaining their wellness and health objectives based on individualized advice on diet and exercise. Upon gathering user information like age, weight, height, and activity level, the application computes optimal daily caloric needs and enables users to record meals and view previous activity. It also offers individualized workout advice based on health levels, supporting a balanced focus on health.
One of the most notable aspects of the app that provides immediate feedback to user questions pertaining to diet, exercise, and overall health, promoting interaction and encouragement. Developed with the Flask framework and SQLite database, the app combines contemporary web technologies such as JavaScript, HTML, and CSS to provide a responsive and intuitive interface. By integrating tracking features, customized suggestions, and real-time support, the Health and Fitness Chatbot enables users to make healthier lifestyle decisions, resulting in better well-being and an active lifestyle.
Keywords: Health & Fitness, Chatbot, Diet Plans, Workout Guidance, Caloric Needs, Meal Tracking, Real-time Feedback, Flask, SQLite, JavaScript, Responsive Design, Wellness, Active Lifestyle.
One of the most notable aspects of the app that provides immediate feedback to user questions pertaining to diet, exercise, and overall health, promoting interaction and encouragement. Developed with the Flask framework and SQLite database, the app combines contemporary web technologies such as JavaScript, HTML, and CSS to provide a responsive and intuitive interface. By integrating tracking features, customized suggestions, and real-time support, the Health and Fitness Chatbot enables users to make healthier lifestyle decisions, resulting in better well-being and an active lifestyle.
Keywords: Health & Fitness, Chatbot, Diet Plans, Workout Guidance, Caloric Needs, Meal Tracking, Real-time Feedback, Flask, SQLite, JavaScript, Responsive Design, Wellness, Active Lifestyle.
Abstract
IoT-Based Anti-Poaching Alarm System for Forest Conservation
Sudharshan Srinivasan. S.L, Shanthini. S
DOI: 10.17148/IJIREEICE.2025.13442
Abstract: Illegal logging and deforestation represent significant threats to global biodiversity, ecological stability, and climate regulation. Traditional forest surveillance methods are often ineffective due to resource constraints and the expansive nature of forested areas. This paper presents an innovative IoT-based anti-poaching alarm system specifically designed to effectively combat illegal logging activities. Leveraging advanced sensors such as tilt sensors, flame sensors, sound sensors, and environmental monitoring modules (DHT11), integrated with ESP32 microcontrollers and Wi-Fi modules, the system ensures real-time detection and alerts of suspicious activities. The sensors trigger alarms or activate preventative measures like water pumps in case of detected threats like fires or illegal logging sounds. Data from these sensors is continuously collected and transmitted via Wi-Fi to a central monitoring station using the ThingSpeak IoT platform, enabling rapid intervention by forest authorities. This automation significantly enhances the efficiency and scalability of forest protection measures, reduces the dependency on human patrols, and provides comprehensive real-time monitoring. Implementation results demonstrate the system's effectiveness in promptly alerting authorities, thereby facilitating swift responses to illegal activities or environmental hazards. Ultimately, the system promotes sustainable forest conservation through technologically advanced methods.
Keywords: IoT, anti-poaching, forest conservation, sensors, ESP32
Keywords: IoT, anti-poaching, forest conservation, sensors, ESP32
Abstract
PERSONALIZED FINANCE MANAGEMENT
Ms. Vinothini S, Mrs. Sowndharya, M.Sc, M.Phil, Ph.D,
DOI: 10.17148/IJIREEICE.2025.13443
Abstract: The Personalized Finance Management Chatbot System is designed to assist individuals in effectively managing their personal finances through a user-friendly web application. This system addresses common challenges faced by users, such as tracking expenses, budgeting, and making informed investment decisions. By leveraging a full-stack development approach with Flask, SQLite, HTML, CSS, and JavaScript, the chatbot provides personalized financial insights, budgeting assistance, and savings recommendations tailored to individual financial behaviors. The system promotes financial literacy and discipline, enabling users to set and achieve their financial goals. Future enhancements, including AI-driven insights and integration with online payment gateways, aim to further improve user experience and functionality. This project represents a significant step towards accessible and efficient financial management solutions.
Keywords: Personalized Finance Management, Chatbot System, Financial Literacy, Budgeting, Expense Tracking, Flask, SQLite, User Experience, AI-driven Insights, Financial Goals
Keywords: Personalized Finance Management, Chatbot System, Financial Literacy, Budgeting, Expense Tracking, Flask, SQLite, User Experience, AI-driven Insights, Financial Goals
Abstract
NUMBER PLATE RECOGNITION
PRAVIN V, Dr K BANUROOPA MCA., M.Phil., Ph.D.
DOI: 10.17148/IJIREEICE.2025.13444
Abstract: Automated Number Plate Recognition (ANPR) is a crucial technology in smart transportation systems, law enforcement, and security monitoring. It involves the automated identification and reading of vehicle registration numbers from images or video feeds through the use of image analysis and machine learning techniques. The rapid increase in urban development and the growing number of vehicles on the roads have generated a significant need for effective, real-time number plate recognition systems to improve traffic management, decrease violations, and enhance public safety. This paper offers a summary of number plate detection techniques, concentrating on approaches such as edge detection, morphological operations, deep learning, and optical character recognition (OCR).
Older methods rely on feature extraction techniques like contour detection and colour segmentation, which perform well in controlled environments but struggle with variations in weather, lighting, and plate designs. Recent advancements in artificial intelligence, particularly deep learning through convolutional neural networks (CNNs), have led to remarkable improvements in the accuracy and robustness of number plate detection under varying conditions. The suggested system combines image preprocessing, plate localization, character segmentation, and OCR-based recognition to attain high detection precision. The implementation of deep learning models facilitates automatic feature extraction and classification, reducing the need for human involvement. Additionally, real-time execution utilizing edge computing and cloud-based processing improves the system’s overall efficiency. Applications of ANPR include automatic toll collection, enforcement of traffic laws, detection of stolen vehicles, management of parking, and control of access. Ongoing research is focused on addressing challenges like occlusions, blurry images, and inconsistencies in number plate formats. Future advancements may encompass hybrid AI models, integration of IoT-based smart surveillance, and advanced real-time processing techniques to further enhance detection accuracy and reliability.
Keywords: Number Plate Recognition, Vehicle registration.
Older methods rely on feature extraction techniques like contour detection and colour segmentation, which perform well in controlled environments but struggle with variations in weather, lighting, and plate designs. Recent advancements in artificial intelligence, particularly deep learning through convolutional neural networks (CNNs), have led to remarkable improvements in the accuracy and robustness of number plate detection under varying conditions. The suggested system combines image preprocessing, plate localization, character segmentation, and OCR-based recognition to attain high detection precision. The implementation of deep learning models facilitates automatic feature extraction and classification, reducing the need for human involvement. Additionally, real-time execution utilizing edge computing and cloud-based processing improves the system’s overall efficiency. Applications of ANPR include automatic toll collection, enforcement of traffic laws, detection of stolen vehicles, management of parking, and control of access. Ongoing research is focused on addressing challenges like occlusions, blurry images, and inconsistencies in number plate formats. Future advancements may encompass hybrid AI models, integration of IoT-based smart surveillance, and advanced real-time processing techniques to further enhance detection accuracy and reliability.
Keywords: Number Plate Recognition, Vehicle registration.
Abstract
SOCIAL MEDIA SENTIMENT ANALYSIS FOR COLLEGE BRANDING
V. Gayathri, Dr. A. Nirmala
DOI: 10.17148/IJIREEICE.2025.13445
Abstract: Social media serves as a vital platform for individuals and organizations to share experiences and opinions. Colleges utilize social media reviews to engage with students, alumni, and prospective applicants while shaping their public perception. This study proposes a sentiment analysis system employing advanced Machine Learning (ML) techniques to analyze college-related social media reviews. Using state-of-the-art models such as BERT (Bidirectional Encoder Representations from Transformers) and LSTMs (Long Short-Term Memory), this approach enhances sentiment classification, including gender-based sentiment analysis. The system provides educational institutions with actionable insights to refine branding strategies, improve student engagement, and strengthen institutional reputation.
Keywords: Social Media, College Branding, Sentiment Analysis, Machine Learning, NLP, BERT, LSTM, Emotion Categorization.
Keywords: Social Media, College Branding, Sentiment Analysis, Machine Learning, NLP, BERT, LSTM, Emotion Categorization.
Abstract
A WEB PORTAL FOR PLANT NURSERY MANAGEMENT
N. Rithanya, Dr.A.Adhiselvam
DOI: 10.17148/IJIREEICE.2025.13447
Abstract: The modern era demands efficient digital solutions for streamlined operations. This paper presents a web portal that enhances accessibility, management, and user engagement. The system provides a seamless interface for users, ensuring real-time updates and security. It aims to resolve existing inefficiencies in data handling and communication. The proposed system integrates advanced technologies, optimizing performance and reliability. This paper discusses its architecture, implementation, and impact. The results demonstrate improved functionality and user satisfaction. Future enhancements will focus on AI integration for predictive analysis. This research contributes to digital transformation by offering a scalable, user-friendly solution. The findings indicate a significant improvement in user engagement and system performance. By adopting the proposed framework, businesses and organizations can optimize their digital operations. The scalability of the system ensures adaptability to future technological advancements, making it a sustainable solution for web portal development.
Abstract
Cyber threat detection using machine learning
SIVAKUMAR.V, Dr. R. PRABHA
DOI: 10.17148/IJIREEICE.2025.13448
Abstract: The project consists of a web-based application build in django (i.e. Python) that includes a dashboard for users to monitor cyberstalking. A machine learning classification algorithm (eg Support Vector Machine) can be trained to identify cyberstalking messages and then the classified messages can be imported into a database and is summarised on a dashboard. The dashboard displays timeseries data, topic models (for cyberstalking messages and non cyber bullying messages) and a summary of the affective dimensions found in the test messages (for cyberstalking messages and non cyber bullying messages). System must be scheduled to perform topic modeling and affective sentiment analysis. A moderation role that is able to mark classified messages as mis-classified. The project entitled “Cyber threat detection using machine learning” is developing an online cloud-based cyberstalking detection system. The system has to implement a cyberstalking detection system that integrates with popular social media services such as Facebook and Twitter. Researchers and IT professionals will have access to download, use, and test the opensource code license for this detection system that will be freely available on the Internet with simple installation instructions, and a highly user- friendly interactive dashboard that can be used by schools and parents as needed. This project will focus on the key natural language processing technologies that will allow traces of cyber-bullying to be captured and classified. The outcomes from this project will lead to free and easy-to-install software where the system picks up patterns in social media interactions that can be construed as potential cyber-bullying.
Keywords: Cyberstalking Detection, Machine Learning Classification, Support Vector Machine (SVM), Natural Language Processing (NLP), Django (Python Web Framework), Social Media Monitoring, Topic Modeling, Sentiment Analysis.
Keywords: Cyberstalking Detection, Machine Learning Classification, Support Vector Machine (SVM), Natural Language Processing (NLP), Django (Python Web Framework), Social Media Monitoring, Topic Modeling, Sentiment Analysis.
Abstract
CHATBOT FOR DOCTOR APPOINMENT SCHEDULING
Ms. GEETHA N, DR. P. SAKTHI MURUGAN M.SC., M.PHIL., NET., PH.D.
DOI: 10.17148/IJIREEICE.2025.13449
Abstract: In today's fast-paced world, efficient healthcare access is crucial. However, traditional methods of booking doctor appointments are often time-consuming and cumbersome. This project aims to develop a Doctor Appointment Scheduling Web Application that enables users to book appointments with doctors seamlessly, interact with a chatbot for basic inquiries, and request to contact doctors. This system will provide a user-friendly interface that simplifies the booking process while ensuring accuracy and accessibility.
The application leverages HTML, CSS, JavaScript, and the Flask framework for the backend, with SQLite as the database. Users can browse available doctors, check their credentials, schedule appointments based on availability, and receive automated notifications. The chatbot integration further enhances user experience by providing answers to common queries.
The application leverages HTML, CSS, JavaScript, and the Flask framework for the backend, with SQLite as the database. Users can browse available doctors, check their credentials, schedule appointments based on availability, and receive automated notifications. The chatbot integration further enhances user experience by providing answers to common queries.
Abstract
Accessible Job Portal Website For Disabled Individuals
Mr. S. Abishek, Mr. A. ADHISELVAM, M.Sc., P.B.D.C.A., M.C.A., M.Phil., Ph.D., SET.
DOI: 10.17148/IJIREEICE.2025.13450
Abstract: In today's competitive job market, accessibility remains a significant barrier for disabled individuals seeking employment opportunities. Traditional job portals often lack essential accessibility features, making it difficult for individuals with disabilities to find and apply for jobs efficiently. This paper presents an Accessible Job Portal Management System designed to provide an inclusive and user-friendly platform for disabled job seekers and employers. The system integrates screen reader compatibility, keyboard navigation, high contrast mode, simplified job listings, and AI-driven job recommendations to ensure usability for all. It enables job seekers to apply for jobs based on their skills, preferences, and accessibility needs while allowing recruiters to identify and connect with suitable candidates effectively. This platform fosters an inclusive hiring ecosystem by bridging the gap between employers and disabled job seekers.
Keywords: Job portal, accessibility, disabled job seekers, inclusive hiring, AI-driven recommendations.
Keywords: Job portal, accessibility, disabled job seekers, inclusive hiring, AI-driven recommendations.
Abstract
DIGITAL LEARNING PLATEFORMS
Bharathi.S, Dr. B. Leelavathi
DOI: 10.17148/IJIREEICE.2025.13451
Abstract: The rapid advancement of technology has revolutionized education by bringing about digital platform that make learning flexible and accessible. This paper discusses an overview This research study offers a thorough analysis of digital learning platforms, including its characteristics, advantages and challenges. The study analyse various types of digital platforms including MOOCs, Learning Management Systems (LMS), and virtual classrooms. The paper also investigates the role of artificial intelligence, gamification and social learning in enhancing digital learning platforms. Lastly, it addresses the challenges and future prospects of these platforms in the context of learning.
Abstract
QUALITY CONTROL SYSTEM FOR GARMENT FACTORY
S.Deepalakshmi, Dr. A.Adhiselvam
DOI: 10.17148/IJIREEICE.2025.13452
Abstract: The apparel and textile industry relies on high-quality products, but traditional quality control methods, like manual inspection, are labor-intensive and error-prone. This study introduces an AI-based Quality Control System for Garment Factories, leveraging machine learning for automated garment quality assessment. Using a Random Forest Classifier, the system evaluates factors like fabric type, texture, thickness, and weave quality to classify garments as high- grade or low-quality. Built with Python and Flask for scalable backend processing, the system ensures real-time predictions. Its frontend, designed in HTML, CSS, and JavaScript, offers a user- friendly interface. The workflow includes a homepage overview, secure user registration, and login for factory users. Once data is submitted, the trained Random Forest model provides results, classifying garment quality effectively. This system enhances efficiency and consistency in quality control processes.
Keywords: Quality Control, Garment Factories, Machine Learning, Random Forest Classifier, User Interface, Weave Quality, Prediction
Keywords: Quality Control, Garment Factories, Machine Learning, Random Forest Classifier, User Interface, Weave Quality, Prediction
Abstract
DIGITAL BUS FARE MANAGEMENT SYSTEM
MEGAVARSHNA V, Dr. P. SAKTHI MURUGAN
DOI: 10.17148/IJIREEICE.2025.13453
Abstract: Efficient fare collection is crucial for the smooth operation of public transportation. Traditional cash-based methods often lead to delays, fraud, and inconvenience for both passengers and conductors. To overcome these challenges, this paper presents a Digital Bus Fare Management System that leverages RFID technology, IoT, and cloud integration to streamline fare collection. The system comprises three key components: a fare collection machine, a passenger mobile application, and an admin web platform. The fare collection machine, operated by the conductor, is built using ESP32, an RFID reader, a 16x6 LCD with an I2C module, and a 4x4 keypad. When passengers tap their RFID card, the fare is automatically deducted, reducing reliance on cash transactions. A Flutter-based mobile application enhances user convenience by allowing passengers to check balances, view transaction history, top up their cards, manage multiple cards, and apply for new cards. Additionally, a web-based admin platform facilitates card issuance, fare tracking, top-up processing, and application management. The system integrates Firebase Realtime Database for real-time data synchronization across all components. This system enhances security, accuracy, and efficiency for both passengers and transport authorities by automating fare collection and eliminating cash handling. The integration of IoT and cloud-based technologies ensures a seamless, reliable, and user-friendly approach to fare management, making public transportation more efficient and accessible.
Keywords: (Contactless Payment, Flutter App, Fare Collecting Machine, Firebase Realtime Database, web application.)
Keywords: (Contactless Payment, Flutter App, Fare Collecting Machine, Firebase Realtime Database, web application.)
Abstract
Why Women Need Exclusive Gyms for a Healthier Future
Hemanth ZB, Ankita Ghosh, Anjali Phukan, Dev Aadhisesh S K, C Khiruthic, G Vedant, Dr. Kalavathy K S
DOI: 10.17148/IJIREEICE.2025.13454
Abstract: EmpowerFit is a research-driven initiative aimed at addressing the low participation of women in fitness activities in India. With over half of Indian women between 18-69 being physically inactive, there is a growing need for fitness environments that cater specifically to their needs. This study identifies key barriers to women’s fitness participation, including societal norms, lack of female trainers, and misconceptions about strength training. Through a mixed-methods research approach, including surveys, interviews, and literature reviews, the study provides data-driven insights into the fitness habits and preferences of women. The findings emphasize the importance of creating safe and inclusive gym spaces, such as EmpowerFit, to encourage participation. Key recommendations include launching exclusive women’s gyms, employing trained female fitness professionals, and implementing awareness programs to address common misconceptions about fitness and health. By removing these barriers, EmpowerFit aims to revolutionize the fitness industry and contribute to healthier lifestyles among women in India.
Abstract
Hostel Gate Pass Management System
Amirthavarshini S, Mrs. L. Ajitha
DOI: 10.17148/IJIREEICE.2025.13455
Abstract: This web-based application is designed for management of gate pass for students in hostels and has been designed to automate and streamline the movement of students at the gate. The computerized system does not allow any manual entry and hence provides a safer, quicker, and more transparent means of checking and verifying the movement of students. The students will online enter the gate pass request alongside the reason for leaving school, date, and return time-all this information then sent to the warden or Head of Department (HOD) for either approval or rejection so that movement is duly checked and controlled. Only authorized personnel will have access, hence securing data and maintaining the integrity of the request management system through role-based authentication. This entire computerized mechanism reduces paperwork, minimizes errors in processing, and optimizes management effectiveness. The system has a centralized database allowing easy accessibility to records, tracking of student movement in real-time, and report generation. Accurate records of passes approved and rejected will enhance accountability and compliance within the institution. The application sends alerts about the status of requests to the students, while actually hostel authorities save time in tracking movements. It acts as a barrier against unauthorized entry, ensures records are maintained without loss, and, of course, sidesteps delays. This results in an organized hostel management system that is credible. Therefore, this system secures and streamlines hostel entry for students while also aiding in providing more safety and organization for hostel life and simplifying all administrative work.
Abstract
BUDGET MANAGER ANDROID APP
KARTHIK G, Dr.A MYTHILI
DOI: 10.17148/IJIREEICE.2025.13456
Abstract: Managing personal finances effectively is essential for financial stability and growth. In today’s digital era, smartphones have become a ubiquitous tool for handling various aspects of daily life, including financial management. This paper introduces an Android-based Budget Manager Application designed to assist users in tracking and managing their financial activities seamlessly.
Developed using Java and SQLite, the application provides users with an intuitive interface to monitor income, expenses, savings, and financial goals efficiently. The core functionalities include real-time expense tracking, automated categorization of transactions, budget planning, and comprehensive financial analytics through interactive charts and graphs. To enhance user experience and provide personalized financial insights, the system integrates machine learning algorithms that analyze spending patterns and offer recommendations for better financial decision-making. The application aims to simplify financial management by offering features such as notifications for budget limits, expense reminders, and secure data storage. This paper explores the design architecture, implementation strategies, and technical challenges encountered during development. Furthermore, it discusses potential future enhancements, such as cloud synchronization, multi-device access, and AI-driven predictive budgeting. By leveraging modern mobile technology, the Budget Manager Application serves as an efficient tool for users to gain better control over their finances, ultimately promoting financial literacy and discipline.
Keywords: Budget Manager, Android, Java, Expense Tracking, Financial Planning, SQLite.
Developed using Java and SQLite, the application provides users with an intuitive interface to monitor income, expenses, savings, and financial goals efficiently. The core functionalities include real-time expense tracking, automated categorization of transactions, budget planning, and comprehensive financial analytics through interactive charts and graphs. To enhance user experience and provide personalized financial insights, the system integrates machine learning algorithms that analyze spending patterns and offer recommendations for better financial decision-making. The application aims to simplify financial management by offering features such as notifications for budget limits, expense reminders, and secure data storage. This paper explores the design architecture, implementation strategies, and technical challenges encountered during development. Furthermore, it discusses potential future enhancements, such as cloud synchronization, multi-device access, and AI-driven predictive budgeting. By leveraging modern mobile technology, the Budget Manager Application serves as an efficient tool for users to gain better control over their finances, ultimately promoting financial literacy and discipline.
Keywords: Budget Manager, Android, Java, Expense Tracking, Financial Planning, SQLite.
Abstract
STUDENT STAFF INTRA CONNECT WEB APP
Mr. Akash G, Dr. P. Sakthi Murugan
DOI: 10.17148/IJIREEICE.2025.13457
Abstract: The project entitled “STUDENT STAFF INTRA CONNECT WEB APP” is a web application it offers an extensive range of functionalities for students and makes better communication between students and staffs. Intra connect provides various services to offer better discussion area in the current system being utilized, hence aiming at improving the quality of the information system to higher standards.
The student registered with this web application can interact with their staffs, see news and events posted by admin, view notifications and get clarified from their technical doubts. This web application allows only registered users to view the posted topics and articles by other members. The main objective of this project is to share the ideas and files of valuable content with their team. This enhances the knowledge and enriches the communication in their area of interest
Keywords: Student engagement, student collaboration, web apllication, Campaus connectivity, internal communication.
The student registered with this web application can interact with their staffs, see news and events posted by admin, view notifications and get clarified from their technical doubts. This web application allows only registered users to view the posted topics and articles by other members. The main objective of this project is to share the ideas and files of valuable content with their team. This enhances the knowledge and enriches the communication in their area of interest
Keywords: Student engagement, student collaboration, web apllication, Campaus connectivity, internal communication.
Abstract
AI-Based Classroom Monitoring System
S. KAVIN, Dr. A. ADHISELVAM
DOI: 10.17148/IJIREEICE.2025.13458
Abstract: Artificial Intelligence (AI) is transforming education by automating crucial administrative tasks, enhancing student engagement, and improving discipline monitoring. Scholar Insight is an AI-based classroom monitoring system designed to automate attendance tracking, observe student behavior, and provide real- time feedback for educators. Using technologies such as OpenCV, TensorFlow/PyTorch, and NLP, the system detects attendance, monitors engagement, and identifies distractions. The backend is developed with Node.js (Express.js), the database is managed with MongoDB, and the frontend interface is built with React/Next.js. Experimental results indicate 95% accuracy in attendance tracking and 86% accuracy in behavior analysis, significantly reducing manual work for educators. This paper explores the role of AI in increasing student participation and maintaining a disciplined learning environment. Future enhancements include emotion detection and AI-driven learning recommendations.
Keywords: AI in Education, Classroom Monitoring, Facial Recognition, Behavior Analysis, Machine Learning, Deep Learning, Student Engagement, Computer Vision.
Keywords: AI in Education, Classroom Monitoring, Facial Recognition, Behavior Analysis, Machine Learning, Deep Learning, Student Engagement, Computer Vision.
Abstract
Fake product identification
SANJAY K A*, Dr .K. BANUROOPA
DOI: 10.17148/IJIREEICE.2025.13459
Abstract: Counterfeit products pose significant financial losses and safety risks to consumers across various industries. Traditional anti-counterfeiting methods, such as visual markers and serial numbers, have proven inadequate in preventing fraud. This paper explores how blockchain technology can offer a more effective solution. By using blockchain’s decentralized ledger and cryptographic security, it creates a transparent and reliable record of every step in the supply chain. Unique product identifiers on this tamper-proof platform ensure accurate verification, building trust among consumers. Smart contracts within the blockchain ecosystem automate verification processes, reducing human error and making transactions more efficient. The proposed system involves registering manufacturers on the blockchain, assigning unique QR codes to products, and creating smart contracts for transferring product ownership. Consumers can verify product authenticity using a mobile app. The use of contract tools and cryptographic methods ensures the integrity and security of the data. This study highlights the potential of blockchain to enhance supply chain transparency, build consumer trust, and combat counterfeit products, ultimately fostering a safer and more transparent marketplace.
Keywords: Anti-counterfeiting, Blockchain, Consumer trust, Supply chain transparency, Tamper-resistant.
Keywords: Anti-counterfeiting, Blockchain, Consumer trust, Supply chain transparency, Tamper-resistant.
Abstract
A Descriptive Study on Consumer Preferences for Natural Moisturizers for Bangalore’s Climate
DharaniLakshmi, Ansita Pattnaik, Shradha Murthy, Dakshatha G, Md Faiz Ansari, Gowtham G, Sayon Paul, Boddepalli Pranay Kumar, Dr Malani V Nair
DOI: 10.17148/IJIREEICE.2025.13460
Abstract: This research explores consumer attitudes towards natural moisturizers in Bangalore, with emphasis on the effects of the city's distinctive climate on skin hydration and well-being. The research seeks to uncover the influence of Bangalore's varying humidity and temperature on skin health, examine consumer desire to make a transition from chemical-based to natural skincare products, and create a scientifically supported natural moisturizer based on the local climate. From a survey of 113 respondents, the study finds that consumers place a high priority on sun protection, hydration, and natural ingredients with a clear preference for lightweight, non-greasy textures. The main findings are that concern over sensitive skin and recommendations through word of mouth are the drivers of natural moisturizer adoption. Floral scents are most sought after, and even as cost is paramount, consumers will pay extra for products providing quality, efficacy, and environmentally friendly attributes. The research suggests creating mid-range products between â‚ą200-â‚ą500, focusing on dermatologist recommendations, and positioning sustainability as a major selling point. By catering to these consumer demands, brands can successfully meet the increasing demand for natural skincare products in Bangalore's weather.
Abstract
INDUSTRY ENERGY CONSUMPTION PREDICTION USING MACHINE LEARNING ALGORITHM
M.Sabarishan, Dr. K. Santhi
DOI: 10.17148/IJIREEICE.2025.13461
Abstract: Energy consumption forecasting plays a crucial role in optimizing industrial processes, reducing operational costs, and promoting sustainability. This study presents a predictive model for industrial energy consumption using machine learning techniques. The model analyzes historical energy usage patterns, environmental factors, and production data to generate accurate forecasts. By leveraging algorithms such as regression models, time-series analysis, and deep learning approaches, the proposed system enhances decision-making for energy management. The results demonstrate improved prediction accuracy, enabling industries to optimize resource allocation and reduce energy wastage. This research contributes to the growing need for efficient and sustainable energy utilization in industrial settings.
Keywords: Industry Energy Consumption Prediction, Electricity consumption, Machine learning, Linear, regression model, Python, Energy consumption, Electricity demand trends, Strategic planning, Energy utilization, Government and industrial stakeholders
Keywords: Industry Energy Consumption Prediction, Electricity consumption, Machine learning, Linear, regression model, Python, Energy consumption, Electricity demand trends, Strategic planning, Energy utilization, Government and industrial stakeholders
Abstract
EEG BASED MENTAL STABILITY ANALYSIS FOR CORPORATE EMPLOYEES USING TRANSFORMER NEURAL NETWORK
Ms. Nivethitha.G, Dr. K. Banuroopa, MCA., M.Phil., Ph.D.,
DOI: 10.17148/IJIREEICE.2025.13462
Abstract: This project explores the novel use of transformer neural networks to assess the mental stability of corporate workers based on electroencephalogram (EEG) data. With rising stress levels and mental health issues among corporate workers, there is a pressing need for sophisticated tools that can track and sustain employee well-being.
Transformers, with their better capacity to handle sequential data, offer a promising method for EEG signal analysis, which is sequential and complex in nature. The research proposes to develop a strong and interpretable model that can detect patterns in EEG data reflecting mental well-being or distress. Transformers have the potential to provide more accurate and detailed insights by extracting long-range dependencies in the temporal dynamics of brain signals compared to conventional models.
The workflow of the project involves a number of key steps. First, raw EEG data preprocessing to eliminate noise and artifacts so that the data is reliable and of good quality. Then the cleaned data from which features related to the aspects most representative of mental health are extracted. The backbone of the project is to train a transformer neural network on this feature-rich dataset so that the model can learn and recognize complex patterns that could reflect different mental states.
The long-term aim is to create a tool that can be fully integrated into corporate wellness initiatives. This tool has the potential to offer actionable insights, enabling businesses to act preemptively on mental health concerns and promote employees' overall well-being. Through the use of state-of-the-art technology, this study not only seeks to improve individual health but also to foster a healthier, more productive workplace. The project underscores the vital importance of innovative technologies in encouraging mental health consciousness and intervention within the business community.
Keywords: Electroencephalogram (EEG), Mental stability, Transformer neural network, Feature extraction, Temporal dynamics, Corporate wellness, Employee support
Transformers, with their better capacity to handle sequential data, offer a promising method for EEG signal analysis, which is sequential and complex in nature. The research proposes to develop a strong and interpretable model that can detect patterns in EEG data reflecting mental well-being or distress. Transformers have the potential to provide more accurate and detailed insights by extracting long-range dependencies in the temporal dynamics of brain signals compared to conventional models.
The workflow of the project involves a number of key steps. First, raw EEG data preprocessing to eliminate noise and artifacts so that the data is reliable and of good quality. Then the cleaned data from which features related to the aspects most representative of mental health are extracted. The backbone of the project is to train a transformer neural network on this feature-rich dataset so that the model can learn and recognize complex patterns that could reflect different mental states.
The long-term aim is to create a tool that can be fully integrated into corporate wellness initiatives. This tool has the potential to offer actionable insights, enabling businesses to act preemptively on mental health concerns and promote employees' overall well-being. Through the use of state-of-the-art technology, this study not only seeks to improve individual health but also to foster a healthier, more productive workplace. The project underscores the vital importance of innovative technologies in encouraging mental health consciousness and intervention within the business community.
Keywords: Electroencephalogram (EEG), Mental stability, Transformer neural network, Feature extraction, Temporal dynamics, Corporate wellness, Employee support
Abstract
E-Parking Lot Booker
Mr. G .ABHIINANTH, Mrs. S. VISHNU PRIYA
DOI: 10.17148/IJIREEICE.2025.13463
Abstract: The increasing demand for efficient parking management systems has led to the development of digital solutions that streamline the process of reserving parking slots. This study presents the development and implementation of an E-Parking Lot Booker System, designed to facilitate both administrators and users in managing and reserving parking spaces efficiently. The system is built using HTML, CSS, JavaScript, PHP, and MySQL, ensuring a seamless user experience and real-time updates. The study highlights the system's architecture, functionality, security measures, and potential impact on urban parking management.
Keywords: Parking Management, Online Reservation, PHP, MySQL, Web Development, Smart Parking.
Keywords: Parking Management, Online Reservation, PHP, MySQL, Web Development, Smart Parking.
Abstract
A STUDY ON DEVELOPING AN EXPANDABLE SPOON FOR THE USER-FRIENDLY CONSUMERS- AN ECO-FRIENDLY INITIATIVE
Dr. L Sudershan Reddy, Samruddhi Pattanashetti, Tejasvani D, Saravana Kumar, Varun N, Shirisha Suresh, Shridhar Srinivas Nagarhalli, Suraj Ranjan Nayak
DOI: 10.17148/IJIREEICE.2025.13464
Abstract: The research explores the conceptualization, design, and market prospect for an expandable, eco-friendly, biodegradable, rice-husk spoon. Responding to mounting environmental degradation contributed by single-use plastic cutlery, the study seeks to come up with a sustainable replacement option that falls in line with customer demand for environment-friendly products. The project, as carried out under the brand label BLOOMWARE, attempts to bridge the gap between sustainability and user-friendly convenience by designing a product which is not just biodegradable but also expandable, being portable and functional.
The research utilizes both primary and secondary research approaches to source information regarding customer preferences, trends in the market, and competitors in the domain of biodegradable cutlery. Surveys, interviews, and observation data were collected to learn about the consumption patterns of environmentally responsible consumers, whereas industry analysis was undertaken to analyze the viability and scalability of the new product proposed. The study finds that consumer awareness and acceptability of green substitutes are on the increase, particularly in food and hospitality segments.
This study adds to the existing literature on sustainable product development and offers real-world implications for startups and industries looking to shift towards environmentally friendly practices. The expandable rice husk spoon is not only unique in its environmental advantage but also in its user-friendly design, which makes it a viable player in the biodegradable cutlery industry.
Keywords: Biodegradable Cutlery, Rice Husk Spoon, Expandable Spoon, Eco-Friendly Product, Sustainable Innovation, Consumer Behavior
BLOOMWARE presents a groundbreaking, environmentally friendly solution to the worldwide problem of single-use plastic cutlery. Made from rice husk fiber and plant-based adhesives, its expandable spoons begin small (1.5–2 cm) and swell when wet—providing a practical, eco-friendly alternative to plastic. These compostable spoons break down naturally into nutrient-rich fertilizer, minimizing waste and maintaining soil health, while using farm by-products and reducing dependence on non-renewable resources. Created for convenience and contemporary lifestyles, BLOOMWARE is ideal for eco-friendly consumers, travelers, and outdoor enthusiasts—ideal for meals on-the-go, picnics, and events.
The research utilizes both primary and secondary research approaches to source information regarding customer preferences, trends in the market, and competitors in the domain of biodegradable cutlery. Surveys, interviews, and observation data were collected to learn about the consumption patterns of environmentally responsible consumers, whereas industry analysis was undertaken to analyze the viability and scalability of the new product proposed. The study finds that consumer awareness and acceptability of green substitutes are on the increase, particularly in food and hospitality segments.
This study adds to the existing literature on sustainable product development and offers real-world implications for startups and industries looking to shift towards environmentally friendly practices. The expandable rice husk spoon is not only unique in its environmental advantage but also in its user-friendly design, which makes it a viable player in the biodegradable cutlery industry.
Keywords: Biodegradable Cutlery, Rice Husk Spoon, Expandable Spoon, Eco-Friendly Product, Sustainable Innovation, Consumer Behavior
BLOOMWARE presents a groundbreaking, environmentally friendly solution to the worldwide problem of single-use plastic cutlery. Made from rice husk fiber and plant-based adhesives, its expandable spoons begin small (1.5–2 cm) and swell when wet—providing a practical, eco-friendly alternative to plastic. These compostable spoons break down naturally into nutrient-rich fertilizer, minimizing waste and maintaining soil health, while using farm by-products and reducing dependence on non-renewable resources. Created for convenience and contemporary lifestyles, BLOOMWARE is ideal for eco-friendly consumers, travelers, and outdoor enthusiasts—ideal for meals on-the-go, picnics, and events.
Abstract
FOOD CALORIES ESTIMATION
Sabarish M, Mrs. M. Sowndharya M.Sc., M.Phil., (Ph. D.)
DOI: 10.17148/IJIREEICE.2025.13465
Abstract: The Food Calories Estimation System provides an efficient and user-friendly approach to tracking daily calorie intake using food image recognition and manual weight input. The system integrates a Flask-based web application with the Gemini API to identify food items from uploaded images and estimate their caloric value per 100g. Users can register, log in, set target calorie goals, and track their remaining calorie intake dynamically. The system calculates Total Daily Energy Expenditure (TDEE) based on user inputs such as weight, height, and activity level. The real-time calorie tracking module updates the consumed and remaining calories after each meal entry. The web application features a responsive frontend using HTML, CSS, and JavaScript, ensuring an interactive user experience. This solution provides a practical tool for individuals to monitor and manage their dietary intake effectively, promoting healthier eating habits.
Keywords: Food calorie estimation, Flask web application, real-time calorie tracking, food image recognition, nutrition monitoring, AI-based food identification.
Keywords: Food calorie estimation, Flask web application, real-time calorie tracking, food image recognition, nutrition monitoring, AI-based food identification.
Abstract
PROJECT REPORT ON ANALYZING THE MARKET POTENTIAL FOR NICHE FOOD DELIVERY SERVICES TARGETING PG ACCOMMODATOR SEGMENTS
Aditya Kedlaya H, Aishwarya Prasad B G, Akshar R, Aneesh S Poojary, Anirudh Santhosh K, Dr. Umesh Chandra
DOI: 10.17148/IJIREEICE.2025.13466
Abstract: The project focuses on examining the market potential for niche food delivery services tailored to the unique needs of Paying Guest (PG) Accommodators. These people represent a dynamic demographic that values convenience, affordability, and personalized experiences in food delivery. The study delves into their dietary preferences, lifestyle behaviors, and challenges, aiming to uncover actionable insights that businesses can leverage to cater to this underserved segment.
The niche food delivery market, marked by increasing demand for customized solutions such as health-conscious meals, dietary-specific options, and culturally relevant cuisines, presents an exciting opportunity for growth. This research evaluates both consumer preferences and the competitive landscape, providing a holistic understanding of the potential in this space.
The niche food delivery market, marked by increasing demand for customized solutions such as health-conscious meals, dietary-specific options, and culturally relevant cuisines, presents an exciting opportunity for growth. This research evaluates both consumer preferences and the competitive landscape, providing a holistic understanding of the potential in this space.
Abstract
RESEARCH REPORT ON ANALYZING THE MARKET POTENTIAL FOR NICHE FOOD DELIVERY SERVICES TARGETING PG ACCOMMODATOR SEGMENTS
Aditya Kedlaya H, Aishwarya Prasad B G, Akshar R, Aneesh S Poojary, Anirudh Santhosh K, Dr. Umesh Chandra
DOI: 10.17148/IJIREEICE.2025.13467
Abstract: The niche food delivery market has witnessed considerable growth in recent years, with a particular focus on specialized segments. This study aims to evaluate the market potential of niche food delivery services tailored for PG accommodators. By analyzing the unique dietary preferences, lifestyle constraints, and purchasing behavior of this demographic, the research identifies key opportunities for businesses to cater to their needs effectively. Employing a mixed-method approach, the study gathers data through surveys and focus group discussions, alongside competitor and trend analyses. Findings reveal a growing demand for convenient, health-conscious, and affordable food options among PG accommodators. Challenges such as logistical costs and customer retention are highlighted, along with potential strategies to address them. Ultimately, this paper concludes with actionable recommendations for implementing a niche food delivery service targeting PG accommodators, backed by both market feasibility and financial projections.
Keywords: Niche food delivery, PG accommodators, Market potential, Dietary preferences, Consumer behavior, Lifestyle constraints, Health-conscious food, Convenience, Purchasing behavior, Feasibility analysis, Logistical challenges, Customer retention strategies, Food delivery trends, Target audience segmentation, Financial projections
Keywords: Niche food delivery, PG accommodators, Market potential, Dietary preferences, Consumer behavior, Lifestyle constraints, Health-conscious food, Convenience, Purchasing behavior, Feasibility analysis, Logistical challenges, Customer retention strategies, Food delivery trends, Target audience segmentation, Financial projections
Abstract
CNC BASED WRITING AND DRAWING MACHINE
Ashvini Shrikrushna Hirve, Rohit Dashrath ade, om prahlad kanse, rashmi rahul rohane, ayush nitin bhagwate, Sanjana ravidas barve, sanidh kamble, umesh kaware
DOI: 10.17148/IJIREEICE.2025.13468
Abstract: In today's world, automation is making everyday tasks easier and more efficient. One area where this is evident is in writing and drawing, where machines are increasingly being used for precision tasks. This manual introduces a CNC-based writing and drawing machine, designed to not only replicate designs but also write in personal handwriting, offering a unique blend of technology and creativity. Using an Arduino Uno microcontroller, stepper motors, and a servo motor, the machine operates across three axes (X, Y, and Z) to create accurate drawings and text. Software like Inkscape helps generate designs, which are converted into instructions that the machine can understand and execute through v3 control board.
The machine is designed to be accessible and useful for individuals with physical disabilities, enabling them to express thoughts through writing by interpreting images and text and converting them into motor actions using new and unique fonts. This versatile device can also be integrated with a computer or mobile device, offering remote operation. The system is both wired and wireless, allowing flexibility in various environments.
The CNC writing machine is an affordable and efficient tool, perfect for personal, educational, or professional use. Whether you're an artist looking for precision or someone wanting to write in your own handwriting through automation, this machine offers endless possibilities. It also includes the integration of Artificial Intelligence which gives one way to move toward the technology efficiently.
Keywords: V3 Control board, Artificial Intelligence, CNC machine.
The machine is designed to be accessible and useful for individuals with physical disabilities, enabling them to express thoughts through writing by interpreting images and text and converting them into motor actions using new and unique fonts. This versatile device can also be integrated with a computer or mobile device, offering remote operation. The system is both wired and wireless, allowing flexibility in various environments.
The CNC writing machine is an affordable and efficient tool, perfect for personal, educational, or professional use. Whether you're an artist looking for precision or someone wanting to write in your own handwriting through automation, this machine offers endless possibilities. It also includes the integration of Artificial Intelligence which gives one way to move toward the technology efficiently.
Keywords: V3 Control board, Artificial Intelligence, CNC machine.
Abstract
ROLE OF AI CHATBOTS IN ENHANCING CUSTOMER ENGAGEMENT ON E-COMMERCE PLATFORMS
Duraiarasu N, Rajlaxmi Narayan Relekar, Rakshitha D Desai, Rishabh Roonwal M, Rohan R, R Sekhar, P K Thomas
DOI: 10.17148/IJIREEICE.2025.13469
Abstract: Artificial intelligence (AI) has revolutionized the e-commerce sector by radically changing how companies engage with their clientele and improving the overall buying experience. The creation of AI-powered chatbots is among the major advancements driven by AI. These chatbots are now essential to e-commerce platforms because of their capacity to offer immediate, individualized, and 24/7 client service. By handling a wide range of customer contacts, from providing personalized product recommendations to responding to simple questions, these AI-driven solutions are intended to increase the efficacy and efficiency of customer support. This study investigates how AI chatbots affect customer satisfaction and engagement by looking at consumer views, how well chatbots handle inquiries, and the difficulties users encounter while dealing with these systems.
The study focuses on how AI chatbots affect consumers' experiences on e-commerce platforms, specifically addressing problems like accuracy, response time, and the capacity to comprehend and handle complex inquiries. Insights into how well these chatbots satisfy customer expectations and enhance their general satisfaction and platform loyalty are the goal of the study. In order to collect data, 30 participants spanning a range of age groups and shopping inclinations responded to a Google Forms survey that was disseminated over social media channels. The survey's questions were intended to assess respondents' opinions of chatbot performance, their experiences with AI chatbots, and any difficulties they ran into when interacting with them. A thorough grasp of how various demographics view and engage with AI chatbots in an e-commerce setting is made possible by the diversity of the responder pool.
The study's conclusions will offer insightful information that can assist e-commerce companies in improving their AI chatbot capabilities and making sure they are better prepared to effectively respond to a variety of consumer questions. The study's goal is to find areas for development by examining customer input. Some of these topics include improving the chatbot's natural language processing capabilities, speeding up response times, and resolving frequent user complaints. In order to increase consumer pleasure and engagement, this research will also provide organizations with practical suggestions on how to enhance personalized shopping support with AI chatbots.
Keywords: AI Chatbots, Customer Engagement, E-commerce Platforms, Customer Satisfaction, Natural Language Processing
The study focuses on how AI chatbots affect consumers' experiences on e-commerce platforms, specifically addressing problems like accuracy, response time, and the capacity to comprehend and handle complex inquiries. Insights into how well these chatbots satisfy customer expectations and enhance their general satisfaction and platform loyalty are the goal of the study. In order to collect data, 30 participants spanning a range of age groups and shopping inclinations responded to a Google Forms survey that was disseminated over social media channels. The survey's questions were intended to assess respondents' opinions of chatbot performance, their experiences with AI chatbots, and any difficulties they ran into when interacting with them. A thorough grasp of how various demographics view and engage with AI chatbots in an e-commerce setting is made possible by the diversity of the responder pool.
The study's conclusions will offer insightful information that can assist e-commerce companies in improving their AI chatbot capabilities and making sure they are better prepared to effectively respond to a variety of consumer questions. The study's goal is to find areas for development by examining customer input. Some of these topics include improving the chatbot's natural language processing capabilities, speeding up response times, and resolving frequent user complaints. In order to increase consumer pleasure and engagement, this research will also provide organizations with practical suggestions on how to enhance personalized shopping support with AI chatbots.
Keywords: AI Chatbots, Customer Engagement, E-commerce Platforms, Customer Satisfaction, Natural Language Processing
Abstract
FOOD RECIPE FINDER
Alagar raja M, Dr. A. Mythili
DOI: 10.17148/IJIREEICE.2025.13470
Abstract: The Food Recipe Finder system is an innovative platform that combines Artificial Intelligence (Gemini AI) and SQL database querying to assist users in discovering recipes. The system offers two key functionalities: the first allows users to upload food images or provide food names to receive a list of ingredients and detailed recipes through Gemini AI, while the second enables users to input available ingredients to receive recipe suggestions from a MySQL database. This approach helps users efficiently plan meals, optimize kitchen resources, and reduce food waste. The system is built with a user-friendly interface, integrating Flask for backend management and Gemini AI for image and text processing. The results show that the system performs well in both food-to-recipe generation and ingredient-to- recipe matching, providing fast and accurate suggestions. Overall, the Food Recipe Finder proves to be an effective tool for home cooks, enhancing convenience and supporting creative meal planning. Future enhancements will aim to expand the recipe database, improve AI capabilities, and optimize the platform for mobile use.
Abstract
Orchid Fixie Flora: The Eco Bloom Initiative
N Roshan Choudhury, Kiruthika G, Kavya Ganji, Arjun R, Murari Jagadeesh, Kanimozhi A
DOI: 10.17148/IJIREEICE.2025.13471
Abstract: The floral industry, valued at $57.4 billion in 2022, is an essential component of global commerce, deeply ingrained in social customs and traditions across cultures. Flowers symbolize love, celebration, condolences, and spiritual reverence, making floristry an indispensable part of human experience. However, beneath this aesthetic charm lies a deeply flawed system marked by unsustainable practices. The use of synthetic pesticides, high levels of plastic packaging, international air transport, and flower wastage collectively contribute to severe ecological degradation. These practices impact air quality, soil health, water bodies, and even wildlife ecosystems.
Orchid fixie flora’s eco bloom initiative seeks to revolutionize this industry through a zero-waste, sustainable floral subscription model. It is a business that intertwines aesthetics with ethics, crafting floral experiences that are not only beautiful but also environmentally responsible. From biodegradable packaging to carbon-neutral delivery and educational outreach, the initiative blends innovation, environmental science, and entrepreneurial spirit. The project supports sustainable development goal 12 (responsible consumption and production) and sustainable development goal 13 (climate action), aiming to be a catalyst for green transformation in the floristry sector. It also addresses the behavioural shift required among consumers to embrace more conscious choices, thus creating a feedback loop where business, environment, and society all benefit.
This summary sets the stage for the detailed breakdown of objectives, methodology, research, and analysis presented in the following sections.
Keywords: Eco-friendly floral business, Circular economy, Return-and-reuse program, Composting floral waste, Zero- waste entrepreneurship, Eco-conscious consumers
Orchid fixie flora’s eco bloom initiative seeks to revolutionize this industry through a zero-waste, sustainable floral subscription model. It is a business that intertwines aesthetics with ethics, crafting floral experiences that are not only beautiful but also environmentally responsible. From biodegradable packaging to carbon-neutral delivery and educational outreach, the initiative blends innovation, environmental science, and entrepreneurial spirit. The project supports sustainable development goal 12 (responsible consumption and production) and sustainable development goal 13 (climate action), aiming to be a catalyst for green transformation in the floristry sector. It also addresses the behavioural shift required among consumers to embrace more conscious choices, thus creating a feedback loop where business, environment, and society all benefit.
This summary sets the stage for the detailed breakdown of objectives, methodology, research, and analysis presented in the following sections.
Keywords: Eco-friendly floral business, Circular economy, Return-and-reuse program, Composting floral waste, Zero- waste entrepreneurship, Eco-conscious consumers
Abstract
RESEARCH REPORT ON ANALYZING THE MARKET POTENTIAL FOR ECO FRIENDLY PACKAGING SOLUTION
Sayan Banik, Srujan P S, Sriram V, Thashwin, Vishnu P, Yajat Sureka, Dr. Umesh Chandra
DOI: 10.17148/IJIREEICE.2025.13472
Abstract: In the wake of accelerating environmental degradation and growing global plastic waste, the call for green packaging has never been more urgent. The rising visibility of plastic marine pollution, littered packaging landfills, and the environmental price of carbon-heavy production processes has driven consumers and companies to look for cleaner, more sustainable packaging options. This report presents a comprehensive examination of sustainable packaging, detailing its suitability, viability, and strategic value in today's market environment.
Green packaging represents a range of sustainable options, from biodegradable and compostable materials to recyclable and reusable packaging material that performs the same functional purpose as regular plastic packaging—but far less harm to the environment. Examples of such materials include, among others, bioplastics made from cornstarch, sugarcane bagasse, mushroom mycelium, recycled kraft paper, and plant fibre composites. Switching to these materials is a part of the global move towards ESG (Environmental, Social, Governance) standards, zero-waste goals, and green product lifecycle.
Market Landscape & Opportunity: The green packaging market is at a crossroads of change, fuelled by consumerism, government policies, and corporate sustainability initiatives. Industries like FMCG (Fast-Moving Consumer Goods), e- commerce, food delivery, retail, and logistics are facing mounting pressure to minimize their carbon footprint. Our market study indicates that:
▪ The global green packaging market is expected to grow at a CAGR of 7–10% to over $300 billion by 2030. ▪ Regulatory frameworks like India's Plastic Waste Management Rules (Amendment), 2022, and the EU Single- Use Plastics Directive are driving the elimination of non-biodegradable packaging.
This report prioritizes cost-effectiveness, material procurement, consumer literacy, and manufacturability scalability as key strategic drivers to success. Our approach involves low-cost prototyping, allowing for quick iterations and early market trials with little capital expenditure.
Business Model & Financial Framework: Operational excellence along with rapid scalability has been designed for by way of lean and efficient business model. This encompasses:
â–Ş B2B collaborations with e-commerce, food technology, and logistics organizations â–Ş Tailored packaging solutions for eco-sensitive consumer goods and cosmetics brands â–Ş White-label production for bulk-buying customers migrating to green packaging
Financial projections suggest break-even within 18–24 months through a modular roll-out strategy facilitated by low- capital-expenditure micro-factories and incentives from the government for green technology. Our cost structure is multi- tiered to facilitate scalability affordability combined with sustainable margins through optimized raw materials and processes.
Keywords: Eco-friendly packaging, Sustainable packaging, Environmental impact, Plastic waste, Consumer behaviour, Regulations, Market analysis, Consumer education, Government incentives, Recycling infrastructure, Greenwashing
Green packaging represents a range of sustainable options, from biodegradable and compostable materials to recyclable and reusable packaging material that performs the same functional purpose as regular plastic packaging—but far less harm to the environment. Examples of such materials include, among others, bioplastics made from cornstarch, sugarcane bagasse, mushroom mycelium, recycled kraft paper, and plant fibre composites. Switching to these materials is a part of the global move towards ESG (Environmental, Social, Governance) standards, zero-waste goals, and green product lifecycle.
Market Landscape & Opportunity: The green packaging market is at a crossroads of change, fuelled by consumerism, government policies, and corporate sustainability initiatives. Industries like FMCG (Fast-Moving Consumer Goods), e- commerce, food delivery, retail, and logistics are facing mounting pressure to minimize their carbon footprint. Our market study indicates that:
▪ The global green packaging market is expected to grow at a CAGR of 7–10% to over $300 billion by 2030. ▪ Regulatory frameworks like India's Plastic Waste Management Rules (Amendment), 2022, and the EU Single- Use Plastics Directive are driving the elimination of non-biodegradable packaging.
This report prioritizes cost-effectiveness, material procurement, consumer literacy, and manufacturability scalability as key strategic drivers to success. Our approach involves low-cost prototyping, allowing for quick iterations and early market trials with little capital expenditure.
Business Model & Financial Framework: Operational excellence along with rapid scalability has been designed for by way of lean and efficient business model. This encompasses:
â–Ş B2B collaborations with e-commerce, food technology, and logistics organizations â–Ş Tailored packaging solutions for eco-sensitive consumer goods and cosmetics brands â–Ş White-label production for bulk-buying customers migrating to green packaging
Financial projections suggest break-even within 18–24 months through a modular roll-out strategy facilitated by low- capital-expenditure micro-factories and incentives from the government for green technology. Our cost structure is multi- tiered to facilitate scalability affordability combined with sustainable margins through optimized raw materials and processes.
Keywords: Eco-friendly packaging, Sustainable packaging, Environmental impact, Plastic waste, Consumer behaviour, Regulations, Market analysis, Consumer education, Government incentives, Recycling infrastructure, Greenwashing
Abstract
THE STUDY ON CUSTOMER SATISFACTION TOWARDS GOOGLE PAY WITH SPECIAL REFERENCE TO TIRUPUR CITY
Rahul. V, Malarvizhi. D
DOI: 10.17148/IJIREEICE.2025.13473
Abstract: In this project, we will discuss functionalities, security, and user experience of Google pay Google Pay which is used for digital payments. Google Pay Description It covers the progression of mobile payment systems, highlighting the technological innovations and security measures that ensure a reliable cashless transaction experience. It also measures user acquisition and transaction efficiency, as well as Google Pay’s effect on digital commerce. This through analyzing its features, benefits and challenges
Keywords: Google pay, Digital Commerce, UPI
Keywords: Google pay, Digital Commerce, UPI
Abstract
“LONG-TERM MENTAL HEALTH EFFECTS OF SEPARATION FROM LOVED ONES: THE ROLE OF EMPATHY AND RELATIONSHIP ATTACHMENT STYLES”
Dr. Kumar Mukul, Mohamed Shihaab Ibrahim M, Noorah Sachora, S Gayan Gowda, Tendulkar Shivam Kishor, Rithikanand Kedanuri, Rupesh M
DOI: 10.17148/IJIREEICE.2025.13474
Abstract: This study investigates the long-term mental health consequences of separation from loved ones—whether due to death, conflict, or disconnection—with a specific focus on the moderating roles of empathy and attachment styles (anxious, avoidant, and secure). Using a cross-sectional design, data were collected from 150 participants through a structured questionnaire based on validated Likert-scale items. The dependent variable was the self-reported mental health impact following separation, while independent variables included empathy levels and attachment styles. Correlation analysis revealed significant associations between these psychological constructs and mental health outcomes. Anxious attachment was strongly positively correlated with mental health impact (r = +0.52), indicating heightened vulnerability. Avoidant attachment also showed a moderate positive correlation (r = +0.27), suggesting internalized emotional distress. Interestingly, empathy—typically seen as a prosocial trait—demonstrated a moderate positive correlation (r = +0.27) with mental health strain, likely due to emotional over-identification with others. Conversely, secure attachment displayed a moderate negative correlation (r = –0.39), serving as a protective factor against long-term psychological distress.
The findings underscore the importance of individual differences in emotional regulation and interpersonal connection when dealing with loss or separation. The study advocates for targeted psychological interventions that consider these traits to enhance resilience and recovery.
Keywords: Empathy, Attachment Styles, Mental Health, Separation, Relationship Dissolution, Emotional Well-being
The findings underscore the importance of individual differences in emotional regulation and interpersonal connection when dealing with loss or separation. The study advocates for targeted psychological interventions that consider these traits to enhance resilience and recovery.
Keywords: Empathy, Attachment Styles, Mental Health, Separation, Relationship Dissolution, Emotional Well-being
Abstract
Turning Plastic into Rewards: A Smart Recycling Solution
Kamalesh S, Brunda B V, Diksha Singh, Dilip Marwal, Bhavish D Kotian, Darshan H, Dr. Uma C Swadimath
DOI: 10.17148/IJIREEICE.2025.13475
Abstract: The plastic pandemic has assumed a worldwide dimension since millions of tons of plastic garbage are dumped onto the landfills and oceans every year. It is in pursuit of reducing this atrocity that this study examines the possibility and viability of having an incentive-based rewards plastic collection vending machine.The research utilizes a mixed- method design using both qualitative and quantitative data gathering. The 100 sample survey was given to gauge recycling behavior, reward preference, and preference to use the vending machine. Research findings are that there is a significant percentage of volunteers among the respondents who would use the vending machine, particularly if what is offered to them as rewards is sufficiently high, e.g., discounts on usual purchases or vouchers in shops nearby. Also, the interview with the experts' findings mirror the usability of the system operationally, e.g., logistical problems, maintenance required, and need for coordination with recycling stations and retail business partners. Moreover, according to the research, the machine has the potential to generate economic value by developing new sources of revenue through partnership with reward coupon sponsorship companies. Also, it can reduce plastic garbage pollution by making disposal effective. Ethical values such as informed consent and data secrecy were ensured throughout the research. Some limitations of the research are also mentioned, i.e., 100 sample size of the respondents, which may not be representative of the total population. Time limit and possibilities of obtaining unbiased responses are also cited as limitations. The research validates that as an incentive reward, plastic collection vending machine is a valuable and deserving undertaking. It is highly successful in bringing about environmental, social, as well as economic gains through promotion of plastic recycling, reward of cleaner behavior, and promotion of enterprise. The research recommends additional pilot trials in the target areas for fine-tuning the model before large-scale implementation.
Keywords: Plastic waste management, Incentive-based recycling, Vending machines, Environmental sustainability.
Keywords: Plastic waste management, Incentive-based recycling, Vending machines, Environmental sustainability.
Abstract
FAMILY FINANCIAL LITERACY IN THE DIGITAL ERA: A SURVEY OF ONLINE INVESTMENT APPLICATION AWARENESS AND USAGE IN COIMBATORE
Dr. S. Kamalaveni, Mr. S. Abhishek, Ms. G. Aarthi
DOI: 10.17148/IJIREEICE.2025.13476
Abstract: This study examines family financial literacy in the context of the digital era, specifically focusing on the awareness and usage of online investment applications in Coimbatore. With the increasing prevalence of digital platforms for managing investments, it becomes essential to understand how families are engaging with these tools. Through a survey-based approach, the research explores the level of knowledge regarding mobile applications for investing in stocks, mutual funds, and managing personal finances. The study also looks into various demographic factors, technology adoption, and the frequency of usage of these platforms. The goal is to identify gaps in financial literacy, challenges faced by families in adopting these digital tools, and the overall impact on financial decision-making. The results aim to offer valuable insights for improving financial education and promoting greater awareness of digital financial resources, ultimately supporting more informed and effective wealth management practices in the digital age.
Keywords: Digital finance, Financial education, Digital literacy
Keywords: Digital finance, Financial education, Digital literacy
Abstract
DESIGN AND FABRICATION OF A LONG-RANGE SPY ROBOT WITH NIGHT VISION USING ULTRASONIC SENSOR
A JHON PRESIN KUMAR, ABHIJEET MONDAL, B YESHWANTH, C S MUGIL
DOI: 10.17148/IJIREEICE.2025.13477
Abstract: This paper presents the design and development of a long-range spy robot equipped with a night vision camera, wireless control, and multiple sensors for obstacle and hazard detection. The robot is designed for military surveillance, industrial monitoring, and disaster response applications. The system integrates an Arduino Uno microcontroller, a night vision wireless camera, and sensors including ultrasonic, metal detection, and fire sensors. Wireless communication via Wi-Fi and DTMF allows remote operation. The results indicate efficient real-time monitoring, obstacle detection, and remote maneuverability, making it a viable solution for autonomous surveillance. Additionally, the robot's mobility is optimized for rough terrains, ensuring functionality in harsh environments.
Keywords: Spy Robot, Night Vision, Surveillance, Obstacle Detection, Wi-Fi, Remote Monitoring, Hazard Detection.
Keywords: Spy Robot, Night Vision, Surveillance, Obstacle Detection, Wi-Fi, Remote Monitoring, Hazard Detection.
Abstract
TREADMILL EXERCISE: EFFECTS ON PSYCHOLOGICAL DISTRESS IN SEDENTARY STUDENTS
Dr. Sinku Kumar Singh, Dr. Shivaji Suryawanshi
DOI: 10.17148/IJIREEICE.2025.13478
Abstract: Aims The primary aim of the study was to examining the effects of Treadmill exercise on psychological distress, Target population and Sample size Two groups were targeted as an experimental group and control group. The 34 male participated in the study and their age ranged between 19-28 years. The all students are sedentary and not participation any sporting or physical activities. Exclusion and inclusion criteria Exclusion criteria were , CVD, Hypertension Hypotension, asthma, Diabetes, etc. that would put the subjects at risk when performing the experimental tests.
Treadmill exercise Experimental group participated in Treadmill exercise Training program which was conducted for four-week, four days in a week and 15 minutes in a day. After the pre-test was over, the entire selected subjects were exposed to four-week Treadmill exercise.
Findings and Conclusions The findings of the show that there were significant differences were found in experimental and control group . significant difference of Psychological distress (t= p<.05) was found between Pre and post test of Experimental group where the score of psychological distress was significantly reduce after treadmill training
Recommendation The findings of the study will be proposing a new conceptual model that may assist the policy makers in framing new policies and strategies to manage the stress problem
Keywords: Distress, Treadmill, experimental, group
Treadmill exercise Experimental group participated in Treadmill exercise Training program which was conducted for four-week, four days in a week and 15 minutes in a day. After the pre-test was over, the entire selected subjects were exposed to four-week Treadmill exercise.
Findings and Conclusions The findings of the show that there were significant differences were found in experimental and control group . significant difference of Psychological distress (t= p<.05) was found between Pre and post test of Experimental group where the score of psychological distress was significantly reduce after treadmill training
Recommendation The findings of the study will be proposing a new conceptual model that may assist the policy makers in framing new policies and strategies to manage the stress problem
Keywords: Distress, Treadmill, experimental, group
Abstract
AWARENESS OF STOCK MARKET INVESTMENT USING AI
Dr. P. KANNAN M.Com., M.Phil., Ph.D. PGDCA, GOKULA KRISHNAN .R
DOI: 10.17148/IJIREEICE.2025.13479
Abstract: In recent years, the stock market has witnessed a dynamic transformation with the integration of Artificial Intelligence (AI), revolutionizing traditional investment strategies. This study aims to explore the extent of awareness among individuals regarding stock market investments that are influenced or supported by AI-based technologies. The research is rooted in the understanding that AI applications—such as algorithmic trading, predictive analytics, and sentiment analysis—are becoming increasingly prominent in aiding investors to make more informed and timely decisions. With rapid advancements in financial technology, it becomes essential to assess whether current and potential investors are aware of the opportunities and tools AI offers in the stock trading landscape. The study specifically focuses on how AI influences the decision-making process of investors, their trust in AI-driven platforms, and the perceived benefits. Data was gathered through a structured questionnaire targeting a diverse group of respondents, varying in age, occupation, and investment experience.
Abstract
IMPACT OF CORPORATE SOCIAL RESPONSIBILITY ON BRAND REPUTATION AMOUNG TATA GROUPS
Dr. P. KANNAN M.Com., M.Phil., Ph.D. PGDCA, GANESAN.R
DOI: 10.17148/IJIREEICE.2025.13480
Abstract: Corporate Social Responsibility (CSR) has become an essential component of business ethics and governance, reflecting a company’s commitment to sustainable development and social welfare. In an era where businesses are expected to go beyond profit-making and contribute to society, CSR plays a pivotal role in shaping corporate reputation and stakeholder trust. This study aims to explore the various dimensions of CSR, including its implementation strategies, regulatory frameworks, and its influence on businesses and communities.
The research delves into how organizations integrate CSR into their operations, the motivations behind adopting socially responsible practices, and the challenges they encounter in the process. It also examines the broader impact of CSR on corporate sustainability, employee engagement, and consumer perception. Furthermore, the study investigates the evolving role of CSR in the face of global challenges such as environmental sustainability, ethical supply chain management, and corporate transparency.
By analyzing real-world examples and industry practices, this study provides insights into how businesses navigate the complexities of CSR while balancing economic objectives with social responsibilities. The findings contribute to a deeper understanding of CSR’s significance in modern business environments and offer strategic recommendations for organizations to enhance their CSR initiatives effectively.
The research delves into how organizations integrate CSR into their operations, the motivations behind adopting socially responsible practices, and the challenges they encounter in the process. It also examines the broader impact of CSR on corporate sustainability, employee engagement, and consumer perception. Furthermore, the study investigates the evolving role of CSR in the face of global challenges such as environmental sustainability, ethical supply chain management, and corporate transparency.
By analyzing real-world examples and industry practices, this study provides insights into how businesses navigate the complexities of CSR while balancing economic objectives with social responsibilities. The findings contribute to a deeper understanding of CSR’s significance in modern business environments and offer strategic recommendations for organizations to enhance their CSR initiatives effectively.
Abstract
A STUDY ON MOBILE APP DEVELOPMENT FOR E-COMMERCE IN COIMBATORE CITY
Dr. S. Kamalaveni, Ms. A.Anish Fathim
DOI: 10.17148/IJIREEICE.2025.13481
Abstract: The study explores the development and impact of mobile applications in the e-commerce domain. It highlights how mobile apps have become a vital component of modern business strategies due to their role in enhancing user engagement, offering convenience, and driving revenue. The research focuses on the essential features, technologies, and challenges involved in creating successful e-commerce mobile apps. Key areas covered include user experience design, performance optimization, security and data privacy, and platform compatibility. The study utilizes a mixed-method research approach, gathering data through questionnaires and analyzing it using statistical tools such as percentage analysis, Like-rt scales, and chi-square tests. The findings emphasize the growing adoption of mobile e- commerce in India, identify user preferences, and suggest strategies to improve app functionality and consumer trust. Ultimately, the research underlines the potential of mobile apps to revolutionize e-commerce by aligning technology with user needs.
Keywords: Mobile App Development, E-Commerce, Mobile Commerce (M-Commerce), Security and Data Privacy, Android, iOS, Order Tracking.
Keywords: Mobile App Development, E-Commerce, Mobile Commerce (M-Commerce), Security and Data Privacy, Android, iOS, Order Tracking.
Abstract
ANALYSING THE IMPACT OF EMPLOYEE ENGAGEMENT ON PRODUCTIVITY IN COTTON CODE GARMENTS TIRUPPUR
Dr. S. Kamalaveni, Ms. S. Aiswariya, Ms. M.T Padmapriya, Mr. S. Abhishek
DOI: 10.17148/IJIREEICE.2025.13482
Abstract: The survey conducted at Cotton Code Garments, Tirupur, to examine the influence of employee engagement on productivity. A sample of 75 employees was selected using convenience sampling and data was collected through structured questionnaires. The research aimed to identify measurable goals and factors contributing to team success. Standard deviation and coefficient of variation were used to analyze the collected data. The findings show that employee engagement significantly impacts performance, morale, and retention. Organizations that foster career growth, psychological safety, and meaningful work see higher engagement. The study emphasizes that tailored engagement strategies are essential for sustained productivity.
Keywords: Employee Engagement, Productivity, Team Success, Motivation, Workplace Culture, Psychological Safety, Career Development, Organizational Performance.
Keywords: Employee Engagement, Productivity, Team Success, Motivation, Workplace Culture, Psychological Safety, Career Development, Organizational Performance.
Abstract
Leveraging Sustainable Urban Development through Smart Waste Management in Bangalore
AISWARYA V B, ALKA SINGH, DILSHAN M, EAPEN JINIX THARAKAN PEREKATT, ADITYA JEETENDRA PORWAL, HARSHITHA L, RUDRA S, KAKULAPATI NAVEEN
DOI: 10.17148/IJIREEICE.2025.13483
Abstract: Urbanization in Bangalore has led to a significant increase in waste generation, posing severe challenges to the city's waste management systems. This research paper explores the potential of smart technologies, such as IoT, artificial intelligence, and mobile applications, to enhance waste management practices in Bangalore. The study evaluates current waste management practices, analyses the role of technology, assesses environmental and economic impacts, and examines the importance of public awareness and community engagement. The paper concludes with policy recommendations to promote sustainable waste management in urban India.
Keywords: Smart Waste Management, IOT, Sustainable Urban Development, Public Awareness, Community Engagement, Environmental Impact.
Keywords: Smart Waste Management, IOT, Sustainable Urban Development, Public Awareness, Community Engagement, Environmental Impact.
Abstract
IMPULSE BUYING BEHAVIOUR OF CONSUMERS TOWARDS FMCG PRODUCTS
Mr. S. Muruganantham, Mr. S. Kaleeswaran, Mr. R. Rohinth, Mr. S. Abhishek
DOI: 10.17148/IJIREEICE.2025.13484
Abstract: This study investigates the impulse buying behavior of consumers with a specific focus on Fast-Moving Consumer Goods (FMCG) in Coimbatore. Impulse buying, a spontaneous and unplanned purchase triggered by various stimuli, plays a significant role in modern consumer behavior. The research aims to analyze demographic influences, emotional and psychological factors, and the impact of marketing strategies such as promotions, packaging, and digital advertisements on impulsive purchases. A sample of 128 respondents was surveyed using a structured questionnaire. Findings reveal that young adults, especially students, are the most active in impulse buying, with snacks and beverages being the most frequently purchased items. Emotional triggers such as happiness and stress, along with digital payment methods and online shopping platforms, further encourage such behavior. The study offers valuable insights for marketers to design targeted strategies and highlights the importance of understanding consumer psychology in the competitive FMCG market.
Keywords: Impulse Buying, FMCG, Consumer Behavior
Keywords: Impulse Buying, FMCG, Consumer Behavior
Abstract
A STUDY ON COMPARING COMPENSATION STRATEGIES FOR STARTUPS VS ESTABLISHED COMPANY
Dr. P. Kannan, Mr. N. Harish Nithin, Mr. S. Abhishek
DOI: 10.17148/IJIREEICE.2025.13485
Abstract: This study compares compensation strategies between startups and established companies, focusing on how each type structures pay, benefits, and incentives to attract and retain talent. While established companies offer structured salary bands, retirement benefits, and stability, startups often rely on flexible work environments, equity options, and performance-linked incentives to compete despite limited financial resources. A survey of 103 respondents revealed that startups prioritize growth potential and non-monetary perks, whereas established firms emphasize security and standardization. Key influencing factors include market demand, financial resources, and industry trends. Findings show that startups appeal more to younger, risk-tolerant individuals, while established companies attract those seeking long- term security. Despite their contrasting approaches, both types face challenges in compensation management. The study highlights the importance of tailoring compensation to organizational context and offers recommendations for improving strategies in both startup and corporate environments.
Keywords: Compensation Strategies, Startups, Established Companies
Keywords: Compensation Strategies, Startups, Established Companies
Abstract
A STUDY ON DATA SECURITY ON MOBILE APPLICATION
Dr. P. Kannan, Ms. T. Ilakkya, Mr. S. Abhishek
DOI: 10.17148/IJIREEICE.2025.13486
Abstract: The widespread adoption of mobile application has introduced significant data security risks, compromising sensitive use information. A mixed method approach was employed combining a literature review, survey of mobile application developer and users and vulnerability testing of selected mobile applications. The results reveal inadequate data security practices, including insecure data storage, insufficient encryption, and weak authentication mechanisms. This project provides recommendations for improving data security in mobile applications, emphasizing the need for robust security measures, user education, and awareness. The findings and recommendations contribute to the development of more secure mobile applications, benefiting developers, users, and organizations.
Keywords: Digital Security, Encryption
Keywords: Digital Security, Encryption
Abstract
A STUDY ON ANALYSIS OF CYBERSECURITY THREATS IN BIG BASKET
Mrs. R. Anitha, D. Udhaya
DOI: 10.17148/IJIREEICE.2025.13487
Abstract: This study provides an in-depth analysis of the cybersecurity threats faced by Big Basket, a leading online grocery delivery service in India. As e-commerce continues to grow rapidly, it presents lucrative opportunities for businesses but also attracts a diverse range of cyber threats. These threats include data breaches, phishing attacks, ransomware, and insider threats, each posing significant risks to customer data and organizational integrity. Given Big Basket's handling of sensitive consumer information, understanding and mitigating these risks is crucial for maintaining customer trust and ensuring business continuity. The research aims to identify specific vulnerabilities in Big Basket’s cybersecurity framework, assess the effectiveness of current security measures, and evaluate the level of awareness and preparedness among employees and customers regarding these threats. Utilizing a descriptive research design, the study collects qualitative and quantitative data from various stakeholders, analysing their perceptions regarding cybersecurity risks. The findings reveal that while a considerable number of users have not experienced a security breach, there is a persistent concern regarding issues such as payment fraud and data leaks. This indicates a pressing need for improved cybersecurity measures. The study highlights the importance of enhancing user education and implementing advanced security protocols like encryption and two-factor authentication. By fostering a culture of cybersecurity awareness and vigilance, Big Basket can bolster its defence’s against potential cyber attacks, thus ensuring a safer online shopping experience for its customers.
Keywords: Cybersecurity, E-commerce, Big Basket, Data Breaches, Phishing, Ransomware, Consumer Trust, Security Measures, Vulnerabilities, Awareness.
Keywords: Cybersecurity, E-commerce, Big Basket, Data Breaches, Phishing, Ransomware, Consumer Trust, Security Measures, Vulnerabilities, Awareness.
Abstract
A STUDY ON COMPARING COMPENSATION STRATEGIES FOR STARTUPS VS ESTABLISHED COMPANY
Dr. P. Kannan, Mr. V. K. Hareesh Raja, Ms. U. Heena Sri, Mr. S. Abhishek
DOI: 10.17148/IJIREEICE.2025.13488
Abstract: This study explores consumer perceptions and preferences towards petrol, diesel, and electric vehicles (EVs), focusing on factors such as environmental awareness, cost, performance, and maintenance. Conducted in Coimbatore and Tiruppur with 125 respondents, the research reveals that while diesel cars remain the most preferred (44.2%), interest in electric vehicles is steadily rising (21.7%), especially among younger demographics. Respondents associate EVs with advantages like instant torque, lower operating costs, and reduced noise pollution. However, concerns such as high upfront costs and limited charging infrastructure persist. Maintenance cost and fuel efficiency are the most influential factors driving consumer choice, overshadowing environmental impact. Most respondents receive information from social media, highlighting its influence on automotive awareness. The study provides valuable insights for manufacturers and policymakers to tailor strategies promoting sustainable mobility and enhancing EV adoption by addressing infrastructure gaps and improving affordability.
Keywords: Consumer Perception, Electric Vehicles, Fuel Type Preference
Keywords: Consumer Perception, Electric Vehicles, Fuel Type Preference
Abstract
A STUDY ON CONSUMER PERCEPTION TOWARDS SMART HOME TECHNOLOGY
Kannan P, Heena Sri U, Hareesh Raja V K, Abhishek S
DOI: 10.17148/IJIREEICE.2025.13489
Abstract: This study explores consumer perception towards smart home technology, examining factors influencing adoption, benefits, and concerns. The research investigates how consumers perceive smart home devices, their willingness to integrate these technologies into daily life, and the impact on their lifestyle and decision-making processes. The findings provide insights into consumer attitudes, preferences, and expectations, offering valuable implications for manufacturers, marketers, and policymakers in the smart home industry.
Abstract
SMART NEST REALITY HUB
Dr. N. Deepa, Mr. Manoj kumar, Mr. S. Abhishek
DOI: 10.17148/IJIREEICE.2025.13490
Abstract: The research evaluates Amazon's data science implementation method as the firm operates within Coimbatore where both technology usage and online customer numbers are steadily increasing. Data-driven tools employed by Amazon operate throughout their Coimbatore operations to advance various retail features including recommendation engines and price variables together with supply chain enhancement and consumer engagement capabilities.
The analysis pertains to how Amazon utilizes data science to personalize online shopping and enhance operational efficiency as well as maintain its strategic market position. The investigators provide significant data which targets businesses and researchers alongside academicians who want to comprehend how data science shapes current retail methods specifically in developing urban consumer markets.
The analysis pertains to how Amazon utilizes data science to personalize online shopping and enhance operational efficiency as well as maintain its strategic market position. The investigators provide significant data which targets businesses and researchers alongside academicians who want to comprehend how data science shapes current retail methods specifically in developing urban consumer markets.
Abstract
A STUDY ON CONSUMER ADOPTION ON DIGITAL WALLET PROJECT REPORT
Mr. R. Surya, Mr. G. Sethuraman
DOI: 10.17148/IJIREEICE.2025.13491
Abstract: This study investigates the determinants influencing consumer adoption of digital wallets, a revolutionary shift in contemporary payment systems. As digital transactions gain traction, understanding the factors that drive user acceptance is crucial for businesses and financial institutions. The research primarily focuses on perceived security, ease of use, and consumer awareness. Data were collected through a survey of 118 respondents over four months, using both primary and secondary sources. The findings reveal that Google Pay is the most preferred digital wallet, with usability and security being key drivers of adoption. While users express satisfactory experiences, significant concerns regarding data protection and identity theft persist. This study contributes valuable insights that can assist businesses in developing marketing strategies aimed at enhancing consumer trust and encouraging wider adoption of digital wallets. Recommendations include reinforcing security measures, improving customer support services, and ensuring a user- friendly interface for digital wallets. The results underscore the importance of addressing consumer concerns to facilitate the successful integration of digital wallets in everyday financial transactions
Keywords: Digital wallets, consumer adoption, perceived security, ease of use, user experience, payment systems.
Keywords: Digital wallets, consumer adoption, perceived security, ease of use, user experience, payment systems.
Abstract
THE IMPACT OF DIGITAL TRANSFORMATION ON RETAIL MANAGEMENT AND CONSUMER BEHAVIOR
M.KUMARAN, Dr. N. DEEPA
DOI: 10.17148/IJIREEICE.2025.13492
Abstract: The swift evolution of digital technology has dramatically transformed the retail environment, impacting retail management practices as well as consumer behavior. This research delves into the dynamic role of digital transformation in retail management practices such as supply chain management, data-driven decision-making, personalized marketing, as well as omni channel retailing. It also looks at how new technologies like artificial intelligence, big data analytics, and mobile platforms are changing consumer expectations, shopping behavior, and loyalty patterns. The research outlines the ways in which retailers are harnessing digital tools in improving customer experience, operations, as well as competing in a more digital economy. It also looks into changing consumer behavior towards convenience, immediate delivery, as well as customization. Using a mixture of literature analysis, case studies, as well as industry analysis, the study brings a holistic comprehension of the ways in which digital transformation is revolutionizing the dynamic between retailers and shoppers, as well as prescribing strategic recommendations for navigating this new environment.
Keywords: Digital Transformation, Retail Management, Consumer Behavior, E-commerce, Artificial Intelligence (AI), Big Data Analytics, Omni channel Retailing, Mobile Platforms, Data-driven Decision Making, Customer Experience
Keywords: Digital Transformation, Retail Management, Consumer Behavior, E-commerce, Artificial Intelligence (AI), Big Data Analytics, Omni channel Retailing, Mobile Platforms, Data-driven Decision Making, Customer Experience
Abstract
CONSUMER BEHAVIOUR TOWARDS PERSONALIZED DIGITAL RECOMMENDER ENGINE – A STUDY IN COIMBATORE CITY
G. RAJESHWARI, M.COM., MBA., M.PHIL., PH.D., S. MANOJKAVIN
DOI: 10.17148/IJIREEICE.2025.13493
Abstract: In the fast-evolving digital landscape, personalization has become a cornerstone of effective marketing strategies. A Personalized Digital Marketing Recommender Engine leverages advanced technologies such as artificial intelligence (AI), machine learning (ML), and data analytics to analyze customer behavior and deliver tailored recommendations that resonate with individual preferences.
This study presents a comprehensive model for implementing a personalized recommender engine to optimize customer engagement, enhance decision-making, and drive sales. The proposed model integrates real-time data processing with diverse selling strategies, including up-selling, crossselling, and consultative selling, while clustering items, customers, and unique selling propositions (USPs) to generate actionable insights.
By gathering, storing, and processing transactional data, the engine delivers highly relevant marketing information, ensuring seamless personalization across online and offline platforms.
This study presents a comprehensive model for implementing a personalized recommender engine to optimize customer engagement, enhance decision-making, and drive sales. The proposed model integrates real-time data processing with diverse selling strategies, including up-selling, crossselling, and consultative selling, while clustering items, customers, and unique selling propositions (USPs) to generate actionable insights.
By gathering, storing, and processing transactional data, the engine delivers highly relevant marketing information, ensuring seamless personalization across online and offline platforms.
Abstract
SECURED CLOUD-BASED FILE STORAGE SYSTEM
G. RAJESHWARI, M.COM., MBA., M.PHIL., PH.D., K. NAVEEN
DOI: 10.17148/IJIREEICE.2025.13494
Abstract: In today’s digital era, data security and accessibility are critical concerns for individuals and organizations. This project aims to develop a secured cloud-based file storage system that enables users to store, manage, and share files with robust security mechanisms. The system will leverage cloud computing technologies to provide scalability, accessibility, and data redundancy while ensuring data confidentiality through encryption techniques.
Key Features: 1. User Authentication & Access Control – Secure login mechanisms with multi-factor authentication (MFA) to prevent unauthorized access.
2. End-to-End Encryption – Implementation of AES-256 encryption for files in storage and SSL/TLS for secure data transmission.
3. Role-Based Access Management – Users can define permissions for file access (e.g., read, write, share) to enhance security.
4. Secure File Sharing – Users can generate time-limited and password-protected file-sharing links. 5. Data Redundancy & Backup – Automated backup mechanisms to ensure data availability in case of failures. 6. Audit Logs & Activity Monitoring – Comprehensive tracking of user actions and access history for security monitoring.
7. Cloud Scalability & Integration – The system is designed to work with multiple cloud platforms (AWS, Azure, Google Cloud) for efficient storage management.
The system will be developed using cloud computing platforms, cryptographic algorithms, and web-based technologies to create a highly secure and user-friendly solution. This project will address concerns related to data breaches, unauthorized access, and secure collaboration, making it suitable for businesses and individuals seeking reliable cloud storage solutions.
Keywords: Cloud Storage, Data Security, Encryption, Access Control, Secure File Sharing, Cloud Computing.
Key Features: 1. User Authentication & Access Control – Secure login mechanisms with multi-factor authentication (MFA) to prevent unauthorized access.
2. End-to-End Encryption – Implementation of AES-256 encryption for files in storage and SSL/TLS for secure data transmission.
3. Role-Based Access Management – Users can define permissions for file access (e.g., read, write, share) to enhance security.
4. Secure File Sharing – Users can generate time-limited and password-protected file-sharing links. 5. Data Redundancy & Backup – Automated backup mechanisms to ensure data availability in case of failures. 6. Audit Logs & Activity Monitoring – Comprehensive tracking of user actions and access history for security monitoring.
7. Cloud Scalability & Integration – The system is designed to work with multiple cloud platforms (AWS, Azure, Google Cloud) for efficient storage management.
The system will be developed using cloud computing platforms, cryptographic algorithms, and web-based technologies to create a highly secure and user-friendly solution. This project will address concerns related to data breaches, unauthorized access, and secure collaboration, making it suitable for businesses and individuals seeking reliable cloud storage solutions.
Keywords: Cloud Storage, Data Security, Encryption, Access Control, Secure File Sharing, Cloud Computing.
Abstract
Exploring Virtualization Techniques for Enhancing Business IT
Kamesh M, Dr. D. Suresh Kumar
DOI: 10.17148/IJIREEICE.2025.13495
Abstract: In today's rapidly evolving technological landscape, virtualization has emerged as a pivotal strategy for enhancing business IT operations. This paper explores various virtualization techniques and their impact on organizational efficiency, cost reduction, and flexibility.
Utilizing a mixed-methods research design, it combines quantitative surveys and qualitative interviews to gather insights from IT professionals and business managers across sectors.
The study reveals significant improvements in resource utilization, disaster recovery, and scalability through virtualization, alongside challenges like setup costs and security vulnerabilities. The integration of AI, ML, and hybrid cloud solutions are highlighted as future trends. This research provides practical recommendations and contributes to the strategic implementation of virtualization in business IT.
Keywords: Virtualization, Business IT, Cloud Computing, Server Virtualization, Virtual Machines, Cost Efficiency, Disaster Recovery
Utilizing a mixed-methods research design, it combines quantitative surveys and qualitative interviews to gather insights from IT professionals and business managers across sectors.
The study reveals significant improvements in resource utilization, disaster recovery, and scalability through virtualization, alongside challenges like setup costs and security vulnerabilities. The integration of AI, ML, and hybrid cloud solutions are highlighted as future trends. This research provides practical recommendations and contributes to the strategic implementation of virtualization in business IT.
Keywords: Virtualization, Business IT, Cloud Computing, Server Virtualization, Virtual Machines, Cost Efficiency, Disaster Recovery
Abstract
ARTIFICIAL INTELLIGENCE IN FINANCIAL SERVICES
Mrs. R. Anitha, P. Vinoth Kumar
DOI: 10.17148/IJIREEICE.2025.13496
Abstract: Artificial Intelligence (AI) is revolutionizing the financial services industry by introducing innovative solutions that enhance efficiency, accuracy, and customer engagement. This paper examines the multifaceted applications of AI in finance, including risk assessment, fraud detection, algorithmic trading, personalized financial advice, and customer service automation through chatbots and virtual assistants. By leveraging machine learning algorithms and big data analytics, financial institutions can analyze vast amounts of data in real-time, enabling them to make informed decisions and mitigate risks more effectively. However, the integration of AI in finance is not without challenges; issues such as data privacy, algorithmic bias, and regulatory compliance pose significant hurdles that must be addressed. Furthermore, the ethical implications of AI deployment in financial decision-making raise questions about accountability and transparency. This study also explores the future trends of AI in financial services, including the potential for enhanced predictive analytics, the rise of decentralized finance (DeFi), and the increasing importance of human-AI collaboration. Ultimately, this paper underscores the transformative potential of AI in reshaping the financial landscape while highlighting the need for a balanced approach that considers both technological advancements and ethical responsibilities.
Keywords: Artificial Intelligence, Financial Services, Risk Assessment, Fraud Detection, Algorithmic Trading, Personalized Financial Advice, Customer Service Automation, Data Privacy, Ethical Implications, Regulatory Compliance, Predictive Analytics, Decentralized Finance (DeFi).
Keywords: Artificial Intelligence, Financial Services, Risk Assessment, Fraud Detection, Algorithmic Trading, Personalized Financial Advice, Customer Service Automation, Data Privacy, Ethical Implications, Regulatory Compliance, Predictive Analytics, Decentralized Finance (DeFi).
Abstract
A Comprehensive Study on the Design and Implementation of a Payroll Management System
S. Abinaya
DOI: 10.17148/IJIREEICE.2025.13497
Abstract: This paper presents the design, development, and implementation of a Payroll Management System aimed at automating employee compensation processes. The system incorporates various modules including employee management, leave tracking, salary computation, payslip generation, and administrative controls. The study outlines both the limitations of traditional manual payroll processes and the enhanced functionality provided by a web-based automated system. It also evaluates the system's software architecture, development framework, database design, and future scalability.
Keywords: Payroll Management, Web Application, ASP.NET, SQL Server, Human Resource Automation, Employee Management, Leave Tracking
Keywords: Payroll Management, Web Application, ASP.NET, SQL Server, Human Resource Automation, Employee Management, Leave Tracking
Abstract
IoT Based Crop Monitoring Drone Using Mobile App
Mrs. M. U. Phutane, Kaushaly Dadaso Jadhav, Shubham Nagoji Kasalkar, Vaibhav Shrikant Chavan
DOI: 10.17148/IJIREEICE.2025.13498
Abstract: Drone technology is revolutionizing agriculture by providing real- time data collection, analysis, and informed decision-making. This research paper explores the development of an IoT-based crop monitoring system using drones and a mobile application. The system aims to optimize resource allocation, improve crop health, and enhance yield. The drone is designed with humidity sensors and cameras, captures detailed images of crops, detecting pests, diseases, and nutrient deficiencies. The improvement of this system has the capability to address issues contributing to poor harvest yields and transform the way farmers monitor and manage their crops.
Keywords: drones, crop, monitoring, food.
Keywords: drones, crop, monitoring, food.
Abstract
"User-Friendliness of the New Income Tax Portal: A Study on Individual Taxpayers’ Experience"
Alka Singh, Amal Fatema Hussain, Patnala Akanksha, Dr. Kiran Kumar M
DOI: 10.17148/IJIREEICE.2025.13499
Abstract: This research focusses on simplifying the tax filing process for individual taxpayers. However, the user- friendliness and accessibility of this system remain critical factors for its effectiveness. This study evaluates the convenience, efficiency, and overall user experience of the new portal through a primary data collection through survey for 51 individual taxpayers. Key parameters such as ease of responsiveness, error handling, and customer support mechanisms were analyzed. The suggestions provide insights into further improvements to enhance the portal’s usability for tax payers.
Keywords: Income Tax Portal, User Experience, Tax Filing, Digital Accessibility, Online Tax System, Usability Evaluation
Keywords: Income Tax Portal, User Experience, Tax Filing, Digital Accessibility, Online Tax System, Usability Evaluation
Abstract
IMPACT OF SOCIAL MEDIA INFLUENCERS ON CONSUMER PURCHASING DECISIONS
Mr. s. Muruganantham, G. sujitha
DOI: 10.17148/IJIREEICE.2025.134100
Abstract: In the digital age, social media influencers have emerged as powerful figures in shaping consumer behavior and purchasing decisions. This study explores the extent to which influencers impact consumer choices, examining factors such as trust, authenticity, engagement, and perceived expertise
Abstract
GUI FOR MESS MANAGEMENT SYSTEMUSING GSM
Mrs. M. U. Phutane, Daksha Nikam, Revati Halingale, Sanika Bodhale
DOI: 10.17148/IJIREEICE.2025.134101
Abstract: The main purpose of this project is to build a fingerprint python-based mess attendance monitoring system for Mess Management to enhance and upgrade the current attendance system into more efficient and effective as compared to before. The current old system has a lot of ambiguity that caused inaccurate and inefficient of attendance taking,bill calculation. The technology working behind will be the finger recognition system. The human finger is one of the natural traits that can uniquely identify an individual. Therefore, it is used to trace identity as the possibilities for a finger to deviate or being duplicated is low. In this project, finger databases will be created to pump data into the recognizer algorithm. Then, during the attendance taking session, finger will be compared against the database to seek for identity. When an individual is identified, its attendance will be taken down automatically saving necessary information into a excel sheet. At the end of the day, the excel sheet containing attendance information, meal usage and billing regarding all individuals is monitored.
Abstract
A Comprehensive Review of Robo Navigator: A Smart, Mobile-Controlled Educational Robot
Dr.P.D.Pawar, Sakshi Yadav, Amit Kasambe, Roshan Jadhao, Chetana Rathod, Anjali Yewale, Aditi Ingole, Bharti Naidu
DOI: 10.17148/IJIREEICE.2025.134102
Abstract: In recent years, the convergence of robotics, sensor technologies, and wireless communication has given rise to intelligent robotic systems capable of performing complex tasks with minimal human intervention. This research presents the development of a mobile-controlled smart robot designed to enhance interactivity, autonomous navigation, and real-time feedback in both academic and assistive environments. The robot is engineered to be controlled wirelessly via a Bluetooth-enabled mobile application, allowing users to direct its movement manually in indoor settings such as educational institutions, offices, or exhibition halls. A key feature of the system is its object detection capability, which is achieved through the integration of ultrasonic sensors. These sensors enable the robot to identify and avoid obstacles in its path, facilitating safe and autonomous operation in dynamic environments. Upon detecting an object, the robot responds accordingly by altering its course or halting movement, depending on the proximity and nature of the obstacle. This enhances the robot’s adaptability and ensures smooth navigation without the need for constant user supervision. In addition to its mobility and obstacle detection, the robot is equipped with a high-resolution display module—such as an OLED or Dot Matrix Display—which visually communicates contextual information to the user. This can include system status, object detection alerts, directional prompts, or predefined messages related to the robot’s environment. Complementing this visual interface, an audio output system powered by a DF Player Mini and speaker setup delivers pre-recorded voice messages or real-time alerts based on user commands and sensor feedback. These auditory cues not only increase the system’s accessibility but also contribute to a more immersive user experience. The robot’s central processing is handled by an Arduino-based microcontroller that coordinates input from various sensors and modules, processes control signals from the mobile application, and manages the output systems for display and sound. The integration of Bluetooth communication, object detection, audio narration, and visual display creates a robust, multi- modal interaction platform. This research aims to highlight the design, implementation, and performance evaluation of the proposed robotic system. The paper discusses the architecture of the robot, the interaction between its hardware and software components, and its potential applications in education, guided tours, customer service, and assistive technologies. The results demonstrate the feasibility and efficiency of using mobile-controlled robots with sensory feedback for dynamic, user-friendly, and intelligent navigation in structured indoor environments.
Keywords: Mobile-Controlled Robot, Object Detection, Bluetooth Communication, Ultrasonic Sensors, Audio Feedback, Display Interface, Arduino-Based System, Human-Robot Interaction, Smart Navigation, Embedded Systems.
Keywords: Mobile-Controlled Robot, Object Detection, Bluetooth Communication, Ultrasonic Sensors, Audio Feedback, Display Interface, Arduino-Based System, Human-Robot Interaction, Smart Navigation, Embedded Systems.
Abstract
AUTOMATED CONTENT GENERATION IN NLP
Ms. TRISHA. A, Dr. K. BANUROOPA, MCA., M.Phil., Ph.D.
DOI: 10.17148/IJIREEICE.2025.134103
Abstract: The rapid advancements in Natural Language Processing (NLP), automated content generation has gained significant traction in various industries, from journalism to marketing. This project presents an AI-powered content generation system leveraging the Mistral AI model via Hugging Face APIs to dynamically produce human-like text based on user inputs. Unlike traditional text-generation models, which often lack contextual relevance and coherence, our approach enhances content quality by incorporating fine-tuned prompts, adjustable creativity levels, and structured output formatting. The system is built using Streamlit for user interaction, LangChain for model integration, and logging mechanisms to track user inputs and generated outputs systematically. Additionally, the modular architecture, based on Object-Oriented Programming (OOP), ensures scalability, maintainability, and efficient debugging. Through extensive testing, we demonstrate the system’s ability to generate high-quality content across various domains, minimizing hallucination while maintaining fluency. Our findings highlight the effectiveness of AI-driven content generation in reducing manual effort, streamlining workflows, and enhancing creativity for businesses and content creators. This work not only contributes to advancements in NLP-based automation but also lays the foundation for future improvements, such as domain-specific fine-tuning, multi-modal content generation, and real-time interactive feedback mechanisms. The proposed system represents a significant step forward in leveraging AI for automated writing, making content creation more efficient, accurate, and scalable in the digital age.
Keyword: Exploring the Frontiers of Natural Language Generation (NLG) Advances in Deep Learning-based Automated Content Creation for Intelligent Journalism and Narrative Generation.
Keyword: Exploring the Frontiers of Natural Language Generation (NLG) Advances in Deep Learning-based Automated Content Creation for Intelligent Journalism and Narrative Generation.
Abstract
The Interplay of Mathematics and Artificial Intelligence: Foundations and Future Directions
Laxmi Bhavani Cheekatimalla
DOI: 10.17148/IJIREEICE.2025.134104
Abstract: The growth of artificial intelligence (AI) is based on the application of mathematics. Why? Mathematics has also played an important role in the development of AI, with topics such as those relating to probability theory, linear algebra (time series), and topology or optimization, among others. Models that use mathematical concepts to make decisions in deep learning, NLP, reinforcement learning and computer vision are developed and trained using mathematics. This is a pioneering implementation of artificial intelligence theory.' The paper delves into the mathematical foundations of AI, their application in modern AI systems, and potential mathematical trends that could shape future research on AI. Some of the key areas of mathematics that are critical to the scalability and reliability of AI include information theory and graph theory (for example. We also discuss the use of quantum mathematics in AI and the growing need for mathematical explanations to XAI. Other research aims to develop more interpretable AI models, to advance topological data analysis (TDA), and to apply quantum computing concepts to AI. This essay will present the mathematical foundations of AI with the aim of integrating theoretical research and applied usage of artificial intelligence.
Keywords: quantitative computing, explainable AI (XAI), deep learning, optimization, linear algebra, probability theory, graph theory, information theories, functional analysis, topological data analysis, artificial intelligence, mathematics, and reinforcement learning (ITL).
Keywords: quantitative computing, explainable AI (XAI), deep learning, optimization, linear algebra, probability theory, graph theory, information theories, functional analysis, topological data analysis, artificial intelligence, mathematics, and reinforcement learning (ITL).
Abstract
A FACE RECOGNITION SECURITY SYSTEM FOR DISABLED PEOPLE
HARIPRIYA R, Dr. PRABA R
DOI: 10.17148/IJIREEICE.2025.134105
Abstract: The face recognition security system for disabled people with a wheelchair ramp is an innovative and practical solution to address the accessibility and security concerns faced by disabled individuals. This system employs state-of- the-art face detection and recognition techniques to provide secure access control to the wheelchair ramp, which can be controlled using GPIO pins. The system is designed to recognize pre-registered users and provide access based on their access level. In addition, the system logs access events and ramp control events for monitoring and analysis. The proposed system is implemented using open-source tools such as OpenCV, which makes it accessible and affordable for deployment in real-world scenarios. The results of the experiment demonstrate that the proposed system is reliable and effective in providing secure and convenient access to the wheelchair ramp for disabled people.
Keywords: Accessibility, Biometrics, Authentication, inclusion, Recognition, Assisstance, Adaption.
Keywords: Accessibility, Biometrics, Authentication, inclusion, Recognition, Assisstance, Adaption.
Abstract
PREDICTION OF CHRONIC KINDNEY DISEASE USING MACHINE LEARNING
PARAMESWARI.N, Mrs. P. MENAKA
DOI: 10.17148/IJIREEICE.2025.134106
Abstract: This project aims to develop a system for predicting Chronic Kidney Disease (CKD) using machine learning method. Specifically, the proposed system employs an XGBoost to predict CKD. The dataset used for training and testing the models is the Chronic Kidney Disease dataset from the UCI Machine Learning Repository.The proposed system also built a web application using Flask framework where the users can enter the details and predict whether the CKD is there or not, which makes the system easier and accessible to every individual. The study contributes to the field of medical diagnosis and highlights the potential of using machine learning techniques for improving CKD prediction This user- friendly interface makes the system practical for both healthcare professionals and individuals seeking early diagnosis.By leveraging machine learning, this study contributes to the field of medical diagnosis, demonstrating the potential of ANN models in improving CKD prediction accuracy. The proposed system can assist in early detection, thereby facilitating timely medical intervention and improving patient outcomes
Keywords: XGBoost UCI machine,Chronic kidney disease,random forest algorithm
Keywords: XGBoost UCI machine,Chronic kidney disease,random forest algorithm
Abstract
RAILWAY ACCIDENT PREVENTION SYSTEM
Prof. Dr. M. B. Bhilavade., Omkar Arun Tibile, Sumit Shivaji Hajare, Rushikesh Sunil Davari
DOI: 10.17148/IJIREEICE.2025.134107
Abstract: This article outlines a design strategy for an Microcontroller ESP8266 based safety system to prevent railway accidents. When a train is some few distance away from an object (a person or an animal), this railway accident prevention safety system commands the train driver (Loco Pilot) if it is on the track. In this system, a high-frequency sound wave is transmitted by an ultrasonic sensor, which then waits for the sound to return before calculating the distance based on the required amount of time. In order to alert people to the impending arrival of a train, an ultrasonic sensor works by scanning for and identifying the vehicle. It then sends a signal to a buzzer to generate an alarm on the railway track. Injuries to train passengers, environmental damage, and property loss can all be caused by the interruption of the rail crossing's right of way. Therefore, there's a need to look into alternate techniques to prevent or mitigate these mishaps, such as using an microcontroller based safety system.
Keywords: Railway Track, Platform, Obstacle.
Keywords: Railway Track, Platform, Obstacle.
Abstract
IOT- Smart Parking System
Mandar Molawade, Swapnil Kante, Tejas Patil, Shrajal Dwivedi, Vishal Jaiswal, Mr. Rahul S Desai, Dr. Mayuri H Molawade
DOI: 10.17148/IJIREEICE.2025.134108
Abstract: The IoT-based Smart Parking System is a cost-effective solution to automate parking management in urban areas. It uses eight IR sensors to monitor parking slot availability and control gate operations via two servo motors. A 16x2 LCD with I2C backpack displays available slots, while the Arduino Uno manages the system. The NodeMCU module ensures real-time updates on slot availability to users' mobile devices from any location. This system optimizes parking efficiency, reduces manual intervention, and supports scalability for large parking areas, making it an ideal solution for smart city projects.
Keywords: IOT, Smart Parking, Arduino, NodeMCU, IR Sensors, Automation, LCD Display, Parking Management.
Keywords: IOT, Smart Parking, Arduino, NodeMCU, IR Sensors, Automation, LCD Display, Parking Management.
Abstract
DESIGN OF A MINIATURIZED ULTRA WIDE BAND MIMO ANTENNA WITH MULTI-BAND CHARACTERISTICS
Sri. A. Rajasekhar, K. Jahnavi, K. Pratheesh, CH.V. Sujith, I. Kranthi babu
DOI: 10.17148/IJIREEICE.2025.134109
Abstract: An ultra-wideband (UWB) multiple-input multiple- output (MIMO) antenna having double band-notched attributes is expected for FR4 substrate (1.6 mm). The antenna is composed of a better ground plane and dual microstrip feeding lines with a minimized dimension. By properly choosing the shape of the patch and the required output is expected. Measurement and simulation are utilized to concentrate on the antenna execution as far as isolation between the two input ports, envelope correlation coefficient, impedance matching, efficiency, radiation pattern and peak gain. CST Studio Suite is used to design the proposed antenna. UWB-MIMO is Better adapted for radar, tracking, monitoring systems of communications equipment, and infrastructure applications.
Keywords: UWB MIMO, Hexagon patch, EM Energy, Communication Technology, WLAN, C-Band, X-Band, Antenna.
Keywords: UWB MIMO, Hexagon patch, EM Energy, Communication Technology, WLAN, C-Band, X-Band, Antenna.
Abstract
“IoT BASED SMART MONITORING & CONTROL SYSTEM FOR GREENHOUSE POWERED BY DUAL AXIS SOLAR TRACKED PV SYSTEM”
Prof. Dr. T. H. Mohite, Sayali Dilip Powar, Siddhi Shital Yelappure, Shraddha Niranjan Tathe
DOI: 10.17148/IJIREEICE.2025.134110
Abstract: Greenhouses have revolutionized the way we grow plants, providing a controlled environment for optimal growth and productivity. As the global population continues to rise, the need for sustainable and efficient agricultural practices has never been more pressing. Greenhouses offer a solution to this challenge, enabling year-round crop production, improved yields, and reduced environmental impact. Greenhouses are essential for modern agriculture, providing a sustainable and efficient solution to global food security challenges. As technology continues to advance, greenhouses will play an increasingly important role in ensuring a food-secure future. Greenhouses provide a controlled environment that extends the growing season beyond what is possible outdoors.
Innovations in greenhouse technology can significantly enhance efficiency and productivity in agriculture. Advanced systems for monitoring, automation, and data analysis can optimize resource use, leading to higher yields and better- quality crops. Innovations like smart irrigation systems, energy-efficient heating and cooling solutions, and sustainable materials contribute to resource conservation. Also, greenhouse innovations help farmers adapt to the challenges posed by climate change.
To achieve all these objectives and a non-manual interference greenhouse, this paper presents an innovative IoT-based smart monitoring and control system for a greenhouse, powered by a dual-axis solar tracked photovoltaic (PV) system. The proposed IoT-based smart monitoring and control system for greenhouses, powered by a dual-axis solar tracked PV system, demonstrates significant improvements in energy efficiency, plant growth, and water conservation. This innovative solution has potential applications in agricultural and horticultural industries. It integrates environmental sensors to monitor key parameters such as temperature, humidity, soil moisture, and light intensity, enabling real-time data collection and analysis. The dual-axis solar tracker optimizes energy generation, ensuring a reliable power supply for the system. By employing IoT connectivity, users can remotely access data and control greenhouse conditions via a mobile application. This system enhances crop yield and resource efficiency, while promoting sustainable agricultural practices. Experimental results demonstrate significant improvements in energy utilization and environmental management, highlighting the feasibility and effectiveness of the integrated approach in modern agriculture.
Keywords: Dual-axis solar tracked PV system, greenhouse, IoT, control system, soil moisture, soil temperature, humidity.
Innovations in greenhouse technology can significantly enhance efficiency and productivity in agriculture. Advanced systems for monitoring, automation, and data analysis can optimize resource use, leading to higher yields and better- quality crops. Innovations like smart irrigation systems, energy-efficient heating and cooling solutions, and sustainable materials contribute to resource conservation. Also, greenhouse innovations help farmers adapt to the challenges posed by climate change.
To achieve all these objectives and a non-manual interference greenhouse, this paper presents an innovative IoT-based smart monitoring and control system for a greenhouse, powered by a dual-axis solar tracked photovoltaic (PV) system. The proposed IoT-based smart monitoring and control system for greenhouses, powered by a dual-axis solar tracked PV system, demonstrates significant improvements in energy efficiency, plant growth, and water conservation. This innovative solution has potential applications in agricultural and horticultural industries. It integrates environmental sensors to monitor key parameters such as temperature, humidity, soil moisture, and light intensity, enabling real-time data collection and analysis. The dual-axis solar tracker optimizes energy generation, ensuring a reliable power supply for the system. By employing IoT connectivity, users can remotely access data and control greenhouse conditions via a mobile application. This system enhances crop yield and resource efficiency, while promoting sustainable agricultural practices. Experimental results demonstrate significant improvements in energy utilization and environmental management, highlighting the feasibility and effectiveness of the integrated approach in modern agriculture.
Keywords: Dual-axis solar tracked PV system, greenhouse, IoT, control system, soil moisture, soil temperature, humidity.
Abstract
Tax Planning Strategies for Small and Medium Enterprises (SMEs): Optimizing Tax Liability and Enhancing Growth
Dr. Kiran Kumar, Nandhini M, Bhavana Varsha Kothapalli, Dhriti Kumari, AnjaliBharti
DOI: 10.17148/IJIREEICE.2025.134111
Abstract: Tax planning is essential for Small and Medium Enterprises (SMEs) as it impacts their profitability, compliance, and long-term sustainability. SMEs play a crucial role in economic growth but face challenges in managing tax obligations due to limited financial expertise and complex regulations. Effective tax planning enables SMEs to optimize liabilities, improve cash flow, and reinvest in growth while maintaining compliance with tax laws. A significant challenge for SMEs is GST compliance. Despite its aim to simplify tax structures, many SMEs struggle with procedural complexities, high compliance costs, and delays in input tax credit (ITC) refunds, which affect liquidity and operations. Digital tax solutions, such as AI-driven planners and automated filing systems, can help SMEs manage taxes efficiently by reducing errors and enhancing compliance. However, limited access and awareness hinder effective use of these tools. Financial literacy is another important factor, as many SMEs lack sufficient tax knowledge, resulting in missed deductions, higher tax burdens, and penalties. To address this, government-backed tax education programs, advisory services, and simplified policies can help SMEs make informed financial decisions. Additionally, business-friendly tax reforms, such as sector-specific benefits and lower corporate tax rates, can further boost SME growth. This study explores strategies to optimize SME tax liabilities and financial stability. By embracing digital solutions, improving tax literacy, and adopting proactive tax policies, SMEs can strengthen compliance, reduce tax burdens, and contribute to economic growth.
Keywords: Tax Planning, SMEs, GST Compliance, Digital Taxation, Financial Management, Policy Reforms
Keywords: Tax Planning, SMEs, GST Compliance, Digital Taxation, Financial Management, Policy Reforms
Abstract
BRAIN-COMPUTER INTERFACES: CAN WE CONTROL MACHINES WITH OUR MINDS?– EXPLORING NEURALINK AND THOUGHT-POWERED DEVICES.
ABIRAMI S, CHITTAL N, DR. K. THENMOZHI
DOI: 10.17148/IJIREEICE.2025.134112
Abstract: A brain-computer interface is a communication pathway between an external equipment and the human brain. The technology, rapidly evolving, holds tremendous power to not only catalyze innovations but also change with applications in health care, neuroprosthetics, cognitive enhancement, and possible areas in AI. The working principle of BCIs consists of capturing signals from the brain, usually with an EEG or electrocorticography, and then applying algorithms to interpret those signals as commands through machine learning. Machine interface systems may also be lumped into three types according to their invasiveness into non-invasive, semi-invasive, and invasive. Non-invasive, unbelievable but easy to work with systems that tend to lose precision; meanwhile, invasive systems enter into the very empowered medical application just for their extreme precision. Neuralink, founded by Elon Musk, is one of the leaders in the field, working on implantable brain-machine interfaces for restoring movement and enhancing cognitive functions. Other players in the field, such as BrainGate and OpenBCI, are thereby rapidly advancing technology to enable people to control robotic limbs and have developed open-source tools for research purposes.
Amazing developments have occurred, but a few challenges remain. Signal fidelity, delay, ethics, and regulation must all be addressed in order for BCIs to gain traction in the mainstream. With future developments in AI, neuroplasticity, and wireless communication, seamless interfacing of BCIs may become a reality.
In this paper BCIs are summarized from the science summit detailing advancements, hurdles within the field that still prevail, and arising concerns regarding ethicality. Comparison of the invasive and non-invasive mechanisms and future insights would form an important part of the paper that would focus on the coming days of this young technology.
Keywords: Brain-Computer Interface, Neuralink, Neurotechnology, Neural Signal Processing, Artificial Intelligence in BCIs, Neuroprosthetics, Neural Data Privacy
Amazing developments have occurred, but a few challenges remain. Signal fidelity, delay, ethics, and regulation must all be addressed in order for BCIs to gain traction in the mainstream. With future developments in AI, neuroplasticity, and wireless communication, seamless interfacing of BCIs may become a reality.
In this paper BCIs are summarized from the science summit detailing advancements, hurdles within the field that still prevail, and arising concerns regarding ethicality. Comparison of the invasive and non-invasive mechanisms and future insights would form an important part of the paper that would focus on the coming days of this young technology.
Keywords: Brain-Computer Interface, Neuralink, Neurotechnology, Neural Signal Processing, Artificial Intelligence in BCIs, Neuroprosthetics, Neural Data Privacy
Abstract
BLOCKCHAIN-BASED LOGGING TO DEFEAT MALICIOUS INSIDERS: THE CASE OF REMOTE HEALTH MONITORING SYSTEMS
Kasi Sailaja, Mudavath Chandi Priya (22WJ5A0522), Md Kareemoddin (22WJ5A0519), Sai Pratham Reddy (21WJ1A05T1), Dr. Mahesh Kotha
DOI: 10.17148/IJIREEICE.2025.134114
Abstract: This project aims to enhance the security and functionality of hospital management systems by implementing robust encryption techniques and access control mechanisms. The system allows the addition of doctors, who are required to obtain permission from the hospital before being granted access. Only authorized doctors are permitted to interact with patient data, ensuring that sensitive medical information remains secure. To protect patient data, the system includes an authentication process where user IDs and passwords are verified. If a mismatch occurs, the system detects and flags the access attempt as coming from a malicious user. The system also allows doctors to view patient details and send prescriptions, ensuring a smooth workflow for healthcare providers. Furthermore, medical staff can add and manage medicines within the system. To safeguard sensitive information, the project integrates the use of SHA-256 (Secure Hash Algorithm) and AES (Advanced Encryption Standard) encryption algorithms. These encryption methods ensure that all user data, patient records, and prescriptions are securely encrypted before being transmitted or stored, preventing unauthorized access and ensuring the privacy of all users.
Keywords: Insider attack, private blockchain, cloud access security broker, remote health.
Keywords: Insider attack, private blockchain, cloud access security broker, remote health.
Abstract
Vertical Axis Wind Turbine
Rohit Rajuke, Pratik Wankhede, Pallavi Imane, Karishma Satpudkar, Akash Girghuse, Sakshi Kotambe, Prof. R.M Bhombe
DOI: 10.17148/IJIREEICE.2025.134115
Abstract: Energy plays a crucial role in our daily lives, but with a rising population and finite natural resources, finding sustainable solutions is essential. Wind energy is a renewable and cost-effective source that can significantly contribute to conserving non-renewable resources. Although wind power has been harnessed for centuries, it has only recently gained prominence in global energy solutions.
This project focuses on utilizing wind energy through a vertical axis wind turbine (VAWT), specifically designed to capture airflow generated by moving vehicles on highways. The VAWT, strategically positioned on highway dividers, features aerofoil-shaped blades that are lightweight yet highly rigid, enabling efficient wind capture. The turbine's blades rotate due to the passing vehicle-induced airflow, transmitting mechanical energy to the generator, which achieves a maximum rotational speed of 500 rpm, generating up to 14.5 volts.
The generated electricity is stored in a battery, ensuring reliable power supply for various applications such as road lamps and other essential infrastructure needs. By harnessing wind energy in high-traffic areas, this innovative approach contributes to sustainable energy solutions while maximizing the efficiency of natural resources.
This project focuses on utilizing wind energy through a vertical axis wind turbine (VAWT), specifically designed to capture airflow generated by moving vehicles on highways. The VAWT, strategically positioned on highway dividers, features aerofoil-shaped blades that are lightweight yet highly rigid, enabling efficient wind capture. The turbine's blades rotate due to the passing vehicle-induced airflow, transmitting mechanical energy to the generator, which achieves a maximum rotational speed of 500 rpm, generating up to 14.5 volts.
The generated electricity is stored in a battery, ensuring reliable power supply for various applications such as road lamps and other essential infrastructure needs. By harnessing wind energy in high-traffic areas, this innovative approach contributes to sustainable energy solutions while maximizing the efficiency of natural resources.