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Keywords: Jackfruit, Baozi, Seed Flour, Functional Healthy Food, Plant-Based Snacks
Keywords: Pie Crust, Coconut Flour, , Lemon, Food Product Development.
VOLUME 13, ISSUE 5, MAY 2026
Publication is in progress..
FORMULATION ANALYSES, AND ACCEPTABILITY OF SINIGANG-FLAVORED CHIPS
DAIZA VENCH S. FRANCISCO, MAEd-TLE-HE
Environmental Challenges and Solutions for Sustainable Development
Sanjeev Kumar Vidyarthi*, Kumari Sushma Saroj, Hari Mohan Prasad Singh
Smart Question Paper Generation System Using NLP
Gavini Venkateswari, Redrouthu Lavanya, Bheemineni Kavya Sudha, Mandava Divya
Plasma Connect
Shabana Khanum, K. Sri Vaishnavi, D. Rajini, K. Madhu Latha
A Voice-Based AI Healthcare Assistant with Multilingual Support and Retrieval-Augmented Medical Reasoning
Madipudi Hema, Pottapenjara Jhansi Lakshmi, M.Lakshmi Kavya Rani, Nagabhairu Gayathri
The Implementation of IoT enabled, AI Driven Water Quality Monitoring and Controlling System for Aquaculture
G. Krishnaveni, Dr. Ch.Hima Bindu, P. Santhi, Y.Naga jyothi, V. Lakshmi
A COMPARATIVE STUDY OF THE DELIVERY APPS BLINKIT AND ZEPTO WITH SPECIAL REFERENCE TO COIMBATORE
Dr. M. K. Palanisamy, Mr. Boobala Krishnan CS
DESIGN AND IMPLEMENTATION OF A PIC-BASED PHASE FAILURE AND THERMAL PROTECTION SYSTEM FOR THREE-PHASE INDUCTION MOTORS
Dr. M. SARITHA, PAWAR ADITHYA, EPPA PRANAYA, DAYYALA BALAJI, NELLUTLA YOGI
Automated Harmful Content Control and Blocking System for Social Media
D. Tejaswi, K. Anusha, G. Vanaja, K. Deepa Sri Bhramaramba
Campus Connect: Smart College Navigation System
Rushikesh Kendre, Premkumar Bandare, Yogita Sontakke, Saniya Shaikh, Aishwarya Hosale
Incremental Passivity Control in 7level Cascaded H-Bridge Converters
Dr. N. Kalpana, Gunti Vishal Babu, Kothedigi Naveen Kumar, M Vardhan patel, Laxmi Vara Prasad
FAULT DETECTION IN GEARBOX USING IoT-BASED MONITORING SYSTEM
DR. A. Sethupathy, BE, ME, PHD., Suryakumar C.K
ECO-FRIENDLY INDUSTRIAL AIR PURIFIER WITH SMART MONITORING
DR. A. Sethupathy, BE, ME, PhD, Rahgul Dickson M.R
Design and Implementation of a 10-bit FSM based Digital SAR Logic in 90 nm CMOS Technology
Keerthana K M. E, Dr. M. Santhi M.E, Ph. D
Design and Analysis of Corrugated Box Elevetor System
Harshwardhan Bhosale*, Amol Dhumal, Akash Dawbhat, Aditya Bhujbal, Narendra Bharti
Combating Antimicrobial Resistance Through Bacteriophage Therapy: A Targeted Therapeutic Alternative
Shruti Umare
COGNITIVE DECLINE PREDICTION: LEVERAGING AI TO DETECT ALZHEIMER’S AT EARLY STAGES
Amulya. S Chandru, Jayalakshmi. M, Sahana. S, Rakshith A.K, Deeksha K.B
“A Real Time Sentiment Adaptive Music Recommendation System using YoloV8”
Sakshi Singh, Shridhi, Pooja Gupta, Ms. Prachi Vashist
Hydro Guard: Strengthening Public Safety Through Advanced Detection And Notification
Mr. L. Anbazhagan, Suriya Prakash M, Ranjith R, Mukesh K
Forest Fire Detection System Using DL
Girish, Sujayeendra Rao, Dr.Ananth.G.S
A Spatial and Socio-Ecological Analysis of Human–Panther Conflicts and Premature Mortality of Panthers in Rajsamand District of Rajasthan
Dr. Devendra Singh Chauhan, Krishna Kanwar
Design and Implementation of a Scalable Web-Based Attendance Monitoring System Utilizing Node.js and MongoDB Architectures
A. Asrin Mahmootha, Dr. B. Aysha Banu, R. Praveen Kumar, R. Vijay, A. Mohamed Ashik Ilahi
Intelligent Indian Sign Language Translator with Real-Time Gesture Recognition and Deep Learning
Dr. B. Aysha Banu, Mrs. A. Asrin Mahmootha, H. Mohamed Fahad Khan, K. Lokesh Krishna, K. Kartheeswaran, M. Mohamed Arshath
Comprehensive Factor Analysis and Risk Quantification Study of Fall from Height Accidents
Jayachandran C. V, Dr. N. Dilip Raja, ME, Ph.D.
Design of Smart Control and Protection System for Three- Phase Generator
Antrang Jitendra Patil, Kunal Anil Mane, Asmita Bhatu Patil, Prof. Ashfaque Ansari
Fault-Aware and Predictive Energy Management for Hybrid Energy Storage Systems in Electric Vehicles Using Mamdani Fuzzy Logic
Rakshan Pradeep K, Dr. J. Rangaraj, M.E., Ph.D.
A Comprehensive Review of IoT-Enabled Smart Traffic Management System Using Raspberry Pi
Ms. Dipali Siddheshwar Pawar, Prof. Kavita H. Waghmode
An AI-Enabled Crop, Fertilizer, And Yield Recommendation System Using Machine Learning
Nayana M P, Mamatha D S, Madhushri, Harshitha Y, Mona M
A REVIEW ON MACHINE LEARNING MODEL FOR AUTOMATIC HEART DISEASE PREDICTION
Ms. Komal Suryakant Kambale, Prof. Namdev M. Sawant
DEVELOPMENT OF REMOTE CONTROLLED MOTORCYCLE HELMET WIPER
CHRISTIAN JAY B. MACARIO
A Study on the Impact of Digital Fatigue and Cognitive Load on Employee Productivity and Work Life Balance with reference to IT Sector in Chennai
Dr G Balamurugan, Joans Balscia V
AN EFFICIENT DEEP NEURAL NETWORK APPROACH FOR DIABETES PREDICTION
Dr. Rajendra Prasad Banavathu, Dr.S. Jayaprada, Dr. Kalpana Devi Bai Mudavathu
Vehicle Classification and Traffic Density Analysis using RT-DETR
Prajwal.M, Sujayeendra Rao, Dr. Ananth. G. S
Design and Circuit-Level Analysis of Low-Power Analog Neural Networks in CMOS Technology
Madhubala R, Dr. J. Rangaraj
DESIGN OF A MIXED SIGNAL VCO BASED ADC FOR HIGH SPEED APPLICATIONS
Akbar Ali A, Dr. O.Saraniya
Implementation of HMI-Based Gesture Recognition and UWB Radar in Autonomous Vehicles
Shrisanjaykumaar K, Dr. O. Saraniya
THE EFFECT OF ACTIVATION TEMPERATURE ON PORE DIAMETER AND ADSORPTION CAPACITY OF ACTIVATED CARBON DERIVED FROM LIGNOCELLULOSIC BIOMASS: A REVIEW
I Gusti Agung Kade Suriadi, Dewa Ngakan Ketut Putra Negara, I Ketut Adi Atmika, Tjokorda Gde Tirta Nindhia*
Hybrid Machine Learning For IoT-Driven Heart Health Prediction
Priyanka Vijay Adate, Prof. A. A. Bhise
Equivalent Circuit Model-Based State of Charge Estimation of Lithium-Ion Batteries Using Kalman Filter Algorithms
Thirumalai V M. E, Dr. A. Anitha M.E, Ph. D
An IoT-Based Smart Smoke Alarm System Using Multi-Sensor Fusion and Intelligent Monitoring for Enhanced Fire Safety
K.Venkatesh, V.Ramesh
A Study on Non-Performing Assets in Selected Non-Banking Financial Companies
Dr. B. Asha Daisy, Dinesh Krishna V
Design and Analysis of Digitally Trimmable Sub – Bandgap Reference in CMOS Technology
Harshavardhini R, Dr. M. Santhi
YOGA POSTURE DETECTION
Keerthana S, Mr.Sujayeendra Rao, Dr. Ananth G S
Automatic Bell System Using Arduino Uno and RTC Module
Ayan Shaik, Tushar Rathod, Uttam Nimbal, Ibrahim Patel, Aishwarya Hosale
Integrated Smart Farming System with IoT Monitoring and AI Assistance
Prof. A. D. Gujar, Shrutika Patil, Ayush Mane, Sneha Gadade, Vaishnavi Bangar
“To study the Role of Augmented Reality on Product marketing”
Pranjal Sharad Mulmule, Prof. Akshay Yeotikar
Explainable Ensemble Machine Learning Framework for Finding Phishing Websites in Real Time
Gangarapu Vasanth Kumar
“Plant Leaf Disease Detector And Solution Recommendation”
Mr.Digamber Shelke, Miss.Nikita Nevase, Gayatri Shinde, Bhakti Kulkarni, Gayatri Kurhade, Anil Karande
Examify: Digital Examination Management & Evaluation System
Prof. Shubhangi S. Bhagat*, Digvijay Shinde, Kulashree Patil, Aditya Nanaware, Roman Shaikh
Thorax MapNet: Attention-Based Architecture with Anatomical Priors for Disease Classification
Harshini V T, Dr. Babu S
V-GPU FOR AI COMPUTING AND PARALLEL COMPUTING
Nayana J, Chethan R, Rakshit J Kashyap, Gurudev B S, Kanderi Karthik
GREEN CORROSION INHIBITORS IN AQUEOUS SYSTEMS: BRIDGING SURFACE CHEMISTRY AND ENVIRONMENTAL SAFETY
Satyendra Sharma
A Critical Analysis in Understanding the Role of AI Influenced Technology in Analysing the Consumer Behavior in Fashion Products: The Mediating Role of Brand Trust
Dr Revathi Anandkumar
AI - Based Real Time Crowd Monitoring and Risk Detection System
Diana PrinceChandran Jeyasingh, Gaanashree BN, K Jyothi, Rachana M, Thanisha G
From Automation Panic to Workforce Resilience: A Governance Framework for Enterprise AI Transformation
Panna Lal Jaiswal, Rajeew Vishvakarma, Sooraj Jacob
Handyman – An Android App for Easy Access to Trusted Professionals
Sowmya N, Harshitha N, Monika R Gowda, Nisha K P, Pavithra C
Prediction of Flood and Planner Towards Emergency Response
Thejaswini S P, Prashant Ankalkoti
Impact of Generative AI Tools on Diploma Engineering Students’ Learning Behaviour
Mrs. Asmita Jagtap, Mr. Sameer Mulik
"AI-Powered Predictive Analytics for Concrete Compressive Strength with Material Impact Interpretation"
Kalpesh Wani, Prashant Shimpi
A Comprehensive DEMATEL Based Framework for Sustainable Decision-Making in Manufacturing Systems: Evaluation, Nomenclature and Cause–Effect Analysis
Somnath Singroul*, Chhatrapal Dewangan, Atul Kumar Sahu, Nitin Kumar Sahu
Engineering Biomass-Based Activated Carbon through Physical and Chemical Activation: A Systematic Review on Pore Structure and Material Performance
Andrian Iswanto, Ade Saepullah, Agri Kala Yosua, Hafda Setyawan, I Gusti Ngurah Putu Tenaya, Tjokorda Gde Tirta Nindhia*
Mechanical Characterization of an Eucheuma cottonii White Seaweed-Based Bioplastic Film: Effect of seafood Protein and Sorbitol Plasticizer on Tensile Properties
Nicolas Meuley, Tjokorda Gde Tirta Nindhia*, Ade Saepullah, Agri Kala Yosua
Empowering Retail Decision-Making: Design and Implementation of an Interactive Visual Analytics Dashboard Using Power BI
Esther Rassou, Mr. T. Amalraj Victoire, Mr. R. Ramakrishnan
An Integrated Machine Learning and Blockchain Framework for E-Waste Forecasting and Traceability
Jaswanth Raj J, Kevin Dhyanesh V R, Krishna P, Dr. Maniraj S P
FORMULATION, ANALYSES, AND ACCEPTABILITY OF TELESCOPE SNAIL AND SQUASH SKINLESS LONGGANISA
MARIE JOY D. MARTINEZ
Formulation and Acceptability of Philippine Catmon Soap Sheets
Maria Teresa Tartana Samillano
FORMULATION, ANALYSES, AND ACCEPTABILITY OF JACKFRUIT BAOZI
JENN ROSE B. DEL MENDO
FORMULATION, ANALYSES, AND ACCEPTABILITY OF SWEET POTATO AND BELL PEPPER SHORTBREAD COOKIES
May B. Develos
Financial Literacy and Investment Behaviour: A Comparative Study among MBA Students and Salaried Professionals
Ms. R. Priyanka, Ms. R. Subiksha
Dietary Habits, Work-Life Balance, and Productivity of Higher Education Institutions (HEIs) Employees in Northern Panay: Implications for Wellness Initiatives
Meriam T. Fernandez
Elevating the Classic Pie Crust with Coconut Flour, Jute, and Citrus Infusions
Miami Rose R. Tabernilla
The Abaca Fiber Industry: Income Generation, Innovations, Challenges, and Future Prospects
Rhyme Ventura Apolinario
CROP DISEASE DETECTION USING DEEP LEARNING
Seema, Sujayeendra Rao, Ananth G S
EMPOWER HER: AN AI-DRIVEN PLATFORM FOR WOMEN SAFETY, LEGAL ASSISTANCE, AND EMERGENCY SUPPORT
Smt. K S Sukrutha, Ms. Ankitha S
Smart Painting Display Board with Adjustable Illumination
Marlo Victor A. Bacuna
Formulation, Analyses, and Acceptability of Crackers with Medicinal Leaves
Rameline Grace D. Borres
ICT Competence and Instructional Delivery of Technology and Livelihood Education Teachers
Ailyn K. Elegino
Blockchain-based Federated Learning with SMPC Model Verification Against Poisoning Attack for Healthcare Systems
Ms. Ankitha S, Shwetha M D, Nisarga, Rakshitha B k, Chandana S
Characterization of Blended Birds of Paradise and Bamban Plant Fibers as Alternative Textile Material
Rey C. Bical
Formulation and Acceptability of Moringa - Jamaican Berry Bar Foot Scrub
Aileen B. Atalisay
FORMULATION AND ACCEPTABILITY OF SHINY BUSH-BETEL LEAF HAND SOAP STRIP
Rose-Ann Daradar Buco
Characterization of Blended Areca Palm And Snake Plant Fiber as Textile Material
Renalyn L. Dela Cruz
ASPIRE AI: An AI-Powered Career Coach
Vishal A. Bagale, Yogesh B. Patil, Hitesh R. Wankhede, Charushila R. Deore, Pranjal S. Chaudhari, Prof. Manesh P. Patil
A Study on Impact Of Tourism Development And Local Employment Opportunities
A. Sowarna lakshmi, P. Sabeena
Formulation, Analyses And Acceptability of Cassava Leaves- Root Crop Patties
EARLEEN MAY BILLONES ACANTO
COGNITIVE CRIME ANALYSIS SYSTEM
Diana Princes Chandran Jeyasingh, Kushal M, Rahul Gowda A, Sharon Arnold, Tarun P
Formulation, Analyses, and Acceptability of Batuan (Garcinia binucao) Syrup
Virgenia D. Villanueva
AI-Driven Crime Prediction Using Machine Learning and Flask
Mrs.V. Anusha, Mrs.S. Sirisha
Characterization of Blended Sugar Palm and Areca Leaf Sheath Fiber as Alternative Textile Material
ENELYN F. PADROGADO
Patient Risk Identification Using Machine Learning
Dr. T. Amalraj Victoire, K. Sanjai
AI-Driven Automation of Centralized Email Triage and SLA Management
Dr.T. Amalraj Victoire, S. Nithyalakshmi
AI POWERED LEARNING AND REVISION WITH CHATBOT SUPPORT SYSTEM
Rajapandian. P, Priyadharshini M
AI-Powered Animal Species Predictor
Rohit B. Magar, Durgesh R. Patil, Pranav B. Patil, Dinesh D. Patil, Amol E. Patil, Prof. Bharti D. Patil
GENDERED STREETS: RETHINKING URBAN PUBLIC SPACES THROUGH INCLUSIVE PLANNING
Kratika Alok Srivastava, Ar. Versha Verma
Sentiment Analysis of YouTube Comments Using TF-IDF and LightGBM with MLflow and Flask Deployment
Rohit Santosh Bedse, Diwansing Shivsing Girase, Krishita Shailendra Patil, Kiran Jitendra Patil, Prof. Reema Kalda
Review on Motorised Cow Dung Collecting Machine
Prof. V. H. Shivsharan, Mr. Chavan Devdatta Vijay, Mr. Devkule Dnyandev Nandkumar, and Mr. Godase Sushant Namdev
Design and Development of Motorised Cow Dung Collecting Machine
Prof. V. H. Shivsharan, Mr. Chavan Devdatta Vijay, Mr. Devkule Dnyandev Nandkumar, and Mr. Godase Sushant Namdev
AI-Driven Blockchain Framework for Trustworthy Data Sharing in IoT Ecosystems
Dr. T. Vamshi Mohana, Salma Begum
Abstract
FORMULATION ANALYSES, AND ACCEPTABILITY OF SINIGANG-FLAVORED CHIPS
DAIZA VENCH S. FRANCISCO, MAEd-TLE-HE
DOI: 10.17148/IARJSET.2026.13501
Abstract: This study focused on the development and evaluation of sinigang-flavored chips, a novel snack inspired by the Filipino sour soup, sinigang. The research aimed to formulate chips using three protein-0based treatments: Treatment A (Pork-Based), Treatment B (Shrimp-Based), and Treatment C (Fish-Based), and to assess their sensory qualities, overall acceptability, shelf-life stability, microbial safety, and nutritional composition. An experimental-developmental design was employed, integrating natural sinigang flavors with tamarind extract and traditional souring agents into a starch-based chip mixture with the designated protein source. The dough was shaped, dried, and fried to achieve consistent crispness, then evaluated by ten semi-trained panelists for appearance, aroma, taste, and texture using a 9-point hedonic scale. General acceptability was assessed by 100 consumer respondents. Kruskal-Wallis tests and ANOVA were applied to determine significant differences among treatments. Results showed that Treatment C (Fish-Based) achieved the highest ratings for appearance and texture, described as extremely appealing and extremely crunchy, while Treatment A (Pork-Based) led in taste and aroma, rated as extremely delicious and very much pleasant. Overall acceptability favored Treatment A (Pork-Based), indicating the most balanced sensory profile. Shelf-life analysis confirmed product stability, with low moisture and proper drying and frying maintaining crispness and sensory quality over time. Microbial assessment verified safety, with no detection of fecal coliform, E. coli, or Salmonella, and yeast and mold counts within acceptable limits. Proximate analysis indicated high carbohydrate content, moderate fat, modest protein, and low moisture, supporting nutritional value and storage stability. The study concludes that the main protein source significantly influenced sensory perception and consumer preference, with Pork-Based chips providing the most favorable combination of flavor, aroma, appearance, and texture. These findings offer practical insights for the production and commercialization of culturally inspired, ready-to-eat snacks that combine traditional flavors with appealing sensory and nutritional qualities.
Keywords: Formulation, Analyses, Acceptability of Sinigang Flavored-Chips
Abstract
Environmental Challenges and Solutions for Sustainable Development
Sanjeev Kumar Vidyarthi*, Kumari Sushma Saroj, Hari Mohan Prasad Singh
DOI: 10.17148/IARJSET.2026.13502
Abstract: India's rapid industrialization, urban expansion, and resource-intensive economic growth have posed significant challenges to environmental sustainability, including air and water pollution, land degradation, loss of biodiversity, and climate change. These environmental issues have direct consequences on public health, agricultural productivity, and ecosystem stability, thus threatening the long-term success of development goals. This paper provides a comprehensive analysis of the intricate challenges to environmental sustainability in India, addressing institutional, technological, and socio-economic constraints. It critically evaluates various contemporary strategies adopted by the Indian government, civil society, and the private sector-such as the incorporation of renewable energy, waste management systems, green urban planning, and environmental regulations. The consequences of inaction are also examined from the standpoint of ecological decline and social disparities. This research seeks to explore the Environmental Challenges and Solutions for Sustainable Development.
Keywords: Environmental issues, socio-economic, public health, sustainable development
Abstract
Smart Question Paper Generation System Using NLP
Gavini Venkateswari, Redrouthu Lavanya, Bheemineni Kavya Sudha, Mandava Divya
DOI: 10.17148/IARJSET.2026.13503
Abstract: The process of designing the examination papers is quite lengthy, biased, and time-consuming. The current research introduces a novel solution to create automatic examination papers based on the syllabus documents in the PDF format. The system uses PyMuPDF for extracting information and processing unstructured text using state-of-the-art Natural Language Processing tools. The generator makes use of a transformer neural network model named Flan-T5 which can produce multiple- choice questions (MCQs) along with contextually appropriate distractors and descriptive long-answer questions. The system also incorporates a login module to ensure secure access and provides the option of exporting the question papers in TXT and PDF formats. According to experimental results, the system shows a remarkable improvement in the speed of generating questions and saves almost 85 percent of the time as compared to the conventional technique. The experiments also confirm the quality of the system as far as coherence and grammaticality of the generated questions are concerned.
Keywords: Natural Language Processing, Automatic Question Generation, Transformer Models, PDF Text Extraction, Educational Technology
Abstract
Plasma Connect
Shabana Khanum, K. Sri Vaishnavi, D. Rajini, K. Madhu Latha
DOI: 10.17148/IARJSET.2026.13504
Abstract: Timely access to plasma donors is critical in medical emergencies, yet traditional coordination methods often rely on phone calls, social media posts, and manual records, which can cause delays. Plasma Connect is a full-stack web platform designed to simplify and improve the plasma donation process by connecting donors, recipients, hospitals, blood banks, and administrators within a single system. The platform allows users to search for plasma donors based on blood group and location, submit donation requests, and track request status in real time. The system is developed using modern web technologies, including React for the frontend, Node.js and Express for backend services, and MySQL for database management. Secure authentication is implemented using JSON Web Tokens and bcrypt encryption. Real-time notifications are provided through Socket.IO, while location-based services are supported using Leaflet maps integrated with OpenStreetMap. The proposed platform improves coordination between donors and recipients, reduces response time in emergency situations, and provides a scalable digital solution for plasma donation management. The results demonstrate that Plasma Connect enhances accessibility, communication, and efficiency in plasma donor coordination.
Keywords: Plasma Donation System, Healthcare Web Platform, Donor Matching System, Blood and Plasma Donation Management, React, Node.js, Real-Time Notifications, Location-Based Services, Web Application Development.
Abstract
A Voice-Based AI Healthcare Assistant with Multilingual Support and Retrieval-Augmented Medical Reasoning
Madipudi Hema, Pottapenjara Jhansi Lakshmi, M.Lakshmi Kavya Rani, Nagabhairu Gayathri
DOI: 10.17148/IARJSET.2026.13505
Abstract: Healthcare accessibility remains a major challenge, particularly for elderly individuals, rural populations, and users with limited technical proficiency. Most existing healthcare applications rely on text-based interfaces and complex navigation, which can delay timely medical assistance during critical situations. This paper proposes a Voice-Based Intelligent Healthcare Assistant that enables users to interact with healthcare services through natural voice commands. The system integrates speech recognition, multilingual translation, Retrieval-Augmented Generation (RAG), and Large Language Models (LLMs) to perform symptom analysis and provide contextual health guidance. In addition to symptom assistance, the platform supports voice-driven doctor appointment booking and emergency detection with nearby hospital identification using location-based services. The system is implemented using a React Native mobile interface, FastAPI backend, MongoDB database, and Qdrant vector database for semantic retrieval. Experimental evaluation demonstrates that the proposed system provides accurate symptom interpretation, multilingual accessibility, and real-time responses within a few seconds. The solution improves healthcare accessibility and provides an intuitive digital healthcare support system for diverse populations.
Keywords: Voice-Based Healthcare System, Artificial Intelligence in Healthcare, Retrieval-Augmented Generation, Multilingual Speech Processing, Medical Symptom Analysis, Digital Health Assistant
Abstract
The Implementation of IoT enabled, AI Driven Water Quality Monitoring and Controlling System for Aquaculture
G. Krishnaveni, Dr. Ch.Hima Bindu, P. Santhi, Y.Naga jyothi, V. Lakshmi
DOI: 10.17148/IARJSET.2026.13506
Abstract: In this paper, we describe about the design of an IoT-enabled, AI-driven water quality monitoring and control system for aquaculture. Maintaining optimal water conditions is essential for healthy aquatic ecosystems and sustainable food production. The system uses sensors to measure key parameters such as temperature, pH, turbidity, total dissolved solids (TDS), and ammonia levels. These sensors are integrated with Arduino Uno and ESP32 microcontrollers, which collect and transmit real-time data to a cloud platform for storage, visualization, and remote access. An AI model analyzes the data to detect anomalies, identify trends, and predict potential water quality issues. When abnormal conditions occur, the system generates instant alerts and classifies water quality into safe, warning, or critical levels using visual indicators. This enables faster decision-making and reduces the need for manual monitoring. Overall, the system improves efficiency, supports sustainable aquaculture practices, and helps ensure better environmental and production outcomes.
Keywords: IoT, Aquaculture, Water Quality Monitoring, Artificial Intelligence, Real-Time Monitoring.
Abstract
A COMPARATIVE STUDY OF THE DELIVERY APPS BLINKIT AND ZEPTO WITH SPECIAL REFERENCE TO COIMBATORE
Dr. M. K. Palanisamy, Mr. Boobala Krishnan CS
DOI: 10.17148/IARJSET.2026.13508
Abstract: This study focuses on a comparative analysis of two leading quick commerce delivery applications, Blinkit and Zepto, with special reference to Coimbatore. The rapid growth of digital technology and increasing consumer demand for convenience have led to the emergence of ultra-fast delivery platforms. These applications promise delivery within 10-20 minutes, transforming traditional retail practices. The research examines consumer awareness, usage patterns, service quality, delivery speed, pricing, and overall satisfaction. Data was collected from 160 respondents using a structured questionnaire. Statistical tools such as percentage analysis, chi-square test, and ranking analysis were used
Keywords: Quick Commerce, Blinkit, Zepto, Consumer Satisfaction, Delivery Speed, Online Grocery, Customer Preference, Coimbatore Market
Abstract
FACTORS AFFECTING POOR RESULTS IN PAPER AND PENCIL TEST AMONG GRADE 7 STUDENTS IN T.L.E.: BASIS FOR REMEDIAL PROGRAM
ROY D. DIAZ
DOI: 10.17148/IARJSET.2026.13509
Abstract: This study investigated the factors contributing to poor results in paper and pencil tests among Grade 7 students in Technology and Livelihood Education (TLE). A descriptive-correlational research design was employed, with a sample of 155 Grade 7 students from Tuburan National High School. The results showed that teaching methods, student engagement, and curriculum alignment are significant factors affecting poor results in paper and pencil tests. Specifically, the study found that current teaching practices may not effectively cater to the learning needs of students, leading to poor test results. Additionally, student engagement was found to be relatively low, and curriculum alignment was found to be high. The study recommends the implementation of interactive teaching strategies, incorporation of technology, and provision of remedial classes to improve student performance. Furthermore, the study suggests that educators should regularly review and update the curriculum to reflect changes in technology and industry standards. The findings of this study have implications for educators, policymakers, and curriculum developers seeking to improve student outcomes in TLE.
Keywords: Curriculum, Teaching Method, Student Engagement
Abstract
DESIGN AND IMPLEMENTATION OF A PIC-BASED PHASE FAILURE AND THERMAL PROTECTION SYSTEM FOR THREE-PHASE INDUCTION MOTORS
Dr. M. SARITHA, PAWAR ADITHYA, EPPA PRANAYA, DAYYALA BALAJI, NELLUTLA YOGI
DOI: 10.17148/IARJSET.2026.13510
Abstract: Three-phase induction motors are widely used in industrial applications but are highly vulnerable to faults such as phase failure and excessive temperature rise, which can lead to severe damage and reduced operational life. This paper presents the design and implementation of a microcontroller-based protection system using PIC16F72 to detect and prevent such faults. The system continuously monitors phase conditions using voltage sensing circuits and measures motor temperature using an LM35 sensor. Upon detecting abnormal conditions such as phase loss or overheating, the system automatically disconnects the motor supply using a relay mechanism. The proposed system provides real-time monitoring, fast response, and improved reliability, making it suitable for industrial motor protection applications. The microcontroller processes these inputs and triggers a relay mechanism to disconnect the motor supply whenever abnormal conditions are detected. The proposed system ensures fast response, improved reliability, and reduced maintenance costs. The design is simple, cost-effective, and suitable for small and medium-scale industrial applications
Keywords: Three-Phase Induction Motor, PIC16F72 Microcontroller, Phase Failure Detection, Temperature Monitoring, Motor Protection System, Relay Control, LM35 Sensor
Abstract
Automated Harmful Content Control and Blocking System for Social Media
D. Tejaswi, K. Anusha, G. Vanaja, K. Deepa Sri Bhramaramba
DOI: 10.17148/IARJSET.2026.13511
Abstract: The rapid growth of multimedia content on digital platforms has created a need for efficient content moderation systems to prevent the spread of harmful material. This project presents an Automated Harmful Content Control and Blocking System that performs analysis of media before it is uploaded. The system allows users to upload images and videos through a web interface, where the content is processed using a Flask-based backend. For image analysis, OpenCV is used to perform preprocessing techniques such as grayscale conversion and pixel intensity evaluation. A threshold-based method is applied to determine whether the uploaded image contains potentially harmful content. If harmful content is detected, the system blocks the upload and notifies the user; otherwise, the file is stored successfully. The system also supports video uploads and provides a deletion feature for managing uploaded files. This approach ensures real-time moderation, reduces dependency on manual monitoring, and enhances platform safety. Although the current implementation uses basic image processing techniques, it can be extended with advanced machine learning models for improved accuracy in future developments.
Keywords: Harmful Content Detection,Image Processing, OpenCV,Flask Content Moderation,Social Media Safety
Abstract
Campus Connect: Smart College Navigation System
Rushikesh Kendre, Premkumar Bandare, Yogita Sontakke, Saniya Shaikh, Aishwarya Hosale
DOI: 10.17148/IARJSET.2026.13512
Abstract: The rapid expansion of educational institutions has led to increasingly complex campus infrastructures, making navigation difficult for students and visitors. Campus Connect is a smart college navigation system designed to provide efficient, user-friendly, and accessible navigation within academic environments. The system enables users to search for classrooms, laboratories, administrative offices, and other facilities through an intuitive interface. Unlike traditional static maps, it provides dynamic search functionality and simulated navigation using structured campus data. The system is implemented using lightweight web technologies and browser-based local storage, ensuring fast performance and offline accessibility. This approach reduces dependency on external servers while improving responsiveness and scalability. The proposed solution enhances user experience, reduces navigation time, and improves accessibility for new users.
Keywords: Smart Campus, Navigation System, Web Application, Local Storage, User Interface, Campus Automation
Abstract
DEVELOPMENT OF AN INTERACTIVE DASHBOARD AND EST-BASED FORECASTING MODEL FOR REAL-TIME DECISION MAKING IN SUPPLY CHAIN MANAGEMENT.
Haritha J
DOI: 10.17148/IARJSET.2026.13513
Abstract: The objective of the project is to develop an Interactive Dashboard for Supplier Weight and Delivery Weight Forecasting using an ETS (Error, Trend, Season) model built using Python to support data-driven decision making within a large engineering manufacturing organisation. The study comprises two integrated components designed to illustrate how visualization and forecasting can facilitate improvements in supply chain monitoring and planning. The organization relies on consistent supplier deliveries and accurate weight forecasting based on historical data for its large-scale manufacturing operations. The manual evaluation of supplier on-time delivery performance and weight of deliveries was often ineffectual and could lead to poor speculation .An Interactive Dashboard was generated in Power BI to visualize supplier performance and delivery timelines. A Forecasting Model was developed in Python with an ETS model predicting the weight of delivery in the future based on historical trends. Combining the analytical power of the Python statistical tool with the data visualization through Microsoft Power BI will help provide actionable insights to improve supply chain management processes and how decisions will be made in the future. The use of an interactive dashboard allowed all users to see how suppliers are performing and the trend regarding weights, and with Python's ETS modelling, a clear prediction of the future delivery weights enables faster decision-making and would lead to increased transparency while using data-informed planning methods to transform supply chain operations.
Keywords: Interactive dashboard, time series forecasting, ETS model, supply chain management, Power BI, Python.
Keywords: Interactive dashboard, time series forecasting, ETS model, supply chain management, Power BI, Python.
Abstract
A STUDY ON INFLUENCE OF SOCIAL MEDIA VIDEOS CONTENT TOWARDS CLIENT SATISFACTION
Karthick Sai M
DOI: 10.17148/IARJSET.2026.13514
Abstract: In recent times, social media has been identified as a major medium of communication in the purpose of business and clients. Among all the content available through social media, videos have been identified as gaining prominence as they are considered more engaging, informative, and understandable. Videos are being utilized on social media for the purpose of communication with clients and to promote business.
The main aim of this research is to identify the impact of social media content on client satisfaction. This research aims to find out how social media content is influencing client satisfaction. To attain this, data is being collected in the form of questionnaires from social media users and is being supplemented with literature. From the results of this research, it is identified that videos are playing a vital role in forming opinions in the minds of clients about a brand.
Keywords: Social media, video marketing, customer engagement, client satisfaction, digital communication
The main aim of this research is to identify the impact of social media content on client satisfaction. This research aims to find out how social media content is influencing client satisfaction. To attain this, data is being collected in the form of questionnaires from social media users and is being supplemented with literature. From the results of this research, it is identified that videos are playing a vital role in forming opinions in the minds of clients about a brand.
Keywords: Social media, video marketing, customer engagement, client satisfaction, digital communication
Abstract
Incremental Passivity Control in 7level Cascaded H-Bridge Converters
Dr. N. Kalpana, Gunti Vishal Babu, Kothedigi Naveen Kumar, M Vardhan patel, Laxmi Vara Prasad
DOI: 10.17148/IARJSET.2026.13515
Abstract: This research presents an advanced evolution of the 7-level Cascaded H-Bridge (CHB) inverter by introducing an 11-level topology integrated with Neural Network-Enhanced Incremental Passivity-Based Control (NN-IPBC). The proposed architecture significantly improves power quality metrics, specifically targeting the reduction of Total Harmonic Distortion (THD) and enhancing the precision of capacitor voltage balancing across five seriesconnected modules. By utilizing a high-density modular topology, the system synthesizes a near-sinusoidal 11step waveform. The NN-IPBC algorithm, executed on an Arduino Mega 2560, provides real-time optimization of energy-shaping parameters to ensure global asymptotic stability and rapid transient response. Experimental validation confirms that the 11-level system achieves a THD of less than 4%, making it highly effective for gridtied renewable energy systems.
Keywords: Cascaded H-Bridge (CHB), 11-Level Inverter, Neural Networks, Incremental Passivity-Based Control (IPBC), Power Quality, THD.
Keywords: Cascaded H-Bridge (CHB), 11-Level Inverter, Neural Networks, Incremental Passivity-Based Control (IPBC), Power Quality, THD.
Abstract
FAULT DETECTION IN GEARBOX USING IoT-BASED MONITORING SYSTEM
DR. A. Sethupathy, BE, ME, PHD., Suryakumar C.K
DOI: 10.17148/IARJSET.2026.13516
Abstract: Gearboxes are critical components in industrial machinery, and their unexpected failure can result in significant operational downtime, elevated maintenance costs, and serious safety hazards. Conventional gearbox maintenance relies on periodic manual inspections that are incapable of detecting developing faults in real time. This paper presents the design and implementation of a low-cost, IoT-based fault detection system for gearboxes that integrates DS18B20 digital temperature sensors and an SW-420 vibration sensor with a NodeMCU (ESP8266/ESP32) microcontroller. The system continuously acquires temperature and vibration data, displays readings on a 16×2 LCD screen for on-site visualization, and transmits the data wirelessly to the Blynk IoT cloud platform, enabling remote monitoring via a smartphone application. Fault conditions such as overheating, excessive vibration, gear misalignment, and lubrication failure are automatically detected when sensor readings exceed predefined safety thresholds, triggering instant mobile alerts. Experimental validation confirms that the system reliably identifies abnormal operating conditions within seconds, enabling proactive maintenance intervention. The proposed system achieves a fault detection accuracy exceeding 94%, reduces manual inspection dependency, and provides a scalable and cost-effective solution for predictive maintenance in industrial environments. The total hardware and software implementation cost is estimated at ₹12,000, making it accessible for small and medium-scale industries.
Keywords: IoT, Gearbox Fault Detection, DS18B20, SW-420, NodeMCU, ESP32, Vibration Monitoring, Predictive Maintenance, Blynk, Condition Monitoring.
Keywords: IoT, Gearbox Fault Detection, DS18B20, SW-420, NodeMCU, ESP32, Vibration Monitoring, Predictive Maintenance, Blynk, Condition Monitoring.
Abstract
ECO-FRIENDLY INDUSTRIAL AIR PURIFIER WITH SMART MONITORING
DR. A. Sethupathy, BE, ME, PhD, Rahgul Dickson M.R
DOI: 10.17148/IARJSET.2026.13517
Abstract: Industrial environments are major sources of air pollution, emitting hazardous gases, dust, fumes, and toxic chemicals that pose serious risks to worker health and workplace productivity. This paper presents the design and implementation of an Eco-Friendly Industrial Air Purifier with Smart Monitoring — an Internet of Things (IoT)-enabled embedded system capable of continuous real-time air quality monitoring and automated air purification. The system is built around an ESP32 microcontroller interfaced with an MQ135 gas sensor for detecting harmful pollutants and a DHT11 sensor for measuring ambient temperature and humidity. When measured pollution levels exceed predefined safe thresholds, the system autonomously activates a 12V industrial air filter fan via a relay module, ensuring immediate air remediation. Environmental data comprising Air Quality Index (AQI), gas concentration, temperature, and relative humidity are transmitted wirelessly to the Blynk IoT mobile application, enabling remote monitoring and informed decision-making. The entire system is powered by a stable 12V, 2Ah Switch-Mode Power Supply (SMPS). Results demonstrate that the proposed system provides low-cost, scalable, and efficient air-quality management suitable for small to medium-scale industrial settings, including welding workshops, manufacturing units, laboratories, and pharmaceutical environments. The total hardware implementation cost is approximately INR 8,000, making it highly accessible for wide industrial adoption.
Keywords: IoT, ESP32, MQ135, DHT11, Air Quality Monitoring, Industrial Air Purifier, Blynk, Smart Monitoring, Embedded Systems, Relay Module
Keywords: IoT, ESP32, MQ135, DHT11, Air Quality Monitoring, Industrial Air Purifier, Blynk, Smart Monitoring, Embedded Systems, Relay Module
Abstract
Design and Implementation of a 10-bit FSM based Digital SAR Logic in 90 nm CMOS Technology
Keerthana K M. E, Dr. M. Santhi M.E, Ph. D
DOI: 10.17148/IARJSET.2026.13518
Abstract: In mixed-signal systems, analog to digital converters (ADCs) are crucial for transforming analog signals into digital data. The Successive Approximation Register (SAR) ADC is one of the most popular ADC architectures because of its low power consumption, moderate resolution, and straightforward hardware design, which make it appropriate for Internet of Things applications, portable electronics, and biomedical devices. The control unit that completes the successive approximation process to produce the final digital output is the digital SAR logic.
In this work, a 10-bit Finite State Machine (FSM) based digital SAR logic using 90 nm CMOS technology is designed and implemented. The binary search conversion from the Most Significant Bit (MSB) to the Least Significant Bit (LSB) is carried out by the suggested architecture.
The Cadence digital design flow, which includes synthesis, timing analysis, power estimation, and physical design processes like floor planning, placement, routing, and GDSII generation, is used to create the design. The results show low power consumption and effective area utilization. For integration in low-power SAR ADCs used in biomedical, wireless sensor, and embedded data acquisition systems, the suggested FSM-based SAR logic provides a small and energy-efficient solution.
Keywords: Successive Approximation Register (SAR), Finite State Machine (FSM), Digital SAR Logic, Verilog HDL, 90 nm CMOS Technology, Low-Power VLSI Design, Analog-to-Digital Converter (ADC).
In this work, a 10-bit Finite State Machine (FSM) based digital SAR logic using 90 nm CMOS technology is designed and implemented. The binary search conversion from the Most Significant Bit (MSB) to the Least Significant Bit (LSB) is carried out by the suggested architecture.
The Cadence digital design flow, which includes synthesis, timing analysis, power estimation, and physical design processes like floor planning, placement, routing, and GDSII generation, is used to create the design. The results show low power consumption and effective area utilization. For integration in low-power SAR ADCs used in biomedical, wireless sensor, and embedded data acquisition systems, the suggested FSM-based SAR logic provides a small and energy-efficient solution.
Keywords: Successive Approximation Register (SAR), Finite State Machine (FSM), Digital SAR Logic, Verilog HDL, 90 nm CMOS Technology, Low-Power VLSI Design, Analog-to-Digital Converter (ADC).
Abstract
Design and Analysis of Corrugated Box Elevetor System
Harshwardhan Bhosale*, Amol Dhumal, Akash Dawbhat, Aditya Bhujbal, Narendra Bharti
DOI: 10.17148/IARJSET.2026.13519
Abstract: In today’s fast-moving manufacturing and logistics environment, efficient material handling plays a key role in improving productivity and maintaining workplace safety. Corrugated boxes are widely used for packaging and storage because they are lightweight, economical, and recyclable. However, manual handling of these boxes, especially when vertical movement between floors is required, leads to low efficiency, higher labor dependency, and increased risk of injuries , this project presents the design and analysis of a corrugated box elevator system developed to transport corrugated boxes vertically in a safe, reliable, and cost-effective manner. The system is designed by carefully considering industrial requirements such as load capacity, lifting height, speed of operation, space limitations, and safety. Major components including the supporting frame, lifting platform, guide rails, drive mechanism, motor, and transmission system are designed using standard mechanical engineering principles, all components are modeled and assembled using SolidWorks software. Structural analysis is carried out using SolidWorks Simulation to study stresses, displacements, and factor of safety under maximum loading conditions. The analysis results show that the elevator system is structurally safe and suitable for industrial use. The proposed design reduces manual effort, improves material handling efficiency, and enhances operational safety, making it ideal for small and medium scale industries.
Abstract
Combating Antimicrobial Resistance Through Bacteriophage Therapy: A Targeted Therapeutic Alternative
Shruti Umare
DOI: 10.17148/IARJSET.2026.13520
Abstract: The rapid emergence of antimicrobial resistance (AMR) poses a significant threat to global public health, rendering conventional broad-spectrum antibiotics increasingly ineffective against multidrug-resistant (MDR) bacterial pathogens. This review investigates the potential of bacteriophage therapy as a targeted therapeutic alternative to combat resistant infections. Unlike traditional antibiotics, which often disrupt the host’s commensal microflora, bacteriophages exhibit high specificity toward target bacterial strains. We examine the biological mechanisms of phage-host interactions, including the lytic cycle, and evaluate the advantages of utilizing phage cocktails over monotherapy to prevent the rapid development of phage resistance. Furthermore, the integration of computational tools and bioinformatics platforms— such as PHASTER—in identifying prophage sequences and optimizing therapeutic efficacy is discussed. While bacteriophage therapy presents a promising avenue for personalized medicine, challenges related to pharmacokinetics, bacterial resistance mechanisms, and regulatory frameworks must be addressed. Ultimately, this paper highlights the necessity of continued research and clinical trials to establish bacteriophage therapy as a safe and viable strategy in the modern management of AMR.
Abstract
COGNITIVE DECLINE PREDICTION: LEVERAGING AI TO DETECT ALZHEIMER’S AT EARLY STAGES
Amulya. S Chandru, Jayalakshmi. M, Sahana. S, Rakshith A.K, Deeksha K.B
DOI: 10.17148/IARJSET.2026.13521
Abstract: Alzheimer’s disease (AD) is a progressive and irreversible neurodegenerative disorder that predominantly affects the brain, resulting in the gradual deterioration of memory, cognitive functions, and behavior. It represents the leading cause of dementia, a clinical condition characterized by a significant decline in cognitive abilities that interferes with daily functioning. Despite extensive research, the precise etiology of Alzheimer’s disease remains unclear; however, it is widely accepted that a combination of genetic predisposition, environmental influences, and lifestyle factors contribute to its onset and progression.
Pathologically, Alzheimer’s disease is marked by the abnormal accumulation of extracellular amyloid-beta plaques and intracellular neurofibrillary tangles composed of hyperphosphorylated tau protein. These abnormalities disrupt neuronal communication, impair synaptic function, and ultimately lead to neuronal degeneration and cell death.
In this context, the present study aims to develop an efficient and accurate automated system for the early detection of Alzheimer’s disease using magnetic resonance imaging (MRI) of the brain. The proposed approach leverages Convolutional Neural Network (CNN) architecture to extract relevant features and perform classification, thereby facilitating improved diagnostic support and early intervention strategies.
Pathologically, Alzheimer’s disease is marked by the abnormal accumulation of extracellular amyloid-beta plaques and intracellular neurofibrillary tangles composed of hyperphosphorylated tau protein. These abnormalities disrupt neuronal communication, impair synaptic function, and ultimately lead to neuronal degeneration and cell death.
In this context, the present study aims to develop an efficient and accurate automated system for the early detection of Alzheimer’s disease using magnetic resonance imaging (MRI) of the brain. The proposed approach leverages Convolutional Neural Network (CNN) architecture to extract relevant features and perform classification, thereby facilitating improved diagnostic support and early intervention strategies.
Abstract
“A Real Time Sentiment Adaptive Music Recommendation System using YoloV8”
Sakshi Singh, Shridhi, Pooja Gupta, Ms. Prachi Vashist
DOI: 10.17148/IARJSET.2026.13522
Abstract: The discussion regarding music recommendation continues to be mentioned repeatedly since this may strengthen consumer interactions through multiple manners, especially psychic and personal memorable ones. Our project highlights a new development, that includes YOLOv8’s deep learning-driven gesture understanding. Applying a live webcam, an individual’s movements get recorded during actual time and are capable of being grouped into happy, sad, angry, neutral or surprised. While a particular mood becomes apparent, an arrangement of soundtracks will be generated according to the underlying feelings, which at first will be chosen by the database of songs that has been previously established. In light of its faster interpretation rate in addition with characteristic extraction skill, the YOLOv8 prototype has been employed for this research to figure out the movement of face. The result of the observation shows that the implemented system is efficient in identifying facial emotions with high accuracy and recommending music that aligns the current mood of a user. This study emphasizes the potential of integrating cognitive computing and Data Science methods to create intelligent multimedia applications that adapt in real-time to an individual's emotional condition.
Keywords: Emotion classification, OpenCV, music suggestion, Facial Extraction, YOLOv8, Real time capturing images.
Keywords: Emotion classification, OpenCV, music suggestion, Facial Extraction, YOLOv8, Real time capturing images.
Abstract
Hydro Guard: Strengthening Public Safety Through Advanced Detection And Notification
Mr. L. Anbazhagan, Suriya Prakash M, Ranjith R, Mukesh K
DOI: 10.17148/IARJSET.2026.13523
Abstract: Ensuring public safety has become a major concern with growing urbanization. Traditional manual monitoring of surveillance cameras is inefficient and prone to human error, leading to delayed responses in critical situations. This paper presents an AI-based system for the real-time detection of violence and weapons from video streams. The proposed system utilizes YOLOv8 for accurate weapon detection and a Transformer model to analyze temporal patterns for violence detection. By integrating feature fusion, the system reduces false alarms and automatically generates alerts for immediate security response. This solution offers a scalable and efficient approach to automated public surveillance.
Keywords: Public Safety, AI-Based Surveillance, YOLOv8, Transformer Model, Weapon Detection, Violence Detection, Deep Learning, Real-Time Monitoring.
Keywords: Public Safety, AI-Based Surveillance, YOLOv8, Transformer Model, Weapon Detection, Violence Detection, Deep Learning, Real-Time Monitoring.
Abstract
Forest Fire Detection System Using DL
Girish, Sujayeendra Rao, Dr.Ananth.G.S
DOI: 10.17148/IARJSET.2026.13524
Abstract: This Forest fires represent a catastrophic threat to global biodiversity and ecological stability, necessitating the development of high-precision, real-time early warning systems. This project introduces a comprehensive monitoring framework that leverages Google’s SigLIP (Sigmoid Loss for Language Image Pre-training), a state-of- the-art Vision Transformer (ViT) architecture, to detect fire and smoke anomalies in various environmental contexts. Unlike traditional Convolutional Neural Networks (CNNs), the implemented SigLIP model utilizes global attention mechanisms to effectively distinguish between subtle visual cues, such as differentiating early-stage smoke from clouds, fog, or thermal haze.The system was fine-tuned on a diverse dataset comprising thousands of images from sources including the FLAME and DeepFire datasets, supplemented by synthetic data for edge-case training. The technical architecture is deployed through a dual-platform approach: a rapid-response Streamlit interface for interactive testing and a full-scale Flask web portal. The Flask-based application provides a production-ready environment featuring secure user authentication, an administrative dashboard for detection logging, and integrated email notification triggers via EmailJS for immediate alert dissemination.Functionally, the application supports both high-resolution static image analysis and sampled video stream processing (MP4/AVI). By utilizing confidence-based thresholding and multi-class probability mapping (Normal, Smoke, and Fire), the system provides actionable intelligence for satellite monitoring, drone surveillance, and fixed CCTV footage. The resulting solution offers a scalable, high-accuracy tool for environmental protection agencies to mitigate devastating impacts of wildfires through rapid, AI-driven detection.
Keywords: Forest Fire Detection, Deep Learning, Computer Vision, Vision Transformer (ViT), SigLIP, Smoke Detection, Fire Detection, Real-time Monitoring, Early Warning System,Transfer Learning, Flask, Streamlit,FLAME Dataset, DeepFire Dataset, Image Classification, Drone Surveillance, Environmental Monitoring.
Keywords: Forest Fire Detection, Deep Learning, Computer Vision, Vision Transformer (ViT), SigLIP, Smoke Detection, Fire Detection, Real-time Monitoring, Early Warning System,Transfer Learning, Flask, Streamlit,FLAME Dataset, DeepFire Dataset, Image Classification, Drone Surveillance, Environmental Monitoring.
Abstract
A Spatial and Socio-Ecological Analysis of Human–Panther Conflicts and Premature Mortality of Panthers in Rajsamand District of Rajasthan
Dr. Devendra Singh Chauhan, Krishna Kanwar
DOI: 10.17148/IARJSET.2026.13525
Abstract: Human–wildlife conflict has emerged as a critical conservation challenge in human-dominated landscapes of India, particularly affecting large carnivores such as the Indian leopard (Panthera pardus). This study examines the patterns, causes, and spatial distribution of premature panther deaths and human–panther conflict in Rajsamand district, Rajasthan. Using a mixed-methods approach, the research integrates geospatial analysis, field surveys, and community- based interviews to identify conflict hotspots and underlying socio-ecological drivers. Secondary data on mortality incidents were analyzed alongside primary data collected from affected villages to assess the role of habitat fragmentation, prey depletion, and anthropogenic pressures.
The findings reveal that a significant proportion of panther deaths are linked to human activities, including retaliatory killings, road accidents, and accidental falls into open wells. Spatial analysis highlights clustering of conflict incidents near forest–agriculture interfaces and rapidly urbanizing zones. Community perceptions indicate a complex relationship characterized by fear, economic loss, and limited awareness of conservation measures.
The study underscores the need for integrated management strategies, including habitat restoration, securing open wells, strengthening compensation mechanisms, and enhancing community participation in conservation programs. By linking ecological patterns with human dimensions, the research contributes to a more nuanced understanding of coexistence challenges and offers practical recommendations for mitigating conflict and reducing premature mortality of panthers in Aravali landscape.
Keywords: Premature Mortality, Human–wildlife conflict, Socio-ecological drivers, Anthropogenic pressures.
The findings reveal that a significant proportion of panther deaths are linked to human activities, including retaliatory killings, road accidents, and accidental falls into open wells. Spatial analysis highlights clustering of conflict incidents near forest–agriculture interfaces and rapidly urbanizing zones. Community perceptions indicate a complex relationship characterized by fear, economic loss, and limited awareness of conservation measures.
The study underscores the need for integrated management strategies, including habitat restoration, securing open wells, strengthening compensation mechanisms, and enhancing community participation in conservation programs. By linking ecological patterns with human dimensions, the research contributes to a more nuanced understanding of coexistence challenges and offers practical recommendations for mitigating conflict and reducing premature mortality of panthers in Aravali landscape.
Keywords: Premature Mortality, Human–wildlife conflict, Socio-ecological drivers, Anthropogenic pressures.
Abstract
Design and Implementation of a Scalable Web-Based Attendance Monitoring System Utilizing Node.js and MongoDB Architectures
A. Asrin Mahmootha, Dr. B. Aysha Banu, R. Praveen Kumar, R. Vijay, A. Mohamed Ashik Ilahi
DOI: 10.17148/IARJSET.2026.13526
Abstract: This paper presents the architectural design and implementation of a robust, scalable web-based attendance monitoring system leveraging the asynchronous, event-driven capabilities of Node.js and the flexible, document-oriented data model of MongoDB. Traditional attendance methods suffer from manual inefficiencies, absence of real-time accessibility, and vulnerability to proxy attendance. The proposed system integrates a responsive React.js front-end with a resilient Node.js/Express.js back-end, providing real-time attendance logging, comprehensive reporting functionalities, and secure role-based access control (RBAC). A microservices-oriented design emphasises modularity and scalability. Experimental evaluation on 1,500 facial images across 50 subjects demonstrates >95% recognition accuracy and average API response times below 200 ms under 50 concurrent users, confirming readiness for real-world educational deployment.
Keywords: Attendance management; web application; Node.js; MongoDB; scalability; real-time monitoring; facial recognition; role-based access control; microservices
Keywords: Attendance management; web application; Node.js; MongoDB; scalability; real-time monitoring; facial recognition; role-based access control; microservices
Abstract
Intelligent Indian Sign Language Translator with Real-Time Gesture Recognition and Deep Learning
Dr. B. Aysha Banu, Mrs. A. Asrin Mahmootha, H. Mohamed Fahad Khan, K. Lokesh Krishna, K. Kartheeswaran, M. Mohamed Arshath
DOI: 10.17148/IARJSET.2026.13527
Abstract: Communication barriers between hearing-impaired individuals and the general public represent one of the most persistent challenges in inclusive society design. Indian Sign Language (ISL) serves as the primary expressive modality for approximately 18 million deaf individuals across India, yet its comprehension remains negligible among the general population. This paper presents the design, development, and rigorous evaluation of an Intelligent Indian Sign Language Translator System (ISLTS) that harnesses deep learning and computer vision to recognize hand gestures and translate them into text and synthesized speech in real time. The system employs a Convolutional Neural Network (CNN) trained on 7,500 custom ISL images augmented to 22,500 samples, achieving an overall gesture recognition accuracy of 92.4% and a mean average precision (mAP) of 0.89 across all gesture classes. MediaPipe Hands is integrated for real- time 21-point landmark detection, feeding a CNN classifier that operates at 28 frames per second on standard laptop hardware with latency below 0.5 seconds per prediction. A text-to-speech (TTS) module converts recognized gestures to audible output, enabling bidirectional communication. Comparative evaluation demonstrates that the proposed system outperforms sensor-based and earlier vision-based methods by 18–22 percentage points in accuracy while eliminating the need for specialized hardware. The proposed system offers a scalable, cost-effective, and non-intrusive solution with strong potential for deployment in educational institutions, healthcare settings, and public
Keywords: Indian Sign Language; Deep Learning; Gesture Recognition; Computer Vision; Real-Time Translation; Accessibility; Convolutional Neural Networks; MediaPipe; Text-to-Speech
Keywords: Indian Sign Language; Deep Learning; Gesture Recognition; Computer Vision; Real-Time Translation; Accessibility; Convolutional Neural Networks; MediaPipe; Text-to-Speech
Abstract
Comprehensive Factor Analysis and Risk Quantification Study of Fall from Height Accidents
Jayachandran C. V, Dr. N. Dilip Raja, ME, Ph.D.
DOI: 10.17148/IARJSET.2026.13528
Abstract: Falls from height (FFH) remain one of the leading causes of fatal and severe injuries in the construction, industrial, and oil & gas sectors worldwide. Despite regulatory advancements and safety interventions, these incidents continue to pose significant challenges to occupational safety professionals. This study aims to conduct a comprehensive factor analysis and risk quantification of fall-from-height accidents to understand their root causes, contributing conditions, and effective preventive strategies.
The study employs a mixed-method approach combining incident data review, Job Hazard Analysis (JHA), behavioural safety audits, and structured interviews with safety professionals. The Factor Analysis of Incident Data (FAID) is used to identify critical causal elements categorized into human, organizational, environmental, and technical domains. Key findings indicate that over 70% of FFH incidents are linked to a combination of unsafe practices, inadequate supervision, and poor planning during work-at-height activities.
A quantitative risk matrix is developed to assign risk scores based on the frequency and severity of each contributing factor. High-risk activities, such as scaffolding erection, roof work, and temporary platform use, were assessed using Bow-Tie analysis and Failure Modes and Effects Analysis (FMEA) to identify escalation factors and opportunities for risk reduction.
Furthermore, the study integrates Human Factors Engineering (HFE) and Safety Culture Assessments to understand the behavioural patterns associated with non-compliance. The findings suggest that targeted training, competent supervision, and a robust Permit-To-Work (PTW) system significantly reduce FFH risks.
This research offers valuable insights for safety professionals and decision-makers aiming to implement evidence-based controls. It emphasizes the importance of integrating predictive analytics, risk quantification, and human-centric design into fall prevention programs to move beyond compliance and foster a resilient safety culture.
Keywords: Fall From Height, Global Statistics on Fall-Related Injuries and Fatalities, Significance of Risk Quantification for Future Prevention, Contributing Factors to Fall from Height Accidents, Regulatory and Technological Interventions.
The study employs a mixed-method approach combining incident data review, Job Hazard Analysis (JHA), behavioural safety audits, and structured interviews with safety professionals. The Factor Analysis of Incident Data (FAID) is used to identify critical causal elements categorized into human, organizational, environmental, and technical domains. Key findings indicate that over 70% of FFH incidents are linked to a combination of unsafe practices, inadequate supervision, and poor planning during work-at-height activities.
A quantitative risk matrix is developed to assign risk scores based on the frequency and severity of each contributing factor. High-risk activities, such as scaffolding erection, roof work, and temporary platform use, were assessed using Bow-Tie analysis and Failure Modes and Effects Analysis (FMEA) to identify escalation factors and opportunities for risk reduction.
Furthermore, the study integrates Human Factors Engineering (HFE) and Safety Culture Assessments to understand the behavioural patterns associated with non-compliance. The findings suggest that targeted training, competent supervision, and a robust Permit-To-Work (PTW) system significantly reduce FFH risks.
This research offers valuable insights for safety professionals and decision-makers aiming to implement evidence-based controls. It emphasizes the importance of integrating predictive analytics, risk quantification, and human-centric design into fall prevention programs to move beyond compliance and foster a resilient safety culture.
Keywords: Fall From Height, Global Statistics on Fall-Related Injuries and Fatalities, Significance of Risk Quantification for Future Prevention, Contributing Factors to Fall from Height Accidents, Regulatory and Technological Interventions.
Abstract
Design of Smart Control and Protection System for Three- Phase Generator
Antrang Jitendra Patil, Kunal Anil Mane, Asmita Bhatu Patil, Prof. Ashfaque Ansari
DOI: 10.17148/IARJSET.2026.13529
Abstract: This paper presents a smart control and protection system for a three-phase generator using an Arduino- based platform. The system continuously monitors voltage, current, and frequency and detects abnormal conditions such as overload, overvoltage, undervoltage, and frequency variations. The system uses sensors and a microcontroller to provide real- time monitoring and protection. Experimental results show improved response time, reliability, and efficiency compared to conventional systems.
Keywords: Arduino, Generator Protection, Voltage Monitoring, Current Sensor, Smart System
Keywords: Arduino, Generator Protection, Voltage Monitoring, Current Sensor, Smart System
Abstract
Fault-Aware and Predictive Energy Management for Hybrid Energy Storage Systems in Electric Vehicles Using Mamdani Fuzzy Logic
Rakshan Pradeep K, Dr. J. Rangaraj, M.E., Ph.D.
DOI: 10.17148/IARJSET.2026.13530
Abstract: Hybrid Energy Storage Systems (HESS) integrating lithium-ion batteries with supercapacitors are increasingly adopted in electric vehicles (EVs) for dynamic power management. While Fuzzy Logic–based Energy Management Systems (EMS) effectively optimize power-split ratios under nominal operating conditions, they remain insensitive to hardware anomalies including battery overcurrent, thermal excursions, supercapacitor degradation, and converter faults. This paper presents a fault-aware intelligent EMS framework built around a Mamdani Fuzzy Inference System (FIS) that continuously monitors four sensor channels—battery voltage, current, temperature, and state-of-charge (SOC)—and classifies six distinct fault categories in real time via a dedicated Severity Index (SI ∈ [0, 1]). Upon fault detection, the controller adaptively modifies the battery duty cycle k_bat and redistributes transient power demands to the supercapacitor, preserving load continuity and system safety. MATLAB/Simulink simulations incorporating non-ideal component models, thermal dynamics, and converter losses demonstrate a 30% reduction in peak battery current, a 29% decrease in thermal rise (ΔT), and a 20% improvement in SOC retention relative to a conventional HESS without fault awareness. DC bus voltage stability (MAD = 8 V) is fully maintained across all injected fault scenarios. The proposed framework bridges the critical gap between energy optimization and hardware fault management in HESS for EV applications.
Keywords: Hybrid Energy Storage System (HESS), Mamdani Fuzzy Inference System, Fault Detection and Classification, Energy Management System, Battery State-of-Charge, Supercapacitor, Thermal Management, Electric Vehicles, DC-DC Converter, Severity Index
Keywords: Hybrid Energy Storage System (HESS), Mamdani Fuzzy Inference System, Fault Detection and Classification, Energy Management System, Battery State-of-Charge, Supercapacitor, Thermal Management, Electric Vehicles, DC-DC Converter, Severity Index
Abstract
A Comprehensive Review of IoT-Enabled Smart Traffic Management System Using Raspberry Pi
Ms. Dipali Siddheshwar Pawar, Prof. Kavita H. Waghmode
DOI: 10.17148/IARJSET.2026.13531
Abstract: Managing traffic efficiently has become one of the most urgent challenges in today’s rapidly growing cities. Traditional fixed-time traffic signals often fall short because they cannot react to changing road conditions, which leads to unnecessary delays, increased fuel consumption, and higher pollution levels. Over the past few years, the combination of Internet of Things (IoT) technologies, edge computing, cloud platforms, and artificial intelligence has opened new possibilities for creating more adaptive and responsive traffic systems. This review brings together recent research from 2020 to 2025 and examines how sensors, embedded devices, and communication networks are being used to monitor real-time traffic flow, prioritize emergency vehicles, and optimize signal timing. The paper also explores advanced methods such as deep reinforcement learning, computer vision–based vehicle detection, blockchain-secured IoT frameworks, federated learning, and digital twin simulations. By comparing these approaches, the review highlights both their strengths and the remaining challenges that need attention. Overall, the study emphasizes that IoT-enabled smart traffic systems especially those combining edge intelligence with cloud analytics offer a practical and scalable pathway toward safer, cleaner, and more efficient urban mobility.
Keywords: Internet of Things (IoT), Smart Traffic Management, Edge Computing, Adaptive Signal Control, Vehicle Detection, Emergency Vehicle Priority, Cloud IoT Platforms, Traffic Flow Prediction, Intelligent Transportation Systems (ITS), Deep Learning, Reinforcement Learning, Urban Mobility.
Keywords: Internet of Things (IoT), Smart Traffic Management, Edge Computing, Adaptive Signal Control, Vehicle Detection, Emergency Vehicle Priority, Cloud IoT Platforms, Traffic Flow Prediction, Intelligent Transportation Systems (ITS), Deep Learning, Reinforcement Learning, Urban Mobility.
Abstract
An AI-Enabled Crop, Fertilizer, And Yield Recommendation System Using Machine Learning
Nayana M P, Mamatha D S, Madhushri, Harshitha Y, Mona M
DOI: 10.17148/IARJSET.2026.13532
Abstract: Agriculture plays a vital role in global food security, and farmers constantly seek ways to optimize crop selection to maximize yield and profitability. However, identifying the most suitable crop for a specific region is challenging due to factors such as climate conditions, soil fertility, rainfall, temperature, and water availability. Traditional farming practices often rely on experience and assumptions, which may lead to reduced productivity and improper fertilizer usage.The proposed Crop and Fertilizer Recommendation System addresses these challenges by utilizing machine learning techniques to analyze environmental and soil-related parameters for intelligent decision- making. The system collects input data such as soil nutrients, temperature, humidity, rainfall, and pH values, and processes them using machine learning algorithms to recommend the most suitable crop and appropriate fertilizer. By providing accurate and data-driven recommendations, the system helps farmers improve crop yield, reduce resource wastage, and enhance sustainable agricultural practices.The developed model aims to support precision agriculture by assisting farmers in selecting crops that are best suited for their land conditions while also suggesting fertilizers to maintain soil health and productivity. Experimental results demonstrate that machine learning-based recommendations can significantly improve agricultural efficiency and contribute to smarter farming solutions.
Abstract
A REVIEW ON MACHINE LEARNING MODEL FOR AUTOMATIC HEART DISEASE PREDICTION
Ms. Komal Suryakant Kambale, Prof. Namdev M. Sawant
DOI: 10.17148/IARJSET.2026.13533
Abstract: Heart disease is a leading cause of mortality worldwide, necessitating early detection and prevention strategies. Machine learning (ML) models have emerged as powerful tools for automatic heart disease prediction. This review paper provides an overview of the recent advancements in ML-based approaches for heart disease prediction. We begin by discussing the significance of early detection and the potential of ML in this domain. Next, we conduct a comprehensive literature survey, summarizing the key findings from previous studies. We then present a comparative study of various ML algorithms commonly used for heart disease prediction, highlighting their strengths and limitations. Additionally, we outline the proposed procedure for developing ML models for heart disease prediction and discuss potential future directions. Finally, we conclude by emphasizing the importance of continued research in this area to improve the accuracy and accessibility of automatic heart disease prediction.
Keywords: Classification, Segmentation, Image Processing, Skin Cancer, Machine Learning, Heart Diseases, Feature Extraction.
Keywords: Classification, Segmentation, Image Processing, Skin Cancer, Machine Learning, Heart Diseases, Feature Extraction.
Abstract
DEVELOPMENT OF REMOTE CONTROLLED MOTORCYCLE HELMET WIPER
CHRISTIAN JAY B. MACARIO
DOI: 10.17148/IARJSET.2026.13534
Abstract: This study focused on the design, development, and evaluation of a remote-controlled motorcycle helmet wiper intended to enhance rider visibility and safety during rainy conditions. Specifically, the study aimed to describe the technical features of the device, determine its sensitivity in terms of wiping speed, assess its effectiveness in clearing raindrops under varying rainfall conditions, and evaluate its acceptability in terms of technical features, composition, operating performance, and safety. A developmental research design was employed in the conduct of the study. The developed device consisted of a compact wiper mechanism mounted on the helmet visor and connected to a lightweight 3D-printed housing containing the power supply, rechargeable battery, RF relay receiver, and control circuitry. A flexible spring wire was incorporated to allow smooth visor movement while minimizing strain on electrical connections. The system operated in two modes, slow and continuous, controlled through a handheld wireless remote. The evaluation involved fifty (50) evaluators composed of motorcycle riders, instructors, and individuals with expertise in mechanical, electrical, and safety-related fields. Data were gathered through structured observation, controlled performance testing, and an acceptability evaluation instrument. Results showed that the developed helmet wiper was functionally integrated and suitable for helmet application. In terms of sensitivity, the device recorded an average wiping speed of 30.66 wipes per minute in slow mode and 75 wipes per minute in continuous mode. The device demonstrated effective raindrop removal under light and moderate rainfall in both modes of operation; however, optimal performance during heavy rainfall was observed only when operated in continuous mode. Moreover, the device obtained a “Very Acceptable” rating across all evaluated criteria, indicating positive user perception of its design, durability, operating performance, and safety. Overall, the findings demonstrate that the developed remote-controlled motorcycle helmet wiper is a functional, safe, and user-acceptable safety device with strong potential for practical application in improving rider visibility during adverse weather conditions.
Keywords: Helmet Wiper, Wireless Control, Rider Safety, Visibility Enhancement, Ergonomic Design, Rain Protection, Operational Performance, User Acceptability.
Keywords: Helmet Wiper, Wireless Control, Rider Safety, Visibility Enhancement, Ergonomic Design, Rain Protection, Operational Performance, User Acceptability.
Abstract
A Study on the Impact of Digital Fatigue and Cognitive Load on Employee Productivity and Work Life Balance with reference to IT Sector in Chennai
Dr G Balamurugan, Joans Balscia V
DOI: 10.17148/IARJSET.2026.13535
Abstract: This study examines the impact of digital fatigue and cognitive load on employee productivity and work–life balance in the IT sector in Chennai. In today’s technology-driven work environment, employees are increasingly exposed to prolonged screen time, multitasking and constant digital connectivity, which often lead to mental exhaustion and reduced efficiency. The study adopts a descriptive research design and uses primary data collected from 113 IT employees through a structured questionnaire using the snowball sampling technique. Secondary data from journals and research articles support the study. Statistical tools such as descriptive statistics, correlation, regression and factor analysis were employed for data analysis. The findings reveal that digital fatigue and cognitive load have a significant negative impact on both employee productivity and work–life balance. Key contributing factors include information overload, continuous virtual meetings and lack of adequate digital breaks. These issues lead to decreased concentration, increased stress and difficulty in managing personal and professional life. The study suggests that practices such as regular digital breaks and better digital management strategies can improve employee well-being, productivity and overall work performance.
Keywords: Digital Fatigue, Cognitive Load, Employee Productivity, Work–Life Balance, IT Sector.
Keywords: Digital Fatigue, Cognitive Load, Employee Productivity, Work–Life Balance, IT Sector.
Abstract
AN EFFICIENT DEEP NEURAL NETWORK APPROACH FOR DIABETES PREDICTION
Dr. Rajendra Prasad Banavathu, Dr.S. Jayaprada, Dr. Kalpana Devi Bai Mudavathu
DOI: 10.17148/IARJSET.2026.13536
Abstract: Millions of people all over the world endure from the incessant condition of Diabetes. Early detection and action can lower the likelihood of problems and assist avoid or delay its development. Diabetes has been predicted using machine learning algorithms using a variety of characteristics, including demographics, clinical data, and lifestyle factors. Using a mix of patient data, including age, body mass index and more we present an approach based on deep learning to predict the chance of acquiring diabetes. K Nearest Neighbor(KNN), Logistic Regression(LR), Support Vector Machine(SVM), Decision Tree(DT) and Random Forest(RF), Deep Neural Networks (DNN) are some of the algorithms used. Each algorithm's accuracy is calculated along with the model's accuracy. The approach with a high accuracy level is used as the model to predict diabetes. This strategy may help medical professionals make knowledgeable judgements and give patients personalized care. A number of metrics, such as accuracy and F1 score, are used to assess the effectiveness of the suggested model. Using deep learning concepts by training the properties of a deep neural network(DNN), we suggest a method for diagnosing diabetes. with 98.49% prediction accuracy, and 93% F1 Score. The experimental findings show that when using Deep learning approach, the suggested system offers good outcomes. This strategy may help medical professionals make knowledgeable judgements and give patients personalized care.
Keywords: Diabetes, Supervised Learning, DL, Data mining, KNN, SVM, Light GBM, DT, RF, DNN
Keywords: Diabetes, Supervised Learning, DL, Data mining, KNN, SVM, Light GBM, DT, RF, DNN
Abstract
Vehicle Classification and Traffic Density Analysis using RT-DETR
Prajwal.M, Sujayeendra Rao, Dr. Ananth. G. S
DOI: 10.17148/IARJSET.2026.13537
Abstract: Traffic congestion and vehicle monitoring have become major challenges in modern urban transportation systems, requiring efficient real-time traffic analysis and management solutions. This project presents an intelligent Vehicle Classification and Traffic Density Analysis System using the advanced RT-DETR deep learning architecture for fast and accurate vehicle detection in traffic environments. Unlike traditional CNN-based models, RT-DETR uses transformer-based attention mechanisms to improve detection accuracy in crowded and complex road conditions. The system can identify multiple vehicle categories such as cars, buses, trucks, motorcycles, bicycles, and auto-rickshaws from images and videos. A dual-platform deployment using Flask and Streamlit supports both testing and large-scale monitoring applications. The application includes secure user authentication, traffic monitoring dashboards, and automated traffic reporting features. Uploaded traffic videos are processed using frame-based inference techniques to estimate traffic density levels such as Low, Medium, and High. Confidence-based filtering helps reduce false detections caused by occlusion, lighting variations, and dense traffic conditions. The system utilizes OpenCV and PyTorch for efficient image and video processing in real-time environments. Experimental results show that the RT- DETR model provides high detection accuracy, stable performance, and an effective AI-driven solution for smart traffic monitoring and intelligent transportation systems.
Keywords: Vehicle Classification, Traffic Density Analysis, RT-DETR, Deep Learning, Computer Vision, Intelligent Transportation System, Object Detection, Real-Time Monitoring, Flask, Streamlit, OpenCV, PyTorch, Traffic Surveillance, Smart City, Vehicle Detection.
Keywords: Vehicle Classification, Traffic Density Analysis, RT-DETR, Deep Learning, Computer Vision, Intelligent Transportation System, Object Detection, Real-Time Monitoring, Flask, Streamlit, OpenCV, PyTorch, Traffic Surveillance, Smart City, Vehicle Detection.
Abstract
Design and Circuit-Level Analysis of Low-Power Analog Neural Networks in CMOS Technology
Madhubala R, Dr. J. Rangaraj
DOI: 10.17148/IARJSET.2026.13538
Abstract: This paper presents the circuit-level design and performance analysis of a low-power Analog Neural Network (ANN) implemented in standard CMOS technology, targeting energy-constrained biomedical and edge-AI VLSI applications. The proposed architecture realises ANN inference entirely in the continuous-time analog domain using four principal circuit primitives: a Current Correlator (CC), an Adaptive Differential Equaliser (ADEL), a Gaussian Activation Function Circuit, and a Synaptic Function Circuit (SFC). All circuits are designed and characterised in Cadence Virtuoso. Simulation results confirm a peak-to-peak differential voltage gain of 2.928×, a −3 dB bandwidth of 15.89 GHz, and near-unity Gaussian voltage transfer (gain ≈ 1.000) with a current gain of 1.266× under low-supply conditions. Comprehensive transient, DC, and AC analyses validate stable, linear operation across the expected operating range. The work establishes a quantitative performance baseline for future integration of SFC and comparator stages toward a fully functional on-chip analog ANN classifier.
Keywords: Analog Neural Network, CMOS VLSI, Low-Power Design, Current Correlator, ADEL, Gaussian Activation Function, Synaptic Function Circuit, Cadence Simulation, Sub-threshold Operation, Edge AI.
Keywords: Analog Neural Network, CMOS VLSI, Low-Power Design, Current Correlator, ADEL, Gaussian Activation Function, Synaptic Function Circuit, Cadence Simulation, Sub-threshold Operation, Edge AI.
Abstract
DESIGN OF A MIXED SIGNAL VCO BASED ADC FOR HIGH SPEED APPLICATIONS
Akbar Ali A, Dr. O.Saraniya
DOI: 10.17148/IARJSET.2026.13539
Abstract: This paper demonstrates a power-efficient implementation of a mixed-signal Analog-to-Digital Converter (ADC) based on a Voltage-Controlled Oscillator (VCO). Conventional Flash ADCs face significant limitations in power and area at multi-GS/s speeds as comparator counts grow exponentially. The proposed hybrid Flash-VCO architecture overcomes these challenges by shifting fine quantization into the time domain. A current-starved ring oscillator scheme is employed for the VCO to achieve high efficiency and CMOS scalability. Simulation results confirm that this approach reduces power consumption and mismatch sensitivity, making it suitable for high-speed, low-resolution biomedical and wireless applications.
Keywords: Voltage-Controlled Oscillator (VCO), Flash ADC, Mixed-Signal Design, Current-Starved Oscillator, Time- Domain Quantization.
Keywords: Voltage-Controlled Oscillator (VCO), Flash ADC, Mixed-Signal Design, Current-Starved Oscillator, Time- Domain Quantization.
Abstract
Implementation of HMI-Based Gesture Recognition and UWB Radar in Autonomous Vehicles
Shrisanjaykumaar K, Dr. O. Saraniya
DOI: 10.17148/IARJSET.2026.13540
Abstract: This paper presents the MATLAB simulation and implementation of a 2–6 GHz CMOS Ultra-Wideband (UWB) radar transceiver front-end designed in 45 nm technology for HMI-based gesture recognition in autonomous vehicles. The transmitter chain employs digital pulse generation (5 ns rectangular pulse), Gaussian pulse shaping for spectral compliance, a Digitally Controlled Oscillator (DCO) providing a 4 GHz carrier, and an up-conversion mixer producing an RF output at 4.5 GHz. The received signal is processed via matched-filter correlation for range estimation, CA-CFAR detection for robust target identification, and a Kalman-filter-based tracker for long-range target following. An 8-gesture recognition vocabulary is implemented, with each gesture mapped to a specific vehicle command. Simulation results confirm FCC Part 15 UWB spectral compliance, accurate range detection at 45.5 m, multi-target resolution of pedestrian-car scenarios at 30–32 m separation, and gesture detection with a sub-5 ns observation window. The system achieves low-power, integrated radar-based HMI suitable for next-generation autonomous vehicles.
Keywords: UWB radar, gesture recognition, autonomous vehicles, CMOS transceiver, CFAR detection, Kalman tracking, HMI, FCC compliance.
Keywords: UWB radar, gesture recognition, autonomous vehicles, CMOS transceiver, CFAR detection, Kalman tracking, HMI, FCC compliance.
Abstract
THE EFFECT OF ACTIVATION TEMPERATURE ON PORE DIAMETER AND ADSORPTION CAPACITY OF ACTIVATED CARBON DERIVED FROM LIGNOCELLULOSIC BIOMASS: A REVIEW
I Gusti Agung Kade Suriadi, Dewa Ngakan Ketut Putra Negara, I Ketut Adi Atmika, Tjokorda Gde Tirta Nindhia*
DOI: 10.17148/IARJSET.2026.13541
Abstract: Activated carbon derived from lignocellulosic biomass has attracted significant attention due to its potential application in wastewater treatment. This review examines the effect of activation temperature on pore structure development and methylene blue adsorption capacity of activated carbon produced from teak sawdust waste. Activation temperature plays a crucial role in determining pore size distribution, surface area, and adsorption performance. Based on previous studies, increasing activation temperature promotes the transformation of micropores into mesopores due to enhanced devolatilization and gasification reactions. This structural evolution improves the adsorption capacity for methylene blue, a large organic molecule commonly used as a model adsorbate. However, excessively high activation temperatures may lead to pore collapse and reduced adsorption efficiency. This review highlights the importance of optimizing activation temperature to achieve a balance between pore development and structural stability. The findings provide insights into the design of efficient biomass-based adsorbents for environmental applications.
Keywords: activated carbon, activation temperature, pore structure, methylene blue, teak sawdust, adsorption
Keywords: activated carbon, activation temperature, pore structure, methylene blue, teak sawdust, adsorption
Abstract
Hybrid Machine Learning For IoT-Driven Heart Health Prediction
Priyanka Vijay Adate, Prof. A. A. Bhise
DOI: 10.17148/IARJSET.2026.13542
Abstract: The rapid growth of intelligent healthcare technologies has enabled continuous monitoring of human health through connected devices and smart sensors. Heart-related illnesses require immediate attention and timely analysis because delayed diagnosis may lead to severe complications. This research presents a smart IoT-based framework designed for early heart risk identification using adaptive learning and sensor-driven analytics. The system acquires real-time physiological information through ECG, pulse oximeter, and temperature sensors connected to an embedded microcontroller platform. Unlike traditional healthcare prediction systems that depend completely on pre-labeled datasets, the proposed framework introduces an adaptive learning strategy capable of dynamically categorizing incoming health data patterns. Processed sensor readings are analyzed using Artificial Neural Network (ANN) and Random Forest techniques to determine the probability of abnormal heart conditions. The proposed approach improves prediction consistency, supports continuous monitoring, and minimizes dependency on cloud-based infrastructure. Experimental evaluation demonstrates that the framework provides reliable prediction performance with improved adaptability for real-time healthcare environments.
Keywords: Smart Healthcare, IoT Sensors, Heart Risk Detection, Artificial Neural Network, Random Forest, Real-Time Monitoring, Intelligent Healthcare
Keywords: Smart Healthcare, IoT Sensors, Heart Risk Detection, Artificial Neural Network, Random Forest, Real-Time Monitoring, Intelligent Healthcare
Abstract
Equivalent Circuit Model-Based State of Charge Estimation of Lithium-Ion Batteries Using Kalman Filter Algorithms
Thirumalai V M. E, Dr. A. Anitha M.E, Ph. D
DOI: 10.17148/IARJSET.2026.13543
Abstract: Estimation of accurate SoC of Li-ion batteries is the basic requirement for a safe and efficient operation of the Battery Management System (BMS) of EVs. A systematic method for SoC estimation using a second order ECM (2RC ECM) calibrated from the data of HPPC tests is proposed in this paper. The OCV-SoC relation is extracted from C/20 charge-discharge test and fitted by a seventh order polynomial function. Parameters of ECM passive components (R₀, R₁, C₁, R₂, C₂) are identified from ten discrete SoC values with Levenberg-Marquardt (LM) nonlinear least-square algorithm, then they are represented by seventh order polynomials with regard to the SoC. EKF and UKF, both recursive Bayesian estimators, are implemented and evaluated for Turnigy Graphene 4.6928 Ah lithium cell at 0C with the standard test profiles (C/20 charge-discharge and HPPC). Error estimation (RMSE, MAE and MAX) is used for SoC and terminal voltage estimation respectively. Experiments show that both filters can always converge at the conditions and the UKF is slightly better than EKF at the SoC estimation under most of drive profiles due to the avoid of Jacobian linearisation. The sensitivity of SoC estimation accuracy with regard to parameters' identification accuracy is discussed and future works are summarized as adaptive noise covariance setting and order elevation to 3RC model.
Keywords: State of Charge (SoC), Equivalent Circuit Model (ECM), Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), HPPC, Levenberg–Marquardt, Battery Management System (BMS), Electric Vehicle (EV).
Keywords: State of Charge (SoC), Equivalent Circuit Model (ECM), Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), HPPC, Levenberg–Marquardt, Battery Management System (BMS), Electric Vehicle (EV).
Abstract
Effect of Yoga on Mental and Physical Health
PUNITH KUMAR G MP. Ed
DOI: 10.17148/IARJSET.2026.13544
Abstract: Yoga is an ancient Indian practice that combines physical postures, breathing exercises, meditation, and relaxation techniques to improve overall well-being. In recent years, yoga has gained worldwide recognition as an effective method for improving both mental and physical health. This research paper examines the effects of yoga on mental stability, stress reduction, emotional balance, flexibility, cardiovascular health, and overall physical fitness. The study also highlights the importance of incorporating yoga into daily life for students, working professionals, and elderly individuals. The findings suggest that regular yoga practice significantly contributes to a healthier lifestyle by reducing anxiety, depression, obesity, hypertension, and lifestyle-related diseases.
Keywords: Yoga, Mental Health, Physical Health, Stress Management, Fitness, Meditation, Wellness.
Keywords: Yoga, Mental Health, Physical Health, Stress Management, Fitness, Meditation, Wellness.
Abstract
ORAL CANCER DETECTION
Chirag R, Sujayeendra Rao
DOI: 10.17148/IARJSET.2026.13545
Abstract: Oral cancer is one of the most common and life-threatening cancers worldwide, particularly in developing regions where tobacco consumption, alcohol usage, and poor oral hygiene are prevalent. Early detection of oral cancer significantly improves survival rates; however, conventional diagnostic methods rely heavily on clinical examination and biopsy procedures, which are invasive, time-consuming, and dependent on expert availability. This project presents an intelligent Oral Cancer Detection System using machine learning and deep learning techniques to assist in early screening. The proposed system employs a Convolutional Neural Network (CNN) for analyzing oral cavity images to classify them as cancerous or non-cancerous. In addition, a machine learning-based clinical prediction model evaluates patient risk factors such as age, tobacco usage, alcohol consumption, and the presence of oral lesions. By integrating image-based analysis with clinical data evaluation, the system enhances diagnostic reliability and decision support. The developed models are deployed using a Streamlit-based web application that allows users to upload oral images and enter clinical details for real-time prediction. Experimental results demonstrate that the image-based model achieves high classification accuracy, while the clinical model effectively supports risk assessment. The proposed system provides a non-invasive, cost effective, and user-friendly solution for preliminary oral cancer screening, aiming to support healthcare professionals and improve early detection outcomes.
Keywords: deep learning, oral cancer detection, oral cancer, risk factors, cancer remedies, hospitals suggested.
Keywords: deep learning, oral cancer detection, oral cancer, risk factors, cancer remedies, hospitals suggested.
Abstract
An IoT-Based Smart Smoke Alarm System Using Multi-Sensor Fusion and Intelligent Monitoring for Enhanced Fire Safety
K.Venkatesh, V.Ramesh
DOI: 10.17148/IARJSET.2026.13546
Abstract: Early detection of fire hazards is essential to minimize loss of life and property in residential and commercial buildings. Traditional smoke alarm systems tend to use a operating system that is based on a single sensor, resulting in a large false alarm rate and lack of situational awareness. In this paper, a smart smoke detector, implemented using Internet of Things (IoT) and multi-sensor fusion, real-time visual and intelligent alert mechanisms to enhance reliability in detecting fire are introduced. The proposed system has a multiple sensors that detect smoke and fire related parameters, thus minimizing false alarms, an integrated web camera, and operated by a servo motor to do a live monitoring of the environment. The predefined floor plan achieves fire localization of the source of fire, which facilitates the evacuation and fire fight work. The functional reliability and practical feasibility of the system is experimentally proven. The proposed solution can be scaled and applicable to implementation in daily residential and commercial locations and is a large improvement over the traditional fire alarm systems.
Keywords: Internet of Things (IoT), Smart Smoke Detector, Fire Safety System, Multi-Sensor Fusion, Intelligible Monitoring, Smart Buildings.
Keywords: Internet of Things (IoT), Smart Smoke Detector, Fire Safety System, Multi-Sensor Fusion, Intelligible Monitoring, Smart Buildings.
Abstract
A Study on Non-Performing Assets in Selected Non-Banking Financial Companies
Dr. B. Asha Daisy, Dinesh Krishna V
DOI: 10.17148/IARJSET.2026.13547
Abstract: Non-Banking Financial Companies are integral to Indian economy as they are in a position to provide loans to areas which might not have access to banks or have not been receiving due attention from the banks. Some risks that NPAs have with regard to smooth operation and long-term survival of NBFCs. The risks originating from NPAs on the economical and efficient functioning of an NBFC come in different forms. In this research paper we would like to discuss the reasons of NPAs and the influence that NPAs have on the operations of the Non-Banking Financial Company. Various reasons for NPAs could include lack of rigorous credit appraisal system, over reliance on select few industries and macroeconomic factors.
Keywords: Non-Performing Assets, Non-Banking Financial Companies, Asset Quality, Risk Management, Credit Appraisal
Keywords: Non-Performing Assets, Non-Banking Financial Companies, Asset Quality, Risk Management, Credit Appraisal
Abstract
Design and Analysis of Digitally Trimmable Sub – Bandgap Reference in CMOS Technology
Harshavardhini R, Dr. M. Santhi
DOI: 10.17148/IARJSET.2026.13548
Abstract: This paper presents the design and implementation of a low-power digitally trimmable CMOS bandgap reference (BGR) circuit intended for high-precision and temperature-stable voltage reference generation in modern VLSI systems. The performance of conventional bandgap reference circuits is significantly affected by process variations, device mismatch, and temperature fluctuations, resulting in reduced output accuracy and long-term stability. To address these limitations, a digitally controlled trimming architecture employing a binary-weighted resistor array integrated with CMOS switching logic is proposed. The trimming network enables fine adjustment of the reference voltage and compensates for fabrication-induced variations, thereby enhancing the overall circuit accuracy and thermal stability. The proposed BGR circuit is designed with emphasis on low power dissipation, compact implementation, and reliable operation over a wide temperature range. Detailed simulation analysis is carried out to evaluate key performance parameters including reference voltage stability, temperature coefficient, line sensitivity, and power consumption under varying operating conditions. Simulation results demonstrate that the proposed digitally trimmable architecture achieves improved voltage precision and reduced temperature dependency when compared with conventional CMOS bandgap reference circuits, making it suitable for low-power analog and mixed- signal integrated circuit applications.
Keywords: Bandgap Reference (BGR), CMOS Technology, Digital Trimming, Low Power VLSI, Voltage Reference Circuit, Binary-Weighted Resistor Array, Temperature Compensation, Analog Integrated Circuits, Process Variation, Low Power Design, CMOS Switches, Mixed-Signal IC Design.
Keywords: Bandgap Reference (BGR), CMOS Technology, Digital Trimming, Low Power VLSI, Voltage Reference Circuit, Binary-Weighted Resistor Array, Temperature Compensation, Analog Integrated Circuits, Process Variation, Low Power Design, CMOS Switches, Mixed-Signal IC Design.
Abstract
YOGA POSTURE DETECTION
Keerthana S, Mr.Sujayeendra Rao, Dr. Ananth G S
DOI: 10.17148/IARJSET.2026.13549
Abstract: Yoga posture correction and recognition systems can help learners practice safely and consistently by providing instant feedback without requiring continuous supervision from an instructor. This project proposes a deep learning–based Yoga Posture Detection system that identifies yoga poses from images and real-time webcam video. The system uses a convolutional neural network (CNN) based classifier trained on a labeled dataset of yoga postures, where each class corresponds to a specific asana. For real-time operation, YOLOv8 is used to detect the person in each frame, the detected region is cropped, and the posture is classified using the trained model. The classification model is trained using transfer learning (MobileNetV2 backbone) to improve accuracy with a limited dataset and reduce training time. The final system is deployed as a web application using Flask with a user-friendly interface built using HTML, CSS, and JavaScript, allowing users to upload images for posture prediction and view top confidence results. Experimental results show that the proposed model achieves around 70% validation accuracy over 41 yoga classes, and performance is analyzed using a confusion matrix, classification report, and Grad-CAM visual explanations. The solution demonstrates an end-to-end pipeline for yoga pose classification and real-time detection, and can be extended further for posture correction and fitness guidance.
Keywords: deep learning, yoga pose detection, Convolutional Neural Network (CNN), Real-Time Detection.
Keywords: deep learning, yoga pose detection, Convolutional Neural Network (CNN), Real-Time Detection.
Abstract
Automatic Bell System Using Arduino Uno and RTC Module
Ayan Shaik, Tushar Rathod, Uttam Nimbal, Ibrahim Patel, Aishwarya Hosale
DOI: 10.17148/IARJSET.2026.13550
Abstract: The Automatic Bell System is a smart electronic system designed to automate the ringing of bells in schools and colleges according to predefined schedules. Traditional manual bell systems require human operation, which may lead to delays and timing errors. The proposed system uses an Arduino Uno, RTC (Real Time Clock) module, buzzer, LCD display, push buttons, and jumper wires to create an automatic and reliable bell mechanism.
The RTC module provides accurate real-time data, while the Arduino Uno controls the entire system. The 16x2 LCD display shows the current time and system status. Push buttons are used to set the bell timing and system configuration. When the stored bell time matches the current RTC time, the buzzer automatically rings. The system reduces manual effort, improves accuracy, and ensures proper time management in educational institutions.
Keywords: Arduino Uno, RTC Module, Automatic Bell System, LCD Display, Buzzer, Embedded System.
The RTC module provides accurate real-time data, while the Arduino Uno controls the entire system. The 16x2 LCD display shows the current time and system status. Push buttons are used to set the bell timing and system configuration. When the stored bell time matches the current RTC time, the buzzer automatically rings. The system reduces manual effort, improves accuracy, and ensures proper time management in educational institutions.
Keywords: Arduino Uno, RTC Module, Automatic Bell System, LCD Display, Buzzer, Embedded System.
Abstract
Integrated Smart Farming System with IoT Monitoring and AI Assistance
Prof. A. D. Gujar, Shrutika Patil, Ayush Mane, Sneha Gadade, Vaishnavi Bangar
DOI: 10.17148/IARJSET.2026.13551
Abstract: Agriculture is one of the most important sectors for food production and economic growth, but farmers often face difficulties in monitoring soil conditions, managing irrigation, identifying crop-related issues, and accessing market resources efficiently. Traditional farming methods require continuous physical presence in the field, which consumes time and labor. To overcome these challenges, this research proposes an AI-Based Smart Agriculture Monitoring and Irrigation System that integrates both hardware and software technologies for efficient farm management. The system uses ESP32, DHT sensor, and Soil Moisture Sensor to continuously monitor environmental and soil conditions such as temperature, humidity, and moisture level. The collected data is transmitted to a Flutter-based mobile application where farmers can monitor real-time readings remotely. When the soil moisture level falls below a predefined threshold, the system alerts the user and also allows one-click water supply control through the application.
In addition, the proposed system includes an AI-powered farming chatbot that assists farmers in solving agriculture- related problems through text chat, voice input, and image upload features for crop analysis and guidance. The application also provides a marketplace platform where farmers can buy or sell crops, fertilizers, seeds, and medicines directly. A dedicated profile section stores farmer information, while the video and news module provides updates regarding modern farming techniques, innovations, and agricultural news. Experimental analysis shows that the system improves irrigation efficiency, reduces unnecessary field visits, supports smart decision-making, and enhances accessibility to agricultural resources. The proposed system is cost-effective, scalable, user-friendly, and suitable for modern smart farming applications.
Keywords: Smart Agriculture System, ESP32, Soil Moisture Sensor, DHT Sensor, Flutter Application, Smart Irrigation, AI Chatbot, Agriculture Monitoring, IoT in Farming, Remote Water Control, Precision Farming, Agricultural Marketplace, Crop Monitoring, Voice Assistance, Image-Based Farming Support, Digital Farming Platform, Real-Time Monitoring, Sustainable Agriculture.
In addition, the proposed system includes an AI-powered farming chatbot that assists farmers in solving agriculture- related problems through text chat, voice input, and image upload features for crop analysis and guidance. The application also provides a marketplace platform where farmers can buy or sell crops, fertilizers, seeds, and medicines directly. A dedicated profile section stores farmer information, while the video and news module provides updates regarding modern farming techniques, innovations, and agricultural news. Experimental analysis shows that the system improves irrigation efficiency, reduces unnecessary field visits, supports smart decision-making, and enhances accessibility to agricultural resources. The proposed system is cost-effective, scalable, user-friendly, and suitable for modern smart farming applications.
Keywords: Smart Agriculture System, ESP32, Soil Moisture Sensor, DHT Sensor, Flutter Application, Smart Irrigation, AI Chatbot, Agriculture Monitoring, IoT in Farming, Remote Water Control, Precision Farming, Agricultural Marketplace, Crop Monitoring, Voice Assistance, Image-Based Farming Support, Digital Farming Platform, Real-Time Monitoring, Sustainable Agriculture.
Abstract
“To study the Role of Augmented Reality on Product marketing”
Pranjal Sharad Mulmule, Prof. Akshay Yeotikar
DOI: 10.17148/IARJSET.2026.13552
Abstract: The rapid adoption of Augmented Reality (AR) has transformed modern marketing strategies, offering immersive and interactive experiences that reshape consumer decision-making. This study aims to analyze the impact of AR on product marketing with a specific focus on Lenskart, a leading eyewear company in India. The research investigates how AR tools, particularly the “3D Virtual Try-On” feature, influence customer engagement, purchase confidence, and brand positioning.
he study employs both qualitative and quantitative methods, including customer surveys, comparative analysis of traditional versus AR-enabled marketing, and statistical tools to measure changes in consumer behavior and sales performance. Secondary data has been collected from industry reports, company publications, and academic literature, while primary data includes customer feedback and usage patterns of AR applications.
The findings indicate that AR significantly enhances customer experience by reducing purchase hesitation, lowering product return rates, and strengthening brand differentiation in a competitive market. Furthermore, the integration of AR in Lenskart’s marketing strategy demonstrates how technology-driven innovation can create sustainable value for both consumers and businesses.
This research contributes to a deeper understanding of how emerging technologies like AR reshape marketing dynamics, offering valuable insights for marketers, policymakers, and businesses seeking to leverage immersive tools for improved customer engagement and risk management in digital commerce.
he study employs both qualitative and quantitative methods, including customer surveys, comparative analysis of traditional versus AR-enabled marketing, and statistical tools to measure changes in consumer behavior and sales performance. Secondary data has been collected from industry reports, company publications, and academic literature, while primary data includes customer feedback and usage patterns of AR applications.
The findings indicate that AR significantly enhances customer experience by reducing purchase hesitation, lowering product return rates, and strengthening brand differentiation in a competitive market. Furthermore, the integration of AR in Lenskart’s marketing strategy demonstrates how technology-driven innovation can create sustainable value for both consumers and businesses.
This research contributes to a deeper understanding of how emerging technologies like AR reshape marketing dynamics, offering valuable insights for marketers, policymakers, and businesses seeking to leverage immersive tools for improved customer engagement and risk management in digital commerce.
Abstract
Explainable Ensemble Machine Learning Framework for Finding Phishing Websites in Real Time
Gangarapu Vasanth Kumar
DOI: 10.17148/IARJSET.2026.13553
Abstract: One of the most significant cyber risks in today's digital communication is phishing websites that prey on users through fake web pages that look like real websites to steal important information such as login passwords, banking details, and personal data. The current blacklist based phishing detection systems are less successful against the emerging and zero-day phishing assaults, as they cannot identify the unseen malicious URLs in real-time. In order to overcome these limitations, this study presents an Explainable Ensemble Machine Learning Framework for Real-Time Phishing Website Detection that utilizes advanced feature engineering, ensemble learning algorithms, and explainable artificial intelligence (XAI) techniques to enhance detection performance and interpretability.
The proposed methodology uses the entire range of URL-based, domain-based and webpage content-based attributes derived from the phishing and legal websites collected from publicly available cyber security datasets PhishTank, UCI Repository and Kaggle. Ensemble method based on voting combines and implements multiple machine learning algorithms such as Random Forest, Gradient Boosting and XGBoost classifiers. Moreover, there is explainable AI based on SHAP for transparent decision-making and feature importance analysis. The experimental results show that the proposed ensemble framework provides better performance than standard standalone machine learning models in terms of accuracy, precision, recall, F1-score, and ROC-AUC score. The platform also offers real-time phishing detection for browser extensions and cybersecurity apps. The suggested research contributes to the development of an intelligent, scalable, accurate and interpretable phishing detection system for modern cyber security infrastructures.
Keywords: Phishing Website Detection, Machine Learning, Ensemble Learning, Explainable AI, SHAP, Cybersecurity, XGBoost, Random Forest, Real-Time Detection
The proposed methodology uses the entire range of URL-based, domain-based and webpage content-based attributes derived from the phishing and legal websites collected from publicly available cyber security datasets PhishTank, UCI Repository and Kaggle. Ensemble method based on voting combines and implements multiple machine learning algorithms such as Random Forest, Gradient Boosting and XGBoost classifiers. Moreover, there is explainable AI based on SHAP for transparent decision-making and feature importance analysis. The experimental results show that the proposed ensemble framework provides better performance than standard standalone machine learning models in terms of accuracy, precision, recall, F1-score, and ROC-AUC score. The platform also offers real-time phishing detection for browser extensions and cybersecurity apps. The suggested research contributes to the development of an intelligent, scalable, accurate and interpretable phishing detection system for modern cyber security infrastructures.
Keywords: Phishing Website Detection, Machine Learning, Ensemble Learning, Explainable AI, SHAP, Cybersecurity, XGBoost, Random Forest, Real-Time Detection
Abstract
“Plant Leaf Disease Detector And Solution Recommendation”
Mr.Digamber Shelke, Miss.Nikita Nevase, Gayatri Shinde, Bhakti Kulkarni, Gayatri Kurhade, Anil Karande
DOI: 10.17148/IARJSET.2026.13554
Abstract: Agricultural productivity and food security are significantly affected by plant diseases, which cause substantial economic losses and threaten global food supply chains. Traditional methods of disease detection rely heavily on manual inspection by experts, which are time-consuming, subjective, and often infeasible for large-scale monitoring. This project presents a mobile application designed for plant leaf disease detection and solution recommendation, with the key advantage of functioning in offline mode. This research proposes an intelligent, automated system for plant leaf disease detection and solution recommendation using computer vision and machine learning techniques.
High-resolution images of plant leaves are preprocessed using contrast enhancement and noise reduction techniques. A Convolutional Neural Network (CNN) model, trained on datasets such as PlantVillage, is used to extract features and classify diseases including bacterial blight, powdery mildew, and leaf spot. The model achieves an average accuracy exceeding 85%.
After detection, the system provides solution recommendations including chemical, biological, and cultural practices. The proposed system supports farmers through a mobile-based interface and promotes sustainable agriculture. Future work includes integrating environmental factors like humidity and temperature for better predictions.
Keywords: Mobile Application, Convolutional Neural Network, Image Processing, Crop Disease Classification.
High-resolution images of plant leaves are preprocessed using contrast enhancement and noise reduction techniques. A Convolutional Neural Network (CNN) model, trained on datasets such as PlantVillage, is used to extract features and classify diseases including bacterial blight, powdery mildew, and leaf spot. The model achieves an average accuracy exceeding 85%.
After detection, the system provides solution recommendations including chemical, biological, and cultural practices. The proposed system supports farmers through a mobile-based interface and promotes sustainable agriculture. Future work includes integrating environmental factors like humidity and temperature for better predictions.
Keywords: Mobile Application, Convolutional Neural Network, Image Processing, Crop Disease Classification.
Abstract
Examify: Digital Examination Management & Evaluation System
Prof. Shubhangi S. Bhagat*, Digvijay Shinde, Kulashree Patil, Aditya Nanaware, Roman Shaikh
DOI: 10.17148/IARJSET.2026.13555
Abstract: Examify is a role-based digital examination management and answer sheet evaluation system developed to automate and simplify the traditional academic evaluation process. The system replaces manual answer-sheet checking with a centralized, secure, and trackable digital workflow. It supports three major roles: Admin, Teacher, and HOD. The Admin manages uploads and assignments, Teachers digitally evaluate answer sheets using annotation tools, and HOD monitors evaluation progress through analytical dashboards. The platform is developed using Flutter, GetX, Supabase, PostgreSQL, and PDF.js. It provides features such as secure authentication, PDF-based evaluation, question-wise marks entry, annotation storage, automatic result generation, and real-time monitoring. The integration of digital annotations and backend-driven analytics improves transparency, efficiency, and evaluation accuracy. The system reduces manual effort, enhances workflow management, and provides a scalable solution for modern educational institutions. Future enhancements may include AI-assisted evaluation, advanced reporting, and enterprise-level deployment support.
Keywords: Digital Examination System, Answer Sheet Evaluation, Flutter, Supabase, PDF Annotation, Academic Analytics, Role-Based Access Control, Online Evaluation System.
Keywords: Digital Examination System, Answer Sheet Evaluation, Flutter, Supabase, PDF Annotation, Academic Analytics, Role-Based Access Control, Online Evaluation System.
Abstract
Thorax MapNet: Attention-Based Architecture with Anatomical Priors for Disease Classification
Harshini V T, Dr. Babu S
DOI: 10.17148/IARJSET.2026.13556
Abstract: The detection of thoracic diseases through chest medical images presents difficulties because of two main issues. The existing deep learning models include YOLOv8 and Faster R-CNN and U-Net-based CNN classifiers which achieve good results but these models struggle with two main issues. The study introduces A2-YOLOv8-ViT as a hybrid deep learning framework that integrates YOLOv8s real-time object detection capabilities with Vision Transformers global feature learning abilities. The proposed model uses spatial and channel attention mechanisms to enhance feature representation while highlighting important clinical areas and it uses a feature fusion strategy to combine local CNN features with transformer-based global dependencies for better detection results. The system uses an adaptive thresholding technique to solve class imbalance problems while boosting prediction accuracy. The proposed framework achieves 99% accuracy through its experimental results while showing better precision and recall and mean average precision results than traditional methods. The system produces interpretable outputs which include bounding boxes and confidence scores and severity indications that enable efficient and accurate clinical decision-making for thoracic disease diagnosis.
Keywords: Thoracic Disease Detection, A2-YOLOv8-ViT, Vision Transformer, Deep Learning, Medical Image Analysis, Attention Mechanism, Chest X-ray, Object Detection, CNN, Adaptive Thresholding.
Keywords: Thoracic Disease Detection, A2-YOLOv8-ViT, Vision Transformer, Deep Learning, Medical Image Analysis, Attention Mechanism, Chest X-ray, Object Detection, CNN, Adaptive Thresholding.
Abstract
V-GPU FOR AI COMPUTING AND PARALLEL COMPUTING
Nayana J, Chethan R, Rakshit J Kashyap, Gurudev B S, Kanderi Karthik
DOI: 10.17148/IARJSET.2026.13557
Abstract: Modern Artificial Intelligence (AI) and Machine Learning (ML) workloads are heavily constrained by the high cost and underutilization of physical hardware acceleration. Traditional hardware-level GPU virtualization methods introduce severe hypervisor overhead, require proprietary licensing, and lack automated multi-dataset parallel orchestration. This paper introduces v-gpu, an open-source, containerized virtual GPU orchestration platform designed to automate machine learning pipeline execution using isolated Docker environments. The framework utilizes a custom engine to build deterministic runtime environments (vgpu-worker), auto-provision containerized workspaces, and route data using custom smart ingestion paths. For parallel computing clusters, the system dynamically balances multi-dataset workloads across isolated, load-balanced container groups to maximize computational throughput. Upon job execution, the core engine extracts analytical performance metrics—including Mean Squared Error (MSE), classification accuracy, and F1-score—delivering real-time telemetry to a centralized control dashboard. Experimental evaluations demonstrate that the architecture achieves zero-overhead process isolation, reliable data routing, and rapid infrastructure teardown under peak workloads.
Keywords: Virtual GPU, Containerization, Parallel Computing, Machine Learning Analytics, Infrastructure Automation, Cloud Orchestration.
Keywords: Virtual GPU, Containerization, Parallel Computing, Machine Learning Analytics, Infrastructure Automation, Cloud Orchestration.
Abstract
Paper Writing Skill Test of ChatGPT for Causal Inference of Physical AI with LMM
Dong Hwa Kim
DOI: 10.17148/IARJSET.2026.13558
Abstract: This paper deals with application ChatGPT for writing engineering paper. Basically, we have to write paper from bottom to final paper such as reference, summarizing, a title selection, and writing work including English skill reviewing. However, the many apples like ChatGPT provide many functions as well as information for producing paper, report, research plan, and etc. This paper focuses physical AI including causal inference and tries its paper writing skill for user for physical causal inference. Causal inference is a big issues and problem in physical AI and LLM (Large Language Model), and LMM (Language Multi-Model) to operate independently in Internet in industrial or small device or robot. The Physical AI have to use LMM (Language Multi-Model) with language sensor, several image sensors, temperature sensor, and others for operation through the communication with user. LMM (Language Multi-Model) such as ChatGPT or AI models has the significant problems in application because of the deficit causal inference of function language recognition, image, audio model, and etc.
That is, the causal inference problems of the large Language Multi-Model (LMM) remain issue for application of physical AI. Because the causal problems are difficult to describe reason in natural language model, this function restricts the application of physical AI in industrial fields. This paper selects this title and to see how they (ChatGPT) can write causal inference issues of this physical AI to see improving skill of ChatGPT for the causal ability of LMM. Of course, there are many limitations in writing paper by ChatGPT and we cannot believe fully the results of ChatGPT. However, this paper tries how to use and how much it is true in the results. In the future, we absolutely need experiment and compare.
Keywords: LMM, ChatGPT, Causal Inference, Physical AI, Robot.
That is, the causal inference problems of the large Language Multi-Model (LMM) remain issue for application of physical AI. Because the causal problems are difficult to describe reason in natural language model, this function restricts the application of physical AI in industrial fields. This paper selects this title and to see how they (ChatGPT) can write causal inference issues of this physical AI to see improving skill of ChatGPT for the causal ability of LMM. Of course, there are many limitations in writing paper by ChatGPT and we cannot believe fully the results of ChatGPT. However, this paper tries how to use and how much it is true in the results. In the future, we absolutely need experiment and compare.
Keywords: LMM, ChatGPT, Causal Inference, Physical AI, Robot.
Abstract
GREEN CORROSION INHIBITORS IN AQUEOUS SYSTEMS: BRIDGING SURFACE CHEMISTRY AND ENVIRONMENTAL SAFETY
Satyendra Sharma
DOI: 10.17148/IARJSET.2026.13559
Abstract: Corrosion remains a major challenge in industrial systems involving metals exposed to aqueous environments. Conventional corrosion inhibitors, although effective, often exhibit toxicity, poor biodegradability, and ecological hazards. Green corrosion inhibitors derived from plant extracts, natural polymers, amino acids, and biodegradable compounds have emerged as sustainable alternatives. This research paper examines the role of green corrosion inhibitors in aqueous systems, with an emphasis on adsorption mechanisms, surface chemistry, electrochemical behaviour, and environmental safety. The study also discusses inhibitor efficiency, adsorption isotherms, surface analytical methods, and ecological assessment. Experimental trends reported in literature indicate that green inhibitors can achieve inhibition efficiencies above 85–90% under optimized conditions while maintaining environmental compatibility. The paper bridges the gap between corrosion science and sustainability by evaluating both protective performance and environmental impact.
Keywords: Green corrosion inhibitors, aqueous systems, adsorption, electrochemistry, environmental safety, plant extracts, sustainable chemistry.
Keywords: Green corrosion inhibitors, aqueous systems, adsorption, electrochemistry, environmental safety, plant extracts, sustainable chemistry.
Abstract
A Critical Analysis in Understanding the Role of AI Influenced Technology in Analysing the Consumer Behavior in Fashion Products: The Mediating Role of Brand Trust
Dr Revathi Anandkumar
DOI: 10.17148/IARJSET.2026.13560
Abstract: The significant integration of artificial intelligence technologies into the worldwide trend of manufacturing intelligence is what is causing a transformation in industrial processes. Because the fashion industry requires a significant amount of resources, it is an essential industry to explore in order to determine the potential for artificial intelligence to support sustainable growth. A substantial shift in the comprehension and administration of consumer buying patterns is marked by the synchronisation of consumer behaviour with artificial intelligence (AI). Enterprises are able to use apps that are powered by artificial intelligence to evaluate human behaviour in ways that are not possible with traditional analytical methods. Through the use of advanced recommendation systems that help consumers make choices about what to buy, personalised content that tailors individual experiences, and behavioural nudges that are generated from the arrangement of information, artificial intelligence has an impact on consumer buying.
Keywords: Consumer Behavior, Brand Trust, Artificial Intelligence
Keywords: Consumer Behavior, Brand Trust, Artificial Intelligence
Abstract
AI - Based Real Time Crowd Monitoring and Risk Detection System
Diana PrinceChandran Jeyasingh, Gaanashree BN, K Jyothi, Rachana M, Thanisha G
DOI: 10.17148/IARJSET.2026.13561
Abstract: For the proposed study, it is anticipated that a software product based on Artificial Intelligence will be developed to enable risk detection and analysis to confirm that there are no security threats in crowded public spaces like malls, temples, and train stations. Crowd management can be quite tricky in such spacesHence, it becomes necessary to monitor them constantly. However, the use of traditional surveillance systems like cameras becomes problematic because it requires one to continuously monitor the video footage for any potential threat.
It becomes possible to resolve this issue by leveraging computer vision, integrated with deep learning capabilities, that enables the system to monitor the video stream recorded through the camera. Furthermore, the system will be capable of detecting individuals and analyzing their behavior patterns to verify if they can be deemed dangerous, including the risk of fire and other similar hazards such a detection capability can prevent accidents, such as stampedes.
Keywords: Artificial Intelligence (AI), Computer Vision, Crowd Monitoring, Risk Detection, RealTime Surveillance, YOLO.
It becomes possible to resolve this issue by leveraging computer vision, integrated with deep learning capabilities, that enables the system to monitor the video stream recorded through the camera. Furthermore, the system will be capable of detecting individuals and analyzing their behavior patterns to verify if they can be deemed dangerous, including the risk of fire and other similar hazards such a detection capability can prevent accidents, such as stampedes.
Keywords: Artificial Intelligence (AI), Computer Vision, Crowd Monitoring, Risk Detection, RealTime Surveillance, YOLO.
Abstract
From Automation Panic to Workforce Resilience: A Governance Framework for Enterprise AI Transformation
Panna Lal Jaiswal, Rajeew Vishvakarma, Sooraj Jacob
DOI: 10.17148/IARJSET.2026.13562
Abstract: Artificial intelligence, especially generative AI, is transforming enterprise operations by automating tasks, enhancing decision-making, and redefining job roles. Public discourse often portrays this as a threat to employment; however, recent evidence has shown a nuanced pattern involving task automation, role transformation, displacement risk, augmentation, and new roles. The International Monetary Fund estimates that nearly 40% of global employment is susceptible to AI, with exposure rising to 60% in advanced economies owing to cognitive task-oriented jobs. The International Labour Organization’s 2025 update highlights the need to assess the exposure of generative AI at the task level using task data, expert input, and AI model predictions. This paper argues that AI-induced workforce disruption is not only a labor market issue but also an enterprise governance challenge. Organizations implementing AI without responsible transition mechanisms may worsen workforce anxiety, skill obsolescence, inequality, and trust erosion. To address this, this study proposes a Workforce Resilience Governance Framework (WRGF) for enterprise AI transformation. This framework includes task-level exposure assessment, human augmentation design, reskilling, redeployment, transparent communication, psychological safety, workforce impact accountability, and policy alignment. This study contributes a taxonomy of AI workforce impact, a Workforce Resilience Readiness Score (WRRS), an AI Workforce Trust Index (AWTI), an Ethical Automation Boundary concept, and a pilot empirical validation design. It concludes that AI's future impact on employment will depend not only on automation capabilities but also on how responsibly enterprises manage workforce transitions.
Keywords: artificial intelligence, generative AI, future of work, workforce resilience, AI governance, enterprise transformation, job displacement, human–AI collaboration, reskilling, responsible AI, automation panic, digital transformation.
Keywords: artificial intelligence, generative AI, future of work, workforce resilience, AI governance, enterprise transformation, job displacement, human–AI collaboration, reskilling, responsible AI, automation panic, digital transformation.
Abstract
Handyman – An Android App for Easy Access to Trusted Professionals
Sowmya N, Harshitha N, Monika R Gowda, Nisha K P, Pavithra C
DOI: 10.17148/IARJSET.2026.13563
Abstract: In everyday life, individuals frequently encounter household and commercial maintenance issues requiring skilled professionals such as electricians, plumbers, pest control agents, and cleaners. Traditional methods of locating and hiring reliable service providers are often inefficient, time-consuming, and lack transparency. This project introduces an Android-based application, Handyman, designed to provide easy access to trusted professionals through a centralized digital platform.
The application enables users to book services, track requests in real time, and provide feedback, while service providers benefit from increased visibility and customer reach. By integrating a dispatcher module, secure payment options, and a rating system, the app ensures efficiency, accountability, and user satisfaction. The proposed system leverages Android Studio, Java, XML, and Firebase Realtime Database to deliver a seamless, scalable, and user-friendly solution that bridges the gap between customers and service professionals.
Keywords: Android Application, Handyman Services, Service Booking Platform, Real-time Tracking, Firebase Realtime Database, Android Studio, Dispatcher Module, On-Demand Services
The application enables users to book services, track requests in real time, and provide feedback, while service providers benefit from increased visibility and customer reach. By integrating a dispatcher module, secure payment options, and a rating system, the app ensures efficiency, accountability, and user satisfaction. The proposed system leverages Android Studio, Java, XML, and Firebase Realtime Database to deliver a seamless, scalable, and user-friendly solution that bridges the gap between customers and service professionals.
Keywords: Android Application, Handyman Services, Service Booking Platform, Real-time Tracking, Firebase Realtime Database, Android Studio, Dispatcher Module, On-Demand Services
Abstract
Prediction of Flood and Planner Towards Emergency Response
Thejaswini S P, Prashant Ankalkoti
DOI: 10.17148/IARJSET.2026.13564
Abstract: FloodGuard is a mobile-based flood prediction and alert system designed for early disaster warning. The application uses machine learning models such as Random Forest, XGBoost, and TensorFlow Neural Network to predict flood risk using weather and environmental data. Data preprocessing techniques including SMOTE, StandardScaler, and cross-validation improve prediction accuracy. The final model is converted to TensorFlow Lite for on-device execution in a Flutter application. The system collects live weather data, analyzes flood risk and sends alerts to emergency contacts during risky situations. FloodGuard provides a fast, low-cost, and user-friendly solution for flood safety and preparedness.
Keywords: flood prediction, mobile app, machine learning, Random Forest, XGBoost, TensorFlow Lite, SMOTE, StandardScaler, Open‑Meteo, emergency alerts
Keywords: flood prediction, mobile app, machine learning, Random Forest, XGBoost, TensorFlow Lite, SMOTE, StandardScaler, Open‑Meteo, emergency alerts
Abstract
Impact of Generative AI Tools on Diploma Engineering Students’ Learning Behaviour
Mrs. Asmita Jagtap, Mr. Sameer Mulik
DOI: 10.17148/IARJSET.2026.13565
Abstract: Generative Artificial Intelligence (AI) tools such as OpenAI ChatGPT, Google Gemini, and AI-powered coding assistants are increasingly being used by diploma engineering students for academic purposes. These tools help students in understanding concepts, writing assignments, solving programming problems, preparing presentations, and improving communication skills. This research paper analyzes the impact of Generative AI tools on the learning behavior of diploma engineering students. The study focuses on student dependency, conceptual understanding, productivity improvement, critical thinking, and academic ethics. Data can be collected through surveys and questionnaires from engineering students across different semesters. The research concludes that while Generative AI significantly improves learning efficiency and accessibility, excessive dependence may reduce analytical thinking and originality if not used responsibly.
Keywords: Generative AI, Engineering Education, Diploma Students, Learning Behavior, Artificial Intelligence in Education, Academic Performance, AI Ethics, Student Productivity
Keywords: Generative AI, Engineering Education, Diploma Students, Learning Behavior, Artificial Intelligence in Education, Academic Performance, AI Ethics, Student Productivity
Abstract
"AI-Powered Predictive Analytics for Concrete Compressive Strength with Material Impact Interpretation"
Kalpesh Wani, Prashant Shimpi
DOI: 10.17148/IARJSET.2026.13566
Abstract: Because it directly affects the quality, longevity, and safety of structures, concrete compressive strength is a crucial factor in the construction sector. Conventional laboratory-based strength testing is expensive, time-consuming, and labor-intensive. This study suggests an AI-powered predictive analytics framework for evaluating concrete compressive strength utilizing machine learning approaches in order to get over these restrictions. A concrete compressive strength dataset that includes mix design factors such cement, water, fly ash, blast furnace slag, superplasticizer, coarse aggregate, fine aggregate, and curing age is used in this work. To find the most effective model for precise strength prediction, several machine learning regression models are put into practice and assessed. Evaluation metrics including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R2 are used to evaluate the models' performance. Additionally, explainable AI algorithms are used for material impact interpretation in order to assess how each input information contributes to the prediction of compressive strength. The experimental findings show that sophisticated ensemble-based regression models outperform conventional regression techniques in terms of prediction accuracy. This study provides engineers with an efficient and comprehensible AI solution for strength assessment and concrete mix design optimization.
Keywords: Concrete Compressive Strength, Artificial Intelligence, Machine Learning, Predictive Analytics, Regression Models, Explainable AI, SHAP.
Keywords: Concrete Compressive Strength, Artificial Intelligence, Machine Learning, Predictive Analytics, Regression Models, Explainable AI, SHAP.
Abstract
A Comprehensive DEMATEL Based Framework for Sustainable Decision-Making in Manufacturing Systems: Evaluation, Nomenclature and Cause–Effect Analysis
Somnath Singroul*, Chhatrapal Dewangan, Atul Kumar Sahu, Nitin Kumar Sahu
DOI: 10.17148/IARJSET.2026.13567
Abstract: In this study, the importance of sustainability and sustainable decision making is highlighted. The study embraces the decision making tools in the form of Decision making trial and evaluation laboratory (DEMATEL) based approach for evaluation in different application related to Industrial and manufacturing filed. The study presented DEMATEL techniques nomenclatures for ease understanding to the users and readers. The developed techniques can helps decision makers in attaining appropriate insights and decision tactics. The approach integrates both classical and extended DEMATEL techniques including fuzzy and rough versions to clarify their terminology and use. A set of sustainability criteria economic, environmental, social and institutional is identified for the manufacturing sector and experts’ judgments about their interrelationships are collected. Using the DEMATEL method, we construct influence matrices and a cause–effect network of criteria. The analysis reveals which factors are the “causes” drivers and which are “effects” in the system. Technical and governance factors emerge as highly influential on others. The developed framework helps decision-makers visualize complex causal links and prioritize actions. For instance, investing in the most influential areas energy efficiency or emissions control can have outsized impact on overall sustainability. The results and illustrative diagrams show how the DEMATEL approach provides actionable insights for sustainable decision tactics. Overall, this paper provides a comprehensive, transparent evaluation model together with clear definitions of DEMATEL nomenclature to support confident, insight-driven sustainable decisions in industry.
Keywords: Decision Making, Industrial and Manufacturing, Sustainability, Critical Thinking
Keywords: Decision Making, Industrial and Manufacturing, Sustainability, Critical Thinking
Abstract
Engineering Biomass-Based Activated Carbon through Physical and Chemical Activation: A Systematic Review on Pore Structure and Material Performance
Andrian Iswanto, Ade Saepullah, Agri Kala Yosua, Hafda Setyawan, I Gusti Ngurah Putu Tenaya, Tjokorda Gde Tirta Nindhia*
DOI: 10.17148/IARJSET.2026.13568
Abstract: Biomass-based activated carbon has attracted widespread attention as a sustainable carbon material due to its abundant availability, renewability, and engineered pore structure. The characteristics and performance of activated carbon are greatly influenced by the type of biomass and the activation strategies used during the synthesis process. This article presents a systematic literature review on the potential of biomass and various activation methods in the production of activated carbon, focusing on the development of pore structure and material performance. The review was conducted on various studies related to physical and chemical activation processes, including activation using steam, CO₂, as well as chemical activation using KOH, H₃PO₄, ZnCl₂, and NaOH. The results of the review indicate that the composition of biomass, particularly the content of lignin, cellulose, and hemicellulose, plays a crucial role in determining carbon yield, thermal stability, and pore evolution during the carbonization and activation processes. Physical activation generally results in stable microporous structures but requires high temperatures and relatively long processing times. In contrast, chemical activation can significantly enhance pore development and specific surface area due to the intensive interaction between the activator and the carbon matrix. Among the various activators used, KOH shows the most effective performance in producing highly developed microporous structures with high surface areas. In addition, the hierarchical pore structure consisting of micropores and mesopores significantly contributes to the adsorption capacity, ion transport, and electrochemical performance of the material. Biomass-based activated carbon shows potential for wide applications in the fields of adsorption, water treatment, energy storage, supercapacitors, and solar energy-based interfacial evaporation systems. Overall, this study emphasizes that activation strategies are the main factor determining the process- structure-property relationship of biomass-based activated carbon in the development of high-performance porous carbon materials for environmental and energy applications.
Keywords: activated carbon, biomass, chemical activation, physical activation, pore structure.
Keywords: activated carbon, biomass, chemical activation, physical activation, pore structure.
Abstract
Mechanical Characterization of an Eucheuma cottonii White Seaweed-Based Bioplastic Film: Effect of seafood Protein and Sorbitol Plasticizer on Tensile Properties
Nicolas Meuley, Tjokorda Gde Tirta Nindhia*, Ade Saepullah, Agri Kala Yosua
DOI: 10.17148/IARJSET.2026.13569
Abstract: This work reports the preliminary mechanical characterization of a bioplastic film prepared from white Eucheuma cottonii seaweed, using sorbitol as a plasticizer and a small fraction of seafood protein as a reinforcing additive. The film was cast from an acid-extracted seaweed slurry and dried under ambient ventilated conditions. Tensile tests were carried out on four specimens whose geometry was inspired by the ASTM D638 Type V standard, using a manual screw-driven apparatus instrumented with an analog force gauge (capacity 10 N, resolution 0.05 N) and a digital dial indicator (resolution 0.01 mm). After removing the zero-load take-up artifacts observed at the beginning of each test, the mean ultimate tensile strength was found to be 2.67(76) MPa, the mean strain at break 1.01 0.17, and the mean Young’s modulus, determined from a linear regression on the initial elastic region, was 1.97(62) MPa. Optical microscopy revealed millimetre-scale residual porosities and an inhomogeneous through- thickness distribution of unfiltered fibers, which are identified as the primary mechanical weak points of the current formulation. Comparison with the existing literature on Eucheuma cottonii and Gracilaria films suggests that the unusually low stiffness and high apparent strain at break observed here reflect both the high plasticizer- to-polymer ratio of the formulation and the limitations of the testing setup, which are discussed in detail.
Keywords: bioplastic; Eucheuma cottonii; alginate; sorbitol; seafood protein; tensile testing.
Keywords: bioplastic; Eucheuma cottonii; alginate; sorbitol; seafood protein; tensile testing.
Abstract
Empowering Retail Decision-Making: Design and Implementation of an Interactive Visual Analytics Dashboard Using Power BI
Esther Rassou, Mr. T. Amalraj Victoire, Mr. R. Ramakrishnan
DOI: 10.17148/IARJSET.2026.13570
Abstract: This paper presents the development of an "Executive Retail Sales Overview Dashboard," an interactive Business Intelligence (BI) solution designed using Microsoft Power BI Desktop. The primary objective is to empower retail executives and analysts with a comprehensive, unified visualization of transactional data spanning a four-year period (2015–2018). The system's architecture is founded on an optimized star-schema data model, connecting a central sales fact table with multiple dimension tables for customers, products, and temporal data. To extract deep analytical insights, thirteen custom Data Analysis Expressions (DAX) metrics were engineered, enabling advanced time- intelligence tracking such as Year-over-Year (YoY) growth and Year-to-Date (YTD) cumulative sales. The resulting dashboard comprises multiple interactive report pages that allow users to dynamically cross-filter data by region, category, and timeframes. By replacing conventional static reports with this self-service visual analytics tool, the project significantly enhances data-driven decision-making and strategic planning in retail management.
Keywords: Business Intelligence, Microsoft Power BI, Star Schema, Data Analysis Expressions (DAX), Retail Analytics, Interactive Visualizations.
Keywords: Business Intelligence, Microsoft Power BI, Star Schema, Data Analysis Expressions (DAX), Retail Analytics, Interactive Visualizations.
Abstract
An Integrated Machine Learning and Blockchain Framework for E-Waste Forecasting and Traceability
Jaswanth Raj J, Kevin Dhyanesh V R, Krishna P, Dr. Maniraj S P
DOI: 10.17148/IARJSET.2026.13571
Abstract: One gadget after another piles up worldwide, piling pressure on nature’s limits - trash from old phones and laptops might hit 80 million tons by 2030. Behind the scenes, today’s recycling networks struggle: they’re run by too few hands, hard to track fully, open to cheating or altered logs. Tackling these two issues at once, a new system links smart forecasts with tamper-proof digital records. Numbers pulled from past trends between 2010 and 2023 feed into a math model; things like people count, money per person, online access help guess future waste amounts. From those patterns, predictions stretch ahead - from 2026 through 2030 - showing where trash tides may rise. One way to track old electronics uses Python, building a chain of records locked with SHA-256 math plus agreement through computing effort. Instead of paper logs, it runs on a live web interface made with Streamlit, storing data in MySQL. Tests show predictions stay close to real outcomes - over 96 percent match by one measure. This setup links smart forecasts to verified tracking, fitting global targets for responsible tech, cleaner production, and climate action.
Keywords: E-Waste, machine learning, blockchain, linear regression, SHA-256, proof of work, forecasting, traceability, streamlit, sustainability.
Keywords: E-Waste, machine learning, blockchain, linear regression, SHA-256, proof of work, forecasting, traceability, streamlit, sustainability.
Abstract
FORMULATION, ANALYSES, AND ACCEPTABILITY OF TELESCOPE SNAIL AND SQUASH SKINLESS LONGGANISA
MARIE JOY D. MARTINEZ
DOI: 10.17148/IARJSET.2026.13572
Abstract: This study developed and evaluated a value-added skinless longganisa using telescope snail (Telescopium telescopium) meat and squash (Cucurbita maxima) pulp as sustainable alternatives to conventional pork. The study aimed to determine the sensory qualities, general acceptability, shelf life, microbial safety, and nutritional composition of the formulated product. An experimental-developmental research design was employed using three formulations: Treatment A (75g telescope snail meat and 25g squash), Treatment B (50g telescope snail meat and 50g squash), and Treatment C (25g telescope snail meat and 75g squash). Sensory evaluation was conducted among 10 semi-trained evaluators and 100 consumers using a 9-point hedonic scale. Data gathered were statistically analyzed using Analysis of Variance (ANOVA) and Kruskal-Wallis test. Results revealed that Treatment B obtained the highest ratings in appearance, aroma, taste, texture, and overall acceptability. Statistical findings showed no significant differences in appearance, aroma, and texture, while significant differences were observed in taste and general acceptability among treatments. Shelf-life evaluation revealed that the product remained acceptable for 30 days under chilling conditions, while spoilage was observed on the 35th day. Microbial analysis confirmed the absence of Salmonella and acceptable microbial counts based on Philippine standards. Proximate analysis revealed that the product contained 10.93% fat, 9.34% protein, and a low caloric value of 31 kcal. The study concluded that telescope snail and squash can be utilized as nutritious, safe, sustainable, and acceptable ingredients in the production of skinless longganisa.
Keywords: Telescope Snail, Squash, Skinless Longganisa, Alternative Protein, Food Innovation, Sensory Evaluation.
Keywords: Telescope Snail, Squash, Skinless Longganisa, Alternative Protein, Food Innovation, Sensory Evaluation.
Abstract
Formulation and Acceptability of Philippine Catmon Soap Sheets
Maria Teresa Tartana Samillano
DOI: 10.17148/IARJSET.2026.13573
Abstract: Soap sheets represent an innovative advancement in personal hygiene products because they provide convenience, portability, and efficient cleansing. This experimental-developmental study developed and evaluated a portable soap sheet formulated with Philippine Catmon (Dillenia philippinensis) extract, a locally sourced natural ingredient for hygiene use. Specifically, the study determined the sensory qualities, general acceptability, and effectiveness of the soap sheets and tested whether significant differences existed among three treatments with varying concentrations of Catmon extract: Treatment A (50 mL), Treatment B (75 mL), and Treatment C (100 mL), while glycerin, clean cotton scent, and foam booster were held constant. The products were evaluated by 100 evaluators composed of cosmetology teachers, students, and consumers using a researcher-made five-point Likert evaluation sheet. Data were analyzed using descriptive means and Analysis of Variance (ANOVA) at the 0.05 level of significance. Results showed that all treatments had an appealing appearance, fine texture, and very foamy consistency, while Treatment C had the most pleasant scent and obtained the highest mean for general acceptability. All treatments were effective in providing smoothing and moisturizing effects, with Treatment C showing the best overall performance, particularly in moisturizing. Statistical results indicated no significant differences among treatments in sensory qualities and in the measured effectiveness attributes. The study concluded that Philippine Catmon extract is a viable ingredient in soap sheet formulation and that higher extract concentration can enhance product performance without compromising acceptability.
Keywords: Soap Sheets, Herbal Soap, Product Acceptability, Natural Products
Keywords: Soap Sheets, Herbal Soap, Product Acceptability, Natural Products
Abstract
FORMULATION, ANALYSES, AND ACCEPTABILITY OF JACKFRUIT BAOZI
JENN ROSE B. DEL MENDO
DOI: 10.17148/IARJSET.2026.13574
Keywords: Jackfruit, Baozi, Seed Flour, Functional Healthy Food, Plant-Based Snacks
Abstract
FORMULATION, ANALYSES, AND ACCEPTABILITY OF SWEET POTATO AND BELL PEPPER SHORTBREAD COOKIES
May B. Develos
DOI: 10.17148/IARJSET.2026.13575
Abstract: This study responds to the growing demand for sustainable, health-oriented snacks by developing a functional shortbread cookie enriched with locally sourced sweet potato and bell pepper. Its objective was to identify the best formulation through sensory evaluation, general acceptability, shelf-life testing, microbial analysis, and proximate analysis, thereby transforming indigenous crops into a market-ready product that supports public health. An experimental-developmental design using a Completely Randomized Design (CRD) was employed, with three formulations containing 100 g of sweet potato and varying amounts of bell pepper (25 g, 50 g, and 75 g). Sensory evaluation by 10 semi-trained panelists and acceptability testing by 100 consumers using the 9-point Hedonic Scale revealed high ratings across all treatments, with Treatment A achieving the highest mean score. Statistical analysis using ANOVA and Kruskal-Wallis Test confirmed no significant differences in sensory attributes and overall acceptability among the three treatments. Shelf-life testing showed stability for 3–4 days at room temperature, while refrigeration extended viability to at least six weeks. Proximate analysis confirmed a balanced nutritional profile, and microbial testing validated product safety and the effectiveness of hygiene protocols. Overall, the study demonstrates that incorporating sweet potato and bell pepper into shortbread cookies is a scientifically sound approach to creating a nutrient-dense, marketable snack.
Keywords: Functional Food, Shortbread Cookie, Sweet Potato, and Bell Pepper.
Keywords: Functional Food, Shortbread Cookie, Sweet Potato, and Bell Pepper.
Abstract
Financial Literacy and Investment Behaviour: A Comparative Study among MBA Students and Salaried Professionals
Ms. R. Priyanka, Ms. R. Subiksha
DOI: 10.17148/IARJSET.2026.13576
Abstract: This comparative descriptive study investigates how financial literacy influences investment behaviour among MBA students (n = 75) and salaried employees (n = 75) in Nagapattinam. Primary data were collected using a structured questionnaire (5-point Likert scale) and analyzed through regression, chi-square, and independent samples t-tests. Instrument reliability was confirmed (Cronbach’s α = 0.859). Regression analysis shows that financial literacy significantly predicts investment behaviour (R² = 0.376, β = 0.597, p < .001), explaining 37.6% of variance. Chi-square results indicate no significant association between respondent category and overall risk tolerance (χ²(4) = 2.782, p = .595). Independent t-tests revealed some item-level differences, but no consistent overall advantage between MBA students and salaried employees. After Holm–Bonferroni adjustment, significant differences remained in budgeting knowledge, investment frequency, reliance on professional advice, and portfolio rebalancing, with small-to-moderate effect sizes. The findings highlight implications for MBA curricula and workplace financial education programs, recommending targeted practical training to help convert financial awareness into effective investment action.
Keywords: Investment behaviour, Digital financial literacy, Risk tolerance, Comparative study
Keywords: Investment behaviour, Digital financial literacy, Risk tolerance, Comparative study
Abstract
Dietary Habits, Work-Life Balance, and Productivity of Higher Education Institutions (HEIs) Employees in Northern Panay: Implications for Wellness Initiatives
Meriam T. Fernandez
DOI: 10.17148/IARJSET.2026.13577
Abstract: Dietary habits and work–life balance are critical determinants of employee productivity, directly impacting the operational efficiency and academic excellence of higher education institutions. This study examined the dietary habits, work–life balance, and productivity of faculty members and non-teaching personnel in Higher Education Institutions (HEIs) in Northern Panay to identify the key drivers of workplace performance. Specifically, it determined the level of dietary habits in terms of meal patterns, food choices, and nutritional behaviors; the level of work–life balance in terms of time management, perceived work-related stress, and personal life management; and the resulting level of employee productivity measured through work efficiency, task effectiveness, and quality output.
The study also investigated the relationship among these variables and identified which factors significantly predict high- level productivity. Employing a mixed-methods sequential explanatory approach, data were gathered from a random sample of 200 faculty and non-teaching personnel in a total population of 415 across selected HEIs in Northern Panay using a validated adopted and modified questionnaire. Quantitative data were analyzed using descriptive and inferential statistics, while qualitative insights were interpreted through direct quotation analysis.
The findings revealed that while dietary habits were generally good and work–life balance was high, employee productivity reached a very high level, suggesting a strong baseline for institutional output. Statistical analysis confirmed significant relationships between these variables, demonstrating that healthier lifestyle choices and balanced personal lives directly manifest as superior workplace productivity. Notably, work–life balance emerged as the primary predictor of productivity, highlighting that an employee's ability to manage professional responsibilities alongside personal well- being is the most influential factor in achieving high-quality output. Based on these findings, the study proposes comprehensive health and wellness initiatives specifically designed to sustain and enhance employee productivity through improved nutritional practices, mental wellness, and engagement strategies.
Keywords: Dietary habits, work–life balance, productivity, higher education institution
The study also investigated the relationship among these variables and identified which factors significantly predict high- level productivity. Employing a mixed-methods sequential explanatory approach, data were gathered from a random sample of 200 faculty and non-teaching personnel in a total population of 415 across selected HEIs in Northern Panay using a validated adopted and modified questionnaire. Quantitative data were analyzed using descriptive and inferential statistics, while qualitative insights were interpreted through direct quotation analysis.
The findings revealed that while dietary habits were generally good and work–life balance was high, employee productivity reached a very high level, suggesting a strong baseline for institutional output. Statistical analysis confirmed significant relationships between these variables, demonstrating that healthier lifestyle choices and balanced personal lives directly manifest as superior workplace productivity. Notably, work–life balance emerged as the primary predictor of productivity, highlighting that an employee's ability to manage professional responsibilities alongside personal well- being is the most influential factor in achieving high-quality output. Based on these findings, the study proposes comprehensive health and wellness initiatives specifically designed to sustain and enhance employee productivity through improved nutritional practices, mental wellness, and engagement strategies.
Keywords: Dietary habits, work–life balance, productivity, higher education institution
Abstract
Elevating the Classic Pie Crust with Coconut Flour, Jute, and Citrus Infusions
Miami Rose R. Tabernilla
DOI: 10.17148/IARJSET.2026.13578
Keywords: Pie Crust, Coconut Flour, , Lemon, Food Product Development.
Abstract
The Abaca Fiber Industry: Income Generation, Innovations, Challenges, and Future Prospects
Rhyme Ventura Apolinario
DOI: 10.17148/IARJSET.2026.13579
Abstract: The abaca fiber industry in the municipalities of Madalag, Libacao, and Balete plays a vital role in sustaining rural livelihoods; however, farmers continue to face income instability, limited innovation, and production challenges affecting long-term sustainability. This study explored the income generation experiences, farming innovations, challenges, and future prospects of abaca farmers in order to generate insights for policy and program interventions that may strengthen the industry. Using a qualitative phenomenological design grounded in constructivist epistemology, the study conducted in-depth interviews with 20 purposively selected participants composed of 16 abaca farmers and 4 key informants from the Philippine Fiber Industry Development Authority (PhilFIDA), LGU, cooperative, and local enterprise sectors. Data were analyzed through thematic analysis with triangulation to ensure credibility and identify patterns across income practices, innovations, challenges, and perceived industry prospects.
Findings revealed four major areas with corresponding themes. In terms of income generation, the themes identified were: (1) abaca stripping as the primary source of income, (2) diversified and supplementary income strategies, and (3) income dependency on labor, time, and market conditions. Farmers primarily depended on fiber stripping as their main livelihood source but supplemented their earnings through paid labor, copra production, carpentry, rice farming, and other agricultural activities due to unstable income and delayed returns from abaca production. Regarding innovations in farming, processing, and marketing, the themes included: (1) persistence of traditional farming and processing practices, (2) selective adoption of agricultural inputs and basic improvements, and (3) limited market and value-adding innovations in production and selling. Farmers largely maintained traditional cultivation and fiber extraction practices while selectively adopting fertilizers, improved abaca varieties, and training-based techniques, although innovation remained constrained by limited capital, inadequate access to technology, and weak institutional support. In relation to challenges experienced by abaca farmers, the themes identified were: (1) market and infrastructure limitations, (2) environmental and biological challenges, and (3) financial constraints and limited resources. Farmers experienced low and unstable market prices, trader dominance, poor transportation systems, pest infestations and diseases, prolonged rainy seasons, climate-related damages, labor shortages, and insufficient financial resources, all of which reduced productivity and income stability. Finally, in terms of future prospects, the themes identified were: (1) optimism for continued production with strong dependence on government support, (2) threat of disease, aging farmers, and declining youth participation, and (3) market instability and price-driven uncertainty of industry sustainability. Despite these challenges, farmers expressed cautious optimism toward the future of the abaca industry through stronger government assistance, cooperative development, market support, and skills training; however, concerns regarding disease outbreaks, limited youth engagement, and unstable market systems continue to threaten the long-term sustainability of the industry.
Keywords: abaca fiber industry, farming innovations, income generation, rural livelihoods,
Findings revealed four major areas with corresponding themes. In terms of income generation, the themes identified were: (1) abaca stripping as the primary source of income, (2) diversified and supplementary income strategies, and (3) income dependency on labor, time, and market conditions. Farmers primarily depended on fiber stripping as their main livelihood source but supplemented their earnings through paid labor, copra production, carpentry, rice farming, and other agricultural activities due to unstable income and delayed returns from abaca production. Regarding innovations in farming, processing, and marketing, the themes included: (1) persistence of traditional farming and processing practices, (2) selective adoption of agricultural inputs and basic improvements, and (3) limited market and value-adding innovations in production and selling. Farmers largely maintained traditional cultivation and fiber extraction practices while selectively adopting fertilizers, improved abaca varieties, and training-based techniques, although innovation remained constrained by limited capital, inadequate access to technology, and weak institutional support. In relation to challenges experienced by abaca farmers, the themes identified were: (1) market and infrastructure limitations, (2) environmental and biological challenges, and (3) financial constraints and limited resources. Farmers experienced low and unstable market prices, trader dominance, poor transportation systems, pest infestations and diseases, prolonged rainy seasons, climate-related damages, labor shortages, and insufficient financial resources, all of which reduced productivity and income stability. Finally, in terms of future prospects, the themes identified were: (1) optimism for continued production with strong dependence on government support, (2) threat of disease, aging farmers, and declining youth participation, and (3) market instability and price-driven uncertainty of industry sustainability. Despite these challenges, farmers expressed cautious optimism toward the future of the abaca industry through stronger government assistance, cooperative development, market support, and skills training; however, concerns regarding disease outbreaks, limited youth engagement, and unstable market systems continue to threaten the long-term sustainability of the industry.
Keywords: abaca fiber industry, farming innovations, income generation, rural livelihoods,
Abstract
CROP DISEASE DETECTION USING DEEP LEARNING
Seema, Sujayeendra Rao, Ananth G S
DOI: 10.17148/IARJSET.2026.13580
Abstract: This project focuses on the development of a web-based system for detecting crop diseases using deep learning techniques. The system uses a Convolutional Neural Network based on the MobileNetV2 architecture trained on the PlantVillage dataset to classify healthy and diseased crop leaves. The trained model is integrated into a Flask-based web application that allows users to upload leaf images and obtain disease predictions in real time, along with confidence scores and precautionary and treatment-related information. The lightweight nature of the selected model ensures fast prediction time while maintaining reliable classification performance. By combining deep learning with web technologies, the proposed system offers an accessible and cost-effective solution for crop disease identification, reducing dependence on manual inspection and supporting timely disease management to improve crop productivity.
Keywords: Crop Disease Detection, Deep Learning, Convolutional Neural Network, MobileNetV2, Flask, PlantVillage Dataset, Image Classification, Transfer Learning.
Keywords: Crop Disease Detection, Deep Learning, Convolutional Neural Network, MobileNetV2, Flask, PlantVillage Dataset, Image Classification, Transfer Learning.
Abstract
EMPOWER HER: AN AI-DRIVEN PLATFORM FOR WOMEN SAFETY, LEGAL ASSISTANCE, AND EMERGENCY SUPPORT
Smt. K S Sukrutha, Ms. Ankitha S
DOI: 10.17148/IARJSET.2026.13581
Abstract: Women often face challenges related to safety, legal rights, and emergencies, but they frequently hesitate to seek help due to social stigma, privacy concerns, and limited access to support systems. Traditional methods for obtaining legal or police assistance typically require physical visits and lengthy processes, which can delay timely intervention. This paper presents "Empower Her," an intelligent web-based platform that gives women secure, confidential, and direct access to legal assistance and emergency support services. The system connects women with verified legal advocates for guidance and case handling and enables emergency police assistance through an AI-driven urgency detection system. AI techniques analyse emergency requests to determine urgency levels and prioritize critical cases. The platform is built using Python, Django, MySQL, HTML5, CSS3, and Bootstrap, ensuring security, scalability, and usability. The proposed system improves accessibility, protects privacy, and promotes faster responses, creating a reliable digital environment that empowers women to seek help confidently and safely.
Keywords: Women Safety, Legal Assistance, Artificial Intelligence, Emergency Support, Emotion Analysis, Django Framework, Web Platform.
Keywords: Women Safety, Legal Assistance, Artificial Intelligence, Emergency Support, Emotion Analysis, Django Framework, Web Platform.
Abstract
Formulation of Blended Herb Spices
Lizlee Veladiez-Fuentes
DOI: 10.17148/IARJSET.2026.13582
Abstract: Blended spices derived from locally available plants tend to achieve higher sensory acceptability compared to single-ingredient spices due to their enhanced flavor complexity and balanced sensory profile. This study aimed to develop and evaluate the acceptability of a blended herb spice composed of dried guava, lemongrass, spring onion, bay leaf, and oregano. An experimental-developmental research design utilizing a Completely Randomized Design (CRD) was employed, consisting of three formulations: Treatment A (25g guava, 75g lemongrass), Treatment B (50g guava, 50g lemongrass), and Treatment C (75g guava, 25g lemongrass), with constant amounts of the remaining ingredients such as dried spring onion, bay leaf and oregano. Sensory evaluation was conducted by 10 semi-trained food technology professors across three trials, followed by acceptability testing involving 100 evaluators using a 9-Point Hedonic Scale. Data were analyzed using mean and Analysis of Variance (ANOVA). Results indicated that all formulations were generally acceptable in terms of appearance, aroma, texture, and overall acceptability. Treatment B (50g guava, 50g lemongrass) emerged as the most preferred formulation, particularly in appearance and aroma, followed by Treatment A (25g guava, 75g lemongrass), while Treatment C (75g guava, 25g lemongrass) remained acceptable across all attributes. Statistical analysis revealed significant differences in appearance and aroma in favor of Treatment B, whereas texture showed no significant variation among treatments. When applied to grilled chicken, all formulations enhanced sensory qualities, with Treatment B (50g guava, 50g lemongrass) yielding the highest acceptability due to its superior aroma, appearance, and taste. Treatment A also showed favorable results, while Treatment C (75g guava, 25g lemongrass), though less preferred, maintained an acceptable flavor profile. The blended herb spices demonstrated good storage stability, remaining unchanged in color, texture, and odor under both room and chilling conditions for up to 30 days. Microbial analysis further confirmed that the product met food safety standards, showing low aerobic plate count and absence of coliform bacteria, thereby indicating its safety for consumption and compliance with FDA guidelines. Overall, the developed herb spice blends demonstrate strong potential as sustainable, plant-based alternatives for commercial application.
Keywords: Blended Herb Spices, Consumer Acceptability, Dried Guava Leaves, Product Development
Keywords: Blended Herb Spices, Consumer Acceptability, Dried Guava Leaves, Product Development
Abstract
Smart Painting Display Board with Adjustable Illumination
Marlo Victor A. Bacuna
DOI: 10.17148/IARJSET.2026.13583
Abstract: Common exhibits use painting stands, display panels, and boards to hold artworks, but lack proper illumination, which is vital for enhancing the visual impact of the displayed artworks. This study used a developmental research method. A researcher-made evaluation sheet, validated by field and academic experts, was used to assess the display board. The main objective of the study was to design and develop a smart painting display board with adjustable illumination. The device could mount paintings ranging from small (16” × 20”) to large (36” × 48”) without displacement, maintaining a vertical upright position at a 90-degree angle. The illuminance coverage was adequate across all size categories, indicating that the painting surfaces were fully illuminated and rated “Good.” The average illuminance was 55.6 lux, which closely aligns with the 50 lux recommendation for highly light-sensitive materials. In addition, the system demonstrated responsive performance in both the color adjustment and on/off functions. It demonstrates excellent operational performance, with high stability in both stationary and moving positions. Overall, the device was rated “Very Acceptable” for usability, illumination quality, visual enhancement, and safety.
Keywords: smart painting display board, adjustable illumination, system responsiveness, operating performance.
Keywords: smart painting display board, adjustable illumination, system responsiveness, operating performance.
Abstract
Formulation, Analyses, and Acceptability of Crackers with Medicinal Leaves
Rameline Grace D. Borres
DOI: 10.17148/IARJSET.2026.13584
Abstract: Snacks provide energy between meals, especially during periods of intense work. Crackers are among the most popular snack foods enjoyed by people of all ages. More than just a convenient treat, they embody versatility, affordability, and innovation in food production. This experimental-developmental research aimed to develop and evaluate a healthier version of crackers fortified with medicinal leaves. Ten semi-trained panelists assessed the sensory qualities of the crackers in terms of appearance, aroma, taste, and texture, while 100 consumers evaluated the general acceptability using a 9-point Hedonic scale. The study employed a Completely Randomized Design (CRD), with data analyzed through mean and ANOVA. In addition, the research examined the microbial safety, shelf-life, and nutritional content of the best treatment. For sensory qualities, three (3) treatments (Treatment A- 10 grams, Treatment B- 15 grams, Treatment C- 20 grams) of each crackers incorporating different medicinal leaves were tested. All treatments were rated from very much to extremely appealing, pleasant, delicious, and crisp. Treatment A of crackers with Alagaw Leaves was the most preferred, also in crackers with Insulin and Tagpo tagpo leaves and showed no significant differences. In terms of general acceptability, crackers with Insulin leaves (Treatment A) were the most preferred among all products. While no significant differences were observed in sensory qualities, overall acceptability showed a significant difference in favor of crackers with Insulin leaves, identifying them as the best product. Microbial analysis revealed that the microbial counts of the best product were within the acceptable limits set by the Department of Science and Technology (DOST) criteria for most parameters. Proximate analysis further confirmed that the product was energy-dense. Regarding shelf life, all treatments remained appealing, crisp, and pleasant in aroma for the first 21 days. Overall, the study demonstrated that crackers fortified with medicinal leaves are viable, nutritious, and sustainable snack foods. They offer a healthier option for consumers while providing economic benefits to local farmers and bakery vendors.
Keywords: Medicinal Leaves, Sensory Evaluation, Shelf Life, Microbial and Proximate Analysis
Keywords: Medicinal Leaves, Sensory Evaluation, Shelf Life, Microbial and Proximate Analysis
Abstract
ICT Competence and Instructional Delivery of Technology and Livelihood Education Teachers
Ailyn K. Elegino
DOI: 10.17148/IARJSET.2026.13585
Abstract: This study determined the relationship between ICT competence and instructional delivery among Junior High School Technology and Livelihood Education (TLE) teachers in the Department of Education Capiz Division during School Year 2025-2026. Anchored on the Technological Pedagogical Content Knowledge (TPACK) framework and Rogers' Diffusion of Innovations Theory, the study examined teachers' technological competence, pedagogical ICT competence, content-specific ICT competence, and attitude toward ICT, as well as their instructional delivery in terms of lesson planning, learning activities, assessment practices, and instructional innovation. The study employed a descriptive-correlational research design. The respondents were 100 Junior High School TLE teachers selected through stratified random sampling from public secondary schools in the DepEd Capiz Division. Data were gathered using a researcher-made and validated questionnaire, which obtained a Cronbach's alpha coefficient of 0.851 during pilot testing. Frequency, percentage, weighted mean, standard deviation, Pearson Product-Moment Correlation, and multiple regression analysis were used in treating the data. Findings showed that the TLE teachers had a high level of ICT competence, with a general mean of 4.20. Content-specific ICT competence and attitude toward ICT were rated very high, while technological competence and pedagogical ICT competence were rated high. Instructional delivery was rated good, with a general mean of 3.91 across lesson planning, learning activities, assessment practices, and instructional innovation. A significant moderate positive relationship was found between ICT competence and instructional delivery (r = 0.613, p = 0.000). Regression results further revealed that pedagogical ICT competence was the only significant predictor of instructional delivery (Beta = 0.735, p = 0.000), while technological competence, content-specific ICT competence, and attitude toward ICT were not significant predictors. The model explained 46.5 percent of the variance in instructional delivery. The study concludes that ICT competence is meaningfully associated with instructional delivery, but the pedagogical use of ICT plays the most decisive role in improving TLE teaching. It is recommended that professional development programs for TLE teachers prioritize pedagogical ICT integration, technology-supported lesson planning, assessment, and instructional innovation.
Keywords: ICT Competence, Instructional Delivery, TLE Teachers, Technology Integration, Pedagogical ICT Competence, Descriptive-Correlational Study.
Keywords: ICT Competence, Instructional Delivery, TLE Teachers, Technology Integration, Pedagogical ICT Competence, Descriptive-Correlational Study.
Abstract
Blockchain-based Federated Learning with SMPC Model Verification Against Poisoning Attack for Healthcare Systems
Ms. Ankitha S, Shwetha M D, Nisarga, Rakshitha B k, Chandana S
DOI: 10.17148/IARJSET.2026.13586
Abstract: Healthcare systems generate large amounts of sensitive patient information through hospitals, labs, wearable devices, diagnostic centres, and electronic medical records. Traditional centralized machine learning approaches require sharing raw healthcare data to train intelligent models. This raises significant concerns about privacy, security, and following regulations. Federated Learning (FL) addresses these issues by allowing multiple healthcare institutions to work together to train machine learning models without transferring raw patient information. However, federated learning environments are still at risk of poisoning attacks, where malicious actors submit altered model updates that harm model performance and reliability. This research presents a secure framework called Blockchain-based Federated Learning with SMPC Model Verification Against Poisoning Attack for Healthcare Systems. The framework combines Federated Learning, Secure Multi-Party Computation (SMPC)-based verification, and Blockchain technology to create a reliable collaborative platform for healthcare intelligence. Healthcare institutions train models locally and share only model parameters instead of patient records. Local model updates undergo SMPC-based verification to flag suspicious contributions before aggregation. Verified updates are combined using federated learning techniques, while malicious updates are discarded. Blockchain technology maintains unchangeable logs to enhance transparency, traceability, and accountability. The proposed system is implemented as a Flask-based web application supported by an SQLite database. This database manages healthcare participants, federated rounds, verification records, global model information, and blockchain event history. The framework strengthens privacy preservation, boosts security against poisoning attacks, and builds trust in distributed healthcare artificial intelligence systems. Experimental implementation shows that combining federated learning with blockchain and verification methods offers a dependable and scalable solution for secure healthcare collaboration.
Keywords: Blockchain Technology, Federated Learning, Healthcare Artificial Intelligence, Secure Multi-Party Computation (SMPC), Poisoning Attack Detection, Privacy Preservation, Distributed Machine Learning, Healthcare Security, Model Verification, Blockchain Auditability.
Keywords: Blockchain Technology, Federated Learning, Healthcare Artificial Intelligence, Secure Multi-Party Computation (SMPC), Poisoning Attack Detection, Privacy Preservation, Distributed Machine Learning, Healthcare Security, Model Verification, Blockchain Auditability.
Abstract
Characterization of Blended Birds of Paradise and Bamban Plant Fibers as Alternative Textile Material
Rey C. Bical
DOI: 10.17148/IARJSET.2026.13587
Abstract: The growing environmental impact of synthetic textiles has intensified the search for sustainable, plant-based alternatives. This study aimed to develop a blended textile from birds of paradise (Strelitzia reginae) and bamban plant (Donax canniformis) fibers and to determine its mechanical properties (tensile strength and breaking elongation), chemical properties (burning rate, washability, and water absorption), and overall acceptability as alternative textile material in terms of hand feel, irregularity of the ribs, color shade, and presence of plugs. A developmental research design was employed to extract, process, blend, and test the fibers under controlled conditions. The statistical tool used was the mean, which determined the acceptability of the blended birds of Paradise (Strelitzia reginae) and bamban plant (Donax canniformis) textile. A total of 25 experts evaluated the product, consisting of garment shop owners, garment teachers, fashion designers, local dressmakers, and end users. An evaluation sheet was utilized to assess the blended textile. The textile underwent a series of tests to determine its mechanical and chemical properties. Findings revealed that the individual fibers of birds of paradise (Strelitzia reginae) and bamban plant (Donax canniformis) were verbally interpreted as durable, while the blended fibers exhibited improved overall strength when combined and were interpreted as very durable, demonstrating enhanced load-bearing performance through fiber blending. In terms of breaking elongation, the individual fibers were verbally interpreted as least elastic, while the blended fibers were interpreted as less elastic, showing improved flexibility compared to the individual fibers, although still within the less elastic range. The burning rate test showed that the blended textile shrank easily when exposed to flame, emitted a pungent urine-like odor, and left a black, hollow, and gritty bead residue. It was interpreted as highly combustible, comparable to cotton and jute. Washability testing showed no noticeable changes after soaking in detergent for thirty minutes, indicating good color stability. The water absorption test revealed that the blended textile has low absorbency, which can be advantageous for applications requiring quick drying and resistance to moisture. Overall, the textile from blended birds of paradise (Strelitzia reginae) and bamban plant (Donax canniformis) textile was rated as “Very Acceptable” in terms of hand feel, irregularity of the ribs, color shade, and presence of plugs based on its level of acceptability.
Keywords: Characterization, Blended Birds of Paradise & Bamban Plant, Alternative Textile Material
Keywords: Characterization, Blended Birds of Paradise & Bamban Plant, Alternative Textile Material
Abstract
Formulation and Acceptability of Moringa - Jamaican Berry Bar Foot Scrub
Aileen B. Atalisay
DOI: 10.17148/IARJSET.2026.13588
Abstract: In the pursuit of innovative and natural treatments, this research explores the synergy between Moringa- Jamaican berry extracts in a solid scrub format. Seeking to balance exfoliation, moisture and smoothness with nourishment. This study aimed to evaluate the acceptability and effectiveness of a moringa seed and Jamaican berry bar foot scrub, focusing on sensory qualities and functional performance. The experimental-developmental research design with a Complete Randomized Design (CRD) was employed, using three treatments that varied in the proportion of the primary ingredients: Treatment A (25g moringa seeds: 75g Jamaican berry), Treatment B (50g:50g), and Treatment C (75g moringa seeds: 25g Jamaican berry), while moringa extract, glycerin, and scent were kept constant. A total of 25 evaluators, including cosmetology teachers, beauticians, students, and consumers, assessed the products using a five- point Likert scale on sensory qualities in terms of appearance, scent, texture and functional performance in terms of exfoliating, moisturizing, and smoothing effects. The findings revealed that Treatment C was consistently rated superior across all sensory and functional attributes. In terms of appearance, it was described as “very attractive”; its scent was “very pleasant,” and its texture was “very rough,” providing a noticeably effective exfoliating feel. Treatment C also demonstrated the highest level of effectiveness, being “very effective” in exfoliating, moisturizing, and smoothing the feet, while Treatments A and B were rated as “effective.” Statistical analyses confirmed significant differences among the treatments in sensory qualities using the Kruskal–Wallis test and in exfoliating and moisturizing effects through one- way ANOVA. Post hoc evaluation indicated that the higher moringa seed content in Treatment C contributed to enhanced abrasive action, superior hydration, and overall user satisfaction. Smoothing effect and general acceptability, although descriptively higher in Treatment C, did not show statistically significant differences. Overall, the study concluded that Treatment C (moringa-dominant formulation) emerged as the best-performing bar foot scrub, offering a synergistic combination of sensory appeal and functional efficacy. The results highlight the importance of optimizing ingredient proportions in plant-based foot care products to maximize exfoliating and moisturizing benefits without compromising user satisfaction. These findings provide practical insights for cosmetic formulation, product development, and commercialization of natural foot care solutions.
Keywords: Moringa seed, foot scrub, bar soap, cosmetic formulation,
Keywords: Moringa seed, foot scrub, bar soap, cosmetic formulation,
Abstract
FORMULATION AND ACCEPTABILITY OF SHINY BUSH-BETEL LEAF HAND SOAP STRIP
Rose-Ann Daradar Buco
DOI: 10.17148/IARJSET.2026.13589
Abstract: The shiny bush-betel leaf hand soap strip is an innovative plant-based hygiene product designed to provide a portable, convenient, and effective alternative to traditional hand soaps. This study addressed the increasing demand for natural and travel-friendly hygiene products by evaluating the acceptability and effectiveness of shiny bush-betel leaf extract hand soap strips in terms of sensory qualities and functional performance. An experimental-development research design using a Complete Randomized Design (CRD) was employed with three treatments (3) that differed in the proportion of the main ingredients: Treatment A (25 ml shiny bush and 75 ml betel leaf), Treatment B (50 ml shiny bush and 50 ml betel leaf), and Treatment C (75 ml shiny bush and 25 ml betel leaf). The glycerin soap base, foam booster, glycerin, and scent were kept constant across all treatments. A total of 25 evaluators, including cosmetology teachers, beauticians, students, and consumers, assessed the products using a 5-point Likert scale based on appearance, foaming ability, scent, texture and functional performance in terms of moisturizing and smoothing effect.
Findings revealed that Treatment C was consistently rated superior across all sensory and functional attributes. In terms of appearance, it was described as very attractive; its foaming ability was very foamy; its scent was very pleasant and its texture was very fine, providing a gentle effect. Treatment C obtained the highest mean scores, being rated as very hydrating in terms of moisturizing effect and ‘smooth’ in smoothing effect. Treatments A and B were likewise rated as smooth in smoothing effect, while their moisturizing performance ranged from moderately hydrating to hydrating. Moreover, general acceptability was rated as effective across all treatments. Statistical analyses confirmed significant differences among the treatments in sensory qualities using the Kruskal–Wallis test and in smoothing and moisturizing effects through one-way ANOVA. Post hoc evaluation indicated that the higher shiny bush content in Treatment C contributed to improved texture, superior hydration, and overall user satisfaction, while the smoothing effect and general acceptability, although descriptively higher Treatment C did not show statistically significant differences. The study concluded that the shiny bush-dominant formulation was the most effective and acceptable treatment, highlighting the importance of proper ingredient proportioning in developing plant-based hand care products.
Keywords: Shiny Bush, Betel Leaf, Hand Soap, Strips, Plant-Based Hand Soap Strip
Findings revealed that Treatment C was consistently rated superior across all sensory and functional attributes. In terms of appearance, it was described as very attractive; its foaming ability was very foamy; its scent was very pleasant and its texture was very fine, providing a gentle effect. Treatment C obtained the highest mean scores, being rated as very hydrating in terms of moisturizing effect and ‘smooth’ in smoothing effect. Treatments A and B were likewise rated as smooth in smoothing effect, while their moisturizing performance ranged from moderately hydrating to hydrating. Moreover, general acceptability was rated as effective across all treatments. Statistical analyses confirmed significant differences among the treatments in sensory qualities using the Kruskal–Wallis test and in smoothing and moisturizing effects through one-way ANOVA. Post hoc evaluation indicated that the higher shiny bush content in Treatment C contributed to improved texture, superior hydration, and overall user satisfaction, while the smoothing effect and general acceptability, although descriptively higher Treatment C did not show statistically significant differences. The study concluded that the shiny bush-dominant formulation was the most effective and acceptable treatment, highlighting the importance of proper ingredient proportioning in developing plant-based hand care products.
Keywords: Shiny Bush, Betel Leaf, Hand Soap, Strips, Plant-Based Hand Soap Strip
Abstract
Characterization of Blended Areca Palm And Snake Plant Fiber as Textile Material
Renalyn L. Dela Cruz
DOI: 10.17148/IARJSET.2026.13590
Abstract: Growing interest in sustainable and eco-friendly materials has driven research into locally available plant fibers as viable alternatives to conventional textiles. This study aimed to develop and characterize an innovative textile material produced by blending fibers from Areca Palm (Areca catechu) and Snake Plant (Sansevieria trifasciata) fibers, with the aim of assessing its mechanical, chemical, and sensory properties. A developmental research design, the study followed a systematic process including fiber extraction, blending, handloom weaving, and comprehensive performance evaluation. Sensory assessment was conducted by 25 qualified evaluators, while laboratory tests were performed to measure physical and chemical characteristics. Mechanical testing results showed that the blended textile exhibited moderate elasticity, with a mean breaking elongation of 24.67%, classified as “less elastic”, while maintaining a high tensile strength of 44.75 N/mm², interpreted as “very durable”. Chemical assessments indicated a controlled burning rate of 0.072 in²/sec, high washability with no observable deformation after 30 minutes of soaking, and water absorption of 71%, classified as absorbent. Sensory evaluation, conducted by 25 expert evaluators, yielded very acceptable ratings for hand feel, rib irregularity, color shade, and plug presence, resulting in a “very acceptable” rating. These findings demonstrated that the blended textile successfully balances flexibility, strength, moisture management, flame resistance, and aesthetic appeal.
Keywords: Blended Textile, Areca Palm, Snake Plant, Mechanical Properties, Chemical Properties, Sustainable Fibers
Keywords: Blended Textile, Areca Palm, Snake Plant, Mechanical Properties, Chemical Properties, Sustainable Fibers
Abstract
ASPIRE AI: An AI-Powered Career Coach
Vishal A. Bagale, Yogesh B. Patil, Hitesh R. Wankhede, Charushila R. Deore, Pranjal S. Chaudhari, Prof. Manesh P. Patil
DOI: 10.17148/IARJSET.2026.13591
Abstract: In the rapidly evolving job market, students and job seekers face major challenges in selecting suitable career paths, identifying industry-required skills, and preparing effectively for recruitment processes. Traditional career counselling [1][2] methods are often manual, time-consuming, and unable to provide personalized guidance at scale. To overcome these limitations, this paper presents ASPIRE AI: An AI-Powered Career Coach, a smart web-based platform designed to provide personalized career recommendations, skill development roadmaps, resume generation, and mock interview practice using Artificial Intelligence and Natural Language Processing (NLP). The proposed system analyses user academic background, interests, and skill set to recommend suitable career domains and learning paths. It integrates modern full-stack technologies such as Next.js and React [7] for frontend development, Node.js for backend services, and PostgreSQL [9] for secure data storage. The platform uses the Google Gemini API [6] to generate intelligent career suggestions, interview feedback, and professional resume/cover letter content. User authentication and secure session management are implemented through Clerk [5], and the system is deployed on Vercel [11] for scalability and accessibility. Experimental evaluation demonstrates that ASPIRE AI provides real-time, adaptive guidance with improved user engagement, making it a reliable tool for bridging the gap between education and employment by enhancing career readiness and employability.
Keywords: AI Career Coach, Career Recommendation System, Google Gemini API [6], Mock Interview System, Resume Builder, Personalized Career Guidance, NLP, Next.js, Node.js, PostgreSQL [9], Web Application
Keywords: AI Career Coach, Career Recommendation System, Google Gemini API [6], Mock Interview System, Resume Builder, Personalized Career Guidance, NLP, Next.js, Node.js, PostgreSQL [9], Web Application
Abstract
A Study on Impact Of Tourism Development And Local Employment Opportunities
A. Sowarna lakshmi, P. Sabeena
DOI: 10.17148/IARJSET.2026.13592
Abstract: Tourism development plays an important role in improving economic growth and generating employment opportunities for local communities. The present study focuses on analyzing the impact of tourism development on local employment opportunities in selected tourism destinations such as Tharangambadi, Velankanni, Thanjavur, Karaikal, and Nagore. The study aims to identify the employment opportunities created through tourism activities, examine income improvement among local residents. The study is based on both primary and secondary data. Primary data were collected from 150 respondents through a structured questionnaire using simple random sampling technique. Statistical tools such as percentage analysis, correlation, ANOVA, Two-Way ANOVA, and MANOVA were used for data analysis and interpretation.
The findings reveal that tourism development significantly contributes to employment generation, business growth, income improvement, and enhancement of living standards among local residents. Tourism-related sectors such as hotels, transportation, restaurants, handicrafts, and local trade provide both direct and indirect employment opportunities. The study concludes that sustainable tourism planning and government support are essential for improving long-term employment opportunities and balanced regional development.
Keywords: Tourism Development, Employment Opportunities, Local Economy, Tourism Employment, Sustainable Tourism, Income Improvement, Living Standards, Tourism Activities, Economic Development, Local Communities.
The findings reveal that tourism development significantly contributes to employment generation, business growth, income improvement, and enhancement of living standards among local residents. Tourism-related sectors such as hotels, transportation, restaurants, handicrafts, and local trade provide both direct and indirect employment opportunities. The study concludes that sustainable tourism planning and government support are essential for improving long-term employment opportunities and balanced regional development.
Keywords: Tourism Development, Employment Opportunities, Local Economy, Tourism Employment, Sustainable Tourism, Income Improvement, Living Standards, Tourism Activities, Economic Development, Local Communities.
Abstract
Formulation, Analyses And Acceptability of Cassava Leaves- Root Crop Patties
EARLEEN MAY BILLONES ACANTO
DOI: 10.17148/IARJSET.2026.13593
Abstract: Cassava leaves and locally grown root crops are widely available in rural communities yet remain underutilized as primary food ingredients despite their nutritional potential. This study explored the development of plant-based patties combining cassava leaves with cassava tubers, sweet potato, and lesser yam to produce a nutritious, affordable, and accessible snack suitable for everyday consumption. An experimental–developmental research design was employed using three formulations per root crop based on varying proportions of cassava leaves to roots: Treatment A (75g cassava leaves:25g root crops), Treatment B (50g cassava leaves:50g root crops), and Treatment C (25g cassava leaves:75g root crops). Sensory qualities in terms of appearance, aroma, taste, and texture were evaluated using a 9-point hedonic scale by semi-trained panelists, while general acceptability was assessed by 100 randomly selected consumers.
Statistical analyses were applied to determine significant differences among treatments. Shelf life was observed under room and refrigerated conditions, and the best-performing formulations underwent microbial and proximate analyses at NPPC Analytical and Diagnostic Laboratory, Inc., Negros to verify safety and nutritional value. This study evaluated the sensory qualities, general acceptability, shelf life, and nutritional density of patties developed from cassava leaves and root crops. Sensory evaluation and ANOVA results revealed that the leaf-to-root ratio is the primary driver of consumer preference, significantly affecting taste while appearance and aroma remained stable. Treatment B (50:50) emerged as the most effective formulation for cassava tuber and lesser yam bases, consistently earning "Extremely Delicious" ratings. However, for sweet potato bases, Treatment C (25:75) was slightly preferred.
Overall, Sweet Potato (Product B) was identified as the superior base, achieving the highest general acceptability score and a "Liked Very Much" rating. Safety and stability tests indicated that the patties are highly perishable, with a room- temperature shelf life of only two hours. Refrigeration (4–6°C) extended quality for two days, though mold growth rendered products unsafe by day five. Laboratory analysis confirmed the product’s safety, with microbial counts well below FDA limits and a total absence of E. coli and Salmonella. Proximate analysis validated the patty as a high-protein, energy-dense supplement, containing 10.93g protein, 9.29g fat, and 0.96g carbohydrates per 100g (270 kcal). The study concludes that a balanced 75:25 formulation using a sweet potato base offers the best combination of nutrition and palatability, providing a viable plant-based protein source for food security initiatives and school feeding programs.
Keywords: Cassava Leaves, Root Crops, Cassava Tubers, Sweet Potato, Lesser Yam, Plant-Based Patties, Patties.
Statistical analyses were applied to determine significant differences among treatments. Shelf life was observed under room and refrigerated conditions, and the best-performing formulations underwent microbial and proximate analyses at NPPC Analytical and Diagnostic Laboratory, Inc., Negros to verify safety and nutritional value. This study evaluated the sensory qualities, general acceptability, shelf life, and nutritional density of patties developed from cassava leaves and root crops. Sensory evaluation and ANOVA results revealed that the leaf-to-root ratio is the primary driver of consumer preference, significantly affecting taste while appearance and aroma remained stable. Treatment B (50:50) emerged as the most effective formulation for cassava tuber and lesser yam bases, consistently earning "Extremely Delicious" ratings. However, for sweet potato bases, Treatment C (25:75) was slightly preferred.
Overall, Sweet Potato (Product B) was identified as the superior base, achieving the highest general acceptability score and a "Liked Very Much" rating. Safety and stability tests indicated that the patties are highly perishable, with a room- temperature shelf life of only two hours. Refrigeration (4–6°C) extended quality for two days, though mold growth rendered products unsafe by day five. Laboratory analysis confirmed the product’s safety, with microbial counts well below FDA limits and a total absence of E. coli and Salmonella. Proximate analysis validated the patty as a high-protein, energy-dense supplement, containing 10.93g protein, 9.29g fat, and 0.96g carbohydrates per 100g (270 kcal). The study concludes that a balanced 75:25 formulation using a sweet potato base offers the best combination of nutrition and palatability, providing a viable plant-based protein source for food security initiatives and school feeding programs.
Keywords: Cassava Leaves, Root Crops, Cassava Tubers, Sweet Potato, Lesser Yam, Plant-Based Patties, Patties.
Abstract
COGNITIVE CRIME ANALYSIS SYSTEM
Diana Princes Chandran Jeyasingh, Kushal M, Rahul Gowda A, Sharon Arnold, Tarun P
DOI: 10.17148/IARJSET.2026.13594
Abstract: The increasing rate of crime and the rapid growth of digital technologies have created a need for intelligent systems that can assist law enforcement agencies in crime analysis and prevention. Traditional methods of crime investigation mainly rely on manual record management and human observation, which are time-consuming and inefficient when handling large volumes of data. The proposed Cognitive Crime Analysis System utilizes Artificial Intelligence, Machine Learning, and Data Analytics to process and analyze crime-related information from multiple sources such as police records, surveillance systems, and public reports. The system identifies crime patterns, predicts potential crime hotspots, and detects suspicious activities in real time. In addition, visualization dashboards and heatmaps are provided for efficient monitoring and decision-making. The project demonstrates the practical application of AI-driven technologies for improving public safety, reducing manual effort, and supporting proactive policing.
Abstract
Utilization of Composite Board from Button Top Shells and Guest Tree Fiber
Sheryl P. Farinas
DOI: 10.17148/IARJSET.2026.13595
Abstract: The growing need for affordable, sustainable and environmentally friendly materials in construction and furniture has encouraged researcher to explore alternatives to traditional wood-based panels. The study aimed to determine the mechanical properties of the composite board in terms of flexural strength, compressive strength, and density; assess its acceptability based on appearance, texture, and firmness; evaluate its applicability for furniture and wall cladding; and test for significant differences among three treatment formulations. An experimental-developmental research design was employed, utilizing a Completely Randomized Design (CRD) with three treatment combinations varying in the proportion of button top shells and guest tree fiber while maintaining epoxy resin as a constant binder. Findings revealed that Treatment C (230g button top shells 20g guest tree fiber) generally exhibited the highest mechanical performance in terms of flexural and compressive strength, while Treatment B (220g button top shells 30g guest tree fiber) showed the most stable density results. In sensory evaluation, all treatments were rated acceptable, with Treatment A (210g button top shells 40g guest tree fiber) obtaining higher scores in appearance and Treatment C (230g button top shells 20g guest tree fiber) rated highest in firmness. In terms of applicability, all treatments were considered suitable for furniture and wall cladding applications. However, only appearance showed a significant difference among treatments, while texture, firmness, acceptability, and applicability showed no significant differences.
Keywords: Composite Board, Button Top Shells, Guest Tree Fiber, Natural Fiber Composite, Shell-Based Composite
Keywords: Composite Board, Button Top Shells, Guest Tree Fiber, Natural Fiber Composite, Shell-Based Composite
Abstract
Formulation, Analyses, and Acceptability of Batuan (Garcinia binucao) Syrup
Virgenia D. Villanueva
DOI: 10.17148/IARJSET.2026.13596
Abstract: This study investigated the potential of Batuan (Garcinia binucao) syrup as a value-added product derived from an indigenous fruit, focusing on its sensory qualities, general acceptability, shelf-life, microbiological safety, and physicochemical properties. The study employed an experimental-developmental research design using a Completely Randomized Design (CRD) with three syrup formulations as treatments. Sensory evaluation was conducted using a structured evaluation sheet administered to 10 semi-trained panelists, while 100 respondents participated in the assessment of general acceptability. Data were analyzed using descriptive statistics (mean) and one-way analysis of variance (ANOVA) to determine differences among treatments and applications, while laboratory analyses were conducted to assess microbial load, ash content, and moisture content. Results revealed that all syrup formulations obtained high sensory ratings in terms of appearance, aroma, taste, and consistency, with no significant differences observed among treatments, indicating comparable sensory quality. Similarly, no significant differences were found when the syrup was applied to waffles, pancakes, and French toast, demonstrating consistency and versatility across food applications. Although Treatment B (Wash Sugar) obtained the highest mean ratings in several attributes, the differences were not statistically significant, indicating that all formulations were generally acceptable. Shelf-life evaluation showed that syrup stored under chilled conditions remained stable for approximately two months, while room temperature storage resulted in mold growth after the fourth week. Microbiological analysis confirmed very low counts of E. coli and molds (<10 CFU/g), indicating that the product is safe for consumption. Physicochemical analysis revealed 0.06% ash and 12.26% moisture content, reflecting acceptable composition and stability for a fruit-based syrup. Overall, the findings suggest that Batuan syrup is a safe, acceptable, and promising value-added product with potential for commercialization and local food innovation.
Keywords: Batuan Syrup, Garcinia binucao, Indigenous Fruit, Value-Added Product, Food Safety, Fruit-Based Syrup
Keywords: Batuan Syrup, Garcinia binucao, Indigenous Fruit, Value-Added Product, Food Safety, Fruit-Based Syrup
Abstract
AI-Driven Crime Prediction Using Machine Learning and Flask
Mrs.V. Anusha, Mrs.S. Sirisha
DOI: 10.17148/IARJSET.2026.13597
Abstract: Crime analysis plays a vital role in ensuring public safety and effective law enforcement. The increasing rate of crimes makes it necessary to analyze large amounts of crime data efficiently. Traditional methods mainly depend on manual analysis of historical records, which are time-consuming and lack predictive capabilities. As a result, identifying crime patterns and trends becomes difficult. To overcome these limitations, a machine learning-based system is proposed. The system is developed using a Flask-based web application and processes crime datasets containing attributes such as crime type, location, and time. Data pre-processing techniques are applied to clean and prepare the data for analysis. Machine learning algorithms are then used to extract meaningful patterns from the data. Classification techniques help in predicting the type of crime, while clustering methods are used to identify crime-prone areas. The system also provides visualizations such as graphs and heatmaps, which make it easier to understand crime patterns and trends. By integrating data processing, prediction, and visualization into a single platform, the system improves decision-making and supports proactive crime prevention.
Keywords: Crime Analysis, Machine Learning, Crime Prediction, Flask, Data Pre-processing, Classification, Clustering, Data Visualization, HeatMaps
Keywords: Crime Analysis, Machine Learning, Crime Prediction, Flask, Data Pre-processing, Classification, Clustering, Data Visualization, HeatMaps
Abstract
Enhancing Students` Technical Competence in Sketching Using Sketchbook Application
MARIONEL D. BRUNA
DOI: 10.17148/IARJSET.2026.13598
Abstract: Sketching is a fundamental skill in technical drafting, yet many students find traditional drawing methods challenging, time-consuming, and error-prone. The study examined the effectiveness of the Sketchbook application in improving the technical competence of Drafting and ICT students and their overall sketching performance. A quasi- experimental research design, specifically a single-group pretest-posttest approach, was utilized. The study involved 30 Grade 9 students who experienced challenges in technical competence in sketching. Their works were evaluated using a validated scoring rubric that was examined by the expert in the field. Based on the findings, the pre-test results showed a “Satisfactory” level of performance, indicating that students had basic sketching skills but still needed improvement. After the introduction of the Sketchbook application, the post-test results improved to an “Excellent” level, indicating a high level of competence in sketching. There was also a significant difference between the pre-test and post-test results, confirming an improvement in students’ performance. The effect size analysis revealed a large effect, indicating that the Sketchbook application had a strong and meaningful impact on enhancing students’ technical competence in sketching. Beyond improving their technical skills, it also fostered greater confidence and engagement in the learning process.
Keywords: Sketchbook Application, Digital Sketching, Technical Competence, Accuracy, Drafting, Efficiency
Keywords: Sketchbook Application, Digital Sketching, Technical Competence, Accuracy, Drafting, Efficiency
Abstract
Development of Eco-Friendly Cork Board
ROMY B. VILLARAIZ
DOI: 10.17148/IARJSET.2026.13599
Abstract: Environmental sustainability drives the search for renewable, eco-friendly alternatives to conventional products. Cork boards are widely used, yet commercial options are often neither sustainable nor cost-effective. This study addresses this by utilizing locally available organic materials—specifically dried Indian Almond tree (Terminalia catappa) leaves, which are abundant and typically treated as waste. By combining crushed leaves with wood adhesive, this research develops an eco-friendly cork board. It evaluates different material ratios to identify the optimal formulation that meets desired quality, sensory characteristics, and acceptability standards, offering a low-cost, sustainable alternative while promoting waste reduction. This study developed a sustainable cork board using crushed, dried Terminalia catappa leaves mixed with wood adhesive. It aimed to assess sensory characteristics, acceptability, and differences among three treatments: A (100g:150ml), B (200g:180ml), and C (300g:200ml). Using an experimental-developmental design under CRD, 30 evaluators rated the products via a 5-Point Likert scale. Data were analyzed using mean and ANOVA at 0.05 significance level. Results showed all treatments were “Very Acceptable”; Treatment C ranked best, rated “Very Appealing, Very Smooth, Very Compact, and Moderately Thin”. While appearance and compactness were similar across groups, texture, thickness, quality, and overall performance differed significantly, with Treatment C outperforming others. The study concludes that Indian Almond leaves are a renewable, low-cost, and eco-friendly material suitable for cork board production, helping reduce waste. Future research is recommended to explore other organic additives and improved processing methods to enhance durability and commercial value.
Keywords: Eco-friendly, cork boards, Indian almond tree, Terminalia catappa, sensory characteristics, acceptability, sustainability materials.
Keywords: Eco-friendly, cork boards, Indian almond tree, Terminalia catappa, sensory characteristics, acceptability, sustainability materials.
Abstract
Enhancing Students’ Animation Production Skills Through Flipaclip Application
MARIEL A. MANONG
DOI: 10.17148/IARJSET.2026.135100
Abstract: While animation production is an essential competency in Arts education, students often find the process of traditional methods time consuming and technically challenging in achieving smooth movement and precision. This study explored how the use of the FlipaClip application could improve the creativity, technical proficiency, and performance of Grade 10 Arts students in animation production. The main objective was to determine whether students could produce better outputs with the help and guidance provided by the digital features of the FlipaClip application. The study utilized a quasi-experimental research design, particularly a single-group pretest-posttest approach. Students were given the requirements and themes for creating animation. In the pretest, they were asked to create an animation using the traditional manual flipbook method on paper but had no formal instruction or prior experience in creating one. As a result, their manual outputs were generally lacking in smoothness, timing, and technical quality. Their outputs were evaluated using a scoring rubric focused on technical execution, accuracy, visual clarity, and speed. The intervention involved introducing the FlipaClip application, which guided students through the animation process features like frame-by-frame editing, onion skinning, and time management. In the post test, students used the application to recreate the same animation. The results showed that with the help and guidance of FlipaClip, students produced smoother, visually engaging, and technically sound outputs. Their average scores improved from “Very Satisfactory” in the pretest to “Excellent” in the post test. There was significant difference in the performance of students before and after using the FlipaClip application. The mean score after the use of the intervention was higher than the mean score before the intervention, with a Cohen's d effect size demonstrating an exceptionally strong positive impact. The findings demonstrate that the application not only supported the students in understanding animation principles but also increased their confidence and ability to meet creative and technical standards. Additionally, the low standard deviation in the post test scores indicates a consistent level of improvement across the participants, demonstrating that FlipaClip helped students achieve more creative and higher quality outputs. Therefore, this study recommends the integration of FlipaClip into the Arts curriculum, as it effectively assists and develop students’ capabilities in producing high-quality outputs through a more guided and engaging process.
Keywords: flipaClip, animation production skills, digital arts, creativity, technical proficiency, performance
Keywords: flipaClip, animation production skills, digital arts, creativity, technical proficiency, performance
Abstract
Characterization of Blended Sugar Palm and Areca Leaf Sheath Fiber as Alternative Textile Material
ENELYN F. PADROGADO
DOI: 10.17148/IARJSET.2026.135101
Abstract: Blending natural fibers combines their desirable qualities to create high performance textile materials while making good use of locally available agricultural waste. This approach reduces environmental strain and supports local economies by turning underutilized resources into valuable, eco-friendly products. This study evaluated the suitability of blended sugar palm and areca leaf sheath fibers as alternative textile materials. Sugar palm fibers offer great strength, while areca leaf sheath fibers provide good flexibility, combining them brings together the best qualities of both. The research used a experimental-developmental design and tested mechanical properties such as breaking elongation and tensile strength, chemical properties including rate of burning, water absorption and wash resistance, as well as user acceptance in terms of hand feel, irregularity of ribs, color shade and presence of plugs. Results showed that the blend has very elastic elongation and durable tensile strength, burns at a moderate rate, absorbs moisture well and keeps its quality after repeated washing. Evaluators rated the material as “Very Acceptable” overall, confirming that it meets practical and aesthetic standards. Using these natural fibers makes good use of agricultural by-products, supports environmental protection and offers economic opportunities for local communities. Future work may explore different mixing proportions, fiber treatments and production methods to further improve performance and expand possible uses. Based on the results, the study concluded that blended sugar palm and areca leaf sheath fibers meet the required quality standards and have good market potential. They serve as a sustainable material suitable for various textile applications.
Keywords: Sugar Palm fiber, Areca leaf sheath fiber, blended textile, sustainable material, eco-friendly products.
Keywords: Sugar Palm fiber, Areca leaf sheath fiber, blended textile, sustainable material, eco-friendly products.
Abstract
Patient Risk Identification Using Machine Learning
Dr. T. Amalraj Victoire, K. Sanjai
DOI: 10.17148/IARJSET.2026.135102
Abstract: These days, hospitals and clinics keep a lot of patient information on computers. Trying to manage all these records by hand gets really hard as more and more patients come in every day. Doctors often have to look at medical reports, lab results, and a patient's past quickly. Because of this, healthcare places are slowly moving towards smart systems that can help them analyze things and make predictions faster.
This project is about finding out patient health risks using computer programs called machine learning algorithms. The system figures out if a patient is at low or high risk by looking at health details like blood pressure, sugar level, cholesterol, heart rate, and age. While we were building it, we tried and tested different algorithms, including Logistic Regression, Decision Tree, Support Vector Machine, and Random Forest.
We built the application using Python and gave it a simple screen so people could easily use it. After trying out the different models, the Random Forest algorithm gave us better results than the other methods we used. This system can help medical staff with their first look at a patient's case and potentially help them spot health risks sooner.
Keywords: Patient Risk Prediction, Machine Learning, Healthcare Analysis, Disease Prediction, Random Forest
This project is about finding out patient health risks using computer programs called machine learning algorithms. The system figures out if a patient is at low or high risk by looking at health details like blood pressure, sugar level, cholesterol, heart rate, and age. While we were building it, we tried and tested different algorithms, including Logistic Regression, Decision Tree, Support Vector Machine, and Random Forest.
We built the application using Python and gave it a simple screen so people could easily use it. After trying out the different models, the Random Forest algorithm gave us better results than the other methods we used. This system can help medical staff with their first look at a patient's case and potentially help them spot health risks sooner.
Keywords: Patient Risk Prediction, Machine Learning, Healthcare Analysis, Disease Prediction, Random Forest
Abstract
AI-Driven Automation of Centralized Email Triage and SLA Management
Dr.T. Amalraj Victoire, S. Nithyalakshmi
DOI: 10.17148/IARJSET.2026.135103
Abstract: As the number of digital messages increases companies have a problem with their customer support processes. This is where they sort through emails and decide how to answer them. When people do this job it can be slow. Cost a lot of money if they do not answer emails on time. The old way of doing things relies on people to look at each email and decide what to do with it. This can lead to mistakes and waste time.
In this paper we talk about a way to automate the process of sorting through emails and managing the rules that companies must follow to answer emails on time. Our system can look at emails understand what they say and decide what to do with them without anyone helping. It is like a first step in answering emails.
We built a system that uses a special computer program to understand what emails say. It can look at emails soon as they arrive and use special techniques to understand what the person who sent the email wants and how they feel. Then it turns these emails into tickets that the company can use to answer the emails. The system also makes sure that the company answers emails on time by tracking how long it takes to answer them and sending reminders if someone is running late.
We also made a website that customer support staff can use to answer emails. This website is very fast and easy to use so staff can answer emails quickly. Do not have to wait. By automating the process of sorting through emails our system helps companies answer emails faster makes sure that no important emails are missed and lets customer support staff focus on answering emails. This is a way to manage customer relationships using artificial intelligence.
Our system is a way to manage customer relationships. It uses intelligence to make the process of answering emails faster and more efficient. Customer support staff can focus on answering emails and the system takes care of the rest. This is an improvement, over the old way of doing things.
Keywords: Artificial Intelligence, Natural Language Processing (NLP), Email Triage, SLA Management, Workflow Automation.
In this paper we talk about a way to automate the process of sorting through emails and managing the rules that companies must follow to answer emails on time. Our system can look at emails understand what they say and decide what to do with them without anyone helping. It is like a first step in answering emails.
We built a system that uses a special computer program to understand what emails say. It can look at emails soon as they arrive and use special techniques to understand what the person who sent the email wants and how they feel. Then it turns these emails into tickets that the company can use to answer the emails. The system also makes sure that the company answers emails on time by tracking how long it takes to answer them and sending reminders if someone is running late.
We also made a website that customer support staff can use to answer emails. This website is very fast and easy to use so staff can answer emails quickly. Do not have to wait. By automating the process of sorting through emails our system helps companies answer emails faster makes sure that no important emails are missed and lets customer support staff focus on answering emails. This is a way to manage customer relationships using artificial intelligence.
Our system is a way to manage customer relationships. It uses intelligence to make the process of answering emails faster and more efficient. Customer support staff can focus on answering emails and the system takes care of the rest. This is an improvement, over the old way of doing things.
Keywords: Artificial Intelligence, Natural Language Processing (NLP), Email Triage, SLA Management, Workflow Automation.
Abstract
AI POWERED LEARNING AND REVISION WITH CHATBOT SUPPORT SYSTEM
Rajapandian. P, Priyadharshini M
DOI: 10.17148/IARJSET.2026.135104
Abstract: Artificial Intelligence (AI) is rapidly changing the modern education environment by introducing intelligent automation, adaptive learning support, and smart academic interaction systems. Traditional learning systems often lack instant doubt clarification, personalized revision assistance, and continuous student interaction. To overcome these limitations, an AI Powered Learning and Revision with Chatbot Support System is developed to improve the learning experience of students through intelligent educational support. The proposed system integrates Artificial Intelligence, Natural Language Processing (NLP), chatbot technology, and Large Language Models (LLMs) to provide smart academic assistance. The system allows students to ask questions, access revision materials, attend quizzes, and receive instant AI- generated responses. Technologies such as HTML, CSS, JavaScript, Flask, MySQL, and Ollama are used for implementation. AI models such as Mistral, Phi, and Llama are integrated to generate meaningful educational responses. The system improves learning efficiency, reduces manual searching efforts, and provides personalized educational guidance. It also helps students improve exam preparation through intelligent revision support and performance tracking.
Keywords: Artificial Intelligence, Chatbot, Natural Language Processing, Ollama, Learning System, Revision Platform, Large Language Model
Keywords: Artificial Intelligence, Chatbot, Natural Language Processing, Ollama, Learning System, Revision Platform, Large Language Model
Abstract
AI-Powered Animal Species Predictor
Rohit B. Magar, Durgesh R. Patil, Pranav B. Patil, Dinesh D. Patil, Amol E. Patil, Prof. Bharti D. Patil
DOI: 10.17148/IARJSET.2026.135105
Abstract: Wildlife conservation, biodiversity monitoring, and ecological research require accurate and efficient identification of animal species. Traditional methods of species identification depend on domain experts and manual examination, which are time-consuming, expensive, and impractical at scale. This paper presents an AI-Powered Animal Species Predictor, a deep learning-based web application that automatically identifies animal species from images using Convolutional Neural Networks (CNN) and Transfer Learning. The proposed system employs Efficient Net [1]. B3 as the backbone feature extractor, fine-tuned on a curated dataset of animal images spanning 10 major species. The platform accepts image input through a web interface, performs preprocessing and feature extraction, and outputs the predicted species name along with its confidence score, habitat information, diet, and conservation status. The system is developed using Python with TensorFlow and Keras for the deep learning model, Flask for the web backend, and React.js for the frontend interface. The model achieves an overall classification accuracy of 93.7%, precision of 92.5%, recall of 91.9%, and F1-score of 92.2% on the test dataset. Experimental results demonstrate that the proposed system outperforms existing baseline models such as VGG16 [3], ResNet50 [2], InceptionV3 [4], and MobileNetV2 [5]. The system is designed to support wildlife conservationists, researchers, educators, and nature enthusiasts by providing instant, reliable, and informative species identification.
Keywords: Animal Species Prediction, Deep Learning, Convolutional Neural Networks, Transfer Learning, EfficientNet [1]B3, Image Classification, Wildlife Conservation, TensorFlow, Flask, React.js, Biodiversity Monitoring.
Keywords: Animal Species Prediction, Deep Learning, Convolutional Neural Networks, Transfer Learning, EfficientNet [1]B3, Image Classification, Wildlife Conservation, TensorFlow, Flask, React.js, Biodiversity Monitoring.
Abstract
GENDERED STREETS: RETHINKING URBAN PUBLIC SPACES THROUGH INCLUSIVE PLANNING
Kratika Alok Srivastava, Ar. Versha Verma
DOI: 10.17148/IARJSET.2026.135106
Abstract: Urban streets are more than transportation corridors; they are social spaces that shape and reflect the inclusivity of cities. This study examines how urban street design, infrastructure, safety, accessibility, and planning policies influence gender inclusivity in public spaces, focusing on Gomti Nagar Extension, Lucknow.
Keywords: Gendered Streets, Urban Planning, Gender Inclusivity, Public Space, Urban Safety, Accessibility, Gender Mainstreaming, Inclusive Design.
Keywords: Gendered Streets, Urban Planning, Gender Inclusivity, Public Space, Urban Safety, Accessibility, Gender Mainstreaming, Inclusive Design.
Abstract
Sentiment Analysis of YouTube Comments Using TF-IDF and LightGBM with MLflow and Flask Deployment
Rohit Santosh Bedse, Diwansing Shivsing Girase, Krishita Shailendra Patil, Kiran Jitendra Patil, Prof. Reema Kalda
DOI: 10.17148/IARJSET.2026.135107
Abstract: Sentiment analysis of user-generated content is a pivotal Natural Language Processing (NLP) task with significant applications in audience analytics, content moderation, and brand monitoring. YouTube comments represent a rich but noisy source of opinionated text, presenting challenges including informal language, abbreviations, sarcasm, and domain-specific terminology. This paper presents an end-to-end multiclass sentiment classification pipeline for YouTube comments built on Term Frequency-Inverse Document Frequency (TF-IDF) feature engineering and a Light Gradient Boosting Machine (LightGBM) classifier. The proposed system integrates structured data ingestion, NLTK- based text preprocessing with negation-aware stopword handling, TF-IDF vectorization with unigram-to-trigram representations, balanced multiclass classification, MLflow experiment tracking, and a Flask REST API deployment layer. An HTML, CSS, and JavaScript frontend provides an interactive interface for YouTube video URL input, real- time comment sentiment prediction, and dashboard visualization. Comparative experimentation across nine machine learning algorithms confirmed LightGBM as the optimal model. The proposed system achieved 89.4% overall accuracy and a macro-averaged F1-score of 88.7% on the held-out test set.
Keywords: Sentiment Analysis, YouTube Comments, TF-IDF, LightGBM, MLflow, Text Classification
Keywords: Sentiment Analysis, YouTube Comments, TF-IDF, LightGBM, MLflow, Text Classification
Abstract
Review on Motorised Cow Dung Collecting Machine
Prof. V. H. Shivsharan, Mr. Chavan Devdatta Vijay, Mr. Devkule Dnyandev Nandkumar, and Mr. Godase Sushant Namdev
DOI: 10.17148/IARJSET.2026.135108
Abstract: The motorised cow dung collecting machine is an innovative agricultural engineering solution designed to automate the labour-intensive process of collecting cow dung from livestock farms, open fields, and dairy establishments. Manual collection of cow dung is unhygienic, time-consuming, and physically demanding, posing significant health hazards to workers. This research paper presents a comprehensive literature review and design analysis of a motorised cow dung collecting machine that integrates mechanical, electrical, and control systems to achieve efficient, automated dung collection.
The proposed machine employs a chassis-mounted motorised platform equipped with a suction-conveyor mechanism, collection hopper, and a microcontroller-based drive system. The collected dung can be utilised for biogas production, organic fertilizer manufacturing, and fuel generation, thus contributing to rural energy sustainability. This paper covers all aspects of design, component selection, working principle, methodology, and future enhancements. The study also reviews relevant prior research to contextualize the proposed design within the current state of the art.
Keywords: Cow dung, motorised collector, biogas, organic fertilizer, rural technology.
The proposed machine employs a chassis-mounted motorised platform equipped with a suction-conveyor mechanism, collection hopper, and a microcontroller-based drive system. The collected dung can be utilised for biogas production, organic fertilizer manufacturing, and fuel generation, thus contributing to rural energy sustainability. This paper covers all aspects of design, component selection, working principle, methodology, and future enhancements. The study also reviews relevant prior research to contextualize the proposed design within the current state of the art.
Keywords: Cow dung, motorised collector, biogas, organic fertilizer, rural technology.
Abstract
Design and Development of Motorised Cow Dung Collecting Machine
Prof. V. H. Shivsharan, Mr. Chavan Devdatta Vijay, Mr. Devkule Dnyandev Nandkumar, and Mr. Godase Sushant Namdev
DOI: 10.17148/IARJSET.2026.135109
Abstract: The Motorised Cow Dung Collecting Machine is an innovative, low-cost automated system designed to address the labour-intensive and unhygienic process of manual cow dung collection in dairy farms and cattle sheds. This project proposes the design, fabrication, and testing of a battery-operated machine capable of collecting, consolidating, and transporting cow dung with minimal human effort.
The machine is constructed around a robust 1-inch square pipe frame structure, mounted on four 4-inch wheels for smooth floor mobility. A 45 RPM DC geared motor drives the machine's forward and rotational motion through a chain and sprocket transmission system. The drivetrain incorporates a 20 mm shaft supported by P204 pedestal bearings. Two 6V DC batteries connected in series supply a 12V DC power source, regulated through an on/off switch and wired with 0.5 mm copper wire. The dung collecting mechanism positioned at the front of the machine scrapes and channels dung into a collection bin.
Keywords: Cow dung, automation, agricultural machinery, motorised collector
The machine is constructed around a robust 1-inch square pipe frame structure, mounted on four 4-inch wheels for smooth floor mobility. A 45 RPM DC geared motor drives the machine's forward and rotational motion through a chain and sprocket transmission system. The drivetrain incorporates a 20 mm shaft supported by P204 pedestal bearings. Two 6V DC batteries connected in series supply a 12V DC power source, regulated through an on/off switch and wired with 0.5 mm copper wire. The dung collecting mechanism positioned at the front of the machine scrapes and channels dung into a collection bin.
Keywords: Cow dung, automation, agricultural machinery, motorised collector
Abstract
AI-Driven Blockchain Framework for Trustworthy Data Sharing in IoT Ecosystems
Dr. T. Vamshi Mohana, Salma Begum
DOI: 10.17148/IARJSET.2026.135110
Abstract: The proliferation of Internet of Things (IoT) infrastructures has accelerated the generation of sensitive, high- volume data across diverse sectors, ranging from healthcare and agriculture to industrial automation and smart mobility. Despite these advancements, the heterogeneity of devices, constrained computational resources, and evolving cyber threats pose significant barriers to secure and reliable data exchange. This study introduces an AI-empowered blockchain framework designed to reinforce trust management and enable transparent communication within distributed IoT environments. The proposed architecture integrates machine learning–driven trust evaluation with a novel Proof-of-Trust (PoT) consensus protocol, ensuring that nodes with verified reliability are prioritized in block validation. By embedding adaptive intelligence into blockchain operations, the framework reduces computational overhead while preserving immutability and accountability. Furthermore, distributed computing at the edge and fog layers enhances scalability and minimizes latency, making the system viable for real-time IoT applications. Simulation outcomes reveal notable improvements, including reduced transaction delays, heightened accuracy in malicious node detection, and stable performance under dense network conditions. The synergy of artificial intelligence, blockchain, and distributed computing establishes a resilient foundation for secure IoT ecosystems, paving the way for trustworthy data sharing in next-generation smart infrastructures.
Keywords: Trust Management, Artificial Intelligence, Blockchain, Distributed Computing
Keywords: Trust Management, Artificial Intelligence, Blockchain, Distributed Computing