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Research Paper

Design and Development of V-Twin Stirling Engine

This project aims to address environmental issues like air pollution and noise generated by internal combustion (IC) engines through the development of a V-Twin Stirling engine. Stirling engines, which operate through cyclic expansion and contraction of gas via external heat sources, offer a more efficient and cleaner alternative to traditional IC engines. The design leverages a unique mechanism where one piston drives the motion of both pistons using a gear system, reducing fuel consumption and emissions. The project involves comprehensive analysis and design, with the engine components, such as flywheels, gears, and pistons, being meticulously crafted for optimized performance. The development process includes part drawings, weight and volume calculations, and precision manufacturing using aluminum. The Stirling engine’s potential to harness renewable energy, integrate into power generation systems, and recover waste heat positions it as a viable alternative for future sustainable automotive technologies. The total project budget is approximately INR 6000, covering materials, manufacturing, and necessary accessories.

Published by: Viraj Tambe, Ravi Singh, Rahul Mayekar, Tanish Tilak, Prof. Nikhil V.S.

Author: Viraj Tambe

Paper ID: V11I2-1166

Paper Status: published

Published: April 10, 2025

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Research Paper

Comparative Analysis of Machine Learning Models for Diabetes Prediction: A Performance Evaluation Study

Diabetes is a chronic disease affecting millions worldwide, necessitating early diagnosis and effective prediction models for improved healthcare outcomes. This study evaluates seven machine learning algorithms for diabetes prediction using healthcare data. We compared Logistic Regression, K-Nearest Neighbors (KNN), Random Forest, Decision Tree, AdaBoost, XGBoost, and Support Vector Machine (SVM) models. The analysis focused on key performance metrics: accuracy, precision, recall, F1-score, and Area Under the Curve (AUC). Results showed that logistic regression achieved the highest overall performance with 79% accuracy and 0.88 AUC, suggesting its potential utility in clinical diabetes prediction applications.

Published by: Taaha Ansari, Vaishali M. Bagade

Author: Taaha Ansari

Paper ID: V11I2-1170

Paper Status: published

Published: April 10, 2025

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Research Paper

Small Businesses as the Basis of the Indian Economy

India's economic progress and GDP growth have been mostly driven by small and medium-sized businesses, or SMEs. As of March 27, 2022, there were over 7.9 million MSMEs in India, according to the Ministry of Micro, Small & Medium Enterprises. India's and the world's economies have grown because of small enterprises. In a nation with an economy the size of India, small businesses make up 95% of the industrial units, and they provide 40% of the nation's total industrial production. Once more, tiny companies account for around 45% of India's overall export earnings. This paper explores the importance of small businesses in India, their contributions, their challenges, and their evolving role in driving sustainable and inclusive economic development.

Published by: Aayaan Sardana

Author: Aayaan Sardana

Paper ID: V11I2-1140

Paper Status: published

Published: April 10, 2025

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Review Paper

Big Data Analytics for Real-Time Fraud Detection in Insurance Claims

The integration of Artificial Intelligence (AI) and Big Data Analytics is revolutionizing industries by optimizing efficiency, accuracy, and security. In healthcare and insurance, AI-driven intelligent Document Processing (IDP) automates workflows such as claims automation, medical data extraction, and regulatory compliance management. By utilizing Machine Learning (ML), Natural Language Processing (NLP), and Optical Character Recognition (OCR), IDP accelerates document classification, data validation, and anomaly detection, reducing errors by 90% and cutting processing time by 80%. In the financial sector, AI enhances fraud analytics, risk modeling, and compliance monitoring. Advanced deep learning architectures, pattern recognition, and predictive analytics improve credit risk assessment and real-time fraud mitigation. AI-powered anomaly detection techniques identify suspicious transactions, reducing cybersecurity threats and financial fraud losses.

Published by: Shaba Khatoon, Ankita Srivastava, Dr. Shish Ahmad

Author: Shaba Khatoon

Paper ID: V11I2-1151

Paper Status: published

Published: April 10, 2025

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Research Paper

AI Based Smart Segregation System

The rising demand for premium agricultural produce underscores the need for efficient, accurate sorting technologies. This paper presents an AI-based smart segregation system designed to automate tomato sorting, integrating deep learning, image processing, and robotic automation. The system employs a conveyor belt, ultrasonic sensors, a high-resolution camera, a weigh scale, and robotic arms to categorize tomatoes into reject, ripe, or unripe classes based on visual and weight attributes. Utilizing the YOLOv8 object detection model trained on 731 tomato images, the system delivers high-precision, real-time classification validated through rigorous testing. Results reveal substantial improvements over manual sorting, reducing labor costs, error rates, and processing time while enhancing operational efficiency. Its scalable design suggests applicability to diverse agricultural contexts, heralding advancements in automated farming.

Published by: Abhay S Rao, Sushanth KM

Author: Abhay S Rao

Paper ID: V11I1-1563

Paper Status: published

Published: April 7, 2025

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Review Paper

A Review of Collaborative Robotics (Cobots) in Industrial Automation

Collaborative robots (cobots) represent a transformative advancement in industrial automation, fundamentally changing production paradigms through seamless human-robot interaction. As a cornerstone of Industry 4.0, cobots integrate advanced force/torque sensing, real-time motion control, and machine learning algorithms to enable safe, efficient collaboration in shared workspaces without traditional safety barriers. Their key technological advantages include adaptive impedance control for precise physical interaction, intuitive programming interfaces reducing deployment time by up to 70%, and flexible reconfigurability supporting high-mix, low-volume production. Major industrial applications demonstrate cobots' versatility: in automotive manufacturing, they enable precision tasks like engine component assembly and quality inspection; in electronics, they handle delicate PCB mounting with micron-level accuracy; in pharmaceuticals, they maintain sterile processes during vaccine packaging. Emerging technological frontiers include cognitive human-robot interaction using computer vision, cloud-based swarm coordination for distributed manufacturing, and digital twin integration for predictive maintenance. This comprehensive review analyses: (1) core technological enablers driving cobot capabilities, (2) implementation case studies across key industries, (3) critical safety considerations and ISO/TS 15066 compliance, (4) Applications of Cobots in Industrial Automation and (5) future research directions in AI-enhanced adaptability and human-centric design. The study provides both a technical reference for engineers and strategic insights for manufacturing decision-makers adopting collaborative automation solutions.

Published by: Aayush Desai

Author: Aayush Desai

Paper ID: V11I1-1562

Paper Status: published

Published: April 7, 2025

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