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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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Research Paper

FESTIVAL BLACK EYES BEANS ou Niébé À PORT-BERGE et MAMPIKONY DANS LA REGION SOFIA, MADAGASCAR

The Black Eyes Beans (BEB) or cowpea festival is a cultural festival in the Sofia region in the northwestern part of Madagascar, during which the organizers aim to promote the agricultural and cultural development of the region. This region is known as the Malagasy capital of BEB, whose farmers represent 80% of the region's inhabitants. The production area is mainly located in two districts: Port-Bergé and Mampikony. This work therefore aims to present the importance of black eyes culture but also to demonstrate the cultural richness in artistic music through the BEB festival in the Sofia region. To this end, many beneficiaries benefit from the organization of the festival. Mainly BEB producers for sale. It is a great opportunity for farmers and even for those who are not involved in the cultivation of said product to exhibit their productions. It is also a highlight for artistic festivities. Artists actively participate during the event by providing entertainment and making the days more festive. Thus, compared to other festivals in Madagascar, the BEB festival is typical of the Sofia region in view of the agricultural and artistic potential presented there. Behind this festival event lies an exploitation of enormous economic potential. Thus, recommendations are then proposed to all stakeholders, both farmers or producers, the organizer, and decentralized local authorities for the improvement and sustainability of the BEB festival.

Published by: Cyprien Zaralahy

Author: Cyprien Zaralahy

Paper ID: V10I6-1367

Paper Status: published

Published: April 7, 2025

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