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
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
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
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
Analysis of Mental Health Economics
Over the past few decades, the economics of mental health has drawn more attention, changing from a neglected area to a crucial one for healthcare policy and resource distribution. The economic aspects of mental health, such as resource allocation, intervention cost-effectiveness, and the financial toll that mental health illnesses have on both individuals and societies, are examined in this study. It examines how funding for mental and physical health care differs, emphasising the problems of stigma, geographical imbalances, and a lack of qualified personnel. The research paper assesses the cost-effectiveness of preventive, treatment, and rehabilitation programs using data from international studies. It also looks into how economic assessments, like quality-adjusted life years (QALYs) and cost-benefit analyses, are used to make policy. The study emphasises the value of allocating resources in an equal and evidence-based manner, encouraging openness, accessibility, and effectiveness in the provision of mental health services. This paper seeks to support policy changes that improve the availability and caliber of mental health services globally by addressing economic inefficiencies and arguing for more financing.
Published by: Parii Jain
Author: Parii Jain
Paper ID: V11I1-1528
Paper Status: published
Published: April 5, 2025
CBIR: Enhancing Image Retrieval through AutoEncoders and Metric-Based Search
The exponential growth of visual data demands robust Content-Based Image Retrieval (CBIR) systems that ac- curately and efficiently retrieve relevant images. In this paper, we present a novel CBIR framework that integrates AutoEncoders for latent feature extraction with hashing and Vantage-Point Trees (VP-Trees) for efficient similarity search. Experimental results on a publicly available dataset demonstrate significant improvements in retrieval precision and computational efficiency.
Published by: Ayush Anand, Shreyansh Narayan, Vinayak Gupta
Author: Ayush Anand
Paper ID: V11I1-1496
Paper Status: published
Published: April 5, 2025
