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Research Paper

Encoding Digital Information in DNA: Advances, Techniques, and Applications

In 2020 approximately 64 zettabytes of Data were generated and it was predicted that by 2025 this number will be greater than twice that of it. This prediction is proving itself as every day approximately 402.74 million Terabytes of data is created and as of 2024 the number has risen to 147 Zettabytes already and it's assumed that this amount will be 181 Zettabytes by the end of 2025. This data primarily includes IoT data which is the fastest growing segment of data which is then followed by social media. The existing storage technologies cannot cater to the needs of the Zettabyte Era, as they have considerable issues like limited durability, high power consumption, and the environmental impact they cause. DNA is nature's best alternative to these problems and can store such high amounts of data for a longer period without very little decay. One gram of DNA can store up to 215 Petabytes of data. Its longevity of thousands of years and enormous information density without harming the environment by generating less e-waste makes it a promising archival storage medium.

Published by: Ananya Chandra, Mahesh Tiwari

Author: Ananya Chandra

Paper ID: V11I1-1285

Paper Status: published

Published: April 10, 2025

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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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