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Research Paper

Re-Imagining Emergency Response System with Geo Fencing

Despite the existence of emergency hotlines, many distress calls go unanswered, leaving individuals in urgent need without timely assistance. This study proposes a system designed to enhance the speed and reliability of Emergency Response Services(ERS). By integrating advanced technologies such as signal triangulation, Geo-fencing, Geo-location, predictive AI, and Geo data analytics, the system aims to provide more accurate and faster responses. The approach works within India’s current governance framework and collaborates with community networks to improve safety, particularly for vulnerable groups like women and children. The proposed system’s objectives include reducing the response time for emergency services, ensuring that help reaches those in need quickly, and strengthening overall crisis management. By leveraging predictive AI and Geo data, it optimizes response times. This study presents an innovative solution for improving emergency services, making them more inclusive, effective, and contributing to the overall safety of communities.

Published by: Aryav Goyal, Isha Goel

Author: Aryav Goyal

Paper ID: V11I3-1290

Paper Status: published

Published: June 5, 2025

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Research Paper

Cyber Warfare: Conflicts and Role of Security in the Digital Age

This research paper looks at a brand new evolving form of warfare, “ cyber warfare”.The main focus is primarily on the internal and external notions of security and emphasises how cybersecurity affects both. Various examples have been taken up to demonstrate how cyber terrorism has created havoc all over the world. An in-depth analysis of the development of international laws was conducted.

Published by: Vanshika Rao

Author: Vanshika Rao

Paper ID: V11I3-1288

Paper Status: published

Published: June 3, 2025

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Research Paper

Divided by Wealth: A Deep Dive into Women’s Access to Credit

Women’s access to credit has historically been restricted by systemic discrimination and by outdated financial structures, limiting female economic independence and business opportunities. While landmark pieces of legislation such as the 1974 Equal Credit Opportunity Act (ECOA) in the United States have attempted to address these issues, research continues to indicate that gender biases in credit persist. While previous academic studies have examined econometrics, Fletschner (2008), and legal reforms, Garikipati (2008), this paper argues that deeply ingrained societal biases continue to shape credit lending systems, disproportionately disadvantaging women. By analyzing case studies from the United States, India, and South Korea, this research evaluates how gender-based disparities in credit access manifest across different economic and cultural contexts. The study incorporates data from financial institutions, government reports, and scholarly articles to highlight ongoing barriers. Ultimately, this paper argues that while progress has been made, financial institutions remain skewed in favor of men, necessitating policy changes that prioritize inclusivity and fairness. Without reform, women will continue to face unnecessary hurdles in obtaining credit, reinforcing long-standing economic inequalities.

Published by: Krish Gupta

Author: Krish Gupta

Paper ID: V11I3-1282

Paper Status: published

Published: June 3, 2025

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Research Paper

AI-Driven Medical Fundraising Verification System to Detect and Prevent Fraudulent Treatment Requests

Medical fund fraud, where individuals fake treatment documents to solicit donations, is a growing concern in crowdfunding. Traditional verification methods are often manual, slow, and prone to error. This project introduces an AI-based system using YOLOv8 to detect text in medical bills and Paddle OCR to extract key information. Extracted data—like hospital names and treatment costs—is verified using fuzzy matching against a trusted hospital database. This automated approach enhances accuracy, blocks fraudulent requests, and helps restore donor trust.

Published by: D V Vidhya Sri, N Aravindhan

Author: D V Vidhya Sri

Paper ID: V11I3-1280

Paper Status: published

Published: June 1, 2025

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Research Paper

An In-Depth Analysis of Dollar Liquidity in the Global Economy

As the US dollar is the basis of international finance and trade, dollar liquidity is vital to the health of the economy. Developing countries such as India feel the brunt of less dollar access through higher import costs, volatile currencies, and reduced corporate competitiveness. Cross-border banks, upon which the availability of dollar financing depends, are also at risk and may produce credit shortages. United States policy making can rock global markets, as was done with the 2008 Financial Crisis and the 2013 Taper Tantrum. The paper puts emphasis on stable dollar liquidity by emphasising the complexity of the global economy and how dislocation of dollar flow impacts banks, companies, and individuals everywhere.

Published by: Jaanya Rathi

Author: Jaanya Rathi

Paper ID: V11I3-1279

Paper Status: published

Published: June 1, 2025

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Research Paper

Thyroid Gland Abnormality Detection Using Pre-Trained Neural Networks

Medical image analysis plays a crucial role in the early detection and diagnosis of thyroid nodules, which are indicative of various thyroid illnesses. Thyroid nodules are classified using machine learning methods like Random Forest and Support Vector Machine in the current framework. In this work, we propose a unique use of transfer learning algorithms to thyroid nodule categorization. Neural network models that have already been trained on large datasets are modified for specific tasks that require less data through the use of transfer learning. Our approach involves using a state-of-the-art convolutional neural network (CNN) that has been pre-trained on a range of medical pictures to extract significant information from thyroid ultrasound scans. To optimize its performance for accurate classification, the model is trained on a particular dataset of thyroid nodule images. We examine the effectiveness of many transfer learning architectures, such as VGG16 and Xception CNN, and assess their overall accuracy, sensitivity, and specificity. The proposed methodology aims to provide physicians with a reliable thyroid problem diagnosis tool by increasing the categorization efficiency of thyroid nodules. The results pave the way for more precise thyroid image analysis, diagnosis by demonstrating how transfer learning can be utilized to maximize model performance even in the presence of sparsely labelled medical data.

Published by: B. Madhu Varshini, S. Sridevi, G. Kokila

Author: B. Madhu Varshini

Paper ID: V11I3-1256

Paper Status: published

Published: May 30, 2025

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