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Young Women and Social Media Feminism

This paper examines whether feminism on social media, despite its empowering appearance, is truly inclusive and accessible to all sections of society, or primarily serves the interests of privileged users. Framed around the concept of 'epistemic injustice,' the study explores how digital platforms like Instagram and TikTok may unintentionally exclude marginalized voices through tools such as algorithmic sorting, aesthetic bias, and engagement-driven content filtering. Key concepts like testimonial injustice, shadowbanning, and report bombing are used to highlight the structural barriers faced by Dalit, queer, disabled, and muslim women online. By analyzing these platforms, the paper questions whether digital feminism truly reaches and democratizes voices or simply echoes dominant narratives.

Published by: Pakhi Kshirsagar

Author: Pakhi Kshirsagar

Paper ID: V11I5-1154

Paper Status: published

Published: September 23, 2025

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Research Paper

Optimising Emergency Vehicle Response Times with Genetic Algorithms: Integrating Routing and Traffic Signal Control

This paper explores the potential of genetic algorithms (GAs) in optimising emergency vehicle response times through both dynamic routing and adaptive traffic signal control. Traditional deterministic routing methods, such as Dijkstra’s and A*, fail to account for real-time traffic fluctuations or signal coordination, often leading to delays that reduce patient survival rates. A review of existing studies demonstrates that GAs outperform static algorithms by dynamically re-evaluating routes, optimising multi-stop journeys, and scaling to fleet-level management. Similarly, GAs have shown effectiveness in adjusting signal timings at intersections to minimise delays under fluctuating traffic volumes. However, most research addresses routing and signal optimisation separately, leaving a gap in integrated systems that combine both strategies. This paper highlights the need for GA-based frameworks capable of jointly coordinating emergency vehicle routing and signal pre-emption, tested on realistic urban networks. Such integration could significantly enhance emergency response efficiency and provide a scalable, adaptable solution for real-world applications.

Published by: Arjun Kulshreshtha

Author: Arjun Kulshreshtha

Paper ID: V11I5-1151

Paper Status: published

Published: September 20, 2025

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Research Paper

A Study on the Dependence of Poverty on Crime: An Interdisciplinary Approach

This paper examines the role played by various elements of socioeconomic status - economic, social, and psychological - in causing criminal behaviour to materialise. Firstly, isolated neighbourhoods often face disconnection from employment in the legitimate economy and encounter income inequality. For the poor, especially, the widening of the gap between the rich and them demonstrates the contrast between earnings from criminal activities and legitimate avenues. These communities often become spatially isolated, causing social mechanisms like collective efficacy and informal social control to break down due to a lack of trust among neighbours. In fact, high-crime urban areas share more or less similar neighbourhood characteristics in Brazil (Nogueira de Melo et al. 2017), China (Liu et al. 2016), South Africa (Breetzke 2010), and the United States (Tuttle 5). Coupled with the economic and social features, various intervening processes like parental discipline, supervision, and attachment factors play an equal role in developing an individual’s psyche. The need to appear “tough” to acquire status and to follow the “code of the streets” can create a mindset among people that can manifest into law-breaking activities. Through this paper, I shed light on the complexity involved in entering crime, and how it can sometimes equally be by circumstance, and not choice.

Published by: Aindri Basu

Author: Aindri Basu

Paper ID: V11I5-1150

Paper Status: published

Published: September 19, 2025

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Research Paper

“What are the Odds?” Improving In-Game Win Probability Models in Football

This paper explores the accuracy and usefulness of football win-probability models for analysts, clubs, and fans. Since football is a low-scoring and unpredictable game, probability models help us interpret uncertain outcomes and support strategies and decisions. The paper looks into Sam Green’s expected goals model and how it laid the foundation for future forecasting. It also evaluates how FiveThirtyEight’s and Herbinet’s models have expanded on this framework by integrating simulations, team strength ratings, and machine learning. By assessing the strengths and weaknesses of these three models, this paper identifies the most accurate approach for forecast match results.

Published by: Arjun Bir Vashisht

Author: Arjun Bir Vashisht

Paper ID: V11I5-1147

Paper Status: published

Published: September 17, 2025

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Research Paper

IoT-Enabled Smart Home Gardening System: An Innovative Approach to Water Conservation and Plant Care

This paper introduces an IoT-based Smart Home Gardening System aimed at efficient water management and optimised plant care. The system leverages advanced soil moisture, temperature, and environmental sensors to monitor conditions and dynamically adjust water distribution using a mobile application. This integration of IoT technology supports water conservation, healthier plant growth, and sustainable gardening practices. The study highlights its potential applications for indoor, outdoor, and urban gardening spaces, focusing on scalability, affordability, and user-friendly features.

Published by: Debjyoti Mukhopadhyay

Author: Debjyoti Mukhopadhyay

Paper ID: V11I5-1149

Paper Status: published

Published: September 16, 2025

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Research Paper

Real-Time Football Player and Ball Detection System Using YOLO Architecture for Automated Sports Analytics

This paper presents a comprehensive AI-powered football analysis system that employs the YOLO (You Only Look Once) detection framework to achieve real-time identification and tracking of players, balls, and referees in football match videos. The system integrates computer vision techniques, including team classification through K-means clustering, optical flow for camera motion compensation, and homography transformation for perspective correction. Our implementation achieved remarkable performance metrics with 82.2% mAP50, 90.2% precision, and 77.0% recall across all object classes. The system successfully processes raw match footage to generate automated analytics, including player tracking, speed calculation, distance metrics, and possession statistics, offering an economical substitute for expensive GPS-based tracking systems used in professional sports.

Published by: Aryan Lalwani

Author: Aryan Lalwani

Paper ID: V11I5-1138

Paper Status: published

Published: September 15, 2025

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