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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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Review Paper

Crypto & Traditional Banking: An Evolving Relationship

The rapid rise of cryptocurrencies over the past decade has catalyzed a major shift in the global financial landscape. Once niche digital experiments, cryptocurrencies powered by blockchain and tokenization technologies have now achieved wider recognition across institutional investors, fintech innovators, and even central banks. A 2025 survey by EY and Coinbase revealed that 83% of institutional investors plan to increase their digital asset allocations, driven by growing regulatory clarity and product innovation.

Published by: Nitant Kalia

Author: Nitant Kalia

Paper ID: V11I5-1142

Paper Status: published

Published: September 15, 2025

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Research Paper

A Comparative Study of Machine Learning Algorithms for Real-Time DDoS Detection in Cloud Environments

Cloud environments are scalable and cost-effective, but they are also highly susceptible to cyberattacks, such as Distributed Denial of Service (DDoS) attacks, which can exhaust resources and impact availability. To counter these threats, this research investigates supervised machine learning techniques for identifying such attacks in real-time based on the BCCC cPacket Cloud DDoS 2024 dataset. Following deep preprocessing and exploratory data analysis, a multi-class classifier was employed to differentiate between benign, suspicious, and attack traffic. Of the models to be compared, the Decision Tree Classifier achieved the highest mark with an accuracy rate of 96.8 percent, which indicates its ability to categorize the majority of cases of traffic accordingly. The other models, including K Nearest Neighbors, Ridge Regression, Logistic Regression, and Linear Support Vector Classifier, had lower levels of accuracy, with Decision Tree always delivering the best. The results confirm that the Decision Tree is the best and most efficient model for precise real-time identification of DDoS attacks in cloud systems.

Published by: Oduwunmi Odukoya, Mariam Adetoun Sanusi, Samuel Adenekan

Author: Oduwunmi Odukoya

Paper ID: V11I5-1146

Paper Status: published

Published: September 11, 2025

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Research Paper

Performance Overview of Light Electric Vehicle Powered by Battery in Assistance with Supercapacitor

The growing electrical vehicle (EV) market has been taking the attention of customers & manufacturers in the last couple of years. EVs with lithium-ion (Li-ion) batteries stand as an alternate to the conventional power source of fossil fuel engines. While sustaining the stand, the limitations of battery as a sole power source surface, like range, temperature, state of health, dynamic power requirement & addressing different duty cycles. In this regard, a hybrid power source with a supercapacitor has emerged as a solution. The combined power source is known as a hybrid energy storage system (HESS). In this report we will see the current progress in technology for the supercapacitor-based powertrain from the viewpoint of range, charge-discharge cycle, overall weight of power source, power split strategies & ultimate cost impact. The HESS has basically three configurations—passive, semi-passive & active. The efficiency of the system improves with the introduction of power electronics. Comparative analysis of passive, semi-passive, and active HESS architectures confirms the superiority of active systems, which enable intelligent power-split strategies through advanced power electronics. Although they involve higher initial costs, lifecycle evaluations highlight reduced battery replacement frequency and lower operating expenses. This study establishes actively managed HESS as a scalable and sustainable solution for three-wheeler electrification, combining enhanced performance and long-term durability.

Published by: Amit Aundhakar, Sateesh Patil, Shashank Gawade

Author: Amit Aundhakar

Paper ID: V11I5-1141

Paper Status: published

Published: September 8, 2025

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Research Paper

A Deep Dive into the Adversarial Training Process of Modern LLMs

Large language models can be trained to resist jailbreak attempts in order to ensure the model’s safety. This literature review analyzes common adversarial training techniques used in modern LLMs to understand and break down the process of adversarial training. The paper looks into 6 training techniques, ranging from PGD to Deployment Time Safety Layers and Model Editing. For each method, we look into how they work behind the scenes, their strengths, weaknesses, and real-world usage. We then synthesize the most effective manner of training models against adversarial inputs. This research revealed that the most effective training technique emphasizes a multilayered defense strategy. Future research can look at improving model-editing coverage, creating open-sourced LLM benchmarks to test against jailbreaks, and closing the divide between embedding-space and discrete input training.

Published by: Armaan Mahajan

Author: Armaan Mahajan

Paper ID: V11I5-1137

Paper Status: published

Published: September 4, 2025

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Research Paper

Analytical Electrochemical– Mechanical Simulation Model for Lithium-Ion Pouch Cells Under External Load

The performance of lithium-ion pouch cells is significantly affected by external mechanical loads in addition to electrochemical operating conditions. This study presents a simplified electrochemical-mechanical model to analyze the influence of compressive forces on a 19.6 Ah lithium-ion pouch cell. External loads are translated into strain using a stiffness-based formulation, which is further linked to porosity variation, capacity fade, and internal resistance rise. A voltage model incorporating open-circuit voltage and ohmic drops is used to simulate discharge characteristics under different charge/discharge rates (0.25C, 0.5C, and 1C) and mechanical loads (0–8 kg). Simulation results indicate that increasing load leads to reduced capacity and elevated resistance, with the effect becoming more pronounced at higher C-rates. Quasi-open circuit voltage (Quasi-OCV) comparisons further reveal measurable shifts in voltage–SOC profiles before and after loading. The proposed framework provides a computationally efficient and adaptable tool for exploring electrochemical–mechanical interactions, offering valuable insights for battery design, safety, and performance optimization.

Published by: Tanmaya Maharana, Dr. Sandeep G Thorat

Author: Tanmaya Maharana

Paper ID: V11I4-1231

Paper Status: published

Published: September 1, 2025

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