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An Analysis of Common Types of Injuries Reported at Out Patient Department of Type a Base Hospital in the Eastern Province of Sri Lanka: A Retrospective Study Using Data from Form Information of Injury (H 1258)

This study aimed to analyze the injury patterns among patients treated in the outpatient department (OPD) to identify key trends in gender, age distribution, mechanisms of injury, and affected body regions. A total of 880 injury cases were reviewed, with male patients constituting 61% and female patients 39%, resulting in a male-to-female ratio of 1.56:1. The majority of injuries (99%) were unintentional, and alcohol use was noted in 2.61% of cases. The age distribution revealed that 91.1% of patients were children, adolescents, or adults, with 59.6% falling within the adult (18-65 years) age group. The most common mechanisms of injury were falls (34%) and being struck by an object (32%), while lower limbs (48%) and palms/fingers (21%) were the most frequently affected body regions. The study also noted that 59% of patients sought treatment by noon, with the remaining patients treated later in the day. These findings suggest that falls, blunt trauma, and upper extremity injuries are most prevalent, with a significant portion of cases occurring in the productive age group. The study highlights the need for targeted injury prevention programs, better data collection on injury specifics, and timely interventions to reduce injury incidence and improve patient outcomes. Keywords: Injury, outpatient department, age distribution, mechanisms of injury, alcohol use, body region affected, fall prevention, injury prevention.

Published by: Thahira Safiudeen, Baminy Navaratnam

Author: Thahira Safiudeen

Paper ID: V11I1-1422

Paper Status: published

Published: March 5, 2025

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Research Paper

Artificial Intelligence and Cyber Law: Navigating Legal Complexities

The rapid advancement of Artificial Intelligence (AI) has revolutionized industries worldwide, offering unprecedented opportunities and challenges. However, as AI systems become more autonomous and integrated into various domains, they raise significant legal and ethical concerns, particularly in cyber law. This article explores the intersection of AI and cyber law, addressing key legal complexities such as data protection, liability, intellectual property rights, cybersecurity, and regulatory frameworks. It provides an in-depth analysis of emerging global legal trends and discusses potential solutions for balancing innovation with legal accountability. The paper further delves into the challenges of AI-driven cybercrimes, ethical AI deployment, and the role of policymakers in shaping comprehensive AI regulations. This article aims to provide valuable insights into navigating the intricate relationship between AI and cyber law by examining case studies and international legal frameworks.

Published by: Mahima Shukla

Author: Mahima Shukla

Paper ID: V11I1-1414

Paper Status: published

Published: February 28, 2025

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Research Paper

Threat Scoring Model Basis Hybrid Attack Emulation

In today's world, as the globe moves towards the Metaverse, all commercial and business transactions are digitalised, and digital transformation is the new need of the hour owing to COVID and other environmental and health problems. On the one hand, digitalisation is transforming the world; on the other, with an expanding attack surface and a variety of attacker modus operandi, it is critical and long overdue to develop a threat classification model that can provide clear insight into adversaries through their appropriate classification and threat scoring. The research aims to emulate “Threat Scoring based on the Hybrid Attack Model”, which consists of Red Teaming, Attack vectors and Threat Hunting models. The model will be simulated to understand the threat landscape for potential trigger points in the network and operation by initiating a wide range of attacks at various levels with respect to security posture. Hypothesis of results based on attack vectors will be mapped with Threat Intel received from various open Threat Scoring models based on analysis of adversities. The threat modelling process will be based on the MITRE & ATTACK Framework, with discovered threats further classified according to MITRE Tactics, Techniques and Procedures.

Published by: Mukul Kulshrestha, Satish Salunkhe, Dr Vaishali Khairnar

Author: Mukul Kulshrestha

Paper ID: V11I1-1390

Paper Status: published

Published: February 27, 2025

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Research Paper

A Review of Artificial Intelligence in Diabetic Retinopathy Detection

Diabetic retinopathy (DR) alongside diabetes is rising among the population twenty-first century and is one of the leading sight threatening cause worldwide. With early treatment in the initial stage possible, detecting the problem before it worsens is important. Different methods of eye evaluation for the changes in retina are mainly hospital based and not accessible to rural remote areas. AI methods can aid the process and provide warnings beforehand in places with inaccessible or poor health facilities. In this review, we discuss four applications - IDx- DR, Eye Art, RetinaLyze, and Bosch DR Algorithm - that are in use in real-world or under study for screening retinopathy.

Published by: Aryan Raj Pradhan

Author: Aryan Raj Pradhan

Paper ID: V11I1-1382

Paper Status: published

Published: February 27, 2025

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Review Paper

Enhancing Solar PV Plant Performance with Digital Twins: Leveraging Data Science and AI for Real-Time Analysis.

As solar power continues to be a crucial element of the global shift towards renewable energy, improving the performance of solar power plants becomes important. Various sensors installed on solar panels, inverters, and other equipment produce data, which is then integrated to create a virtual model, the digital twin, that reflects the solar power plant’s operational behavior. For precise representation and control, the system dynamically models the actions of the plant by integrating MATLAB-simulink simulations. Furthermore, fault detection is achieved using Neural Networks, Adaboost, Naïve Bayes, and Decision Trees. This unification of AI-based Digital Twins with energy generation prediction and forecasting, fault detection, and corrective maintenance technologies amplifies solar power plants' operational efficiency, reliability, and sustainability.

Published by: Tanmay Mane, Arpita Kulkarni, Anurhuta Kulkarni, Omkar Shrotri, Dr. Sinu Nambiar, Prof. Sonali Potadar

Author: Tanmay Mane

Paper ID: V11I1-1394

Paper Status: published

Published: February 27, 2025

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Research Paper

Gig Labor Market Dynamics – A Case Study on Challenges of Gig Economy in Tamil Nadu

The gig economy is a marketplace where individuals take temporary/contingent jobs at organisations or work as freelancers. It is a booming sector but has remained an unregulated space on a larger scale. Given the nature of their role, they do not maintain a stable relationship with their clients and management and are always open to risk. With the rise of internet users and city growth via investment and urbanisation, Gig workers and their services have become a crucial part of our rapid lives. With the amount of effort, risks, and complexities the Gig workers face in their day-to-day operations, their plight must be considered. In this study, we are focused on understanding and highlighting the challenges of Gig workers. We are trying to work towards addressing an enduring version of the Gig economy, which can help its participant maintain a primary source of income from their gigs. It requires the digital infrastructure, standardized policies, legislations, and corporate work structure to be improved to create space and viability for this growing sector. The research will collect data on real-time Gig workers by understanding their experiences, challenges, social-security requirements, and technological assistance in operations. It is necessary to build on the available secondary data by identifying the areas for improvement in this sector. The study is looking for a scope for improvement in the current framework to result in an environment where the gig economy can thrive and become a perennial source of employment.

Published by: Shikhar Sinha, Rashi K

Author: Shikhar Sinha

Paper ID: V11I1-1392

Paper Status: published

Published: February 27, 2025

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