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All Journal Bulletin of Electrical Engineering and Informatics Journal of Robotics and Control (JRC)
Reddy, Sareddy Venkata Rami
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Mechanical Engineering

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Injury Prediction in Sports using Artificial Intelligence Applications: A Brief Review Kumar, G. Syam; Kumar, M. Dilip; Reddy, Sareddy Venkata Rami; Kumari, B. V. Seshu; Reddy, Ch. Rami
Journal of Robotics and Control (JRC) Vol 5, No 1 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i1.20814

Abstract

Avoiding injuries in sports has always depended on historical records and human experience. This is despite using injuries being a major and unsolvable issue. The development of more precise preventative procedures using the now available approaches has been excruciatingly sluggish. The development of artificial intelligence (AI) and machine learning (ML) as potentially valuable procedures to improve damage prevention and recovery procedures has been made possible by technological advances that have made these areas more accessible. This article presents a detailed summary of ML approaches as they have been used to predict and anticipate sports injuries to this point in time. The research conducted over the last five years has been collated, and its results have been untaken. Assuming the present absence of accessible sources, standardized statistics, and a dependence on obsolete deterioration prototypes, it is impossible to draw any definitive conclusions regarding the real-world effectiveness of machine learning in terms of its application to the prediction of sports injuries. However, it has been hypothesized that resolving these two problems would make it possible to deploy innovative, strong machine-learning architectures, which will hasten the process of increasing the state of this area while also offering proven clinical tools.
Reconfiguration of the radial distribution network using an artificial rabbits optimization approach Rao, Ganney Poorna Chandra; Krishna, Puvvula Venkata Rama; Rupesh, Mailugundla; Karike, Swathi; Polisetty, Sathyanarayana; Reddy, Sareddy Venkata Rami; Sreedhar, Jadapalli
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.7260

Abstract

Lowering system power losses along with improving voltage profile have been major concerns for researchers for the past few decades. The performance of an electrical distribution system (EDS) is dependent on these two factors. This work’s main emphasis is on reconfiguring the radial distribution network (RDN) to diminish system power losses and strengthen the voltage profile. The process of network reconfiguration (NR) involves state transitions of sectionalizing and tie switches while still adhering to the limitations. In this work, the optimal reconfiguration network is determined using the artificial rabbits optimization (ARO) approach. The adopted method is tested using IEEE 119 bus RDN under low, normal, and heavy load conditions. When compared to the current approaches, the adopted methodology produced favorable results.
Co-Authors Karike, Swathi Krishna, Puvvula Venkata Rama Kumar, G. Syam Kumar, M. Dilip Kumari, B. V. Seshu Polisetty, Sathyanarayana Rao, Ganney Poorna Chandra Reddy, Ch. Rami Rupesh, Mailugundla Sreedhar, Jadapalli