Neeraj Dhanraj Bokde

Banoth Thulasya Naik, Mohammad Farukh Hashmi, Neeraj Dhanraj Bokde

Recent developments in video analysis of sports and computer vision techniques have achieved significant improvements to enable a variety of critical operations. To provide enhanced information, such as detailed complex analysis in sports like soccer, basketball, cricket, badminton, etc., studies have focused mainly on computer vision techniques employed to carry out different tasks. This paper presents a comprehensive review of sports video analysis for various applications high-level analysis such as detection and classification of players, tracking player or ball in sports and predicting the trajectories of player or ball, recognizing the teams strategies, classifying various events in sports. The paper further discusses published works in a variety of application-specific tasks related to sports and the present researchers views regarding them. Since there is a wide research scope in sports for deploying computer vision techniques in various sports, some of the publicly available datasets related to a particular sport have been provided. This work reviews a detailed discussion on some of the artificial intelligence(AI)applications in sports vision, GPU-based work stations, and embedded platforms. Finally, this review identifies the research directions, probable challenges, and future trends in the area of visual recognition in sports.

Neeraj Dhanraj Bokde

The Jaya R package offers a robust and versatile implementation of the parameter-free Jaya optimization algorithm, suitable for solving both single-objective and multi-objective optimization problems. By integrating advanced features such as constraint handling, adaptive population management, Pareto front tracking for multi-objective trade-offs, and parallel processing for computational efficiency, the package caters to a wide range of optimization challenges. Its intuitive design and flexibility allow users to solve complex, real-world problems across various domains. To demonstrate its practical utility, a case study on energy modeling explores the optimization of renewable energy shares, showcasing the package's ability to minimize carbon emissions and costs while enhancing system reliability. The Jaya R package is an invaluable tool for researchers and practitioners seeking efficient and adaptive optimization solutions.

Neeraj Dhanraj Bokde, Zaher Mundher Yaseen, Gorm Bruun Andersen

This paper introduces an R package ForecastTB that can be used to compare the accuracy of different forecasting methods as related to the characteristics of a time series dataset. The ForecastTB is a plug-and-play structured module, and several forecasting methods can be included with simple instructions. The proposed test-bench is not limited to the default forecasting and error metric functions, and users are able to append, remove, or choose the desired methods as per requirements. Besides, several plotting functions and statistical performance metrics are provided to visualize the comparative performance and accuracy of different forecasting methods. Furthermore, this paper presents real application examples with natural time series datasets (i.e., wind speed and solar radiation) to exhibit the features of the ForecastTB package to evaluate forecasting comparison analysis as affected by the characteristics of a dataset. Modeling results indicated the applicability and robustness of the proposed R package ForecastTB for time series forecasting.