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The study investigates the intricate influence of gender and age variability in individuals diagnosed with diabetes, aiming to gain a comprehensive understanding of the diverse impact and implications of this prevalent metabolic disorder. A real-world dataset, obtained from a renowned diabetologist and meticulously maintained by Dr. Reddys’ Lab, serves as the foundation for rigorous analysis. Leveraging the capabilities of ensemble learning, an advanced technique that combines multiple models, the predictive model’s efficiency is substantially enhanced, resulting in precise and reliable predictions of individuals’ diabetic status. Addressing the challenge of diabetes prediction, a novel ensemble learning model was proposed. The model combines the strengths of three distinct algorithms: Random Forest, Extra Trees, and Multilayer Perceptron (MLP). The model’s output comprises a ternary label categorizing individuals as “diabetic, non-diabetic, or pre-diabetic”, while the accompanying prediction score quantifies the likelihood of individuals belonging to each respective category. The findings of this research expand the existing body of knowledge on diabetes prediction, underscoring the untapped potential of ensemble learning methodologies in augmenting accuracy and predictive performance for diabetic patients.

The outbreak of COVID-19, a little more than 2 years ago, drastically affected all segments of society throughout the world. While at one end, the microbiologists, virologists, and medical practitioners were trying to find the cure for the infection; the Governments were laying emphasis on precautionary measures like lockdowns to lower the spread of the virus. This pandemic is perhaps also the first one of its kind in history that has research articles in all possible areas as like: medicine, sociology, psychology, supply chain management, mathematical modeling, etc. A lot of work is still continuing in this area, which is very important also for better preparedness if such a situation arises in future. The objective of the present study is to build a research support tool that will help the researchers swiftly identify the relevant literature on a specific field or topic regarding COVID-19 through a hierarchical classification system. The three main tasks done during this study are data preparation, data annotation and text data classification through bi-directional long short-term memory (bi-LSTM).

Selecting team players is a crucial and challenging task demanding a considerable amount of thinking and hard work by the selectors. The present study formulated the selection of an IPL squad as a multi-objective optimization problem with the objectives of maximizing the batting and bowling performance of the squad, in which a player’s performance is estimated using an efficient Batting Performance Factor and Combined Bowling Rate. Also, the proposed model tries to formulate a balanced squad by constraining the number of pure batters, pure bowlers, and all-rounders. Bounds are also considered on star players to enhance the performance of the squad and also from the income prospects of IPL. The problem in itself is treated as a 0/1 knapsack problem for which two combinatorial optimization algorithms, namely, BNSGA-II and INSGA-II, are developed. These algorithms were compared with existing modified NSGA-II for IPL team selection and three other popular multi-objective optimization algorithms, NSGA-II, NSDE, and MOPSO-CD, on the basis of standard performance metrics: hypervolume, inverted generational distance, and number of Pareto optimal solutions. Both algorithms performed well, with BNSGA-II performing better than all the other algorithms considered in this study. The IPL 2020 players’ data validated the applicability of the proposed model and algorithms. The trade-off squads contained players of each expertise in appropriate proportions. Further analysis of the trade-off squads demonstrated that many theoretically selected players performed well in IPL 2020 matches.

Optimization algorithms have proven to be a useful tool in different areas of water resource management including water distribution network (WDN), which may be modelled as a large scale, combinatorial problem subject to constraints with the typical objective of minimizing the cost. In this study, a fuzzy C-means adaptive differential evolution (FCADE) is proposed to solve three well-known WDN problems: the two-loop distribution network problem, the Hanoi distribution network problem, and New York tunnel distribution network problem. FCADE is integrated with a well-known simulation software EPANET to manage the pressure constraints requirement. A comparative analysis of results through various performance metrics indicate the competence of FCADE for solving complex WDN problems.

This paper provides an extensive review of the popular multi-objective optimization algorithm NSGA-II for selected combinatorial optimization problems viz. assignment problem, allocation problem, travelling salesman problem, vehicle routing problem, scheduling problem, and knapsack problem. It is identified that based on the manner in which NSGA-II has been implemented for solving the aforementioned group of problems, there can be three categories: Conventional NSGA-II, where the authors have implemented the basic version of NSGA-II, without making any changes in the operators; the second one is Modified NSGA-II, where the researchers have implemented NSGA-II after making some changes into it and finally, Hybrid NSGA-II variants, where the researchers have hybridized the conventional and modified NSGA-II with some other technique. The article analyses the modifications in NSGA-II and also discusses the various performance assessment techniques used by the researchers, i.e., test instances, performance metrics, statistical tests, case studies, benchmarking with other state-of-the-art algorithms. Additionally, the paper also provides a brief bibliometric analysis based on the work done in this study.

ABC (Artificial Bee Colony) is one of the most recent nature inspired algorithm (NIA) based on swarming metaphor. Proposed by Karaboga in 2005, ABC has proven to be a robust and efficient algorithm for solving global optimization problems over continuous space. In this paper, we propose a modified version of the ABC. In modified version called Dichotomous ABC (DABC), the idea is to move dichotomously in both directions to generate a new trial point. The results of a trade study carried out on three classical structural optimization problems taken from literature indicate the validity of the proposed algorithm.

Digital image watermarking is the process of concealing secret information in a digital image for protecting its rightful ownership. Most of the existing block based singular value decomposition (SVD) digital watermarking schemes are not robust to geometric distortions, such as rotation in an integer multiple of ninety degree and image flipping, which change the locations of the pixels but don’t make any changes to the pixel’s intensity of the image. Also, the schemes have used a constant scaling factor to give the same weightage to the coefficients of different magnitudes that results in visible distortion in some regions of the watermarked image. Therefore, to overcome the problems mentioned here, this paper proposes a novel image watermarking scheme by incorporating the concepts of redistributed image normalization and variable scaling factor depending on the coefficient’s magnitude to be embedded. Furthermore, to enhance the security and robustness the watermark is shuffled by using the piecewise linear chaotic map before the embedding. To investigate the robustness of the scheme several attacks are applied to seriously distort the watermarked image. Empirical analysis of the results has demonstrated the efficiency of the proposed scheme.