The Round Robin (RR) CPU scheduling algorithm is a fair scheduling algorithm that gives equal time quantum to all processes. The choice of the time quantum is critical as it affects the algorithm's performance. This paper proposes a new algorithm that further improved on the Improved Round Robin CPU (IRR) scheduling algorithm by Manish and AbdulKadir. The proposed algorithm was implemented and benchmarked against five other algorithms available in the literature. The proposed algorithm compared with the other algorithms, produces minimal average waiting time (AWT), average turnaround time (ATAT), and number of context switches (NCS). Based on these results, the proposed algorithm should be preferred over other scheduling algorithms for systems that adopt RR CPU scheduling.

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Summary Scientific and technological advancements lead to the continuous generation of a large amount of data. These datasets are analyzed computationally to reveal patterns and trends. While the presence of noisy and irrelevant features or attributes in these datasets is unavoidable, they negatively impact the performance of classification techniques. Feature selection is a method to pre‐process these datasets by selecting the most informative features while concurrently improving the classification accuracy. Recently, several metaheuristic algorithms were employed in this feature selection process, including particle swarm optimization (PSO). PSO is prominent in the field of feature selection due to its simplicity and global search abilities. However, it may get stuck in local optima. To solve this problem, a new update mechanism in PSO is proposed and the PSO is hybridized with a local search method. To evaluate the performance of the proposed algorithm, benchmark datasets from the University of California in Irvine (UCI) repository were utilized, the k‐nearest neighbor as the classifier. Results show that the proposed feature selection technique outperforms other optimization algorithms on these feature selection problems.

Feature Selection (FS) is an efficient technique use to get rid of irrelevant, redundant and noisy attributes in high dimensional datasets while increasing the efficacy of machine learning classification. The CSA is a modest and efficient metaheuristic algorithm which has been used to overcome several FS issues. The flight length (fl) parameter in CSA governs crows' search ability. In CSA, fl is set to a fixed value. As a result, the CSA is plagued by the problem of being hoodwinked in local minimum. This article suggests a remedy to this issue by bringing five new concepts of time dependent fl in CSA for feature selection methods including linearly decreasing flight length, sigmoid decreasing flight length, chaotic decreasing flight length, simulated annealing decreasing flight length, and logarithm decreasing flight length. The proposed approaches' performance is assessed using 13 standard UCI datasets. The simulation result portrays that the suggested feature selection approaches overtake the original CSA, with the chaotic-CSA approach beating the original CSA and the other four proposed approaches for the FS task.

Representation of results/data graphically depicts a better understanding of the behavior of the results/data. Contour plotting is an easy way of representing results/data. Contouring algorithms use linear interpolation in determining the point of the intersection between contour lines and grid segments when drawing contour lines. Using linear interpolation is not very precise and results in discontinuities at end points. This paper presents and examines the contouring algorithm that uses inverse distance weighting interpolation in determining the point of the intersection between contour lines and grid segments of randomly generated data. Comparison made between the maps produced by these algorithms that use linear, cubic and inverse distance weighting interpolation, showed that the map produced by inverse distance weighting interpolation is wrong because different contour lines cross each other, the map produced by cubic interpolation depicts less information because some contour lines are missing when compared with the map produced by linear interpolation