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- W4327926850 abstract "Numerous search techniques have been created to address diverse optimization problems in a variety of domains, grabbing the interest of many author/researchers. Optimization algorithm is frequently influenced by the environment/nature inspired or involves modifying current algorithms. The novel methods are currently configured and concentrated on reaching the intended optimization objective. While this is productive and beneficial in the short term, it is insufficient in the long term because it needs to be repeated for all novel issues that appear in addition to specialized challenges, completely consistent technique can be utilized for all real-world situations. In this research, we have to improve its global optimization search space for optimal selection is a modern meta-heuristic algorithm based on crow intelligence large dimensions of constraint variables. The experimental findings demonstrate that when compared to other feature selection algorithms, the feature subset obtained by Constrained Crow Search Algorithm (CCSA) has higher classification accuracy and higher dimensional compression capabilities, indicating that Constrained Crow Search Algorithm (CCSA) has the ability to deal with feature selection problems with strong competitiveness and sufficient superiority." @default.
- W4327926850 created "2023-03-21" @default.
- W4327926850 creator A5004712690 @default.
- W4327926850 creator A5066181482 @default.
- W4327926850 date "2023-02-18" @default.
- W4327926850 modified "2023-09-28" @default.
- W4327926850 title "Incubation of a Metaheuristic Searching Approach for Intrusion Detection into A New Constrained Crow Search Algorithm" @default.
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- W4327926850 doi "https://doi.org/10.1109/sceecs57921.2023.10063120" @default.
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