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Titlebook: Applied Computer Sciences in Engineering; 11th Workshop on Eng Juan Carlos Figueroa-García,German Hernández,Elvis Conference proceedings 20

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期刊全称Applied Computer Sciences in Engineering
期刊简称11th Workshop on Eng
影响因子2023Juan Carlos Figueroa-García,German Hernández,Elvis
视频video
学科分类Communications in Computer and Information Science
图书封面Titlebook: Applied Computer Sciences in Engineering; 11th Workshop on Eng Juan Carlos Figueroa-García,German Hernández,Elvis Conference proceedings 20
影响因子.The two-volume set CCIS 2222 and 2223 constitutes the proceedings of the 11th Workshop on Engineering Applications, WEA 2024, which took place in Barranquilla, Colombia, during October 23–25, 2024...The 42 full papers presented here were carefully reviewed and selected from 97 submissions. The papers are organized in the following topical sections:..Part I - Artificial Intelligence...Part II - Optimization; Simulation; Applications..
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Social and Environmental Factorsblem. Additionally, we explore the incorporation of chaotic maps into the binarization process. To benchmark the binary Fox Optimizer, we compare it with two well-known and documented metaheuristics: Particle Swarm Optimization and Grey Wolf Optimizer. Each algorithm is tested with standard, sine ch
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Sexually transmitted infections and distributing the products to end consumers. In this context, it is proposed to design a model for locating rural centers that serves as logistical support for the yam agri-food supply chain. This location model for hubs aims to plan the distribution in the agri-food supply chain of yams in the
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Sexually transmitted infections sector that is crucial in addressing these challenges. In this context, this work conducted a theoretical review of the optimization techniques used and the information available in a specific case study. From the above, two techniques were selected, linear programming and stochastic linear program
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Natural History of HIV Infectionon approach based on a non-convex cardinality constraint approximation of the ℓ.-norm to identify the optimal asset weights of the tracking portfolio. The results demonstrate that the ℓ.-norm-constrained sparse tracking portfolio is computationally efficient and exhibits a notable reduction in track
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