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Titlebook: Intelligent Computing & Optimization; Proceedings of the 5 Pandian Vasant,Gerhard-Wilhelm Weber,J. Joshua Tho Conference proceedings 2023 T

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樓主: Malicious
51#
發(fā)表于 2025-3-30 11:19:57 | 只看該作者
On Coupling a Content-Based Routing Process with a Multi-label Residual Convolutional Neural Network in order to effectively categorize texts in numerous applications. In natural language processing, several machine learning techniques have shown astounding outcomes. These learning algorithms’ effectiveness depends on their ability to comprehend intricate models and non-linear correlations in data
52#
發(fā)表于 2025-3-30 15:12:51 | 只看該作者
Solving a Generalized Network Design Problem Using the Archimedes Optimization Algorithmce real-world cases are complex and computationally challenging systems that require effective optimization techniques, therefore recently, approximate resolution methods, specifically metaheuristics frameworks are in focus. In this research, we investigate a specific variant of Network Design Probl
53#
發(fā)表于 2025-3-30 19:49:11 | 只看該作者
54#
發(fā)表于 2025-3-30 23:16:40 | 只看該作者
Metaheuristic Solver for?Problems with?Permutative Representationver. However, exact solvers do not scale well and thus custom metaheuristic algorithms are being designed to provide better scalability at the cost of no optimality guarantees and time-consuming development. This paper proposes a novel formalism for a large class of problems with permutative represe
55#
發(fā)表于 2025-3-31 03:25:51 | 只看該作者
56#
發(fā)表于 2025-3-31 07:28:27 | 只看該作者
57#
發(fā)表于 2025-3-31 09:16:10 | 只看該作者
Bell Pepper Leaf Disease Classification Using Convolutional Neural Networkr the quality of agricultural products. Leaf disease can develop as a result of bacterial, fungal, or other causes. Finding and detecting sick plants in the open eye takes a long time. As a result, automatically detecting and resolving plant disease is critical. Pepper Bacterial spot disease is gene
58#
發(fā)表于 2025-3-31 16:19:53 | 只看該作者
Surrogate-assisted Genetic Algorithm for?Multi-project Schedulingthm (SaGA) is presented for solving the RCMPSP. A non-random initialization starts the SaGA with a certain diversity and quality. A forward-backward improvement (FBI) based local search is utilized to intensify high quality solutions. Surrogation in genetic algorithm (GA) estimates few individual’s
59#
發(fā)表于 2025-3-31 21:08:27 | 只看該作者
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