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Titlebook: Advances in Data-driven Computing and Intelligent Systems; Selected Papers from Swagatam Das,Snehanshu Saha,Jagdish Chand Bansal Conference

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31#
發(fā)表于 2025-3-26 23:24:02 | 只看該作者
32#
發(fā)表于 2025-3-27 04:37:23 | 只看該作者
33#
發(fā)表于 2025-3-27 05:54:20 | 只看該作者
34#
發(fā)表于 2025-3-27 09:40:52 | 只看該作者
https://doi.org/10.1007/978-3-030-50797-8rning models, i.e. multiple linear regression, artificial neural network, support vector machine and random forest. Finally, these models were compared to select the best model for prediction of biological oxygen demand using a set of indicators, i.e. coefficient of determination and root mean square error.
35#
發(fā)表于 2025-3-27 14:25:38 | 只看該作者
36#
發(fā)表于 2025-3-27 18:58:01 | 只看該作者
Soils in Harmony with the Environment,nd the GSA techniques. The simulation results confirm that the GSA exhibits more robust performance and accurate identification results as compared with the RGA and FIPSO methods which have been verified by using various quantitative metrics.
37#
發(fā)表于 2025-3-27 22:03:47 | 只看該作者
,Moments of Vision — and After, a multi-agent system (MAS) approach for solving the FJSP. To optimize the results, genetic algorithm (GA) is also implemented with a multi-agent system. The proposed approach is evaluated by solving standard problem instances, and the results are promising.
38#
發(fā)表于 2025-3-28 04:13:51 | 只看該作者
39#
發(fā)表于 2025-3-28 09:36:55 | 只看該作者
40#
發(fā)表于 2025-3-28 11:16:29 | 只看該作者
https://doi.org/10.1007/978-3-030-50797-8sed method is trained and tested with the google speech command (GSC) dataset by considering eight speech commands. The experimental results demonstrate the efficiency and effectiveness of the proposed method for solving speech features-based classification problems like SCR.
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