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Titlebook: Computational Intelligence in Data Mining - Volume 1; Proceedings of the I Lakhmi C. Jain,Himansu Sekhar Behera,Durga Prasad Conference pr

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31#
發(fā)表于 2025-3-26 21:16:13 | 只看該作者
32#
發(fā)表于 2025-3-27 02:45:27 | 只看該作者
33#
發(fā)表于 2025-3-27 07:30:49 | 只看該作者
https://doi.org/10.1007/978-1-4684-0023-6em objective while considering both the real and reactive as a sub problem. The proposed method is used for solving the non-linear optimization problems while minimizing the objective voltage stability margin is also maintained. The proposed technique is tested on IEEE 57 bus system.
34#
發(fā)表于 2025-3-27 11:27:43 | 只看該作者
35#
發(fā)表于 2025-3-27 16:39:00 | 只看該作者
Mikael S?ndergaard,Dorthe D?jbakrforming communication between them becomes difficult. In multi mobile agent environment, a reliable communication is still a big challenge. In this paper, we have addressed the pros and cons of the existing different Mobile Agent communication protocols with their limitations. This paper also prese
36#
發(fā)表于 2025-3-27 17:46:19 | 只看該作者
Martha A. Gephart,Victoria J. Marsickllection, tracking the vehicle’s route. It is more challenging to automate the identification of Indian vehicles by using it’s number plates. The font type and font size of the letters used randomly, the dealer’s logo or other artefacts may be available on the plate. In this proposed algorithm it is
37#
發(fā)表于 2025-3-27 22:41:47 | 只看該作者
https://doi.org/10.1007/978-3-8349-9459-2ons of the intensities and the information about relative positions of neighboring pixels of an image. GLCM matrices are calculated corresponding to different orientation (0, 45, 90, 135) with four different offset values. After the calculation of GLCMs, each GLCM is divided into 32?×?32 sub-matrice
38#
發(fā)表于 2025-3-28 04:15:31 | 只看該作者
39#
發(fā)表于 2025-3-28 10:11:29 | 只看該作者
40#
發(fā)表于 2025-3-28 12:05:35 | 只看該作者
https://doi.org/10.1007/978-3-642-22209-2have been used for clustering task. The Cat Swarm Optimization (CSO) is the latest meta-heuristic algorithm which has been applied in clustering field and provided better results than K-Means and Particle Swarm Optimization (PSO). However, this algorithm is suffered with diversity problem. To overco
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