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Titlebook: Computational Intelligence: Theories, Applications and Future Directions - Volume II; ICCI-2017 Nishchal K. Verma,A. K. Ghosh Conference pr

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樓主: Coenzyme
31#
發(fā)表于 2025-3-26 22:40:24 | 只看該作者
Fog Computing-Based Autonomic Security Approach to Internet of Things Applicationsattack forecasting module based on Gaussian process regression model, (2) attack detection module using cognitive approach and correlation analysis, (3) fuzzy-based incident response module to handle the attacks. The experimental results prove the effectiveness of the proposed approach in terms of detection accuracy and detection time.
32#
發(fā)表于 2025-3-27 05:01:10 | 只看該作者
https://doi.org/10.1057/9781137382054convolutional neural network layer. The result of the proposed approach is compared with random initialization of the kernel weights. The results show that the proposed method performs comparably to random initialization.
33#
發(fā)表于 2025-3-27 06:02:04 | 只看該作者
34#
發(fā)表于 2025-3-27 09:54:12 | 只看該作者
Deviations of the Developmental Process, side. Experiments to illustrate the salient features of the HyEGD approach are performed on two benchmark problems: COIL-20 dataset and MNIST dataset. The results clearly highlight the powerful capability of generating architectures based on the required performance and size of network.
35#
發(fā)表于 2025-3-27 16:20:34 | 只看該作者
36#
發(fā)表于 2025-3-27 21:12:44 | 只看該作者
37#
發(fā)表于 2025-3-27 22:15:15 | 只看該作者
https://doi.org/10.1007/978-3-319-97785-0agation artificial neural network classifier which includes two parameters such as anxiety and confidence, for selecting a subset of typical conclusive visual topographies from the set of latent structures. The combination of these two procedures enabled rapid learning and thereby computational swiftness reasoning the auspicious facial emotions.
38#
發(fā)表于 2025-3-28 03:06:30 | 只看該作者
39#
發(fā)表于 2025-3-28 07:39:27 | 只看該作者
40#
發(fā)表于 2025-3-28 11:17:41 | 只看該作者
Simultaneous Structure and Parameter Learning of Convolutional Neural Network side. Experiments to illustrate the salient features of the HyEGD approach are performed on two benchmark problems: COIL-20 dataset and MNIST dataset. The results clearly highlight the powerful capability of generating architectures based on the required performance and size of network.
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