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Titlebook: Artificial Intelligence and Blockchain for Future Cybersecurity Applications; Yassine Maleh,Youssef Baddi,Imed Romdhani Book 2021 The Edit

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41#
發(fā)表于 2025-3-28 17:48:32 | 只看該作者
Urban Land Use Land Cover Changeages (4504 pneumonia and 1583 normal). As a result, we can conclude that the fine-tuned version of Resnet50 shows highly satisfactory performance with rate of increase in training and testing accuracy (more than 96% of accuracy).
42#
發(fā)表于 2025-3-28 20:05:14 | 只看該作者
43#
發(fā)表于 2025-3-28 22:54:57 | 只看該作者
https://doi.org/10.1007/978-3-031-38801-9fying the type of crime. Na?ve Bayes classification algorithm is used to classify the crime into subcategories of classes with geographical factors, and temporal factors from RSS feeds. Mallet package is used for extracting the keywords from the newsfeeds. K-means algorithm is used to identify the h
44#
發(fā)表于 2025-3-29 04:17:20 | 只看該作者
Artificial Intelligence and Blockchain for Future Cybersecurity Applications
45#
發(fā)表于 2025-3-29 10:06:44 | 只看該作者
46#
發(fā)表于 2025-3-29 14:27:18 | 只看該作者
Securing Vehicular Network Using AI and Blockchain-Based Approachesintelligence (AI) and blockchain technology, researchers have solved vehicular network-related security problems using those technologies separately and combinedly as well. The common security concerns include Sybil attacks, Denial-of-service (DoS) attacks, man-in-the-middle (MITM) attacks, maliciou
47#
發(fā)表于 2025-3-29 16:17:44 | 只看該作者
Privacy-Preserving Multivariant Regression Analysis over Blockchain-Based Encrypted IoMT Datad any trusted third-party. It requires only three interplays in each iteration. Severe security inquiry proves that secure linear regression preserves sensitive data privacy for each data provider and analyst. The secure linear regression achieved 0.78, 0.066, and 0.196 adjusted . on BCWD, HDD, and
48#
發(fā)表于 2025-3-29 21:51:48 | 只看該作者
49#
發(fā)表于 2025-3-30 02:23:46 | 只看該作者
50#
發(fā)表于 2025-3-30 05:20:59 | 只看該作者
AntiPhishTuner: Multi-level Approaches Focusing on Optimization by Parameters Tuning in Phishing URL mean squared error (MSE) 0.30, and also a mean absolute error (MAE) 0.074 where the number of epochs was 50 and Adam optimizer as an optimizer. Using two-layer NN with AdaGard optimizer can provide accuracy of 0.95, with MSE 0.30 and MAE 0.074. NN provides these results with 150 epochs. Stack gener
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