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Titlebook: Computer and Information Sciences; 31st International S Tadeusz Czachórski,Erol Gelenbe,Ricardo Lent Conference proceedings‘‘‘‘‘‘‘‘ 2016 Th

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31#
發(fā)表于 2025-3-27 00:08:16 | 只看該作者
https://doi.org/10.1007/978-3-319-04135-3arning algorithm to classify a new android malware. We extract a fairly adequate set of malware features and we evaluate a machine learning based classification method. The runtime model is built and it can be implemented to detect variants of an android malware. The metrics illustrate the effectiveness of the proposed classification method.
32#
發(fā)表于 2025-3-27 04:44:37 | 只看該作者
33#
發(fā)表于 2025-3-27 08:47:16 | 只看該作者
34#
發(fā)表于 2025-3-27 13:26:54 | 只看該作者
A Novel Multi-criteria Inventory Classification Approach: Artificial Bee Colony Algorithm with VIKORhe objective function. The results of our proposed approach were obtained from a widely used data set in the literature, and outperforms the existing classification models from the literature, by obtaining better inventory cost.
35#
發(fā)表于 2025-3-27 17:28:10 | 只看該作者
Android Malware Classification by Applying Online Machine Learningarning algorithm to classify a new android malware. We extract a fairly adequate set of malware features and we evaluate a machine learning based classification method. The runtime model is built and it can be implemented to detect variants of an android malware. The metrics illustrate the effectiveness of the proposed classification method.
36#
發(fā)表于 2025-3-27 20:03:29 | 只看該作者
37#
發(fā)表于 2025-3-28 01:05:24 | 只看該作者
38#
發(fā)表于 2025-3-28 05:46:15 | 只看該作者
The Ethics of Energy Sustainabilityeferences. Our ISA tracks the user’s preferences and makes a selection on the output of one or more search engines using the preferences that it has learned. We also introduce a “relevance metric” to compare the performance of our Intelligent Search Assistant against a few search engines, showing that it provides better performance.
39#
發(fā)表于 2025-3-28 09:25:19 | 只看該作者
The Ethics of Information Warfarequality of prepared decision system. The set of classifiers was selected and different discretization algorithms were applied to obtain method invariant outcomes. The comparative results of experiments performed using cross-validation and test sets approaches to system evaluation, and conclusions are presented.
40#
發(fā)表于 2025-3-28 10:50:18 | 只看該作者
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