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Titlebook: International Conference on Communication, Computing and Electronics Systems; Proceedings of ICCCE V. Bindhu,Jo?o Manuel R. S. Tavares,Chan

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樓主: audiogram
61#
發(fā)表于 2025-4-1 05:12:57 | 只看該作者
U. B. Mahadevaswamy,D. Aashritha,Nikhil S. Joshi,K. N. Naina Gowda,M. N. Syed Asifme a world leader in drug law reform. Real world policy changes, beginning with innovative harm reduction programs that challenged zero-tolerance ideologies, through the rapid roll out of state level medical cannabis programs, and concluding with the recent moves to regulate non-medical cannabis mar
62#
發(fā)表于 2025-4-1 08:13:01 | 只看該作者
Sarka Hubackoval Code (COIP), which establishes threshold quantities for consumers; an innovative approach that is supposed to distinguish consumers from producers and drug traffickers. Regardless of the achievements made during the last years, the new legal Ecuadorian structure has some limits that block effectiv
63#
發(fā)表于 2025-4-1 12:47:44 | 只看該作者
64#
發(fā)表于 2025-4-1 14:52:44 | 只看該作者
65#
發(fā)表于 2025-4-1 20:53:21 | 只看該作者
Distributed DBSCAN Protocol for Energy Saving in IoT Networks,dic cluster head strategy is proposed based on certain criteria like remaining energy, number of neighbors, and the distance for each node in the cluster. The cluster head will be chosen in a periodic and distributed way to consume the power in a balanced way in the IoT sensor devices inside each cl
66#
發(fā)表于 2025-4-2 01:32:47 | 只看該作者
Texture-Based Face Recognition Using Grasshopper Optimization Algorithm and Deep Convolutional Neuror to select the optimal feature vectors. At last, deep convolutional neural network (DCNN) was applied to classify the person’s facial image. The experimental result proves that the proposed model improved recognition accuracy up to 1.78–8.90% compared to the earlier research works such as improved
67#
發(fā)表于 2025-4-2 05:33:40 | 只看該作者
An Interactive Framework to Compare Multi-criteria Optimization Algorithms: Preliminary Results on cal interface that allows non-expert users in multi-objective optimization is proposed to interact and compare the performance of the NSGA-II and MOPSO algorithms. It is chosen qualitatively from a group of five preselected algorithms as members of evolutionary algorithms and swarm intelligence. The
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