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Titlebook: Advances in Signal Processing and Intelligent Recognition Systems; 5th International Sy Sabu M. Thampi,Rajesh M. Hegde,Juan M. Corchado Con

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發(fā)表于 2025-3-30 09:42:20 | 只看該作者
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發(fā)表于 2025-3-30 12:42:36 | 只看該作者
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發(fā)表于 2025-3-30 20:07:44 | 只看該作者
https://doi.org/10.1007/0-387-31262-5r, using the edge-computing capability of the smart plugs, some lightweight artificial intelligence-based NILM algorithms are developed and implemented inside the smart plugs. These practical algorithms are validated using massive hardware experiments. Case studies indicate that high accuracy can be
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發(fā)表于 2025-3-31 00:26:54 | 只看該作者
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發(fā)表于 2025-3-31 02:05:25 | 只看該作者
Richard H. Enns,George C. McGuire which limits the overall performance of the LID-Mono system that suffers with high PERs at small windows (poor LID performance) and mismatched decoding conditions at long windows (due to poor code-switching detection time resolution). We show that the Multi-PRS, by virtue of not having to do a fron
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發(fā)表于 2025-3-31 06:53:32 | 只看該作者
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發(fā)表于 2025-3-31 12:12:37 | 只看該作者
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發(fā)表于 2025-3-31 16:30:54 | 只看該作者
https://doi.org/10.1007/978-0-387-49333-6a vector. We generated our data-set consisting of 40 patients. For patient diagnosed with Obstructive Pulmonary Disease we assigned the label as +1 and for those who have other disease as ?1. Different machine learning algorithms such as kNN, SVM, Random Forest and Naive Bayes were applied to the da
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發(fā)表于 2025-3-31 18:20:16 | 只看該作者
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