cinder
发表于 2025-3-26 21:23:22
Neferti X. M. Tadiar,Angela Y. Davisr time. Online data-stream outlier detection can indeed be more difficult and challenging. This is because new data points are continuously arriving, and the outlier detection algorithm must process them in real-time. Our idea is to use online evolving spiking neural network classifier and dynamic o
Aggrandize
发表于 2025-3-27 01:52:43
https://doi.org/10.1007/978-1-4039-8261-2r system. With the development of social production, the electricity consumption in people’s daily life, factories and enterprises is continuously increasing, it also increases the difficulty of electric load forecasting. Traditional methods are difficult to analyze the huge and complex electricity
Explicate
发表于 2025-3-27 08:38:37
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方舟
发表于 2025-3-27 11:10:03
https://doi.org/10.1007/978-1-4039-8261-2f cancer can be predicted by analyzing lncRNAs. However, lncRNA is characterized by a limited amount of data samples and a large number of expression levels of gene features, where there exist much redundancy. It results in difficulty in cancer predicting. To solve the problem, this paper proposes a
爆炸
发表于 2025-3-27 15:28:39
https://doi.org/10.1007/978-1-4039-8261-2 propose a new algorithm based on the manifold tangent space, called the manifold tangent space-based 2D-DLPP algorithm. This algorithm embeds the covariance matrix into the tangent space of the SPD manifold and utilizes Log-Euclidean Metric Learning (LEM) to fully extract feature information, thus
Canyon
发表于 2025-3-27 19:13:23
Advanced Intelligent Computing Technology and Applications978-981-99-4752-2Series ISSN 0302-9743 Series E-ISSN 1611-3349
宪法没有
发表于 2025-3-27 23:19:44
https://doi.org/10.1007/978-981-99-4752-2Evolutionary Computation and Learning; Swarm Intelligence and Optimization; Information Security; Theor
Grievance
发表于 2025-3-28 05:52:22
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是剥皮
发表于 2025-3-28 06:19:58
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FRAUD
发表于 2025-3-28 12:26:19
https://doi.org/10.1007/978-1-4039-8261-2er fit the non-convex distribution of data (MKTL, Multi-core K-means Transfer Learning). The experimental results show that MKTL achieves the best average accuracy in 3 datasets. Compared with the original methods (kNN, TCA, GFK, JDA), the performance of MKTL is improved by 2.5 ~ 12.8 percentage in high computational efficiency.