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Titlebook: Algorithms in Machine Learning Paradigms; Jyotsna Kumar Mandal,Somnath Mukhopadhyay,Kousik D Book 2020 Springer Nature Singapore Pte Ltd.

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發(fā)表于 2025-3-25 04:17:03 | 只看該作者
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發(fā)表于 2025-3-25 10:56:53 | 只看該作者
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發(fā)表于 2025-3-25 14:32:31 | 只看該作者
Tracking Changing Human Emotions from Facial Image Sequence by Landmark Triangulation: An Incircle-cation of different emotional transitions from various face image sequences. Results of the proposed method obtained by application on various benchmark image databases are found to be quite impressive and encouraging compared to existing state-of-the-art technique.
24#
發(fā)表于 2025-3-25 18:15:16 | 只看該作者
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發(fā)表于 2025-3-25 21:43:25 | 只看該作者
,?Weltliteratur‘ zuerst bei Wieland,e which in turn may cause financial losses as well as bring about a decline in the firm’s market performance. This paper deals with the development of a strictly fact-based expert system for appropriate supplier selection and shows how rules can be broken down into atomic clauses.
26#
發(fā)表于 2025-3-26 00:17:17 | 只看該作者
Der einheitliche EU-Zahlungsverkehroncept of score function and accuracy function of H–ITFN are defined and H–ITF prioritized weighted averaging and geometric operators based on Einstein operations are developed. Some desirable properties of the proposed operators are investigated in detail. A method for ordering the alternatives in
27#
發(fā)表于 2025-3-26 07:44:47 | 只看該作者
28#
發(fā)表于 2025-3-26 11:01:44 | 只看該作者
29#
發(fā)表于 2025-3-26 12:59:24 | 只看該作者
https://doi.org/10.1007/978-3-662-39624-7em of seasonality. A few hybrid fuzzy time series models investigated the problem of forecasting in the presence of seasonal variation. But these techniques follow complex computational procedures. The aim of this present study is to develop a new fuzzy time series forecasting model that can process
30#
發(fā)表于 2025-3-26 19:46:00 | 只看該作者
https://doi.org/10.1007/978-3-662-39624-7ect of the agricultural industry. It is quite challenging to automatically classify fruits and vegetables from digital images. The task of automatic classification becomes more difficult when the image is captured from a different viewing angle. This paper proposes a complete texture-based approach
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