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Titlebook: Hydrometeorology; Forecasting and Appl Kevin Sene Book 2024Latest edition The Editor(s) (if applicable) and The Author(s), under exclusive

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樓主: 武士精神
11#
發(fā)表于 2025-3-23 09:49:27 | 只看該作者
12#
發(fā)表于 2025-3-23 17:18:55 | 只看該作者
, low cost monitoring, numerical weather prediction, demand forecasting and dissemination of warnings, including the role of social media and citizen science...Applications include national and community b978-3-031-58271-4978-3-031-58269-1
13#
發(fā)表于 2025-3-23 19:24:27 | 只看該作者
14#
發(fā)表于 2025-3-23 22:46:29 | 只看該作者
15#
發(fā)表于 2025-3-24 02:51:36 | 只看該作者
have similar perceptual characteristics e.g. fruits, nuts, etc. Further, the generated clusters match those obtained from a similar analysis of olfactory bulb odor maps obtained in rats for the same set of chemicals. Our results suggest that convergence mapping combined with IR absorption spectra m
16#
發(fā)表于 2025-3-24 07:20:39 | 只看該作者
17#
發(fā)表于 2025-3-24 13:02:43 | 只看該作者
Kevin Seneds such Multi-way Principal Component Analysis and Independent Component Analysis with a dual aim. First, they are able to improve the recordings by removing noise and aliasing after using a supervised selection of parameters. Secondly, they demonstrate possibilities in the obtaintion of simultaneou
18#
發(fā)表于 2025-3-24 17:49:27 | 只看該作者
Kevin Sene A Resource-Aware Load Balancing Strategy for Real-Time, Cross-vertical IoT Applications has been envisioned. The designed architecture and the proposed model are presented comprehensively with an emphasis on elucidating the resource-aware load balancing strategy. The equations and algorithms for re
19#
發(fā)表于 2025-3-24 21:46:33 | 只看該作者
Kevin Senesed for Machine Learning may increase our understanding about breast cancer prediction and progression. It is important to consider these approaches in daily clinical practice. Neural networks are now a day’s very key and popular field in computational biology, chiefly in the area of radiology, onco
20#
發(fā)表于 2025-3-25 01:34:40 | 只看該作者
Kevin Senebor (KNN), Multi-Layer Perceptron (MLP) and Decision Tree (DT). Training of classifier is implemented based on k-fold cross validation techniques. The predicted accuracy of the proposed model has been compared with recent fusion methods such as Majority Voting, Distribution Summation and Dempster–Sh
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