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Titlebook: Intelligent Computing for Sustainable Development; First International S. Satheeskumaran,Yudong Zhang,Danilo Pelusi Conference proceedings

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樓主: Remodeling
21#
發(fā)表于 2025-3-25 04:45:35 | 只看該作者
Md Noorullah Khan,E. Srinivasit Hand in Hand. Anf?nglich wurden fast ausschlie?lich tierische und pflanzliche Fette und ?le zur Herabminderung der Reibung verwendet. Schweineschmalz, Unschlitt, Oliven?l, Rüb?l, Tran und andere ?le bildeten die Hauptbestandteile der Schmiermaterialien, die zu jener Zeit in Gebrauch standen. Inde
22#
發(fā)表于 2025-3-25 09:58:29 | 只看該作者
23#
發(fā)表于 2025-3-25 14:21:22 | 只看該作者
24#
發(fā)表于 2025-3-25 17:01:31 | 只看該作者
A Senthil Kumar,Selvaraj Kesavan,Kumar Neeraj,N Sharath Babu,K Sasikala,Bethelegem Addisu?lteren Vorstellungen von . und ., wonach das Erd?l durch Einwirkung von Wasserd?mpf en auf Eisenkarbid entstanden w?re, sind heute verlassen, weil sie unter anderem die optische Aktivit?t des Erd?les nicht zu erkl?ren verm?gen. Nach der Theorie von ., welche lange Zeit die gr??te Wahrscheinlichkeit
25#
發(fā)表于 2025-3-25 20:02:46 | 只看該作者
26#
發(fā)表于 2025-3-26 01:53:52 | 只看該作者
Darwinian Lion Swarm Optimization-Based Extreme Learning Machine with Adaptive Weighted Smote for Hg values based on the class distribution. Thirdly, Adaptive Weighted Synthetic Minority Oversampling Technique with Natural Neighbors (AWSMOTE-NN) is proposed to resolve the drawbacks of collinearity in the imbalanced dataset and improve the class balancing. After pre-processing, the features are ex
27#
發(fā)表于 2025-3-26 07:48:23 | 只看該作者
State of the Art Analysis of Word Sense Disambiguation,eviews various approaches, and resources, used in WSD. The survey includes papers from various journals and discusses recent trends and competitions in the field, as well as future directions for research.
28#
發(fā)表于 2025-3-26 09:26:33 | 只看該作者
29#
發(fā)表于 2025-3-26 15:07:45 | 只看該作者
An Effective Framework for the Background Removal of Tomato Leaf Disease Using Residual Transformermance, which is contrasted with more conventional approaches. The RTN model classified tomato leaf diseases successfully. The accuracy analysis for the suggested tomato leaf classification model, RTN, demonstrated 8.04 percent better results than CNN, 6.81 percent better results than Res-net, and 4.
30#
發(fā)表于 2025-3-26 18:07:18 | 只看該作者
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