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Titlebook: Advances in Electrical and Computer Technologies; Select Proceedings o Thangaprakash Sengodan,M. Murugappan,Sanjay Misra Conference proceed

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41#
發(fā)表于 2025-3-28 14:35:15 | 只看該作者
Multi-Dimensional Incompressible Flows, varieties of their thinking sorts on each time based tweeting. The principle intention of this work is to demonstrate the performance execution between KNN and LR algorithms. Moreover, KNN performs superior to LR with 98% precision of genuine positivity upon individuals thinking examples or changes from constant data.
42#
發(fā)表于 2025-3-28 20:06:02 | 只看該作者
Computational Fluid and Solid Mechanicsain 99.95% of the variability in the shrinkage, and the relation of the shrinkage with the humidity percentage is inversely proportional, but the relation of this variable with the color of roasted coffee is directly proportional. The tests applied to the model wastes proved that the model is suitable for predicting the shrinkage in the process.
43#
發(fā)表于 2025-3-29 02:07:22 | 只看該作者
44#
發(fā)表于 2025-3-29 03:39:54 | 只看該作者
1876-1100 .Covers a broad range of topics useful for students as well The book comprises select proceedings of the first International Conference on Advances in Electrical and Computer Technologies 2019 (ICAECT 2019). The papers presented in this book are peer reviewed and cover wide range of topics in Electr
45#
發(fā)表于 2025-3-29 09:04:31 | 只看該作者
46#
發(fā)表于 2025-3-29 14:38:54 | 只看該作者
47#
發(fā)表于 2025-3-29 16:38:08 | 只看該作者
State Equations for Reacting Air,does not compute the expression concerned. This leads to unnecessary edge splitting. In this paper, the insert equation of the PRE algorithm is updated to avoid the edge splitting as far as possible, and hence the algorithm becomes more compact and beautiful.
48#
發(fā)表于 2025-3-29 22:04:26 | 只看該作者
49#
發(fā)表于 2025-3-30 01:38:16 | 只看該作者
50#
發(fā)表于 2025-3-30 07:04:23 | 只看該作者
Euler and Navier Stokes Systems,ters, CNN model learns spatial information of given RGB image and creates a robust system for classification. The results are tested on benchmark PatternNet[.] dataset with different image augmentation parameters and size of it. Significant amount of accuracy is achieved using the proposed technique.
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