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Titlebook: Congress on Smart Computing Technologies; Proceedings of CSCT Jagdish Chand Bansal,Harish Sharma,Antorweep Chakr Conference proceedings 20

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51#
發(fā)表于 2025-3-30 10:30:35 | 只看該作者
52#
發(fā)表于 2025-3-30 15:44:15 | 只看該作者
Tariffs and Imperfect Competition(threshold) is selected in the feature subset which represents that star. The feature subsets associated with each star are then evaluated using a random forest classifier, while the rest of the algorithm follows the same steps as the Binary Black Hole algorithm. These two Black Hole algorithms are
53#
發(fā)表于 2025-3-30 18:00:11 | 只看該作者
Transport Pricing of Electricity Networksing. It gives an accurate forecast to better understand and plan for the short term. The predictions would be presented along with recent accuracy statistics in absolute and in terms of percentage to get a better context of the predictions. The data are collected from official data endpoints of sour
54#
發(fā)表于 2025-3-30 22:52:53 | 只看該作者
https://doi.org/10.1007/978-1-4757-3756-1 as age, gender, and emotion of the person will be given as a voice message to blind people. Due to the pandemic, the people are wearing clinical masks which makes the recognition process difficult as half of the face is covered, addressed by the system. Proposed system provides efficient communicat
55#
發(fā)表于 2025-3-31 03:09:10 | 只看該作者
56#
發(fā)表于 2025-3-31 07:52:06 | 只看該作者
57#
發(fā)表于 2025-3-31 10:32:01 | 只看該作者
58#
發(fā)表于 2025-3-31 13:51:23 | 只看該作者
A Wavelet Thresholding-Based Approach for Denoising the Color Image Affected by Gaussian Noise,he results confirm that the proposed method gives better results than existing algorithms. It is noticed that most of the noise affecting in the communication channel can be modeled as Gaussian noise. As per the central limit theorem, although the random variables are not normally distributed, they
59#
發(fā)表于 2025-3-31 18:16:06 | 只看該作者
60#
發(fā)表于 2025-4-1 01:21:23 | 只看該作者
Classification of Esophageal Cancer Using Ensembled CNN with Generalized Normal Distribution Optimi an accuracy of 98.02%, sensitivity and .-score of 98.26, the proposed network combination of Xception, ResNet50, DarkNet53, and ShuffleNet with GNDO and M-SVM outperforms all existing techniques. The experimental research shows that the multi-CNN model is more efficient than those using individual
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