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Titlebook: High Performance Computing and Networking; Select Proceedings o Ch. Satyanarayana,Debasis Samanta,Rajiv Kumar Kapo Conference proceedings 2

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樓主: CT951
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發(fā)表于 2025-3-23 12:43:16 | 只看該作者
12#
發(fā)表于 2025-3-23 14:25:33 | 只看該作者
Kunal Khadkee certain characteristics are left behind disproportionately. It is wise investing our policy efforts that leverage the most return for limited resources and effort. This chapter frames systems structure in light of health-related disparities found in appropriate housing policy to uplift struggling urban populations.
13#
發(fā)表于 2025-3-23 18:51:26 | 只看該作者
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發(fā)表于 2025-3-23 23:41:50 | 只看該作者
Generative Adversarial Network for Music Generation,tor and on the discriminator. The same pre-trained model is used for several other music tracks like drums, flute, etc., provided the model is trained with appropriate libraries. The outcome of this experiment is evaluated using conventional evaluators and also the esthetics by human observer.
15#
發(fā)表于 2025-3-24 05:26:39 | 只看該作者
A Comparative Study on Network Intrusion Detection System Using Deep Learning Algorithms and EnhancIn this paper, we have calculated evaluation metrics for every algorithm using various data set NSL-KDD to select the best algorithm to fit for the network intrusion detection system (NIDS) and enhanced its accuracy by generating synthetic data using GAN.
16#
發(fā)表于 2025-3-24 10:03:17 | 只看該作者
Indian Sign Language Detection Using YOLOv3,using the trained model. YOLOv3 convolutional neural Network (CNN) uses darknet-53 as a backbone network. CNN is responsible for feature extraction. The effectiveness of the proposed method is validated on a test dataset containing 50 images. The proposed system achieves an average accuracy of 82%.
17#
發(fā)表于 2025-3-24 11:06:03 | 只看該作者
Conference proceedings 2022CHSN 2021). This book highlights the high-quality research articles in machine learning, computer vision, and networks. The content of this volume gives the reader an up-to-date picture of the state-of-the-art connection between computational intelligence, machine learning, and IoT. The papers inclu
18#
發(fā)表于 2025-3-24 15:12:39 | 只看該作者
,Ink Recognition Using TDNN and?Bi-LSTM,were implemented for this task. The analysis showed that the model with TDNN and Bi-LSTM architecture with an additional Trie beam search decoder with Kneser–Ney Interpolated smoothing algorithm using 10,000-word lexicon performed better than the model without a decoder.
19#
發(fā)表于 2025-3-24 20:00:16 | 只看該作者
20#
發(fā)表于 2025-3-25 00:32:29 | 只看該作者
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