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Titlebook: Conference Proceedings of ICDLAIR2019; Meenakshi Tripathi,Sushant Upadhyaya Conference proceedings 2021 The Editor(s) (if applicable) and

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樓主: 人工合成
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發(fā)表于 2025-3-28 16:45:48 | 只看該作者
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發(fā)表于 2025-3-28 21:55:37 | 只看該作者
Meenakshi Tripathi,Sushant UpadhyayaPresents new technologies and applications in DL, artificial intelligence, and robotics.Includes the results from the International Conference on Deep Learning, Artificial Intelligence and Robotics he
43#
發(fā)表于 2025-3-29 00:07:53 | 只看該作者
Lecture Notes in Networks and Systemshttp://image.papertrans.cn/c/image/235272.jpg
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發(fā)表于 2025-3-29 06:48:52 | 只看該作者
Alternative Splicing of Pre-messenger RNAIt has always been a human desire to see how far up into the sky their creation can reach. It is like humanity to push further than ever and create things better than anyone before they only dreamt of. This complexity of achievements achieved by human beings, however, brings forward other challenges that need to be faced by present scientists.
45#
發(fā)表于 2025-3-29 10:31:06 | 只看該作者
Indoor Navigation Techniques: A Comparative Analysis Between Radio Frequency and Computer Vision-BaIt has always been a human desire to see how far up into the sky their creation can reach. It is like humanity to push further than ever and create things better than anyone before they only dreamt of. This complexity of achievements achieved by human beings, however, brings forward other challenges that need to be faced by present scientists.
46#
發(fā)表于 2025-3-29 13:07:38 | 只看該作者
47#
發(fā)表于 2025-3-29 17:12:48 | 只看該作者
48#
發(fā)表于 2025-3-29 21:29:10 | 只看該作者
A Survey: Secure Indexing & Storage of Big Data,hich is dealing with the large amount of data expected to increase its capacity in the next upcoming years where large amount of data is still unstructured, which is present in the silos as well as in the form of the images, medical notes of prescription, insurance claims data, EPR (Electronic Patie
49#
發(fā)表于 2025-3-30 03:41:58 | 只看該作者
50#
發(fā)表于 2025-3-30 04:44:22 | 只看該作者
Detecting Fake News with Machine Learning, impacting society and politics negatively. Most existing works have used supervised learning but given importance to the words used in the dataset. The approach may work well when the dataset is huge and covers a wide domain. However, getting the labeled dataset of fake news is a challenging proble
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