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Titlebook: Digital Transformation and Emerging Technologies for Fighting COVID-19 Pandemic: Innovative Approach; Aboul Ella Hassanien,Ashraf Darwish

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41#
發(fā)表于 2025-3-28 16:07:09 | 只看該作者
Deep Learning Technology for Tackling COVID-19 Pandemicchnology have led to the rise of new distributed and learning studies. Throughout this chapter, we discuss how deep learning can contribute to these goals by stepping up ongoing research activities, improving the efficiency and speed of existing methods, and proposing original lines of research.
42#
發(fā)表于 2025-3-28 19:18:26 | 只看該作者
43#
發(fā)表于 2025-3-28 23:54:51 | 只看該作者
Lecture Notes in Computer Scienceed to support the solution proposed to ensure the integration of these technologies to fight the pandemic. Also, numerous emerging technologies used for the COVID-19 fight have been highlighted. Finally, the impact of COVID-19 is discussed, and applications showing how to mitigate this impact using the emerging technologies are outlined.
44#
發(fā)表于 2025-3-29 06:39:33 | 只看該作者
Erkl?rungsf?higkeit semantischer Systemehe examination investigates the method of reasoning of human-robot groups to increase creation utilizing preferences of both the simplicity of coordination and keeping up social removing. This chapter highlights the role of social robotic in fighting COVID-19. Also, it presents the requirements of social robotics.
45#
發(fā)表于 2025-3-29 10:05:29 | 只看該作者
46#
發(fā)表于 2025-3-29 12:59:49 | 只看該作者
https://doi.org/10.1007/978-0-387-72926-8sector worker and reduce the spread of COVID-19 pandemic. The chapter also presents the problems and challenges and present to the researchers and academics some future research points from the AI point of view that can help healthcare sectors and curbing the COVID-19 spread.
47#
發(fā)表于 2025-3-29 16:26:24 | 只看該作者
Semantik der Adjektive des Deutschen using the transfer learning method and InceptionV3 algorithm has been presented to classify the X-ray images into COVID-19, Normal, and Pneumonia classes. The experimental results show that the proposed model achieved 98% Accuracy on the test set for classifying the images from the 3 different classes.
48#
發(fā)表于 2025-3-29 20:48:18 | 只看該作者
49#
發(fā)表于 2025-3-30 00:11:33 | 只看該作者
50#
發(fā)表于 2025-3-30 04:41:15 | 只看該作者
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