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pro vyhledávání: '"Aditya Pribadi Kalapaaking"'
Publikováno v:
IEEE Transactions on Emerging Topics in Computing. :1-11
Due to the rising awareness of privacy and security in machine learning applications, federated learning (FL) has received widespread attention and applied to several areas, e.g., intelligence healthcare systems, IoT-based industries, and smart citie
Autor:
Nour Moustafa, Abdelaziz Bouras, Mohammad Saidur Rahman, Aditya Pribadi Kalapaaking, Ibrahim Khalil
Publikováno v:
IEEE Transactions on Industrial Informatics. 18:5007-5017
Advanced Industrial Internet-of-Things (IIoT), such as smart grids, 5G-enabled unmanned aerial vehicles (UAV), and supply chain 4.o, can be used to facilitate smart management. Nevertheless, IIoT systems generate huge amounts of data that need to be
The widespread adoption of Internet of Things (IoT) devices in smart cities, intelligent healthcare systems, and various real-world applications have resulted in the generation of vast amounts of data, often analyzed using different Machine Learning
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::668ccb185a6af042ba4fe3e04d6bde66
Autor:
Aditya Pribadi Kalapaaking, Veronika Stephanie, Ibrahim Khalil, Mohammed Atiquzzaman, Xun Yi, Mahathir Almashor
Rapidly developing intelligent healthcare systems are underpinned by Sixth Generation (6G) connectivity, ubiquitous Internet of Things (IoT), and Deep Learning (DL) techniques. This portends a future where 6G powers the Internet of Medical Things (Io
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cd8b3b4a014fddf737d4f8d6a329087c
Publikováno v:
Network and System Security ISBN: 9783031230196
Smart manufacturing systems involve a large number of interconnected devices resulting in massive data generation. Cloud computing technology has recently gained increasing attention in smart manufacturing systems for facilitating cost-effective serv
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::eff69626131886e2cf00015e1cfe63e4
https://doi.org/10.1007/978-3-031-23020-2_35
https://doi.org/10.1007/978-3-031-23020-2_35