Acta Informatica Pragensia 2022, 11(1), 141-144 | DOI: 10.18267/j.aip.1755601
Call for Special Issue Papers: Deep Learning Blockchain-enabled Technology for Improved Healthcare Industrial Systems
- Mazin Abed Mohammed
ORCID...1, Seifedine Kadry ORCID...2, Oana Geman ORCID...3 - 1 College of Computer Science and Information Technology, University of Anbar, Iraq
- 2 Department of Applied Data Science, Noroff University College, Norway
- 3 Department of Health and Human Development, Ștefan cel Mare University of Suceava, Romania
The main objective of this special issue is to bring together diverse, novel and impactful research work on explainable deep learning for medicine based on the Internet of Medical Things, thereby accelerating research in this field. The regulation of Internet of Medical Things (IoMT) aware industrial networks for medical science applications has been evolving day by day. An IoMT industrial network consists of different bio-medical sensors, wireless technologies and cloud computing services to run different healthcare applications. However, IoMT industrial networks also suffer from dynamics uncertainties, such as intermittent changes in wireless network values, availability of cloud services and security issues, and require flexible systems to cope with these challenges for healthcare applications in the network.
Keywords: Internet of medical things; Blockchain; Cloud computing; Artificial intelligence; Special issue.
Accepted: February 2, 2022; Prepublished online: February 2, 2022; Published: March 13, 2022 Show citation
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References
- Lakhan, A., Mohammed, M. A., Kozlov, S., & Rodrigues, J. J. P. C. (2021a). Mobile-fog-cloud assisted deep reinforcement learning and blockchain-enable IoMT system for healthcare workflows. Transactions on Emerging Telecommunications Technologies. https://doi.org/10.1002/ett.4363
Go to original source... - Lakhan, A., Mohammed, M. A., Rashid, A. N., Kadry, S., Panityakul, T., Abdulkareem, K. H., & Thinnukool, O. (2021b). Smart-Contract Aware Ethereum and Client-Fog-Cloud Healthcare System. Sensors, 21(12), 4093. https://doi.org/10.3390/s21124093 Go to original source...
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- Lakhan, A., Abed Mohammed, M., Kadry, S., Hameed Abdulkareem, K., Taha AL-Dhief, F., & Hsu, C.-H. (2021d). Federated learning enables intelligent reflecting surface in fog-cloud enabled cellular network. PeerJ Computer Science, 7, e758. https://doi.org/10.7717/peerj-cs.758 Go to original source...
- Lakhan, A., Mohammed, M. A., Obaid, O. I., Chakraborty, C., Abdulkareem, K. H., & Kadry, S. (2022). Efficient deep-reinforcement learning aware resource allocation in SDN-enabled fog paradigm. Automated Software Engineering, 29(1). https://doi.org/10.1007/s10515-021-00318-6 Go to original source...
- Mutlag, A. A., Ghani, M. K. A., Mohammed, M. A., Lakhan, A., Mohd, O., Abdulkareem, K. H., & Garcia-Zapirain, B. (2021). Multi-Agent Systems in Fog-Cloud Computing for Critical Healthcare Task Management Model (CHTM) Used for ECG Monitoring. Sensors, 21(20), 6923. https://doi.org/10.3390/s21206923 Go to original source...
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