Call for Special Issue Papers: Deep Learning Blockchain-enabled Technology for Improved Healthcare Industrial Systems

ing and Indexing: Scopus (Elsevier), DBLP Computer Science Bibliography, RSCI – Russian Science Citation Index, Open J-Gate, CEEOL, ERIH PLUS, DOAJ and other databases. Acta Informatica Pragensia Volume 11, 2022 https://doi.org/10.18267/j.aip.175 4 Notes for prospective authors Submitted papers should not have been previously published nor be currently under consideration for publication elsewhere. All papers must be submitted online. To submit a paper, please read our Submitting articles page. When you are submitting a manuscript, please select the “Special issue” option in “Section”. If you have any suggestions or questions regarding the subject matter, please contact the special issue editor Mazin Abed Mohammed (mazinalshujeary@uoanbar.edu.iq). Before submitting your paper, please make sure you carefully read the instructions to authors. The journal has no strict formatting requirements on submission. There is no restriction on the length of manuscripts. Accepted articles will be published immediately on the journal website with a digital object identifier (DOI) prior to the release of the special issue.


Special issue information
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; see, e.g., Lakhan et al. (2021a).
This special issue focuses on dynamic deep reinforcement learning and blockchain-enabled task scheduling systems for improved IoMT industrial applications. The goal is to persist in dynamic environments, adopt network and node changes and minimise processing costs while ensuring data security in the network. The initial purpose of dynamic deep reinforcement learning and blockchain-enabled task scheduling is to use deep reinforcement learning techniques and an efficient allocation mechanism to plan all Internet of Things (IoT) workflow jobs for different heterogeneous nodes. The created data are then saved and sent to a blockchain-enabled fog-cloud network in certified and secure formats. The direct policy quest in dynamic deep reinforcement learning and blockchain-enabled task scheduling aims to find the best scheduling strategy for dealing with adaptive changes in the problem state. The decentralized protection in a heterogeneous distributed network in dynamic deep reinforcement learning and blockchain-enabled task scheduling is validated using blockchain methods. The suggested dynamic deep reinforcement learning and blockchainenabled task scheduling framework may achieve equivalent outcomes and flexible and realtime task scheduling for healthcare processes compared to traditional scheduling approaches; see, e.g., Lakhan et al. (2021bLakhan et al. ( , 2021cLakhan et al. ( , 2021d or Mutlag et al. (2021).
The use of cloud-based healthcare technologies has been steadily increasing since the advent of 5G wireless networking, blockchain technology and fog computing. The fog cloud is a cooperative paradigm that boosts the performance of IoT applications with both data-intensive analysis and compute-intensive execution at different nodes in distributed computing. Many research studies have investigated the task scheduling problem, the resource allocation problem and application partitioning for healthcare applications in cooperative fog-cloud computing networks with efficient 5G technologies. At the same time, blockchain encourages decentralized security in distributed networks. Different machine learning algorithms have been introduced to adopt dynamic changes in resource and communication nodes during the processing of applications in various locations, such as the Deep-Q-Network resource allocation for IoT healthcare aware meta-heuristics; see, e.g., Lakhan et al. (2022).
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. Therefore, suitable topics for manuscripts include, but are not limited to, the following: • The role of blockchain technology and machine learning in human healthcare • Artificial intelligence approaches based on patient-centric healthcare systems

Seifedine Kadry is currently a full professor of Data Science at the Noroff University
College in Norway. He received a BSc degree in 1999 from the Lebanese University, MSc degree in 2002 from the University of Reims (France) and the EPF Lausanne (Switzerland), PhD in 2007 from the Blaise Pascal University (France), HDR degree in 2017 from the University of Rouen Normandy (France). At present, his research focuses on data science, education using technology, system prognostics, stochastic systems and applied mathematics. He is an ABET program evaluator for computing and for engineering technology. He is a Fellow of IET, a Fellow of IETE, and a Fellow of IACSIT. He is a distinguished speaker of the IEEE Computer Society.
Oana Geman is currently an associate professor at the Ștefan cel Mare University of Suceava in Romania. She received a PhD degree in Electronics and Telecommunication from the Ștefan cel Mare University of Suceava, Romania, in 2005, and later also a habilitation degree. Within the past five years she has published ten books, over 70 articles (50 articles in Web of Science journals; 15 papers in ISI indexed conference volumes and 11 articles in Q1 and Q2 journals as the main author). She is a member of the steering committee of the Medical and Application Symposium and an Elsevier and Springer editor. She was the General Chair of sessions at international conferences in the field of sensors and instrumentation for Internet of Things (IoT) organized in 2019 (ISSI 2019 in Lisbon and AEIT 2019 in Guangzhou). She has been a director or a member in 12 national and international grants. Her current research interests include: non-invasive measurements of biomedical signals, wireless sensors, e-Health, telemedicine, signal processing, nonlinear dynamics analysis, classification and prediction, data mining, deep learning, intelligent systems, bioinformatics and biomedical applications.

About Acta Informatica Pragensia journal
Acta Informatica Pragensia (ISSN 1805-4951) is a peer-reviewed journal on social and business aspects of informatics. It covers mainly the theory, application and management of information systems, as well as interactions between information and communication technologies and people. All articles are published in DIAMOND OPEN ACCESS. The journal has NO CHARGE for article publication. All accepted manuscripts have free professional English proofreading.

Notes for prospective authors
Submitted papers should not have been previously published nor be currently under consideration for publication elsewhere. All papers must be submitted online. To submit a paper, please read our Submitting articles page. When you are submitting a manuscript, please select the "Special issue" option in "Section". If you have any suggestions or questions regarding the subject matter, please contact the special issue editor Mazin Abed Mohammed (mazinalshujeary@uoanbar.edu.iq).
Before submitting your paper, please make sure you carefully read the instructions to authors. The journal has no strict formatting requirements on submission. There is no restriction on the length of manuscripts. Accepted articles will be published immediately on the journal website with a digital object identifier (DOI) prior to the release of the special issue.

Important dates:
Deadline for manuscript submissions: 20 December 2022 Notification to authors within four weeks.
Special issue will be published in June 2023.

Visit the instructions for authors
Submit your paper for peer review online