Introduction

In healthcare industry from diagnosis of disease and treatment planning to the patient and population health management the Machine Learning technique has shown a significant development. The rapid growth of healthcare data makes scalable computing solutions increasingly necessary for fully utilizing the promise of machine learning algorithms. This proposed special issue aims to explore the interaction between scalable computing and machine learning in the healthcare sector, addressing problems and showcasing innovative solutions.

To provide a platform for researchers and physicians to express their insights and methodologies, and progressions in scalable machine learning solutions designed for healthcare industry. The special issue aims:

• To research the most recent advancements and difficulties in scalable machine learning methods in health care.
• To discuss cutting-edge strategies and techniques for implementing scalable machine learning systems in healthcare environments.
• To address challenges with model complexity, computing resources, and data volume scalability in machine learning applications for the healthcare industry.
• To promote interdisciplinary cooperation among computer scientists, medical practitioners, and domain experts in order to advance scalable machine learning solutions that tackle practical healthcare issues.
• To present best practices and standards for creating and implementing scalable machine learning (ML) solutions in the healthcare industry that guarantee scalability, dependability, and efficiency.

Recommended topics (but not limited to)

This special issue aims to bring together original research papers, case studies, and literature reviews that address the challenges and solutions related to scalable computing in online and blended learning environments. The scope of this special issue includes:

• Medical image analysis using scalable deep learning networks.
• Frameworks for distributed computing for processing massive amounts of medical data.
• Scalable machine learning algorithms for predictive modeling in clinical decision support systems.
• Federated learning approaches for privacy-preserving healthcare data analysis.
• Scalable natural language processing techniques for clinical text mining and electronic health record analysis.
• Edge computing solutions for real-time processing of healthcare sensor data.
• Scalability challenges and solutions in genomic data analysis for personalized medicine.
• Adaptive learning algorithms for continuous model updates and optimization in healthcare applications.
• Scalable anomaly detection techniques for healthcare fraud detection and cybersecurity.
• Integration of scalable ML techniques in telemedicine and remote patient monitoring systems.
• Scalable reinforcement learning algorithms for healthcare resource allocation and management.
• Explainable AI techniques for interpretable and transparent scalable ML models in healthcare.
• Scalability considerations in AI-driven drug discovery and pharmaceutical research.
• Scalable machine learning approaches for population health management and disease surveillance.
• Resilience and fault tolerance in scalable ML systems for healthcare applications.

Important dates

Submission deadline: 30 September 2024, 31 January 2025

Authors notification: 1 December 2024, 30 April 2025

Revision submission: 15 December 2024, 30 June 2025

Publication deadline: 1 September 2025

Submission guidelines

Original and unpublished works on any of the topics aforementioned or related are welcome. The SCPE journal has a rigorous peer-reviewing process and papers will be reviewed by at least two referees. All submitted papers must be formatted according to the journal's instructions, which can be found here.

During submission please select a Special Issue that you want to submit to and provide this information in the Comments for the Editor field.

Guest Editors

Lead: Dr. Prakash Mohan, Associate Professor, School of Computer Science and Engineering, Vellore Institute of Technology (VIT University), Vellore, India, email: m.prakash@vit.ac.in

Prakash Mohan, SM IEEE, Top 2% Scientist in 2022 by Standford University and Elsevier database works as an Associate Professor in the School of Computer Science and Engineering at VIT University in Vellore, Tamil Nadu, India. He completed his Bachelor of Engineering (Computer Science and Engineering) in 2001 from the University of Madras, his Master of Engineering (Computer Science and Engineering) in 2007 from Sathyabama Institute of Science and Technology, and his Doctor of Philosophy (Computer Science and Engineering) in 2014 from Jawaharlal Nehru Technological University Hyderabad. With over 20 years of experience in teaching and research, his areas of interest include Data Analytics, Big Data, and Machine Learning. He has published more than 80 research papers in international journals and conferences and has also served as a lead guest editor for the journals Inderscience, EAI Endorsed Transition, Bentham Publishers, and Tech Science Press. He is an Editorial Review Board member of IGI Global and has reviewed journals for publishers such as IEEE, Elsevier, Springer, Taylor & Francis, and Inderscience. He has received awards from the Computer Society of India for hosting the highest number of CSI events and is an active CSI member in the Coimbatore Chapter. He is also a member of IEEE, the Association for Computing Machinery, the Computer Society of India, ISTE, and IAENG.

Dr. Saurav Mallik, Research Scientist, The University of Arizona, USA, email: smallik@arizona.edu

Dr. Saurav Mallik is currently working as Research Scientist in the Department of Pharmacology and Toxicology, The University of Arizona, USA. Previously, he worked as Postdoctoral Fellow in the Department of Environmental Health, Harvard T H Chan School of Public Health, Boston, USA for more than three years (2019-2022), the Center of Precision Health, Department of School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, Texas, USA for one and half year (2018-2019), and in the Division of Bio-statistics, Department of Public Health Sciences, University of Miami Miller School of Medicine, Miami, Florida, USA for more than one year (2017-2018). He obtained his PhD degree in the Department of Computer Science & Engineering (C.S.E.) from Jadavpur University, Kolkata, India in 2017 while his PhD works carried out in Machine Intelligence Unit (MIU), Indian Statistical Institute (ISI), Kolkata, India. He worked previously at ISI, Kolkata, India as a Junior Research Fellow (JRF) in DST (Department of Science & Technology, New Delhi, Govt. of India)-sponsored Swarnajayanti project and as a Visiting Scientist (VS) for a total of more than 3 years (2011-2013 & 2015-2016). He also worked in the Department of Computer Science & Engineering, Jadavpur University, Kolkata, India as UGC (University Grant Commission, Govt. of India) Research Fellow for 2 years (2014-2015 & 2016-2017). He is the recipient of Research Associate-ship (RA) from CSIR (Council of Scientific and Industrial Research), MHRD, Govt. of India in 2017. He also worked as assistant professor in the Department of Computer Science & Engineering in Mallabhum Institute of Technology, Bisnupur, WB, India (currently on leave). He is also recipient of "Emerging Researcher In Bioinformatics" award from Bioclues & BIRD Award steering committee, India (http://bioclues.org/) in the year 2020 and "Young scientist award" on “International Scientist Awards on Engineering, Science and Medicine" (ISAO 2021) from a non-profit organization, VDGOOD Professional Association of Scientists, Engineers and Doctors, India in the year 2021. He received two times Travel Grant Award for International Conference on Intelligent Biology and Medicine (ICIBM), June 2018 at Los Angeles, California, USA and August 2021 at Philadelphia, PA, USA. Dr. Mallik has coauthored more than 130 research papers in various peer-reviewed International Journals, Conferences and Book Chapters. He also has several authored/edited book publication in Taylor & Francis, River publishers, IET, etc. He attended many national and international conferences in USA and India. He is currently an active member of IEEE, AACR, and ACM, USA and life member of BIOCLUES, India. He is editors of many journals like Frontiers in Genetics, BMC Bioinformatics, Frontiers in Bioinformatics, Frontiers in Applied Mathematics and Statistics, Archives of Medical Sciences, Mathematics, Electronics, Bioengineered (Taylor & Francis), International Journal of Biomedical Imaging, Chemistry & Biodiversity (Wiley), International Journal of Molecular Sciences, etc. He is also member of international advisory committee of many reputed engineering colleges in India. His research areas include Data mining, computational biology, bioinformatics, Bio-statistics and machine learning.

Dr. Surbhi Bhatia Khan, School of Science, Engineering and Environment, University of Salford, United Kingdom, email: s.khan138@salford.ac.uk

Surbhi Bhatia Khan is doctorate in Computer Science and Engineering in machine learning and social media analytics. She is listed in the top 2% researchers released by the Stanford University, USA. She earned Project management Professional Certification from reputed Project Management Institute, USA. She is currently working in the Department of Data Science, School of Science, Engineering and environment, University of Salford, Manchester, United Kingdom. She holds research positions Lebanese American University. She also enjoys adjunct professor position from Shoolini University, Himachal Pradesh, India. She has more than 12 years of academic and teaching experience in different universities. She has published 100+ papers in many reputed journals in high indexed outlets. She has around 12 international patents from India, Australia and USA. She has successfully authored and edited 12 books. She has completed research funded projects from Deanship of Scientific Research, Ministry of Education from Saudi Arabia, and India. She is working in the research projects with UKRI, EU, RDIA and also with King Salman Disability research programme. She is a senior member of IEEE, a member of IEEE Young Professionals, and ACM. She has chaired several international conferences and workshops and has delivered over 20 invited and keynote talks across the globe. She is serving as an Academic editor, Associate Editor and Guest editor in many reputed journals including Springer, Tech science press, Bentham Science, PLOS ONE journals, MDPI and HCIS. She is the awardee of the Research Excellence award given by King Faisal University, Saudi Arabia in 2021. Her area of interests are Information Systems, Sentiment Analysis, Machine Learning, Databases, and Data Science.

Dr. Nithiyananthan Kannan, Professor, Department of Electrical and Computer Engineering, King Abdul Aziz Univeristy Rabigh Branch Kingdom of Saudi Arabia, email: nmajaknap@kau.edu.sa

Nithiyananthan Kannan is currently working as a Professor in the Department of Electrical and Electronics Engineering, King Abdul Aziz University Rabigh Branch Kingdom of Saudi Arabia. He has 18 years of teaching/research experience. He completed his PhD in Power System Engineering from the College of Engineering Guindy Campus, Anna University, India in 2004. He is an active member of IET (UK) and he received Charted Engineer title in 2016 from Engineering Council, UK. His areas of interest are computer applications to power system engineering, modelling of modern power systems, renewable energy, smart grid, micro grid, and genetic algorithms. He has published more than 80 articles in reputed journal and acted as a editor for CRC book series and guest editor for Inderscience, EAI Endorsed Transition, Bentham publisher and Tech science press.

Dr. Tamil Selvi Madeswaran, College of Computing and Information Science, University of Technology and Applied Sciences-Nizwa, Sultanate of Oman, email: tamilselvi.madeswaran@nct.edu.om

Tamilselvi Madeswaran holds a Ph.D. degree, from Anna University Chennai. She worked as Senior Lecturer at Sona College of Technology from 2000 to 2008. She served in the TULEC Computer Education Center as Programmer and acted as Guest Lecturer in the Government College, Salem, India. She is also worked as Academic counselor and University coordinator in the Indira Gandhi Open University and Tamil Nadu Open University. She is also a member in Computer society of India. She serves as reviewer for well known national and international journals. She is currently working as Lecturer in College of Computing and Information Science , University of Technology and Applied Sciences - Nizwa, Sultanate of Oman. She works in the field of Fuzzy Logic, Semantic Web, machine learning, data analytics and Networks. She received appreciation award for the producing Best Results in the University Exam. She has 22 years of teaching experience and 15 years of Research Experience.