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including Applied Soft Computing, IEEE Access, Evolutionary Intelligence, and IET Quantum Communications. He is the editor of the International Journal of Pattern Recognition Research and the Founding Editor in Chief of the International Journal of Hybrid Intelligence, Inderscience. He has guest‐edited several issues with several international journals and is serving as the Series Editor of the IGI Global Book Series “Advances in Information Quality and Management (AIQM)”, De Gruyter Book Series “Frontiers in Computational Intelligence (FCI)”, CRC Press Book Series(s) “Computational Intelligence” and “Applications & Quantum Machine Intelligence”, Wiley Book Series “Intelligent Signal and Data Processing”, Elsevier Book Series “Hybrid Computational Intelligence for Pattern Analysis and Understanding”, and Springer “Tracts on Human Centered Computing”.

      His research interests include hybrid intelligence, pattern recognition, multimedia data processing, social networks, and quantum computing.

      Dr. Bhattacharyya is a life fellow of the Optical Society of India (OSI), India; life fellow of the International Society of Research and Development (ISRD), UK; a fellow of the Institution of Engineering and Technology (IET), UK; a fellow of the Institute of Electronics and Telecommunication Engineers (IETE), India; and a fellow of the Institution of Engineers (IEI), India. He is also a senior member of the Institute of Electrical and Electronics Engineers (IEEE), USA; International Institute of Engineering and Technology (IETI), Hong Kong; and the Association for Computing Machinery (ACM), USA. He is a life member of the Cryptology Research Society of India (CRSI), Computer Society of India (CSI), Indian Society for Technical Education (ISTE), Indian Unit for Pattern Recognition and Artificial Intelligence (IUPRAI), Center for Education Growth and Research (CEGR), Integrated Chambers of Commerce and Industry (ICCI), and Association of Leaders and Industries (ALI). He is a member of the Institution of Engineering and Technology (IET), UK; International Rough Set Society; International Association for Engineers (IAENG), Hong Kong; Computer Science Teachers Association (CSTA), USA; International Association of Academicians, Scholars, Scientists and Engineers (IAASSE), USA; Institute of Doctors Engineers and Scientists (IDES), India; The International Society of Service Innovation Professionals (ISSIP); and The Society of Digital Information and Wireless Communications (SDIWC). He is also a certified Chartered Engineer of the IEI and is on the Board of Directors of IETI, Hong Kong.

       Sourav De1*, and Rik Das2

       1 Department of Computer Science & Engineering, Cooch Behar Government Engineering College, Vill‐ Harinchawra, P.O.‐ Ghughumari, Cooch Behar, West Bengal, 736170, India

       2 Department of Information Technology, Xavier Institute of Social Service, Post Box No‐7, Dr Camil Bulcke Path, Ranchi, Jharkhand, 834001, India

      Health care informatics combine the fields of information technology, science, and medicine for a better seamless and speedy management process that serves people worldwide. The main objective for health care informatics is to render effective health care to patients with the help of technologic advancements in public health, drug discovery, pharmacy, etc. However, there is an insufficient understanding of the computational methodologies that will be highly efficient for the health care sector and its approach for patients worldwide (Durcevic, 2020).

      Big data analytics can be applied in different areas of medicine. The concept of big data analytics can be employed in different areas and among them image processing, signal processing, and genomics (Ritter et al., 2011) are primarily noted.

      Medical images are a vital source of data, and they are frequently employed for diagnosing, assessing therapy, and designing (Ritter et al., 2011) algorithms. X‐ray, magnetic resonance imaging (MRI), molecular imaging, computerized tomography (CT) images, photo acoustic imaging, ultrasound, fluoroscopy, and mammography are some instances of imaging methods that are found inside clinical settings (Belle et al., 2015). Medical images can run from a couple of megabytes to process a solitary report to many megabytes per analysis: for example, thin‐slice CT studies (Seibert, 2010). These types of information need huge capacity limits for long term data retention and require accurate and fast algorithms for decision‐assisting automation. Likewise, if other sources of information obtained for an individual patient are additionally applied at diagnoses, prognosis, and treatment, then the issue of proving reliable storage and increasingly advantageous methods for this large scope of records turns into a challenge.

      Like health care images, medical signals likewise present quantity and speed snags, particularly

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