1st Edition
Soft Computing Techniques in Connected Healthcare Systems
This book provides an examination of applications of soft computing techniques related to healthcare systems and can be used as a reference guide for assessing the roles of various techniques. Soft Computing Techniques in Connected Healthcare Systems presents soft computing techniques and applications used in healthcare systems, along with the latest advancements. The authors examine how connected healthcare is the essence of combining a practical operative procedure of interconnectedness of electronic health records, mHealth, clinical informatics, electronic data exchange, practice management solutions, and pharmacy management. The book focuses on different soft computing techniques, such as fuzzy logic, ANN, and GA, which will enhance services in connected health systems, such as remote diagnosis and monitoring, medication monitoring devices, identifying and treating the underlying causes of disorders and diseases, improved access to specialists, and lower healthcare costs. The chapters also examine descriptive, predictive, and social network techniques and discuss analytical tools and the important role they play in enhancing the services to connected healthcare systems. Finally, the authors address real-time challenges with real-world case studies to enhance the comprehension of topics. This book is intended for under graduate and graduate students, researchers, and practicing professionals in the field of connected healthcare. It provides an overview for beginners while also addressing professionals in the industry on the importance of soft computing approaches in connected healthcare systems.
1. Automation in Healthcare: Forecast and Outcome: A Case Study
Kshatrapal Singh, Ashish Kumar, Manoj Kumar Gupta, and Ashish Seth
2. Optimizing Smartphone Addiction Questionnaires with Smartphone Application and Soft Computing: An Intelligent Smartphone Usage Behavior Assessment Model
Anshika Arora, Pinaki Chakraborty, and M.P.S. Bhatia
3. Artificial Neural Network Model for Automated Medical Diagnosis
Shambhavi Mishra, Tanveer Ahmed, Mohd. Abuzar Sayeed, and Umesh Gupta
4. Analysing of Heterogeneous Perceptions of a Mutually Dependent Health Ecosystem System Survey
Manish Bhardwaj, Sumit Kumar Sharma, Jyoti Sharma, and Vivek Kumar
5. Intuitionsitic Fuzzy-Based Technique Ordered Preference by Similarity to the Ideal Solution (TOPSIS) Method: An MCDM Approach for the Medical Decision Making of Diseases
Vijay Kumar, H. D. Arora, and Kiran Pal
6. Design of a Heuristic IoT-Based Approach as a Solution to a Self-Aware Social Distancing Paradigm
Amit Kumar Bhuyan and Hrishikesh Dutta
7. Combined 3D Mesh and Generative Adversarial Network-Based Improved Liver Segmentation in Computed Tomography Images
Mriganka Sarmah and Arambam Neelima
8. Applying Privacy by Design to Connected Healthcare Ecosystems
Naomi Tia Chantelle Freeman
9. Next-Generation Platforms for Device Monitoring, Management, and Monetization for Healthcare
Aparna Agarwal, Khoula Al Harthy, and Robin Zarine
10. Real-Time Classification and Hepatitis B Detection with Evolutionary Data Mining Approach
Asadi Srinivasulu, CV Ravikumar, Goddindla Sreenivasulu, Olutayo Oyeyemi Oyerinde, Siva Ram Rajeyyagari, Madhusudana Subramanyam, Tarkeshwar Barua, and Asadi Pushpa
11. Healthcare Transformation Using Soft Computing Approaches and IoT Protocols
Sakshi Gupta and Manorama Mohapatro
12. Automated Detection and Classification of Focal and Nonfocal EEG Signals Using Ensemble Empirical Mode Decomposition and ANN Classifier
C. Ruth Vinutha, S. Thomas George, J. Prasanna, Sairamya Nanjappan Jothiraj, and M.S.P Subathra
13. Challenges and Future Directions of Fuzzy System in Healthcare Systems: A Survey
Manish Bhardwaj, Jyoti Sharma, Yu-Chen Hu, and Samad Noeiaghdam
14. Perceptual Hashing Function for Medical Images: Overview, Challenges, and the Future
Arambam Neelima and Heisnam Rohen Singh
15. Deploying Machine Learning Methods for Human Emotion Recognition
Ansu Elsa Regi and A. Hepzibah Christinal
16. Maternal Health Risk Prediction Model using Artificial Neural Network
Divya Kumari
Biography
Moolchand Sharma is currently an Assistant Professor in the Department of Computer Science and Engineering at Maharaja Agrasen Institute of Technology, GGSIPU, Delhi. He has published scientific research publications in reputed International Journals and Conferences, including SCI indexed and Scopus indexed Journals such as Cognitive Systems Research (Elsevier), Physical Communication (Elsevier), Intelligent Decision Technologies: An International Journal, Cyber-Physical Systems (Taylor & Francis Group), International Journal of Image & Graphics (World Scientific), International Journal of Innovative Computing and Applications (Inderscience) & Innovative Computing and Communication Journal (Scientific Peer reviewed Journal). He has authored/co-authored chapters with international publishers like Elsevier, Springer, Wiley, and De Gruyter. He has authored/edited four books with a national/international level publisher (CRC Press, Bhavya Publications). His research area includes Artificial Intelligence, Nature-Inspired Computing, Security in Cloud Computing, Machine Learning, and Search Engine Optimization. He is associated with various professional bodies, such as IEEE, ISTE, IAENG, ICSES, UACEE, Internet Society, Universal Inovators research lab life membership, etc. He possesses teaching experience of more than 10 years. He is the co-convener of ICICC, DOSCI, ICDAM & ICCCN Springer Scopus Indexed conference series and ICCRDA-2020 Scopus Indexed IOP Material Science & Engineering conference series. He is also the organizer and Co-Convener of the International Conference on Innovations and Ideas toward Patents (ICIIP) series. He is also the Advisory and TPC committee member of the ICCIDS-2022 Elsevier SSRN Conference. He is also the reviewer of many reputed journals, such as Springer, Elsevier, IEEE, Wiley, Taylor & Francis Group, IJEECS, and World Scientific Journal, and many springer conferences. He has also served as a session chair in many international Springer conferences. He is currently a doctoral researcher at DCR University of Science and Technology, Haryana. He completed his Post Graduation in 2012 from SRM UNIVERSITY, NCR CAMPUS, GHAZIABAD, and Graduated in 2010 from KNGD MODI ENGG. COLLEGE, GBTU.
Prof. Suman Deswal holds a Ph.D. from DCR University of Science and Technology, Murthal, India. She completed her M.Tech. (CSE) from Kurukshetra University, Kurukshetra, India, and B.Tech. (Computer Science and Engg.) from CR State College of Engg., Murthal, India, in 2009 and 1998, respectively. She possesses 21 years of teaching experience and is presently working as a Professor in the Department of Computer Science and Engineering at DCR University of Science and Technology, Murthal, India. Her research area includes Wireless Networks, Heterogeneous Networks, Distributed Systems, Machine Learning, Deep Learning Approaches, and Bioinformatics. She has many research papers to her credit in reputed journals, including SCI indexed, and Scopus Indexed Journals and conferences. Her publications have more than 143 citations. She is also the reviewer of many reputed journals, such as Springer, Elsevier, IEEE, Wiley, and International IEEE and Springer Conferences. She is a member of IAENG and Computer Society of India (CSI).
Dr. Umesh Gupta is currently an Associate Professor at Bennett University, India. He received a Doctor of Philosophy (Ph.D.) (Machine Learning) from the National Institute of Technology, Arunachal Pradesh, India. He was awarded a gold medal for his Master of Engineering (M.E.) from the National Institute of Technical Teachers Training and Research (NITTTR), Chandigarh, India, and Bachelor of Technology (B.Tech.) from Dr. APJ, Abdul Kalam Technical University, Lucknow, India. His research interests include SVM, ELM, RVFL, Machine Learning, and Deep Learning Approaches. He has published over 35 referred journal and conference papers of international repute. His scientific research has been published in reputable international journals and conferences, including SCI-indexed and Scopus-indexed journals like Applied Soft Computing (Elsevier) and Applied Intelligence (Springer), each of which is a peer-reviewed journal. His publications have more than 158 citations with an h-index of 8 and an i10-index of 8 on Google Scholar as of March 1, 2023. He is a senior Member of IEEE (SMIEEE) and an active member of ACM, CSTA, and other scientific societies. He has also reviewed papers for many scientific journals and conferences in the United States and other foreign countries. He led the sessions at the 6th International Conference (ICICC-2023), 3rd International Conference on Data Analytics and Management (ICDAM 2023), the 3rd International Conference on Computing and Communication Networks (ICCCN 2022), and other international conferences like Springer ETTIS 2022 and 2023. He is currently supervising two Ph.D. students. He is the co-principal investigator (co-PI) of two major research projects. He published three patents in the years 2021–2023. He also published four book chapters with Springer, CRC.
Mujahid Tabassum is a lecturer at Noroff University College (Noroff Accelerate), Kristiansand, Norway. He has completed a Master of Science (Specialization in Computer System Engineering) degree from Halmstad University, Sweden, and a bachelor’s degree from the University of Wollongong, Australia. He has worked in various international universities in Malaysia and the Middle East, making his profile well reputed. He has managed and led several student and research projects and published several research articles in well-known SCI journals and Scopus conferences. He is a qualified “Chartered Engineer—CEng” registered with the Engineering Council, UK. He has 13 years of teaching experience. He is a Cisco, Microsoft, Linux, Security, and IoT-certified instructor. His research interests include Computer Networks, AI, Wireless Sensor networks, IoT, Security, and Applications. He has published several Scopus papers, journals, and book chapters. He is a Member of IEEE, a Member of the Institution of Engineering and Technology, a Member of IAENG, a Member of the Australia Computing Society (ACS), and a Member of MBOT Malaysia. He is an active member of the Society of IT Engineers and Researchers, UK.
Dr. Isah A. Lawal was an Erasmus Mundus Joint Doctorate Fellow with over 10 years of professional work experience, including teaching and research. He has participated in several collaborative multidisciplinary research projects at different universities, including in Europe (Italy and United Kingdom) and the Middle East (Saudi Arabia). He has authored several articles in peer-reviewed journals and conferences ranging from data-driven predictive modeling to machine learning for intelligent systems. In addition to actively engaging in research, he has taught Data Mining, Innovative Systems, and Artificial Intelligence courses at both undergraduate and postgraduate levels. Dr. Isah’s research interests include the multidisciplinary application of Machine Learning Techniques, Data Mining, and Smart Systems. He has supervised and examined several undergraduate projects and master’s thesis in Statistical Data Analysis, Data Visualisation, and Natural Language Processing. Dr. Isah is currently participating in the EEA granted data-driven public administration project as a consultant for the Department for Strategic Development and Coordination of Public Administration, Ministry of the Interior of the Czech Republic. The project involves using big data analytics to analyze public mobility from mobile positioning data to efficiently plan and distribute public services and public administration in the Czech Republic.
