Augmented Intelligence in Healthcare: A Pragmatic and Integrated Analysis

The book discusses how augmented intelligence can increase the efficiency and speed of diagnosis in healthcare organizations. The concept of augmented intelligence can reflect the enhanced capabilities of human decision-making in clinical settings when augmented with computation systems and methods. It includes real-life case studies highlighting impact of augmented intelligence in health care. The book offers a guided tour of computational intelligence algorithms, architecture design, and applications of learning in healthcare challenges. It presents a variety of techniques designed to represent, enhance, and empower multi-disciplinary and multi-institutional machine learning research in healthcare informatics. It also presents specific applications of augmented intelligence in health care, and architectural models and frameworks-based augmented solutions.

Provides a unique compendium of current and emerging augmented intelligence paradigms for healthcare informatics Appeals to a broader section of audience outreachmedical staffs, researchers, academicians, and common public Reflects the diversity, complexity, and the depth and breadth of this critical domain

Auteur

Dr. Sushruta Mishra is working as Assistant Professor in the School of Computer Engineering, KIIT University, Bhubaneswar, Odisha, India. He pursued his M.Tech. from IIIT Bhubaneswar in 2012 and has completed his Ph.D. in Computer Science from KIIT University, Bhubaneswar, Odisha, India, in 2017. He has more than 8 years of teaching experience in various educational institutions. He has handled many subjects such as computer networks, data mining, software engineering, and machine learning during his academic experience. His research interest includes image processing, machine learning, Internet of things, and cognitive computing. He has published several research articles in reputed SCIE journals, Scopus indexed international journals, edited books, and conferences.

Dr. Hrudaya Kumar Tripathy is presently working as Associate Professor and Program Head of Master Program at the School of Computer Engineering, KIIT (Deemed to be University), Bhubaneswar, India. He has completed Ph.D. in Computer Science (Berhampur University) and an M.Tech. in Computer Science and Engineering from the Indian Institute of Technology Guwahati. He had been Visiting Faculty at Asia Pacific University, Kuala Lumpur, Malaysia, and Universiti Utara Malaysia, Sintok, Malaysia. He has received the research Post Doctoral research fellowship from the Ministry of Higher Education Malaysia. He has 20 years of teaching experience with post-doctorate research experience in the field of artificial intelligence, machine learning, mobile robotics, and data analysis. He received many certificates of merit and highly applauded in a presentation of research papers at international conferences. He has published many research papers in reputed international and national refereed journals and conferences. The Computer Society of India (CSI) has awarded the young IT professional award 2013 to him. He is Senior Member of IEEE, Life Member of CSI, and having membership in other professional bodies such as IET, IACSIT, and IAENG.

Dr. Pradeep Kumar Mallick is currently working as Associate Professor in the School of Computer Engineering, Kalinga Institute of Industrial Technology (KIIT) Deemed to be University, Odisha, India. He has also served as Professor and Head Department of Computer Science and Engineering, Vignana Bharathi Institute of Technology, Hyderabad. He has completed his Post Doctoral Fellow (PDF) in Kongju National University South Korea, Ph.D. from Siksha Ó Anusandhan University, M. Tech. (CSE) from Biju Patnaik University of Technology (BPUT), and MCA from Fakir Mohan University, Balasore, India. Besides academics, he is also involved in various administrative activities, Member of Board of Studies, Member of Doctoral Research Evaluation Committee, Admission Committee, etc. His area of research includes algorithm design and analysis, data mining, image processing, soft computing, and machine learning. Now, he is Editorial Member of the Korean Convergence Society for SMB. He has published 9 books and more than 70 research papers in national and international journals and conference proceedings to his credit.

Dr. Khaled Shaalan is Full Professor and Programme Head of Computer Science at the British University in Dubai (BUiD), UAE. He is ranked among the top 2% of scientists in 2019 according to a study led by Dr. Ioannidis and his research team at Stanford University. He is Honorary Fellow at the School of Informatics, University of Edinburgh (UoE), UK. Over the last two decades, he has been contributing to a wide range of research topics in AI, Arabic NLP, knowledge management, health informatics, and educational technology. He has published 240+ refereed publications. His research work is cited extensively worldwide, and the impact of his research using Google Scholar s H-index metric is 40+. He has been actively and extensively supporting the local and international academic community. He acts as the chair of international conferences, journals and books editor, keynote speaker, an external member of promotions committees, among others. He is Associate Editor on ACM Transactions of Asian and Low Resource Language Information Processing (TALLIP) editorial board, published by the Association for Computing Machinery (ACM), USA. He is also Member of the editorial board of the AKCE International Journal of Graphs and Combinatorics (AKCE), Taylor and Francis.

Contenu
Chapter 1. A bibliometric analysis on the role of artificial intelligence in healthcare.- Chapter 2. Supervised Intelligent Clinical Approach for Breast Cancer Tumour Categorisation.- Chapter 3. Health Monitoring and Integrated Wearables.- Chapter 4. A Comprehensive Review Analysis of Alzheimer Disorder using Machine Learning Approach.- Chapter 5. Machine Learning Techniques in Medical Image: A Short Review.- Chapter 6. Analysis of Diabetic Retinopathy Detection Techniques using CNN Models.- Chapter 7. Experimental Evaluation Of Brain Tumor Image Segmentation and Detection Using CNN Model.- Chapter 8. Effective Deep Learning Algorithms for Personalized Healthcare Services.- Chapter 9. Automatic lung carcinoma identification and classification in CT images using CNN deep learning model.- Chapter 10. Augmented Intelligence: Deep Learning Models for Healthcare.- Chapter 11. Sentiment analysis and emotion detection with healthcare perspective.- Chapter 12. Augmented Intelligence in Mentalhealthcare: Sentiment analysis & emotion detection with healthcare perspective.- Chapter 13. NLP applications for big data analytics within healthcare.- Chapter 14. Cognitive Computing Driven Healthcare: A Precise Study.- Chapter 15. Cognitive Techniques for Brain Disorder Management: A Future Trend.- Chapter 16. Relevance of Blockchain in Revolutionizing Health Records.- Chapter 17. A Systematic Review on Blockchain Technology: Concepts, Applications, and Prospects in Healthcare.- Chapter 18. Integrated Machine Learning Models for Enhanced Security of Healthcare data.- Chapter 19. Symptoms based Biometric Pattern Detection and Recognition.- Chapter 20. Time Series Analysis of COVID 19 waves in India for Social Good.- Chapter 21. Detection of COVID-19 using A Multi-Scale Deep Learning Network:Covid-MSNet.- Chapter 22. Immersive Technologies in the Healthcare Space.- Chapter 23. Artificial Intelligence in Telemedicine: A Brief Survey.- Chapter 24. Infectious Diseases Reporting System Using Naïve Bayes Classification Algorithm.- Chapter 25. A Comprehensive Study of Explainable Artificial Intelligence In Healthcare.