Computational Intelligence Aided Systems for Healthcare Domain

This book covers recent advances in artificial intelligence, smart computing, and their applications in augmenting medical and health care systems. It will serve as an ideal reference text for graduate students and academic researchers in diverse engineering fields including electrical, electronics and communication, computer, and biomedical.

This book:

• Presents architecture, characteristics, and applications of artificial intelligence and smart computing in health care systems
• Highlights privacy issues faced in health care and health informatics using artificial intelligence and smart computing technologies
• Discusses nature-inspired computing algorithms for the brain-computer interface
• Covers graph neural network application in the medical domain
• Provides insights into the state-of-the-art artificial intelligence and smart computing enabling and emerging technologies

This book discusses recent advances and applications of artificial intelligence and smart technologies in the field of healthcare. It highlights privacy issues faced in health care and health informatics using artificial intelligence and smart computing technologies. It covers nature-inspired computing algorithms such as genetic algorithms, particle swarm optimization algorithms, and common scrambling algorithms to study brain-computer interfaces. It will serve as an ideal reference text for graduate students and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer engineering, and biomedical engineering.

本書涵蓋了人工智慧、智能計算及其在增強醫療和健康護理系統中的應用的最新進展。它將成為電氣、電子與通信工程、計算機和生物醫學等不同工程領域的研究生和學術研究人員的理想參考書。

本書包括以下內容：
- 介紹人工智慧和智能計算在醫療系統中的架構、特性和應用
- 強調使用人工智慧和智能計算技術在醫療保健和健康信息學中面臨的隱私問題
- 討論基於自然啟發的計算算法在腦-計算機界面中的應用
- 探討圖神經網絡在醫學領域的應用
- 提供對最先進的人工智慧和智能計算啟用和新興技術的深入洞察

本書討論了人工智慧和智能技術在醫療領域的最新進展和應用。它強調了使用人工智慧和智能計算技術在醫療保健和健康信息學中面臨的隱私問題。它涵蓋了基於自然啟發的計算算法，如遺傳算法、粒子群優化算法和常見的混淆算法，用於研究腦-計算機界面。它將成為電氣工程、電子與通信工程、計算機工程和生物醫學工程等領域的研究生和學術研究人員的理想參考書。

Dr Akshansh Gupta is a scientist at CSIR-Central Electronic Engineering Research Institute Pilani Rajasthan. He has worked as a DST-funded postdoctoral research fellow as a principal investigator under the scheme of the Cognitive Science Research Initiative (CSRI) from the Department of Science and Technology (DST), Ministry of Science and Technology, Government of India, from 2016 to 2020 in School of Computational Integrative and Science, Jawaharlal Nehru University, New Delhi. He has many publications, including Springer, Elsevier, and IEEE Transaction. He received his master's and a PhD degree from the School of computer and systems sciences, JNU, in 2010 and 2015, respectively. His research interests include Pattern Recognition, Machine Learning, Data Mining Signal Processing, Brain Computer Interface, Cognitive Science, and IoT. He is also working as CO-PI on a consultancy project named "Development of Machine Learning Algorithms for Automated Classification Based on Advanced Signal Decomposition of EEG Signals" ICPS Program, DST Govt. of India.

Dr Hanuman Verma received the PhD and M.Tech degrees in Computer Science and Technology from the School of Computer and Systems Sciences (SC&SS) at Jawaharlal Nehru University (JNU), New Delhi, India, in 2015 and 2010, respectively. He also did his master of Science (M.Sc.) degree in Mathematics & Statistics from Dr R. M. L. Avadh University, Ayodhya, Uttar Pradesh, India. He has worked as a junior research fellowship (JRF) and senior research fellowship (SRF) from 2009 to 2013, received from the Council of Scientific and Industrial Research (CSIR), New Delhi, India. Currently, he is working as Assistant Professor at the Department of Mathematics, Bareilly College, Bareilly, Uttar Pradesh, India. He has published research papers in reputed international journals, including Elsevier, Wiley, World Scientific, and Springer, in machine learning, deep learning and medical image computing. His primary research interest includes machine learning, deep learning, medical image computing, and mathematical modelling.

Dr Mukesh Prasad (SMIEEE, ACM) is a Senior Lecturer in the School of Computer Science (SoCS), Faculty of Engineering and Information Technology (FEIT), University of Technology Sydney (UTS), Australia. His research expertise lies in developing new methods in artificial intelligence and machine learning approaches like big data analytics, and computer vision within the healthcare domain, biomedical research. He has published more than 100 articles, including several prestigious IEEE Transactions and other Top Q1 journals and conferences in the areas of Artificial Intelligence and Machine Learning. His current research interests include pattern recognition, control system, fuzzy logic, neural networks, the internet of things (IoT), data analytics, and brain-computer interface. He received an M.S. degree from the School of Computer Systems and Sciences, Jawaharlal Nehru University, New Delhi, India, in 2009, and a PhD degree from the Department of Computer Science, National Chiao Tung University, Hsinchu, Taiwan, in 2015. He worked as a principal engineer at Taiwan Semiconductor Manufacturing Company, Hsinchu, Taiwan, from 2016 to 2017. He started his academic career as a Lecturer with the University of Technology Sydney in 2017. He is also an Associate/Area Editor of several top journals in the field of machine learning, computational intelligence, and emergent technologies.

Prof. Chin-Teng Lin Distinguished Professor Chin-Teng Lin received a Bachelor's of Science from National Chiao-Tung University (NCTU), Taiwan, in 1986, and holds Master's and PhD degrees in Electrical Engineering from Purdue University, USA, received in 1989 and 1992, respectively. He is currently a distinguished professor and Co-Director of the Australian Artificial Intelligence Institute within the Faculty of Engineering and Information Technology at the University of Technology Sydney, Australia. He is also an Honorary Chair Professor of Electrical and Computer Engineering at NCTU. For his contributions to biologically inspired information systems, Prof Lin was awarded Fellowship with the IEEE in 2005 and the International Fuzzy Systems Association (IFSA) in 2012. He received the IEEE Fuzzy Systems Pioneer Award in 2017. He has held notable positions as editor-in-chief of IEEE Transactions on Fuzzy Systems from 2011 to 2016; seats on the Board of Governors for the IEEE Circuits and Systems (CAS) Society (2005-2008), IEEE Systems, Man, Cybernetics (SMC) Society (2003-2005), IEEE Computational Intelligence Society (2008-2010); Chair of the IEEE Taipei Section (2009-2010); Chair of IEEE CIS Awards Committee (2022); Distinguished Lecturer with the IEEE CAS Society (2003-2005) and the CIS Society (2015-2017); Chair of the IEEE CIS Distinguished Lecturer Program Committee (2018-2019); Deputy Editor-in-Chief of IEEE Transactions on Circuits and Systems-II (2006-2008); Program Chair of the IEEE International Conference on Systems, Man, and Cybernetics (2005); and General Chair of the 2011 IEEE International Conference on Fuzzy Systems. Prof Lin is the co-author of Neural Fuzzy Systems (Prentice-Hall) and the author Neural Fuzzy Control Systems with Structure and Parameter Learning (World Scientific). He has published more than 400 journal papers, including over 180 IEEE journal papers in neural networks, fuzzy systems, brain-computer interface, multimedia information processing, cognitive neuro-engineering, and human-machine teaming, that have been cited more than 30,000 times. Currently, his h-index is 82, and his i10-index is 356.

Dr. Akshansh Gupta是印度科學與技術部(DST)資助的博士後研究員，曾在2016年至2020年期間擔任印度新德里Jawaharlal Nehru大學計算整合科學學院的認知科學研究計劃(CSRI)的主要研究員。他在Springer、Elsevier和IEEE Transaction等出版物上發表了許多論文。他於2010年和2015年分別在JNU的計算機與系統科學學院獲得碩士和博士學位。他的研究興趣包括模式識別、機器學習、數據挖掘、信號處理、腦機接口、認知科學和物聯網。他還在一個名為“基於高級信號分解的腦電圖信號自動分類機器學習算法開發”的顧問項目中擔任合作研究員，該項目由印度科學與技術部(ICPS計劃)資助。

Dr. Hanuman Verma於2015年和2010年分別在Jawaharlal Nehru大學的計算機與系統科學學院獲得計算機科學和技術的博士和碩士學位。他還在印度Dr R. M. L. Avadh大學獲得了數學和統計學的碩士學位。他曾在2009年至2013年期間擔任印度新德里的科學與工業研究委員會(CSIR)的初級研究員和高級研究員。目前，他在印度北方邦Bareilly的Bareilly學院的數學系擔任助理教授。他在機器學習、深度學習和醫學影像計算等領域的研究論文發表在知名國際期刊，包括Elsevier、Wiley、World Scientific和Springer。他的主要研究興趣包括機器學習、深度學習、醫學影像計算和數學建模。

Dr. Mukesh Prasad (SMIEEE, ACM)是澳大利亞悉尼科技大學(UTS)工程與資訊技術學院(FEIT)計算機科學學院(SoCS)的高級講師。他的研究專長在於人工智能和機器學習方法的新方法，如大數據分析和計算機視覺在醫療領域和生物醫學研究中的應用。他發表了100多篇文章，包括幾篇著名的IEEE Transactions和其他頂級Q1期刊和會議，涉及人工智能和機器學習領域。他目前的研究興趣包括模式識別、控制系統、模糊邏輯、神經網絡、物聯網(IoT)、數據分析和腦機接口。他於2009年在Jawaharlal Nehru大學的計算機系統與科學學院獲得碩士學位，並於2015年在台灣國立交通大學的計算機科學系獲得博士學位。他曾在2016年至2017年期間在台灣台積電擔任首席工程師。他於2017年開始在悉尼科技大學擔任講師。他還是機器學習、計算智能和新興技術領域的幾本頂級期刊的副編輯/領域編輯。

Chin-Teng Lin教授是澳大利亞悉尼科技大學工程與資訊技術學院的傑出教授，也是澳大利亞人工智能研究所的聯合主任。他於1986年在台灣國立交通大學獲得理學學士學位，並於1989年和1992年在美國普渡大學獲得電機工程的碩士和博士學位。他目前是悉尼科技大學工程與資訊技術學院的傑出教授和澳大利亞人工智能研究所的聯合主任。