Computational Intelligence Aided Systems for Healthcare Domain  book cover
SAVE
$36.00
1st Edition

Computational Intelligence Aided Systems for Healthcare Domain



  • Available for pre-order on April 21, 2023. Item will ship after May 12, 2023
ISBN 9781032210339
May 12, 2023 Forthcoming by CRC Press
432 Pages 85 Color & 20 B/W Illustrations

FREE Standard Shipping
 
SAVE $36.00
was $180.00
USD $144.00

Prices & shipping based on shipping country


Preview

Book Description

The text 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.

The book-

  • Presents architecture, characteristics, and applications of artificial intelligence and smart computing in health care systems

  • Highlight 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 text 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.

Table of Contents

Chapter 1 
Introduction to Computational Methods-Machine and Deep Learning Perspective 
Hanuman et al.

Chapter 2 
Deep Learning and Machine Learning methods for healthcare  
Kushagra Krishnan and Pinki Dey

Chapter 3 
Deep Multi-view Learning for Healthcare Domain 
Dr. Vipin Kumar, Dr. Bharti, Dr. Pawan Kumar Chaurasia, Dr. Prem Shankar Singh Aydav

Chapter 4 
Precision Healthcare in the Era of IoT and Big Data: Monitoring of Self-care Activities
Sujit Bebortta and Dilip Senapati

Chapter 5 
Machine learning approach for classification of stroke patients using clinical data 
Md. Rafiqul Islam and Bharti

Chapter 6 
Comparative Analysis of noise robust Fuzzy C-means based Image Segmentation Algorithm
Inder Khatri, Dhirendra Kumar and Aryan Gupta

Chapter 7 
Exploring the role of AI, IoT and Blockchain during Covid-19: A bibliometric and network analysis   
Kiran Sharma and Parul Khurana

Chapter 8 
Biomedical Named Entity Recognition using Natural Language Processing 
Shaina Raza, Syed Raza Bashir, Vidhi Thakkar, Usman Naseem

Chapter 9 
Image segmentation of skin disease using deep learning approach 
Manbir Singh and Maninder Singh

Chapter 10 
Multimodality dementia detection system using machine and deep learning 
Shruti Srivatsan, Sumneet Kaur Bamrah, Gayathri K

Chapter 11 
Heart Attack Analysis and Prediction System 
Ashish Kumar Das, Ravi Vishwakarma, Adarsh Kumar Bharti, Binu Singh, Jyoti Singh Kirar
Chapter 12 
Utility of Exploratory Data Analysis for Improved Diabetes Prediction 
Jyoti et al.

Chapter 13 
Fundus Image Classification for Diabetic Retinopathy Using Deep Learning 
Monika Mokan, Soharab Hossain Shaikh, Devanjali Relan

Chapter 14 
Emerging role of Artificial intelligence in precision oncology 
Vinod Singh

Chapter 15 
Artificial Intelligence in Precision Oncology: Research Trends (2015-2021), Challenges and Future Perspectives 
Ranjeet Kaura, Sabiya Fatimaa, Amit Doegara, Dr. Suyash Singh

Chapter 16 
Computer-Aided Breast Cancer Diagnosis using Machine Learning and Deep Learning methods: A Comprehensive Survey 
Vipin et al.

Chapter 17 
Understanding complex dynamical evolution and interplay of health, climate and conflicts using data driven approach 
Syed Shariq Husain

 

...
View More

Editor(s)

Biography

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.