The book covers the strategies and frameworks for designing and developing intelligent systems to solve real-life problems related to digital health, telemedicine, and smart-cities applications.
- Covers the essence of human-computer interaction in healthcare applications, along with possibilities with generative artificial intelligence
- Discusses quality use of human cognition for the design and development of information processing systems from an industry perspective with human-computer interaction-based technologies
- Presents the strategies or framework for designing and developing intelligent systems to solve real-life problems related to digital health, telemedicine, and smart-cities applications
- Explains security and privacy issues in designing and developing of cyber-physical systems applications for healthcare and smart cities
- Explores emerging trends, challenges, and opportunities in the fields of augmented reality interfaces, affective computing, and ethical considerations in human-computer interaction design
The text is primarily written for senior undergraduates, graduate students, and academic researchers in the fields of electrical engineering, electronics and communications engineering, computer science and engineering, biomedical engineering, and information technology.
Table of Contents:
Chapter 1: Issues and Challenges: Towards a Secure Metaverse Chapter 2: Intelligent Systems - The Role of Wireless Positioning in Autonomous Vehicles Chapter 3: Advanced Encryption Techniques for Wireless Networks: A Comparative Study Chapter 4: Enhancing Security and Threat Detection through Machine Learning Models Chapter 5: Privacy-Preserving Federated Learning for Securing the IoT Based Edge Devices in Human Computer Interaction Applications Chapter 6: Blockchain-Enabled IoT for Green Finance: Fostering Eco-Conscious Behavior and Financial Innovation Chapter 7: Comparative Analysis of Advanced Transformer Architectures for Online Rumor Veracity Detection Chapter 8: Nearest Neighbour Search with Applications in Medical Image Manipulation Chapter 9: Emotional Anatomy of Fake News: Leveraging Sentiment and Explainable AI for Misinformation Detection Chapter 10: A Comprehensive Review of Deep Learning, Traditional Methods, and Gen AI Techniques for lung nodule segmentation Chapter 11: MERDiff-ODE: Redefining Multimodal Emotion Recognition with Diffusion Models in Healthcare Chapter 12: A Novel Poisoned Water Detection Method Using Smartphone Embedded Wi-Fi Technology and Machine Learning Algorithms Chapter 13: Leafy Vegetables Growth Analysis using Machine Learning at Different pH and TDS Levels for Hydroponics Chapter 14: Detecting Newly Emerging Apple Blotch Leaf Miner (ABLM) using Deep Learning Approach Chapter 15: Cognitive Intelligent Duct Systems for Providing Cooling Comfort as per the Climate Chapter 16: AI-Driven BookSwap Connect Chapter 17: Bridging the Gaps: An Extensive Survey on Knowledge Distillation for Computer Vision
About the Author :
Arvind Sharma is an alumna of Guru Nanak Dev University India. He is an academician having 8+ years of experience, and currently working in the capacity of Associate Professor in Shoolini University, India. Apart from academics, he also holds certain administrative responsibilities with current or former employers, and currently serves as Faculty In-Charge in Shoolini University ACM Student Chapter. Earlier he worked for iHub and HCI Foundation, Indian Institute of Technology Mandi (IITM); Maharishi Markandeshwar (deemed to be) University; Chandigarh University; Trinity College; in India. He had Ph.D. in Computer Science and Engineering with specialization in Network Security from Rayat Bhara University, India; he received Master of Technology (M.Tech) in Computer Science and Engineering from Lovely Professional University, India along with another masters M.Sc. in Computer Science from Guru Nanak Dev University, India. His research interests span in Cryptography, Computer Networks, Cloud Computing, Internet of Things, and Interdisciplinary research in Healthcare. With over 35+ research papers, his scholarly work, journal articles, conference papers, is widely recognized in international/national publications. He served as book editor in several reputed publishing houses and journals. He also served as Guest Editor in Journal Special Issues. Currently 2 Ph.D. research scholars are pursuing their research work under the supervision of Dr. Sharma in Shoolini University.
Samiya Khan a University of Delhi alumna holds a PhD in Computer Science from Jamia Millia Islamia, India. She is currently working as Lecturer at the University of Greenwich, United Kingdom. Prior to this, she worked on an ERDF Project as Postdoctoral Research Fellow at the University of Wolverhampton, United Kingdom. With over 25+ research papers, her scholarly work, encompassing journal articles, conference papers and book chapters, is widely recognized in high-impact, international publications. Samiya has also served as a guest speaker for numerous events and conferences hosted by universities and organizations across the globe. In her editorial capacity, she has contributed as Guest Associate Editor for high impact journals such as Expert Systems and Frontiers in Big Data, and as a reviewer for numerous prestigious journals and conferences. Having a PhD focused on Big Data, she has also authored a book entitled, ‘Big Data and Analytics. Besides this, she has co-edited five books published by renowned publishers such as Taylor & Francis and other reputed journals. Samiya’s recent research endeavours focus on the application of data science in digital health.
Ashima Yadav is an Assistant Professor at the School of Computer Science Engineering and Technology, Bennett University, Greater Noida, India. She earned her Ph.D. in Deep Learning from Delhi Technological University, New Delhi, India. She was honored with the “Research Excellence Award” by Delhi Technological University in 2020, 2021, and 2023 and “Dean’s Research Award” by Bennett University in the year 2024. Additionally, she has been recognized in the top 2% of scientists globally by Stanford University in the year 2024. Her research interests includes deep learning, natural language processing, machine learning, fake news detection, Deepfake identification, emotion recognition, and sentiment analysis.
