Emerging Trends and Applications of Deep Learning for Biomedical Data Analysis

Auteur

Smita Sharma is a Senior IEEE member who her PhD degree from Uttarakhand Technical University in the field of Wireless Body Area Sensor Networks. She is currently working as an Assistant Professor at Amity University, Uttar Pradesh, India. She has published dozens of articles in peer-reviewed journals and conferences of international repute. Apart from that, several book chapters and Indian patents are under her credit. Her current research interests include IoT, Wireless Sensor Networks, Network Security, and AI. Her focus in WSN is improving the lifetime and efficiency of sensor networks. She serves as a reviewer of many peer-reviewed journals and conferences. She is the organizer for several IEEE conferences. She has also served on the publication team of several conferences of internationally reputed journals and is member of IAENG and CSI societies.

Dr. Balamurugan Balusamy is currently working as an Associate Dean Student in Shiv Nadar Institution of Eminence, Delhi-NCR. He is part of the Top 2% Scientists Worldwide 2023 by Stanford University in the area of Data Science/AI/ML. He is also an Adjunct Professor, Department of Computer Science and Information Engineering, Taylor University, Malaysia. His contributions focus on engineering education, block chain, and data sciences.

S. Ramesh held various administrative positions in premier institutions including the Principal of the Engineering College, Head of the Department, etc. He is currently working as a Principal at Jerusalem College of Engineering, Chennai, India. In addition to his academic experience, he has 3 years of industrial experience.

Ali Kashif Bashir is an Associate Professor at the School of Computing and Mathematics of Manchester Metropolitan University, United Kingdom, an Adjunct Professor at the School of Electrical Engineering and Computer Science at the National University of Science and Technology, Islamabad (NUST), Pakistan, an Honorary Professor at the School of Information and Communication Engineering of the University of Electronics Science and Technology of China (UESTC) and a Chief Advisor at the Visual Intelligence Research Center, UESTC, China. He is a senior member of Institute of Electrical and Electronics Engineers (IEEE), USA and Distinguished Speaker of Association for Computing Machinery (ACM), USA.

Texte du rabat

Emerging Trends and Applications of Deep Learning for Biomedical Data Analysis introduces the latest emerging trends and applications of deep learning in biomedical data analysis. The book delves into various use cases where deep learning is applied in industrial, social, and personal contexts within the biomedical domain. By gaining a comprehensive understanding of deep learning in biomedical data analysis, readers will develop the skills to critically evaluate research papers, methodologies, and emerging trends. In 14 chapters this book provides both insights into the fundamentals as the latest research trends in the applications of deep learning in biosciences. With several case studies and use cases it familiarizes the reader with a comprehensive understanding of deep learning algorithms, architectures, and methodologies specifically applicable to biomedical data analysis. This title is an ideal reference for researchers across the biomedical sciences.

Contenu

1. A detailed perspective of biomedical engineering
   
   2. Latest research trends and Transitions in the Domain of biomedical engineering
   3. The brewing challenges and concerns of biomedical engineering
   4. The significance of computer vision for biomedical engineering
   5. Fundamentals and Technology Roadmap of Deep Learning
   6. Deep Learning Techniques for Biomedical Image Analysis
   7. Convergence of Deep Learning, AI for Genomic Data Analysis
   8. Deep Learning in Electronic Health Records (EHR) Analysis
   9. Deep Learning for Wearable Sensor Data Analysis
   10. Deep Learning for Drug Discovery and Development
   11. Deep Learning for Pharmacokinetics and Pharmacodynamics
   12. Ethical Considerations and Challenges in Deep Learning for Biomedical Data Analysis
   13. The Societal Impact and Emerging Trends
   14. Case Studies and Use Cases of Deep Learning for biomedical applications
   15. The Role of Generative AI for biomedical engineering