Emerging Trends and Applications of Deep Learning for Biomedical Data Analysis

Erscheinungsjahr 2025
ISBN: 978-0-443-26765-9
Verlag: Elsevier Science

Buch, Englisch, 330 Seiten, Format (B × H): 191 mm x 235 mm

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.

Autoren/Hrsg.

Sharma, Smita

Balusamy, Balamurugan

Ramesh, S.

Bashir, Ali Kashif

Fachgebiete

• Naturwissenschaften Biowissenschaften Biowissenschaften

Inhaltsverzeichnis

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

Über Autor(innen)

Balusamy, Balamurugan
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.

Bashir, Ali Kashif
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.

Sharma, Smita
Dr. Smita Sharma is a Senior Member of IEEE and currently serves as the WIE Chair of the IEEE Uttar Pradesh Section. She holds a Ph.D. in Wireless Body Area Sensor Networks from Uttarakhand Technical University, a Central Government University, along with a B.Tech from Galgotias College of Engineering and an M.Tech from Madan Mohan Malviya Engineering College, Uttar Pradesh, specializing in Electronics and Communication Engineering.

Dr. Sharma is associated with the National Institute of Electronics & Information Technology (NIELIT), New Delhi. Previously, she was an Associate Professor at Amity University, Uttar Pradesh, where she dedicated 14 years to teaching and research.

With a remarkable academic and research portfolio, Dr. Sharma has authored over 50 peer-reviewed articles published in prestigious international journals and conferences. She has contributed to numerous book chapters, edited several books, and holds multiple Indian patents. She actively collaborates with distinguished professors from globally renowned QS-ranked universities and plays a key role in organizing IEEE conferences.

Dr. Sharma's research focuses on cutting-edge areas including the Internet of Things (IoT), wireless sensor networks (WSNs), network security, artificial intelligence, and machine learning. Within WSNs, her work emphasizes improving network efficiency and extending sensor lifespan.

A dedicated contributor to the academic community, Dr. Sharma serves as a reviewer for leading journals and conferences, is a sought-after speaker at global events, and is an integral part of publication teams for internationally recognized journals.

She is also an active member of IAENG and CSI societies, promoting diversity, inclusion, and technological advancement.

Ramesh, S.
Dr. S. Ramesh is an Assistant Professor (SS) in the Department of Mechatronics Engineering, Rajalakshmi Engineering College, Thandalam, Chennai. He has completed his Ph.D. degree in Embedded Systems/Machine Learning from VIT University, Chennai in 2020, an M. Tech. Degree in Embedded Systems from SRM University, Chennai, Tamilnadu, India in 2011, B.E. Degree from National Engineering College, Kovilpatti, Tamilnadu, India in 2008. In addition to this, he is currently doing Post Doctoral Research in Malaysia. He has over 13 years of Teaching and Research Experience at various Universities and Engineering Colleges around India.