Deep Learning and Parallel Computing Environment for Bioengineering Systems delivers a significant forum for the technical advancement of deep learning in parallel computing environment across bio-engineering diversified domains and its applications. Pursuing an interdisciplinary approach, it focuses on methods used to identify and acquire valid, potentially useful knowledge sources. Managing the gathered knowledge and applying it to multiple domains including health care, social networks, mining, recommendation systems, image processing, pattern recognition and predictions using deep learning…mehr
Deep Learning and Parallel Computing Environment for Bioengineering Systems delivers a significant forum for the technical advancement of deep learning in parallel computing environment across bio-engineering diversified domains and its applications. Pursuing an interdisciplinary approach, it focuses on methods used to identify and acquire valid, potentially useful knowledge sources. Managing the gathered knowledge and applying it to multiple domains including health care, social networks, mining, recommendation systems, image processing, pattern recognition and predictions using deep learning paradigms is the major strength of this book. This book integrates the core ideas of deep learning and its applications in bio engineering application domains, to be accessible to all scholars and academicians. The proposed techniques and concepts in this book can be extended in future to accommodate changing business organizations' needs as well as practitioners' innovative ideas.
Prof. Arun Kumar Sangaiah received his PhD from the School of Computer Science and Engineering, VIT University, Vellore, India. He is currently a Full Professor with National Yunlin University of Science and Technology, Taiwan. He is also a Professor at the School of Computing Science and Engineering, VIT University, Vellore, India. His areas of research interest include machine learning, Internet of Things, Sustainable Computing. He has published more than 300 research articles in refereed journals, 11 edited books, one patent (held and filed), as well as four projects funded by MOST-TAIWAN, one funded by Ministry of IT of India, and several international projects (CAS, Guangdong Research fund, Australian Research Council). Dr. Sangaiah has received many awards, Yushan Young Scholar, Clarivate Top 1% Highly Cited Researcher (2021,2022, 2023), Top 2% Scientist (Standord Report-2020,2021,2022, 2023), PIFI-CAS fellowship, Top-10 outstanding researcher, CSI significant Contributor etc. He is also serving as Editor-in-Chief and/or Associate Editor of various reputed ISI journals. Dr. Sangaiah is a visiting scientist (2018-2019) with Chinese Academy of Sciences (CAS), China and visiting researcher of Université Paris-Est (UPEC), France (2019-2020) and etc.
Inhaltsangabe
1. Introductory 2. Theoretical results on representation of deep learning and parallel architectures for bioengineering 3. Parallel Machine Learning and Deep Learning approaches for Bio-informatics 4. Parallel programming, architectures and machine intelligence for bioengineering 5. Deep Randomized Neural Networks for Bioengineering applications 6. Artificial Intelligence enhance parallel computing environments 7. Parallel computing, graphics processing units (GPU) and new hardware for deep learning in Computational Intelligence research 8. Novel feature representation using deep learning, dictionary learning for face, fingerprint, ocular, and/or other biometric modalities 9. Novel distance metric learning algorithms for biometrics modalities 10. Machine learning techniques (e.g., Deep Learning) with cognitive knowledge acquisition frameworks for sustainable energy aware systems 11. Deep learning and semi-supervised and transfer learning algorithms for medical imaging 12. Biological plausibility/inspiration of Randomized Neural Networks 13. Genomic data visualisation and representation for medical information 14. Applications of deep learning and unsupervised feature learning for prediction of sustainable engineering tasks 15. Inference and optimization with bioengineering problems
1. Introductory 2. Theoretical results on representation of deep learning and parallel architectures for bioengineering 3. Parallel Machine Learning and Deep Learning approaches for Bio-informatics 4. Parallel programming, architectures and machine intelligence for bioengineering 5. Deep Randomized Neural Networks for Bioengineering applications 6. Artificial Intelligence enhance parallel computing environments 7. Parallel computing, graphics processing units (GPU) and new hardware for deep learning in Computational Intelligence research 8. Novel feature representation using deep learning, dictionary learning for face, fingerprint, ocular, and/or other biometric modalities 9. Novel distance metric learning algorithms for biometrics modalities 10. Machine learning techniques (e.g., Deep Learning) with cognitive knowledge acquisition frameworks for sustainable energy aware systems 11. Deep learning and semi-supervised and transfer learning algorithms for medical imaging 12. Biological plausibility/inspiration of Randomized Neural Networks 13. Genomic data visualisation and representation for medical information 14. Applications of deep learning and unsupervised feature learning for prediction of sustainable engineering tasks 15. Inference and optimization with bioengineering problems
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