Deep Learning and Parallel Computing Environment for Bioengineering Systems

Häftad, Engelska, 2019

AvArun Kumar Sangaiah,Taiwan) Sangaiah, Arun Kumar (National Yunlin University of Science and Technology

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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.

Presents novel, in-depth research contributions from a methodological/application perspective in understanding the fusion of deep machine learning paradigms and their capabilities in solving a diverse range of problems
Illustrates the state-of-the-art and recent developments in the new theories and applications of deep learning approaches applied to parallel computing environment in bioengineering systems
Provides concepts and technologies that are successfully used in the implementation of today's intelligent data-centric critical systems and multi-media Cloud-Big data

Produktinformation

Utgivningsdatum2019-07-27
Mått191 x 235 x 14 mm
Vikt570 g
FormatHäftad
SpråkEngelska
Antal sidor280
FörlagElsevier Science
ISBN9780128167182

Tillhör följande kategorier

Biokemisk teknik inom Naturvetenskap och teknik
Hårdvara inom Data och IT
Teknik: allmänt inom Naturvetenskap och teknik

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.

1. Introductory2. Theoretical results on representation of deep learning and parallel architectures for bioengineering3. Parallel Machine Learning and Deep Learning approaches for Bio-informatics4. Parallel programming, architectures and machine intelligence for bioengineering5. Deep Randomized Neural Networks for Bioengineering applications6. Artificial Intelligence enhance parallel computing environments7. Parallel computing, graphics processing units (GPU) and new hardware for deep learning in Computational Intelligence research8. Novel feature representation using deep learning, dictionary learning for face, fingerprint, ocular, and/or other biometric modalities9. Novel distance metric learning algorithms for biometrics modalities10. Machine learning techniques (e.g., Deep Learning) with cognitive knowledge acquisition frameworks for sustainable energy aware systems11. Deep learning and semi-supervised and transfer learning algorithms for medical imaging12. Biological plausibility/inspiration of Randomized Neural Networks13. Genomic data visualisation and representation for medical information14. Applications of deep learning and unsupervised feature learning for prediction of sustainable engineering tasks15. Inference and optimization with bioengineering problems

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