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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…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.
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
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Dr. Arun Kumar Sangaiah received his Master of Engineering from Anna University and Ph.D. from VIT University, India. He is currently as 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. Moreover, he has holding visiting professor positions in China, France, Japan, South Korea. Further, he has been visited many research centers and universities in China, Japan, France, Singapore and South Korea for join collaboration towards research projects and publications. Dr. Sangaiah's outstanding scientific production spans over 200+ contributions published in high standard ISI journals, such as IEEE-TII, IEEE-Communication Magazine, IEEE Systems and IEEE IoT. In addition, he has authored/edited 8 books (Elsevier, Springer and others) and edited 50 special issues in reputed ISI journals, such as IEEE-Communication Magazine, IEEE-TII, IEEE-IoT, ACM
transaction on Intelligent Systems and Technology etc. He has also registered one Indian patent in the area of Computational Intelligence. His Google Scholar Citations reached 5000+ with h-index: 40+ and i10-index: 150+. Further, Dr. Sangaiah is responsible for EiC, Editorial Board Member and Associate Editor of many reputed ISI journals.Finally, he has received many awards that includes,Chinese Academy of Sciences-PIFI overseas visiting scientist award, UPEC-France Visiting Scholar award, Carrers-360 Top-10 Outstanding Researchers award 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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