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Deep learning and parallel computing environment for bioengineering

Author: Arun Kumar Sangaiah
Publisher: St. Louis, Missouri : Elsevier, 2019.
Edition/Format:   eBook : Document : EnglishView all editions and formats

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Genre/Form: Electronic books
Additional Physical Format: (OCoLC)1082969589
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Arun Kumar Sangaiah
ISBN: 9780128172933 0128172932
OCLC Number: 1110719145
Description: 1 online resource
Contents: 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
Responsibility: edited by Arun Kumar Sangaiah.


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