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

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