Deep Learning and Parallel Computing Environment for Bioengineering Systems

1. Introductory<br>2. Theoretical results on representation of deep learning and parallel architectures for bioengineering<br>3. Parallel Machine Learning and Deep Learning approaches for Bio-informatics<br>4. Parallel programming, architectures and machine intelligence for bioengineering<br>5. Deep Randomized Neural Networks for Bioengineering applications<br>6. Artificial Intelligence enhance parallel computing environments<br>7. Parallel computing, graphics processing units (GPU) and new hardware for deep learning in Computational Intelligence research<br>8. Novel feature representation using deep learning, dictionary learning for face, fingerprint, ocular, and/or other biometric modalities<br>9. Novel distance metric learning algorithms for biometrics modalities<br>10. Machine learning techniques (e.g., Deep Learning) with cognitive knowledge acquisition frameworks for sustainable energy aware systems<br>11. Deep learning and semi-supervised and transfer learning algorithms for medical imaging<br>12. Biological plausibility/inspiration of Randomized Neural Networks<br>13. Genomic data visualisation and representation for medical information<br>14. Applications of deep learning and unsupervised feature learning for prediction of sustainable engineering tasks<br>15. Inference and optimization with bioengineering problems