DLBES-2019: Elsevier Call For Book Chapter |
Website | https://sites.google.com/site/elsevierdlbes/ |
Submission link | https://easychair.org/conferences/?conf=dlbes2019 |
Abstract registration deadline | April 1, 2018 |
Submission deadline | July 1, 2018 |
Deep Learning and Parallel Computing Environment for Bio-Engineering Systems
Volume Editor
Dr. Arun Kumar Sangaiah,
School of Computing Science and Engineering,
Vellore Institute of Technology, Vellore 632014, India
Overview & Aim of the Book
The new frontier research era and convergence of deep machine learning and parallel computing with reference to bio-engineering has three main streams needs to be addressed in the current scenario: bio informatics, medical imaging, and sustainable engineering. This book is integrating machine learning, cognitive neural computing, parallel computing paradigms, advanced data analytics and optimization opportunities to bring more compute to the bio-engineering problems and challenges. Further, it is importance to make a note that convergence of parallel computing architectures, deep machine learning and its intelligence techniques has not been adequately investigated from the perspective of bio engineering research streams (bio-informatics, medical imaging, and sustainable engineering) and its related research issues. Obviously, these challenges also create immense opportunities for researchers.
The book will present novel in depth fundamental research contributions either from a methodological/application perspective in understanding the fusion of deep machine learning paradigms and their capabilities in solving a diverse range of problems in for bio-engineering and its real-world applications. The overall objective of the book is to illustrate the state-of-the-art and recent developments in the new theories and applications of deep learning approaches applied to parallel computing environment in bio engineering systems.
Recommended Topics
This book solicits contributions that also include the basics, fundamentals of the deep learning in parallel computing environment across bio-engineering diversified domains and its applications. It is supported by practical examples and case studies apart from in-depth analysis of real life engineering applications.
- Theoretical results on representation of deep learning and parallel architectures for bio engineering
- Parallel Machine Learning and Deep Learning approaches for Bio-informatics
- Parallel programming, architectures and machine intelligence for bio engineering
- Deep Randomized Neural Networks for Bio-engineering applications
- Artificial Intelligence enhance parallel computing environments
- Parallel computing, graphics processing unit (GPU) and new hardware for deep learning in Computational Intelligence research
- Novel feature representation using deep learning, dictionary learning for face, fingerprint, ocular, and/or other biometric modalities
- Novel distance metric learning algorithms for biometrics modalities
- Machine learning techniques (e.g., Deep Learning) with cognitive knowledge acquisition frameworks for sustainable energy aware systems
- Deep learning and semi-supervised and transfer learning algorithms for medical imaging
- Biological plausibility/inspiration of Randomized Neural Networks
- Genomic data visualization and representation for medical information
- Applications of deep learning and unsupervised feature learning for prediction of sustainable engineering tasks.
- Inference and optimization with bio-engineering problems
Tentative Publication Schedule
April 25, 2018: Abstract registration due. The authors are encouraged to submit a 1-2 page abstract clearly explaining the purpose and concerns of the chapter as it relates to deep learning for bio engineering systems.
May 1, 2018: Abstract notification due. Authors will be notified about the acceptance of the abstract based on the volume editors’ review assessment.
July 1, 2018: Full chapter submission due. Deadline for submission of full chapters. Chapters are not to exceed 40 pages in length and will be peer-reviewed by at least three anonymous referees.
August 15, 2018: First Review notification with peer-reviewers comments on the chapter.
September 15, 2018: Chapter revised version submission upon the reviewer’s comments. Addressing all reviewers’ comments is a precondition for publication of the final version of an accepted chapter.
October 1, 2018: Full chapter notification due. Authors of submitted chapters are notified about their acceptance.
October 15, 2018: Camera-ready submission due. Submission deadline for the camera-ready chapters.
Submission Procedure
Full chapters of this book can be submitted through
Easy Chair: https://easychair.org/conferences/?conf=dlbes2019
Please follow the instructions posted in : https://sites.google.com/site/elsevierdlbes/
Prospective authors are encouraged to indicate their interests any time before the submission deadline.
Elsevier Chapter - Author Preparation Tool Kit
The authors are refer the chapter preparation guidelines in the below link.
Further information related to this book and chapter submission contact editor:
Dr. Arun Kumar Sangaiah
School of Computing Science and Engineering,
Vellore Institute of Technology, Vellore-632014, Tamil Nadu, India.
Tel: +91 9842935634
Email: arunkumarsangaiah@gmail.com