BDDL 2020: Bioinformatics and Medical Applications: Big Data Using Deep Learning Algorithms |
Abstract registration deadline | September 30, 2020 |
Submission deadline | October 30, 2020 |
Submission of Full Chapter : 20th November 2020 | November 20, 2020 |
Communication of Reviewer’s Comment | December 10, 2020 |
Final Submission of Chapter | December 30, 2020 |
Wiley- Scrivener Publishing
Call For Book Chapters
Bioinformatics and Medical Applications: Big Data Using Deep Learning Algorithms
About the Book:
This book aims to provides an insight to the readers in way of inculcating the need for applying Mobile edge Data Analytics in Bioinformatics and Medicine that overviews the current state of medical treatments and systems and offers emerging solutions for a more personalized approach to the healthcare field. Biologists no longer use traditional laboratories to discover a novel biomarker for a disease, rather they rely on huge and continuously growing genomic data made available by various research groups. The data size in bioinformatics is increasing dramatically in the recent years. Applying deep learning techniques for data-driven solutions in health information allows automated analysis of both structured and unstructured data without the intervention of a human, this method can be more advantageous in supporting the problems arising from medical and health related information.
Topics Covered:
It solicits high quality and original contribution and will feature thematic content across the following topics of interest, but not limited to
- Deep learning methods for applications in object detection and identification, object tracking, human action recognition, cross-modal and multimodal data analysis
- High performance Computing systems for applications in Healthcare and recommendation systems.
- Hyperspectral data analysis and intelligent systems
- Microarray data analysis, Sequence analysis, genomics based analytics, Disease network analysis, Techniques for big data Analytics and health information technology
- Deep Learning and Cross-Media Methods for Big Data Representation
- Mobile edge computing for Large-scale multimodal data acquisition techniques
- Personal Big data driven approaches to collect and analyze large volumes of information from emerging technologies (e.g., IoT, remote sensors, wireless sensor networks, RFIDs, mobile)
- Mobile edge computing techniques for healthcare applications
- Swarm intelligence big data computing for healthcare applications
- Hybrid DL methods for bioinformatics and healthcare applications
- Automatic design of DL architectures
- Semantic machine learning for big biological data analysis
- Deep Learning for Imaging Informatics in Healthcare Sector
- Deep Learning for Large-Scale Health Data Classification
- Recent Applications of Deep learning Techniques in Health Data Modeling
- Deep learning and graph neural networks for network biology
- Learning meaningful representations for bioinformatics in DL
- Learning node, edge, higher-order, and graph-level embeddings for biomedical applications
- Next-generation graph embedding techniques for important problems, including node classification, link prediction, graph classification and network alignment.
- Graph representation learning for visualizing and interpreting interaction data
- Next-generation network science through network embeddings
- Relevant benchmark datasets, initial solutions for new challenges and new directions in DL bioinformatics
- Applications of network embeddings broadly in computational biology, genomics, medicine, and health.
- Deep learning for sensor informatics and behavioral / activity profiling
- Deep learning for imaging informatics and large-scale classification
- Deep learning for translational bioinformatics and drug discovery
- Deep learning for medical informatics and public health
- Integration of Deep Learning and Big Data for Healthcare Analytics
- Deep learning for Sensor Informatics and Behavioral Profiling
- The Need for Deep Learning-Based Pervasive Health Systems and Services
- Deep Learning and Health Informatics for Smart Monitoring and Diagnosis
Important Dates:
Submission of Chapter Abstract : 30th September 2020
Notification for Conditional Acceptance : 10th October2020
Submission of Full Chapter : 20th November 2020
Communication of Reviewer’s Comment : 10th December 2020
Final Submission of Chapter : 30th December 2020
Submission Procedure:
All the submission for the book chapter should be sent to prisu6esh@ieee.org (or) svimalphd@gmail.com
The published book will be submitted to scopus for indexing ( There is no publication fee for this book)
For any query please contact: Dr.A.Suresh, Email: prisu6esh@ieee.org
Dr.S.Vimal, Email: svimalphd@gmail.com
Editor(s) Name
1. Dr. A. Suresh,
Professor & Head,
Department of Computer Science and Engineering,
Nehru Institute of Engineering and Technology,
T.M.Palayam, Coimbatore. 641105
Tamil Nadu, India
Email: prisu6esh@ieee.org
2. Dr. S. Vimal,
Assistant Professor(Senior Grade)
Department of Information Technology,
National Engineering College,
K.R.Nagar, Kovilpatti,
Thoothukudi District,
Tamil Nadu, India
Email:svimalphd@gmail.com
3. Dr. Y. Harold Robinson,
School of Information Technology and Engineering,
Vellore Institute of Technology,
Vellore, India.
Email: yhrobinphd@gmail.com
4. Mr. Dhinesh Kumar Ramaswami,
Senior Consultant,
Capgemini US,
333 W Wacker Dr #300
Chicago, IL 60606
Email: dhineshkuma66@gmail.com
5. Mr. R. Udendhran,
Bharathidasan University,
Department of Computer Science,
Palkalaiperur, Tiruchirappalli,
Tamil Nadu 620024.
Email: udendran@gmail.com
About the Editors:
Dr. A. Suresh, B.E., M.Tech., Ph.D works as the Professor & Head, Department of the Computer Science and Engineering in Nehru Institute of Engineering & Technology, Coimbatore, Tamil Nadu, India. He has been nearly two decades of experience in teaching and his areas of specializations are Data Mining, Artificial Intelligence, Image Processing, Multimedia and System Software. He has published two patents and 85 papers in International journals. He has book authored Industrial IoT Application Architectures and use cases in CRC press and currently editing two books namely Deep learning and Edge Computing solutions for High Performance Computing in Springer Publisher and Sensor Data Management and Analysis: The Role of Deep Learning in Wiley publisher. He has published 10 chapters in the book title An Intelligent Grid Network Based on Cloud Computing Infrastructures in IGI Global Publisher and Internet of Things for Industry 4.0 in EAI/Springer Innovations in Communication and Computing. He has published more than 40 papers in National and International Conferences. He has served as editor / reviewer for Springer, Elsevier, Wiley, Inderscience journals etc... He is a member of ISTE, MCSI, IACSIT, IAENG, MCSTA and Global Member of Internet Society (ISOC). He has organized several National Workshop, Conferences and Technical Events. He is regularly invited to deliver lectures in various programmes for imparting skills in research methodology to students and research scholars. He has published four books, in the name of Data structures & Algorithms, Computer Programming, Problem Solving and Python Programming and Programming in “C”. He has hosted two special session for IEEE sponsored conference in Osaka, Japan and Thailand.
Dr. S. Vimal is working as an Assistant professor (Senior Grade) in Department of Information Technology, National Engineering College, Kovilpatti, Tamilnadu, India. He has around Thirteen years of teaching experience, EMC certified Data science Associate and CCNA certified professional too. He holds a Ph.D in Information and Communication Engineering from Anna University Chennai and he received Masters Degree from Anna University Coimbatore. He is a member of various professional bodies and organized various funded workshops and seminars. He has wide publications in the highly impact journals in the area of Data Analytics, Networking and Security issues. He has hosted two special session for IEEE sponsored conference in Osaka, Japan and Thailand. His areas of interest include Game Modelling, Cognitive radio networks, Network security, Machine Learning and Big data Analytics. He is a Senior member in IEEE and ISTE.
Dr. Y. Harold Robinson is currently working in School of n Technology and Engineering, Vellore Institute of Technology, Vellore, India. He has received Ph.D degree in Information and Communication Engineering from Anna University, Chennai in the year 2016. He is having more than15 years of experience in teaching. He has published more than 50 papers in various International Journals and presented more than 70 papers in both national and International Conferences. He has written 10 book chapters by Springer, IGI global Publication. He acted as editor for a book Title as “Successful Implementation and Deployment of IoT Projects in Smart Cities “. IGI Global in the The Advances in Environmental Engineering and Green Technologies (AEEGT) book series. He is one of the editor for the book “Handbook of Research on Blockchain Technology: Trend and Technologies” published by Elsevier.
Dhinesh Kumar Ramaswami, B.E., Computer Science, is a Senior Consultant at Capgemini America Inc. He has over 9 years of experience in software development and specializes in various .net technologies. He has an expertise in Docker, Kubernetes and Cloud practices. He has good problem solving skills and proposing software design solutions and practicing solution architecture over the past couple of years. He has vast domain knowledge in Cards and Payment processing. His areas of specializations are Deep Learning, Data Mining, Artificial Intelligence and Image Processing. He has published more than 15 papers in International Journals and National and International Conferences.
Mr. Udendhran R, Dignified computer science researcher on Deep learning. He worked as a data scientist and presented research work in international conferences held in University of Cambridge and published many research papers which are available in ACM digital library. He has published research 10 papers in International journals his research area extensively focus deep learning. He has completed M.Tech in computer science and engineering. His research work focus on deep learning and cryptography. He has published10 chapters in the book title An Intelligent Grid Network Based on Cloud Computing Infrastructures in IGI Global Publisher and Internet of Things for Industry 4.0 in EAI/Springer Innovations in Communication and Computing