Cognitive Data Science in Sustainable Computing
Aim & scope
Cognitive data science is the creation of self-learning systems that use data mining, pattern recognition and natural language processing to solve complicated problems without constant human oversight. Cognitive data analytics applies humanlike intelligence to certain tasks, such as understanding not only the words in a text, but the full context of what is being written or spoken, or recognizing objects in an image with large amounts of information. Sustainable computing is an interdisciplinary field that aims to apply intelligent techniques for balancing environmental, economic and societal needs for sustainable development.
Cognitive Data Science in Sustainable Computing publishes volumes which aim to develop cognitive data computational models and methods for decision-making concerning the most challenging problems related to sustainability. Hence, the main objective is to facilitate a forum for a broad range of researchers, where decision-making approaches under cognitive data science and computing paradigms are adapted to demonstrate how proposed procedures, as well as big data and IoT approaches, can be applied in practice within sustainable computing environments, such as predictive analytics, optimization, and policies for harvesting renewable resources.
Topical coverage includes but is not limited to:
Cognitive data models
Cognitive systems
Deep learning models
Models for IoT devices and systems
Cognitive computing models and prediction analytics for sustainable engineering
Parallel programming, architectures and machine intelligence
Applications: cognitive IoT (CIoT) with big data systems; AI modelling and performance analysis in edge computing for IoT; networks for data management; predictive analytics in IoT based solutions
New Volume Proposals:
Volumes can be Edited, Multi-Authored, or Authored Monographs
New volume proposals should:
Include a well-structured Table of Contents
Be innovative, including original features, and any overlaps with published titles in the CDSSC Series should be explained
Include a list of confirmed or tentative, geographically distributed, authors (for Edited volumes)
Indexing: All published volumes in this book series are submitted for indexing in:
Scopus
EI Indexing / Compendex
Book Citation Index
Audience
Graduate students, researchers, and professionals in fields of computational modelling, applied mathematics, and applied engineering, interested in interdisciplinary research at the intersection of computational intelligence, applied mathematics, and sustainability
Publish a book with Elsevier
Series Editor
PAKS
Prof. Arun Kumar Sangaiah
Disguised Professor
National Yunlin University of Science and Technology, Taiwan
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