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Elsevier
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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 cover

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

Series Editor

Arun Kumar Sangaiah

PAKS

Prof. Arun Kumar Sangaiah

Full Professor

National Yunlin University of Science and Technology, Taiwan

Read more about Prof. Arun Kumar Sangaiah