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 science has broad horizons, which cover different characteristics of cognition. 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. The main focus of this book series is to develop cognitive data computational models and methods for decision-making concerning some of the most challenging problems related to sustainability.

The main focus of this book series is integrating cognitive data science with sustainable computing. It can be extended into existing data technologies and approaches by incorporating knowledge from experts as well as a notion of artificial intelligence (AI). The main focus is designing the cognitive-embedded data technologies to aid better decision-making and process and analyze a large amount of data collected through the IoT. The book series targets the adaptation of decision-making approaches under cognitive computing paradigms to demonstrate how the proposed procedures, as well as big data and Internet of Things (IoT) problems, can be handled in practice.

Topical coverage includes but is not limited to:

Data Science, Cognitive, IoT, Big Data, Models Deep learning Model and Methods for Bio Informatics Modern Data Science Models for IoT Devices and Systems Cognitive computing models and prediction analytics for sustainable engineering Parallel programming, architectures and machine intelligence for bio medical engineering Predictive analytics and AI human intelligence in IoT based medical solutions Cognitive Intelligent systems based on connected vehicles Applications of cognitive IoT (CIoT) with big data systems Artificial Intelligence modelling and performance analysis in edge computing for IoT Smart things networks for real world data management

This book series provides a comprehensive overview of the constituent paradigms that underlie cognitive data science approaches and sustainable computing paradigms, paying more attention to big data systems over Internet of Things (IoT) and its research challenges. 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.

Publications in this Book Series include:

Edited volumes Authored volumes Monographs

Cognitive Data Science in Sustainable Computing

Aim & scope

Series Editor

Prof. Arun Kumar Sangaiah

Cognitive Data Science in Sustainable Computing