Overview
- Addresses recent research on soft computing techniques in data science, IoT, and cloud computing
- Presents the latest advances and developments in the field of multimedia retrieval, fault tolerance, system modeling, network architecture, Web semantics, big data analytics, time series, and biomedical and health informatics
- Focuses on both soft computing techniques, multimedia retrieval, cloud computing, image analysis, and data science and binds these fields very closely
Part of the book series: Studies in Big Data (SBD, volume 89)
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Table of contents (18 chapters)
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Front Matter
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Soft Computing Techniques for IoT Devices
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Front Matter
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Soft Computing Techniques in Cloud Computing and Computer Networking
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Front Matter
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Soft Computing Techniques in Data Science
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Front Matter
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Keywords
About this book
This book plays a significant role in improvising human life to a great extent. The new applications of soft computing can be regarded as an emerging field in computer science, automatic control engineering, medicine, biology application, natural environmental engineering, and pattern recognition. Now, the exemplar model for soft computing is human brain. The use of various techniques of soft computing is nowadays successfully implemented in many domestic, commercial, and industrial applications due to the low-cost and very high-performance digital processors and also the decline price of the memory chips. This is the main reason behind the wider expansion of soft computing techniques and its application areas. These computing methods also play a significant role in the design and optimization in diverse engineering disciplines. With the influence and the development of the Internet of things (IoT) concept, the need for using soft computing techniques has become more significant than ever. In general, soft computing methods are closely similar to biological processes than traditional techniques, which are mostly based on formal logical systems, such as sentential logic and predicate logic, or rely heavily on computer-aided numerical analysis. Soft computing techniques are anticipated to complement each other. The aim of these techniques is to accept imprecision, uncertainties, and approximations to get a rapid solution.
However, recent advancements in representation soft computing algorithms (fuzzy logic,evolutionary computation, machine learning, and probabilistic reasoning) generate a more intelligent and robust system providing a human interpretable, low-cost, approximate solution. Soft computing-based algorithms have demonstrated great performance to a variety of areas including multimedia retrieval, fault tolerance, system modelling, network architecture, Web semantics, big data analytics, time series, biomedical and health informatics, etc. Soft computing approaches such as genetic programming (GP), support vector machine–firefly algorithm (SVM-FFA), artificial neural network (ANN), and support vector machine–wavelet (SVM–Wavelet) have emerged as powerful computational models. These have also shown significant success in dealing with massive data analysis for large number of applications. All the researchers and practitioners will be highly benefited those who are working in field of computer engineering, medicine, biology application, signal processing, and mechanical engineering. This book is a good collection of state-of-the-art approaches for soft computing-based applications to various engineering fields. It is very beneficial for the new researchers and practitioners working in the field to quickly know the best performing methods. They would be able to compare different approaches and can carry forward their research in the most important area of research which has direct impact on betterment of the human life and health.This book is very useful because there is no book in the market which provides a good collection of state-of-the-art methods of soft computing-based models for multimedia retrieval, fault tolerance, system modelling, network architecture, Web semantics, big data analytics, time series, and biomedical and health informatics.
Editors and Affiliations
Bibliographic Information
Book Title: Advanced Soft Computing Techniques in Data Science, IoT and Cloud Computing
Editors: Sujata Dash, Subhendu Kumar Pani, Ajith Abraham, Yulan Liang
Series Title: Studies in Big Data
DOI: https://doi.org/10.1007/978-3-030-75657-4
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021
Hardcover ISBN: 978-3-030-75656-7Published: 06 November 2021
Softcover ISBN: 978-3-030-75659-8Published: 06 November 2022
eBook ISBN: 978-3-030-75657-4Published: 05 November 2021
Series ISSN: 2197-6503
Series E-ISSN: 2197-6511
Edition Number: 1
Number of Pages: XXV, 434
Number of Illustrations: 66 b/w illustrations, 192 illustrations in colour
Topics: Computational Intelligence, Data Engineering, Cyber-physical systems, IoT, Big Data