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Soft Computing In Data Science

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Soft Computing In Data Science

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Author : Michael W. Berry
language : en
Publisher: Springer Nature
Release Date : 2019-09-23

Soft Computing In Data Science written by Michael W. Berry and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-23 with Computers categories.

This book constitutes the refereed proceedings of the 5th International Conference on Soft Computing in Data Science, SCDS 2019, held in Iizuka, Japan, in August 2019. The 30 revised full papers presented were carefully reviewed and selected from 75 submissions. The papers are organized in topical sections on information and customer analytics; visual data science; machine and deep learning; big data analytics; computational and artificial intelligence; social network and media analytics.

Soft Computing In Data Science

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Author : Azlinah Mohamed
language : en
Publisher:
Release Date : 2017

Soft Computing In Data Science written by Azlinah Mohamed and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with Artificial intelligence categories.

Soft Computing For Data Analytics Classification Model And Control

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Author : Deepak Gupta
language : en
Publisher: Springer Nature
Release Date : 2022-01-30

Soft Computing For Data Analytics Classification Model And Control written by Deepak Gupta and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-01-30 with Technology & Engineering categories.

This book presents a set of soft computing approaches and their application in data analytics, classification model, and control. The basics of fuzzy logic implementation for advanced hybrid fuzzy driven optimization methods has been covered in the book. The various soft computing techniques, including Fuzzy Logic, Rough Sets, Neutrosophic Sets, Type-2 Fuzzy logic, Neural Networks, Generative Adversarial Networks, and Evolutionary Computation have been discussed and they are used on variety of applications including data analytics, classification model, and control. The book is divided into two thematic parts. The first thematic section covers the various soft computing approaches for text classification and data analysis, while the second section focuses on the fuzzy driven optimization methods for the control systems. The chapters has been written and edited by active researchers, which cover hypotheses and practical considerations; provide insights into the design of hybrid algorithms for applications in data analytics, classification model, and engineering control.

Soft Computing In Data Science

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Author : Azlinah Mohamed
language : en
Publisher: Springer
Release Date : 2017-11-23

Soft Computing In Data Science written by Azlinah Mohamed and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-11-23 with Computers categories.

This book constitutes the refereed proceedings of the International Conference on Soft Computing in Data Science, SCDS 2017, held in Yogyakarta, Indonesia, November 27-28, 2017. The 26 revised full papers presented were carefully reviewed and selected from 68 submissions. The papers are organized in topical sections on deep learning and real-time classification; image feature classification and extraction; classification, clustering, visualization; applications of machine learning; data visualization; fuzzy logic; prediction models and e-learning; text and sentiment analytics.

Soft Computing In Data Science

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Author : Michael W. Berry
language : en
Publisher: Springer
Release Date : 2016-09-17

Soft Computing In Data Science written by Michael W. Berry and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-09-17 with Computers categories.

This book constitutes the refereed proceedings of the International Conference on Soft Computing in Data Science, SCDS 2016, held in Putrajaya, Malaysia, in September 2016. The 27 revised full papers presented were carefully reviewed and selected from 66 submissions. The papers are organized in topical sections on artificial neural networks; classification, clustering, visualization; fuzzy logic; information and sentiment analytics.

Soft Computing In Data Science

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Author : Azlinah Mohamed
language : en
Publisher: Springer Nature
Release Date : 2021-10-28

Soft Computing In Data Science written by Azlinah Mohamed and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10-28 with Computers categories.

This book constitutes the refereed proceedings of the 6th International Conference on Soft Computing in Data Science, SCDS 2021, which was held virtually in November 2021. The 31 revised full papers presented were carefully reviewed and selected from 79 submissions. The papers are organized in topical sections on AI techniques and applications; data analytics and technologies; data mining and image processing; machine & statistical learning.

Soft Computing In Data Science

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Author : Bee Wah Yap
language : en
Publisher: Springer
Release Date : 2018-12-10

Soft Computing In Data Science written by Bee Wah Yap and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-12-10 with Computers categories.

This book constitutes the refereed proceedings of the 4th International Conference on Soft Computing in Data Science, SCDS 2018, held in Bangkok, Thailand, in August 2018. The 30 revised full papers presented were carefully reviewed and selected from 75 submissions. The papers are organized in topical sections on machine and deep learning, image processing, financial and fuzzy mathematics, optimization algorithms, data and text analytics, data visualization.

Soft Computing In Data Science

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Author : Michael W. Berry
language : en
Publisher: Springer
Release Date : 2015-09-02

This book constitutes the refereed proceedings of the International Conference on Soft Computing in Data Science, SCDS 2015, held in Putrajaya, Malaysia, in September 2015. The 25 revised full papers presented were carefully reviewed and selected from 69 submissions. The papers are organized in topical sections on data mining; fuzzy computing; evolutionary computing and optimization; pattern recognition; human machine interface; hybrid methods.

Soft Computing In Data Science

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Author :
language : en
Publisher:
Release Date : 2019

Soft Computing In Data Science written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with Data mining categories.

Advanced Soft Computing Techniques In Data Science Iot And Cloud Computing

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Author : Sujata Dash
language : en
Publisher: Springer Nature
Release Date : 2021-11-05

Advanced Soft Computing Techniques In Data Science Iot And Cloud Computing written by Sujata Dash and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-11-05 with Technology & Engineering categories.

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.

Soft Computing In Data Science

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