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Big Data in Engineering Applications

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Big Data in Engineering Applications/ edited by Sanjiban Sekhar Roy, Pijush Samui, Ravinesh Deo, Stavros Ntalampiras.
other author:	Roy, Sanjiban Sekhar.
Description:	VI, 384 p. 135 illus., 88 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Computational intelligence. -
Online resource:	https://doi.org/10.1007/978-981-10-8476-8
ISBN:	9789811084768

Big Data in Engineering Applications
Big Data in Engineering Applications[electronic resource] /edited by Sanjiban Sekhar Roy, Pijush Samui, Ravinesh Deo, Stavros Ntalampiras. - 1st ed. 2018. - VI, 384 p. 135 illus., 88 illus. in color.online resource. - Studies in Big Data,442197-6503 ;. - Studies in Big Data,8.

Big Data Applications in Education and Health Care -- Analysis of Compressive strength of alkali activated cement using Big data analysis -- Application of cluster based AI methods on daily streamflows -- Bigdata applications to smart power systems -- Big Data in e-commerce -- Interaction of Independent Component Analysis (ICA) and Support Vector Machine (SVM) in exploration of Greenfield areas -- Big Data Analysis of decay Coefficient of Naval Propulsion Plant -- Information Extraction and Text Summarization in documents using Apache Spark -- Detecting Outliers from Big Data Streams -- Machine Learning in Big Data Applications.

This book presents the current trends, technologies, and challenges in Big Data in the diversified field of engineering and sciences. It covers the applications of Big Data ranging from conventional fields of mechanical engineering, civil engineering to electronics, electrical, and computer science to areas in pharmaceutical and biological sciences. This book consists of contributions from various authors from all sectors of academia and industries, demonstrating the imperative application of Big Data for the decision-making process in sectors where the volume, variety, and velocity of information keep increasing. The book is a useful reference for graduate students, researchers and scientists interested in exploring the potential of Big Data in the application of engineering areas.

ISBN: 9789811084768

Standard No.: 10.1007/978-981-10-8476-8doiSubjects--Topical Terms:

568984
Computational intelligence.

LC Class. No.: Q342

Dewey Class. No.: 006.3

Big Data in Engineering Applications
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