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001 9910299911603321
005 20200701115440.0
010   $a981-10-8476-9
024 7 $a10.1007/978-981-10-8476-8
035   $a(CKB)4100000004244309
035   $a(DE-He213)978-981-10-8476-8
035   $a(MiAaPQ)EBC5379963
035   $a(PPN)227402987
035   $a(EXLCZ)994100000004244309
100   $a20180502d2018      u|         0
101 0 $aeng
135   $aurnn|008mamaa
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aBig Data in Engineering Applications /$fedited by Sanjiban Sekhar Roy, Pijush Samui, Ravinesh Deo, Stavros Ntalampiras
205   $a1st ed. 2018.
210  1$aSingapore :$cSpringer Singapore :$cImprint: Springer,$d2018.
215   $a1 online resource (VI, 384 p. 135 illus., 88 illus. in color.) 
225 1 $aStudies in Big Data,$x2197-6503 ;$v44
311   $a981-10-8475-0 
327   $aBig 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.
330   $aThis book presents the current trends, technologies, and challenges in Big Data in the diversified field of engineering and sciences. It covers the applications of Big Data ranging from conventional fields of mechanical engineering, civil engineering to electronics, electrical, and computer science to areas in pharmaceutical and biological sciences. This book consists of contributions from various authors from all sectors of academia and industries, demonstrating the imperative application of Big Data for the decision-making process in sectors where the volume, variety, and velocity of information keep increasing. The book is a useful reference for graduate students, researchers and scientists interested in exploring the potential of Big Data in the application of engineering areas.
410  0$aStudies in Big Data,$x2197-6503 ;$v44
606   $aComputational intelligence
606   $aBig data
606   $aComputer mathematics
606   $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014
606   $aBig Data$3https://scigraph.springernature.com/ontologies/product-market-codes/I29120
606   $aComputational Science and Engineering$3https://scigraph.springernature.com/ontologies/product-market-codes/M14026
615  0$aComputational intelligence.
615  0$aBig data.
615  0$aComputer mathematics.
615 14$aComputational Intelligence.
615 24$aBig Data.
615 24$aComputational Science and Engineering.
676   $a006.3
702   $aRoy$b Sanjiban Sekhar$4edt$4http://id.loc.gov/vocabulary/relators/edt
702   $aSamui$b Pijush$4edt$4http://id.loc.gov/vocabulary/relators/edt
702   $aDeo$b Ravinesh$4edt$4http://id.loc.gov/vocabulary/relators/edt
702   $aNtalampiras$b Stavros$4edt$4http://id.loc.gov/vocabulary/relators/edt
906   $aBOOK
912   $a9910299911603321
996   $aBig Data in Engineering Applications$92513036
997   $aUNINA