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Data, engineering and applications.V...

Shukla, Rajesh Kumar.

Data, engineering and applications.Volume 1

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Data, engineering and applications.edited by Rajesh Kumar Shukla ... [et al.].
其他作者:	Shukla, Rajesh Kumar.
出版者:	Singapore :Springer Singapore :2019.
面頁冊數:	viii, 191 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer eBooks
標題:	Data mining.
電子資源:	https://doi.org/10.1007/978-981-13-6347-4
ISBN:	9789811363474$q(electronic bk.)

Data, engineering and applications.Volume 1
Data, engineering and applications.Volume 1[electronic resource] /edited by Rajesh Kumar Shukla ... [et al.]. - Singapore :Springer Singapore :2019. - viii, 191 p. :ill. (some col.), digital ;24 cm.

A review of Recommender System and related Dimensions -- Collaborative Filtering Techniques in Recommendation Systems -- Predicting Users' Interest through ELM basedCollaborative Filtering -- Application of Community Detection Technique in Text Mining -- Sentiment Analysis on WhatsApp Group Chat using R -- A Recent Survey on Information Hiding Techniques -- Investigation of Feature Selection Techniques on Performance of Automatic Text Categorization -- Identiﬁcation and Analysis of Future User Interactions Using Some Link Prediction Methods in Social Networks -- Sentiment Prediction of Facebook Status updates of youngsters -- Logistic Regression for the Diagnosis of Cervical Cancer -- Automatic Examination Timetable Scheduling Using Particle Swarm Optimization and Local Search Algorithm -- Personality Trait Identification for Written Texts Using MLNB -- Deep neural network compression via knowledge distillation for embedded vision applications.

This book presents a compilation of current trends, technologies, and challenges in connection with Big Data. Many fields of science and engineering are data-driven, or generate huge amounts of data that are ripe for the picking. There are now more sources of data than ever before, and more means of capturing data. At the same time, the sheer volume and complexity of the data have sparked new developments, where many Big Data problems require new solutions. Given its scope, the book offers a valuable reference guide for all graduate students, researchers, and scientists interested in exploring the potential of Big Data applications.

ISBN: 9789811363474$q(electronic bk.)

Standard No.: 10.1007/978-981-13-6347-4doiSubjects--Topical Terms:

184440
Data mining.

LC Class. No.: QA76.9.D343

Dewey Class. No.: 006.312

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505 0 $a A review of Recommender System and related Dimensions -- Collaborative Filtering Techniques in Recommendation Systems -- Predicting Users' Interest through ELM basedCollaborative Filtering -- Application of Community Detection Technique in Text Mining -- Sentiment Analysis on WhatsApp Group Chat using R -- A Recent Survey on Information Hiding Techniques -- Investigation of Feature Selection Techniques on Performance of Automatic Text Categorization -- Identiﬁcation and Analysis of Future User Interactions Using Some Link Prediction Methods in Social Networks -- Sentiment Prediction of Facebook Status updates of youngsters -- Logistic Regression for the Diagnosis of Cervical Cancer -- Automatic Examination Timetable Scheduling Using Particle Swarm Optimization and Local Search Algorithm -- Personality Trait Identification for Written Texts Using MLNB -- Deep neural network compression via knowledge distillation for embedded vision applications.
520 $a This book presents a compilation of current trends, technologies, and challenges in connection with Big Data. Many fields of science and engineering are data-driven, or generate huge amounts of data that are ripe for the picking. There are now more sources of data than ever before, and more means of capturing data. At the same time, the sheer volume and complexity of the data have sparked new developments, where many Big Data problems require new solutions. Given its scope, the book offers a valuable reference guide for all graduate students, researchers, and scientists interested in exploring the potential of Big Data applications.
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