國立虎尾科技大學 |

Data, Engineering and Applications = Volume 1 /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Data, Engineering and Applications/ edited by Rajesh Kumar Shukla, Jitendra Agrawal, Sanjeev Sharma, Geetam Singh Tomer.
其他題名:	Volume 1 /
其他作者:	Shukla, Rajesh Kumar.
面頁冊數:	VIII, 191 p. 89 illus., 60 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Big data. -
電子資源:	https://doi.org/10.1007/978-981-13-6347-4
ISBN:	9789811363474

Data, Engineering and Applications = Volume 1 /
Data, Engineering and ApplicationsVolume 1 /[electronic resource] :edited by Rajesh Kumar Shukla, Jitendra Agrawal, Sanjeev Sharma, Geetam Singh Tomer. - 1st ed. 2019. - VIII, 191 p. 89 illus., 60 illus. in color.online resource.

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

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

981821
Big data.

LC Class. No.: QA76.9.B45

Dewey Class. No.: 005.7

Data, Engineering and Applications = Volume 1 /
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