東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Fog computing, deep learning and big...

Prabhu, C. S. R.

FindBook

Google Book

Amazon

博客來

Fog computing, deep learning and big data analytics-research directions

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Fog computing, deep learning and big data analytics-research directions/ by C. S. R. Prabhu.
作者:	Prabhu, C. S. R.
出版者:	Singapore :Springer Singapore : : 2019.,
面頁冊數:	xiii, 71 p. :ill., digital ;24 cm.
內容註:	Introduction -- Fog Application management -- Fog Analytics -- Fog Security and Privary -- Research Directions -- Conclusion.
Contained By:	Springer eBooks
標題:	Cloud computing. -
電子資源:	https://doi.org/10.1007/978-981-13-3209-8
ISBN:	9789811332098

Fog computing, deep learning and big data analytics-research directions
Prabhu, C. S. R.

Fog computing, deep learning and big data analytics-research directions[electronic resource] /by C. S. R. Prabhu. - Singapore :Springer Singapore :2019. - xiii, 71 p. :ill., digital ;24 cm.

Introduction -- Fog Application management -- Fog Analytics -- Fog Security and Privary -- Research Directions -- Conclusion.

This book provides a comprehensive picture of fog computing technology, including of fog architectures, latency aware application management issues with real time requirements, security and privacy issues and fog analytics, in wide ranging application scenarios such as M2M device communication, smart homes, smart vehicles, augmented reality and transportation management. This book explores the research issues involved in the application of traditional shallow machine learning and deep learning techniques to big data analytics. It surveys global research advances in extending the conventional unsupervised or clustering algorithms, extending supervised and semi-supervised algorithms and association rule mining algorithms to big data Scenarios. Further it discusses the deep learning applications of big data analytics to fields of computer vision and speech processing, and describes applications such as semantic indexing and data tagging. Lastly it identifies 25 unsolved research problems and research directions in fog computing, as well as in the context of applying deep learning techniques to big data analytics, such as dimensionality reduction in high-dimensional data and improved formulation of data abstractions along with possible directions for their solutions.

ISBN: 9789811332098

Standard No.: 10.1007/978-981-13-3209-8doiSubjects--Topical Terms:

1016782
Cloud computing.

LC Class. No.: QA76.585

Dewey Class. No.: 004.6782

Fog computing, deep learning and big data analytics-research directions
LDR:02383nmm a2200325 a 4500 001 2178309
003 DE-He213
005 20190104211344.0
006 m d
007 cr nn 008maaau
008 191122s2019 si s 0 eng d
020 $a 9789811332098 $q (electronic bk.)
020 $a 9789811332081 $q (paper)
024 7 $a 10.1007/978-981-13-3209-8 $2 doi
035 $a 978-981-13-3209-8
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.585
072 7 $a UN $2 bicssc
072 7 $a COM021000 $2 bisacsh
072 7 $a UN $2 thema
082 0 4 $a 004.6782 $2 23
090 $a QA76.585 $b .P895 2019
100 1 $a Prabhu, C. S. R. $3 3382328
245 1 0 $a Fog computing, deep learning and big data analytics-research directions $h [electronic resource] / $c by C. S. R. Prabhu.
260 $a Singapore : $b Springer Singapore : $b Imprint: Springer, $c 2019.
300 $a xiii, 71 p. : $b ill., digital ; $c 24 cm.
505 0 $a Introduction -- Fog Application management -- Fog Analytics -- Fog Security and Privary -- Research Directions -- Conclusion.
520 $a This book provides a comprehensive picture of fog computing technology, including of fog architectures, latency aware application management issues with real time requirements, security and privacy issues and fog analytics, in wide ranging application scenarios such as M2M device communication, smart homes, smart vehicles, augmented reality and transportation management. This book explores the research issues involved in the application of traditional shallow machine learning and deep learning techniques to big data analytics. It surveys global research advances in extending the conventional unsupervised or clustering algorithms, extending supervised and semi-supervised algorithms and association rule mining algorithms to big data Scenarios. Further it discusses the deep learning applications of big data analytics to fields of computer vision and speech processing, and describes applications such as semantic indexing and data tagging. Lastly it identifies 25 unsolved research problems and research directions in fog computing, as well as in the context of applying deep learning techniques to big data analytics, such as dimensionality reduction in high-dimensional data and improved formulation of data abstractions along with possible directions for their solutions.
650 0 $a Cloud computing. $3 1016782
650 0 $a Big data. $3 2045508
650 1 4 $a Big Data. $3 3134868
650 2 4 $a Data Structures. $3 891009
650 2 4 $a Computer Appl. in Administrative Data Processing. $3 892567
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
856 4 0 $u https://doi.org/10.1007/978-981-13-3209-8
950 $a Computer Science (Springer-11645)