國立高雄大學圖資館 |

語系: 繁體中文

說明(常見問題)

圖資館首頁

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Deep learning and missing data in en...

Leke, Collins Achepsah.

Deep learning and missing data in engineering systems

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Deep learning and missing data in engineering systemsby Collins Achepsah Leke, Tshilidzi Marwala.
作者:	Leke, Collins Achepsah.
其他作者:	Marwala, Tshilidzi.
出版者:	Cham :Springer International Publishing :2019.
面頁冊數:	xiv, 179 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer eBooks
標題:	Engineering.
電子資源:	https://doi.org/10.1007/978-3-030-01180-2
ISBN:	9783030011802$q(electronic bk.)

Deep learning and missing data in engineering systems
Leke, Collins Achepsah.

Deep learning and missing data in engineering systems[electronic resource] /by Collins Achepsah Leke, Tshilidzi Marwala. - Cham :Springer International Publishing :2019. - xiv, 179 p. :ill. (some col.), digital ;24 cm. - Studies in big data,v.482197-6503 ;. - Studies in big data ;v.1..

Introduction to Missing Data Estimation -- Introduction to Deep Learning -- Missing Data Estimation Using Bat Algorithm -- Missing Data Estimation Using Cuckoo Search Algorithm -- Missing Data Estimation Using Firefly Algorithm -- Missing Data Estimation Using Ant Colony Optimization Algorithm -- Missing Data Estimation Using Ant-Lion Optimizer Algorithm -- Missing Data Estimation Using Invasive Weed Optimization Algorithm -- Missing Data Estimation Using Swarm Intelligence Algorithms from Reduced Dimensions -- Missing Data Estimation Using Swarm Intelligence Algorithms: Deep Learning Framework Analysis -- Conclusion.

Deep Learning and Missing Data in Engineering Systems uses deep learning and swarm intelligence methods to cover missing data estimation in engineering systems. The missing data estimation processes proposed in the book can be applied in image recognition and reconstruction. To facilitate the imputation of missing data, several artificial intelligence approaches are presented, including: deep autoencoder neural networks; deep denoising autoencoder networks; the bat algorithm; the cuckoo search algorithm; and the firefly algorithm. The hybrid models proposed are used to estimate the missing data in high-dimensional data settings more accurately. Swarm intelligence algorithms are applied to address critical questions such as model selection and model parameter estimation. The authors address feature extraction for the purpose of reconstructing the input data from reduced dimensions by the use of deep autoencoder neural networks. They illustrate new models diagrammatically, report their findings in tables, so as to put their methods on a sound statistical basis. The methods proposed speed up the process of data estimation while preserving known features of the data matrix. This book is a valuable source of information for researchers and practitioners in data science. Advanced undergraduate and postgraduate students studying topics in computational intelligence and big data, can also use the book as a reference for identifying and introducing new research thrusts in missing data estimation.

ISBN: 9783030011802$q(electronic bk.)

Standard No.: 10.1007/978-3-030-01180-2doiSubjects--Topical Terms:

210888
Engineering.

LC Class. No.: Q342 / .L454 2019

Dewey Class. No.: 006.3

Deep learning and missing data in engineering systems
LDR:03206nmm a2200337 a 4500 001 555680
003 DE-He213
005 20190705140414.0
006 m d
007 cr nn 008maaau
008 191121s2019 gw s 0 eng d
020 $a 9783030011802$q(electronic bk.)
020 $a 9783030011796$q(paper)
024 7 $a 10.1007/978-3-030-01180-2 $2 doi
035 $a 978-3-030-01180-2
040 $a GP $c GP
041 0 $a eng
050 4 $a Q342 $b .L454 2019
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.3 $2 23
090 $a Q342 $b .L536 2019
100 1 $a Leke, Collins Achepsah. $3 837888
245 1 0 $a Deep learning and missing data in engineering systems $h [electronic resource] / $c by Collins Achepsah Leke, Tshilidzi Marwala.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2019.
300 $a xiv, 179 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Studies in big data, $x 2197-6503 ; $v v.48
505 0 $a Introduction to Missing Data Estimation -- Introduction to Deep Learning -- Missing Data Estimation Using Bat Algorithm -- Missing Data Estimation Using Cuckoo Search Algorithm -- Missing Data Estimation Using Firefly Algorithm -- Missing Data Estimation Using Ant Colony Optimization Algorithm -- Missing Data Estimation Using Ant-Lion Optimizer Algorithm -- Missing Data Estimation Using Invasive Weed Optimization Algorithm -- Missing Data Estimation Using Swarm Intelligence Algorithms from Reduced Dimensions -- Missing Data Estimation Using Swarm Intelligence Algorithms: Deep Learning Framework Analysis -- Conclusion.
520 $a Deep Learning and Missing Data in Engineering Systems uses deep learning and swarm intelligence methods to cover missing data estimation in engineering systems. The missing data estimation processes proposed in the book can be applied in image recognition and reconstruction. To facilitate the imputation of missing data, several artificial intelligence approaches are presented, including: deep autoencoder neural networks; deep denoising autoencoder networks; the bat algorithm; the cuckoo search algorithm; and the firefly algorithm. The hybrid models proposed are used to estimate the missing data in high-dimensional data settings more accurately. Swarm intelligence algorithms are applied to address critical questions such as model selection and model parameter estimation. The authors address feature extraction for the purpose of reconstructing the input data from reduced dimensions by the use of deep autoencoder neural networks. They illustrate new models diagrammatically, report their findings in tables, so as to put their methods on a sound statistical basis. The methods proposed speed up the process of data estimation while preserving known features of the data matrix. This book is a valuable source of information for researchers and practitioners in data science. Advanced undergraduate and postgraduate students studying topics in computational intelligence and big data, can also use the book as a reference for identifying and introducing new research thrusts in missing data estimation.
650 0 $a Engineering. $3 210888
650 0 $a Big data. $3 609582
650 0 $a Artificial intelligence. $3 194058
650 1 4 $a Computational Intelligence. $3 338479
650 2 4 $a Big Data. $3 760530
650 2 4 $a Artificial Intelligence. $3 212515
700 1 $a Marwala, Tshilidzi. $3 470478
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
830 0 $a Studies in big data ; $v v.1. $3 675357
856 4 0 $u https://doi.org/10.1007/978-3-030-01180-2
950 $a Intelligent Technologies and Robotics (Springer-42732)