عنوان

Deep learning and missing data in engineering systems /

شابک

3030011801

شابک

303001181X

شابک

9783030011802

شابک

9783030011819

شابک اشتباه

3030011798

شابک اشتباه

9783030011796

عنوان اصلي

Deep learning and missing data in engineering systems /

نام عام مواد

[Book]

نام نخستين پديدآور

Collins Achepsah Leke, Tshilidzi Marwala.

محل نشرو پخش و غیره

Cham :

نام ناشر، پخش کننده و غيره

Springer,

تاریخ نشرو بخش و غیره

2019.

نام خاص و کميت اثر

1 online resource (xiv, 179 pages) :

ساير جزييات

illustrations (some color)

عنوان فروست

Studies in Big Data,

مشخصه جلد

48

شاپا ي ISSN فروست

2197-6503 ;

متن يادداشت

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.

بدون عنوان

0

متن يادداشت

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.

منبع سفارش / آدرس اشتراک

Springer Nature

شماره انبار

com.springer.onix.9783030011802

شماره استاندارد بين المللي کتاب و موسيقي

9783030011796

شماره استاندارد بين المللي کتاب و موسيقي

9783030011819

موضوع مستند نشده

Artificial intelligence.

موضوع مستند نشده

Engineering.

موضوع مستند نشده

Mathematical statistics.

موضوع مستند نشده

Artificial intelligence.

موضوع مستند نشده

Big data.

موضوع مستند نشده

Engineering.

موضوع مستند نشده

COM004000

موضوع مستند نشده

UYQ

موضوع مستند نشده

UYQ

شماره

ويراست

شماره رده

نشانه اثر

مستند نام اشخاص تاييد نشده

Leke, Collins Achepsah

مستند نام اشخاص تاييد نشده

Marwala, Tshilidzi

تاريخ عمليات

20200823072658.0

قواعد فهرست نويسي ( بخش توصيفي )

pn

نام الکترونيکي

نوع ماده

[Book]

تكميل شده

Y