Deep Learning and Missing Data in Engineering Systems

Name: Deep Learning and Missing Data in Engineering Systems
Brand: Springer Nature
SKU: 9783030011796
Availability: InStock

Best Price (Coupon Required):
Buy Deep Learning and Missing Data in Engineering Systems for $116.10 at @ Link.springer.com when you apply the 10% OFF coupon at checkout.
Click “Get Coupon & Buy” to copy the code and unlock the deal.

Set a price drop alert to never miss an offer.

1 Offer Price Range: $129.00 - $129.00

BEST PRICE

Single Product Purchase

$116.10

@ Link.springer.com with extra coupon

Price Comparison

Seller	Contact Seller	List Price	On Sale	Shipping	Best Promo	Final Price	Volume Discount	Financing	Availability	Seller's Page
BEST PRICE 1 Product Purchase @ Link.springer.com		$129.00	$129.00		10% OFF This deals requires coupon	$116.10		See Site	In stock	Visit Store

Product Details

Brand

Springer Nature

Manufacturer

N/A

Part Number

GTIN

9783030011796

Condition

New

Product Description

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.

Available Colors

N/A

Available Sizes

N/A