20% off all books with the code: BOOKS
  • check 10+ million books
  • check New arrivals every day
  • check Trusted by 1M+ customers
  • check Great prices & discounts
  • check Shipping across Europe

Deep Learning and Missing Data in Engineering Systems - Tshilidzi Marwala,Collins Achepsah Leke

English
2019-01-31
€203.26 €254.08

-20% with code BOOKS

In stock at our supplier

Shipping in 17-23 days

30-day return policy

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 neu ... Full description

You May Also Like

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.

More Information

Author Tshilidzi Marwala, Collins Achepsah Leke
Publisher Springer Nature Switzerland
Series Studies in Big Data
Release year 2019
Cover type Hardcover
EAN 9783030011796
Write Your Own Review
You're reviewing: Deep Learning and Missing Data in Engineering Systems
Your Rating:

Goodreads Reviews

€203.26 €254.08