Deep Learning Applications, Volume 3 (Paperback, 1st ed. 2022)

This book presents a compilation of extended version of selected papers from the 19th IEEE International Conference on Machine Learning and Applications (IEEE ICMLA 2020) and focuses on deep learning networks in applications such as pneumonia detection in chest X-ray images, object detection and classification, RGB and depth image fusion, NLP tasks, dimensionality estimation, time series forecasting, building electric power grid for controllable energy resources, guiding charities in maximizing donations, and robotic control in industrial environments. Novel ways of using convolutional neural networks, recurrent neural network, autoencoder, deep evidential active learning, deep rapid class augmentation techniques, BERT models, multi-task learning networks, model compression and acceleration techniques, and conditional Feature Augmented and Transformed GAN (cFAT-GAN) for the above applications are covered in this book. Readers will find insights to help them realize novel ways of using deep learning architectures and algorithms in real-world applications and contexts, making the book an essential reference guide for academic researchers, professionals, software engineers in the industry, and innovative product developers.

General

Imprint:	Springer Verlag, Singapore
Country of origin:	Singapore
Series:	Advances in Intelligent Systems and Computing, 1395
Release date:	November 2021
First published:	2022
Editors:	M. Arif Wani • Bhiksha Raj • Feng Luo • Dejing Dou
Dimensions:	235 x 155mm (L x W)
Format:	Paperback
Pages:	322
Edition:	1st ed. 2022
ISBN-13:	978-981-16-3356-0
Categories:	Books > Science & Mathematics > Mathematics > Applied mathematics > Mathematical modelling Books > Computing & IT > Applications of computing > Artificial intelligence > General
LSN:	981-16-3356-8
Barcode:	9789811633560

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