Can I read Deep Learning Applications in Image Analysis (Studies in Big Data, 129) on EtoBox?

Deep Learning Applications in Image Analysis (Studies in Big Data, 129) by Sanjiban Sekhar Roy (editor), Ching-Hsien Hsu (editor), Venkateshwara Kagita (editor) is a nonfiction available to read on EtoBox.

What is Deep Learning Applications in Image Analysis (Studies in Big Data, 129) about?

This book provides state-of-the-art coverage of deep learning applications in image analysis. The book demonstrates various deep learning algorithms that can offer practical solutions for various image-related problems; also how these algorithms are used by scientists and scholars in industry and academia. This includes autoencoder and deep convolutional generative adversarial network in improving classification performance of Bangla handwritten characters, dealing with deep learning-based approaches using feature selection methods for automatic diagnosis of covid-19 disease from x-ray images, imbalance image data sets of classification, image captioning using deep transfer learning, developing a vehicle over speed detection system, creating an intelligent system for video-based proximity analysis, building a melanoma cancer detection system using deep learning, plant diseases classification using AlexNet, dealing with hyperspectral images using deep learning, chest x-ray image classification of pneumonia disease using efficient net and inceptionv3. The book also addresses the difficulty of implementing deep learning in terms of computation time and the complexity of reasoning and

Who reads Deep Learning Applications in Image Analysis (Studies in Big Data, 129)?

Author

Sanjiban Sekhar Roy (editor), Ching-Hsien Hsu (editor), Venkateshwara Kagita (editor)

Publisher

Springer Nature Singapore Pte Ltd Fka Springer Science + Business Media Singapore Pte Ltd

Published

2023

Language

EN

ISBN

9789819937844

Category

nonfiction

Subjects

Engineering, Mathematics, Computer Science