Deep Learning Applications in Image Analysis

Name: Deep Learning Applications in Image Analysis
Brand: Springer Nature Singapore
SKU: 9789819937868
Price: 5706 CZK
Availability: InStock

Autor: Venkateshwara Kagita, Ching-Hsien Hsu

Jazyk:

Vazba: Brožovaná

Vydavatel: Springer Nature Singapore

Dostupnost: Skladem u dodavatele

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5 706 Kč

This book provides state-of-the-art coverage of deep learning applications in image analysis. The bo...

Informace o knize

Autor

Venkateshwara Kagita, Ching-Hsien Hsu

Jazyk

Angličtina

Vazba

Kniha - Brožovaná

Vydáno

2024

Stránek

224

EAN

9789819937868

ISBN

9819937868

Enbook ID

46228233

Vydavatel

Springer Nature Singapore

Hmotnost

347

Rozměry

155 x 235 x 13

Kompletní popis

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 modelling different types of data where information is currently encoded. Each chapter has the application of various new or existing deep learning models such as Deep Neural Network (DNN) and Deep Convolutional Neural Networks (DCNN). The detailed utilization of deep learning packages that are available in MATLAB, Python and R programming environments have also been discussed, therefore, the readers will get to know about the practical implementation of deep learning as well. The content of this book is presented in a simple and lucid style for professionals, nonprofessionals, scientists, and students interested in the research area of deep learning applications in image analysis.

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