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Deep learning and computer vision = models and biomedical applications.. Volume 2 /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Deep learning and computer vision/ edited by Uma N. Dulhare, Essam Halim Houssein.
Reminder of title:	models and biomedical applications.
other author:	Dulhare, Uma N.
Published:	Singapore :Springer Nature Singapore : : 2025.,
Description:	xviii, 198 p. :ill. (chiefly color), digital ;24 cm.
[NT 15003449]:	Sequence Alignment Using Deep Learning -- Maximizing Renewable Energy: Harnessing the Power of Metaheuristic Optimization Techniques -- Medical Computer Vision -- Detection of oral Cavity using Convolution Neural Networks for Density -- Verification and Analysis of Covid-19 Diagnostic Process using the concept of Process Mining -- Design of Nano Magnetorheological fluid damper - based Leg prosthesis for Amputees -- Nano encapsulation for the targeted drug delivery to enhance the efficacy of drugs -- Transcriptomics: Mapping the RNA Landscape.
Contained By:	Springer Nature eBook
Subject:	Deep learning (Machine learning) -
Online resource:	https://doi.org/10.1007/978-981-96-3648-8
ISBN:	9789819636488

Deep learning and computer vision = models and biomedical applications.. Volume 2 /
Deep learning and computer visionmodels and biomedical applications.Volume 2 /[electronic resource] :edited by Uma N. Dulhare, Essam Halim Houssein. - Singapore :Springer Nature Singapore :2025. - xviii, 198 p. :ill. (chiefly color), digital ;24 cm. - Algorithms for intelligent systems,2524-7573. - Algorithms for intelligent systems..

Sequence Alignment Using Deep Learning -- Maximizing Renewable Energy: Harnessing the Power of Metaheuristic Optimization Techniques -- Medical Computer Vision -- Detection of oral Cavity using Convolution Neural Networks for Density -- Verification and Analysis of Covid-19 Diagnostic Process using the concept of Process Mining -- Design of Nano Magnetorheological fluid damper - based Leg prosthesis for Amputees -- Nano encapsulation for the targeted drug delivery to enhance the efficacy of drugs -- Transcriptomics: Mapping the RNA Landscape.

This book takes a balanced approach between theoretical understanding and real time applications. All topics show how to explore, build, evaluate and optimize deep learning models with computer vision. Deep learning is integrated with computer vision to enhance the performance of image classification with localization, object detection, object recognition, object segmentation, image style transfer, image colorization, image reconstruction, image super-resolution, image synthesis, motion detection, pose estimation, semantic segmentation in biomedical field. Huge number of efficient approaches/applications and models support medical decisions in the fields of cardiology, dermatology, and radiology. The content of book elaborates deep learning models such as convolution neural networks, deep learning, generative adversarial network, long short-term memory networks (LSTM), autoencoder (AE), restricted Boltzmann machine (RBM), self-organizing map (SOM), deep belief network (DBN), etc.

ISBN: 9789819636488

Standard No.: 10.1007/978-981-96-3648-8doiSubjects--Topical Terms:

3538509
Deep learning (Machine learning)

LC Class. No.: Q325.73

Dewey Class. No.: 006.31

Deep learning and computer vision = models and biomedical applications.. Volume 2 /
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520 $a This book takes a balanced approach between theoretical understanding and real time applications. All topics show how to explore, build, evaluate and optimize deep learning models with computer vision. Deep learning is integrated with computer vision to enhance the performance of image classification with localization, object detection, object recognition, object segmentation, image style transfer, image colorization, image reconstruction, image super-resolution, image synthesis, motion detection, pose estimation, semantic segmentation in biomedical field. Huge number of efficient approaches/applications and models support medical decisions in the fields of cardiology, dermatology, and radiology. The content of book elaborates deep learning models such as convolution neural networks, deep learning, generative adversarial network, long short-term memory networks (LSTM), autoencoder (AE), restricted Boltzmann machine (RBM), self-organizing map (SOM), deep belief network (DBN), etc.
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