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Concepts and Real-Time Applications of Deep Learning

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Cover of 'Concepts and Real-Time Applications of Deep Learning'

Table of Contents

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    Book Overview
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    Chapter 1 Emotion Recognition from Speech Using Deep Neural Network
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    Chapter 2 Text-Independent Speaker Recognition Using Deep Learning
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    Chapter 3 A Qualitative and Quantitative Research of Machine Learning Algorithms for Gait Analysis and Recognition
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    Chapter 4 Emotion Recognition from Speech Signals Using Machine Learning and Deep Learning Techniques
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    Chapter 5 Micro-expression Detection Using Main Directional Maximal Differential Analysis (MDMD) Method
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    Chapter 6 Survival Prediction of Cancer Patient Using Machine Learning
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    Chapter 7 Skin Lesion Segmentation Using Deep Convolutional Networks
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    Chapter 8 Bone Cancer Survivability Prognosis with KNN and Genetic Algorithms
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    Chapter 9 BeamAtt: Generating Medical Diagnosis from Chest X-Rays Using Sampling-Based Intelligence
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    Chapter 10 CNN-Based Driver Drowsiness Detection System
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    Chapter 11 Forecasting Using Deep Learning Approaches
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    Chapter 12 A Low-Cost IOT and Deep Learning Enabled Precision Agriculture Support System for Indian Diverse Environment
Attention for Chapter 4: Emotion Recognition from Speech Signals Using Machine Learning and Deep Learning Techniques
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Citations

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4 Dimensions

Readers on

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6 Mendeley
Summary Dimensions citations
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Chapter title
Emotion Recognition from Speech Signals Using Machine Learning and Deep Learning Techniques
Chapter number 4
Book title
Concepts and Real-Time Applications of Deep Learning
Published by
Springer, Cham, September 2021
DOI 10.1007/978-3-030-76167-7_4
Book ISBNs
978-3-03-076166-0, 978-3-03-076167-7
Authors

Kataria, Gaurav, Gupta, Akansh, Kaushik, V. Sirish, Chaudhary, Gopal

View on publisher site
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 6 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 6 100%

Demographic breakdown

Readers by professional status Count As %
Lecturer 1 17%
Lecturer > Senior Lecturer 1 17%
Unknown 4 67%
Readers by discipline Count As %
Computer Science 2 33%
Unknown 4 67%

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