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Produktbild: Machine Learning Algorithms and Applications

Machine Learning Algorithms and Applications

234,99 €

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Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

24.08.2021

Herausgeber

Mettu Srinivas + weitere

Verlag

John Wiley & Sons Inc

Seitenzahl

368

Maße (L/B/H)

23,6/16,1/2,3 cm

Gewicht

626 g

Auflage

1. Auflage

Sprache

Englisch

ISBN

978-1-119-76885-2

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

24.08.2021

Herausgeber

Verlag

John Wiley & Sons Inc

Seitenzahl

368

Maße (L/B/H)

23,6/16,1/2,3 cm

Gewicht

626 g

Auflage

1. Auflage

Sprache

Englisch

ISBN

978-1-119-76885-2

Herstelleradresse

Libri GmbH
Europaallee 1
36244 Bad Hersfeld
DE

Email: gpsr@libri.de

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  • Produktbild: Machine Learning Algorithms and Applications
  • Acknowledgments xv
     
    Preface xvii
     
    Part 1: Machine Learning for Industrial Applications 1
     
    1 A Learning-Based Visualization Application for Air Quality Evaluation During COVID-19 Pandemic in Open Data Centric Services 3
    Priyank Jain and Gagandeep Kaur
     
    1.1 Introduction 4
     
    1.1.1 Open Government Data Initiative 4
     
    1.1.2 Air Quality 4
     
    1.1.3 Impact of Lockdown on Air Quality 5
     
    1.2 Literature Survey 5
     
    1.3 Implementation Details 6
     
    1.3.1 Proposed Methodology 7
     
    1.3.2 System Specifications 8
     
    1.3.3 Algorithms 8
     
    1.3.4 Control Flow 10
     
    1.4 Results and Discussions 11
     
    1.5 Conclusion 21
     
    References 21
     
    2 Automatic Counting and Classification of Silkworm Eggs Using Deep Learning 23
    Shreedhar Rangappa, Ajay A. and G. S. Rajanna
     
    2.1 Introduction 23
     
    2.2 Conventional Silkworm Egg Detection Approaches 24
     
    2.3 Proposed Method 25
     
    2.3.1 Model Architecture 26
     
    .3.2 Foreground-Background Segmentation 28
     
    2.3.3 Egg Location Predictor 30
     
    2.3.4 Predicting Egg Class 31
     
    2.4 Dataset Generation 35
     
    2.5 Results 35
     
    2.6 Conclusion 37
     
    Acknowledgment 38
     
    References 38
     
    3 A Wind Speed Prediction System Using Deep Neural Networks 41
    Jaseena K. U. and Binsu C. Kovoor
     
    3.1 Introduction 42
     
    3.2 Methodology 45
     
    3.2.1 Deep Neural Networks 45
     
    3.2.2 The Proposed Method 47
     
    3.2.2.1 Data Acquisition 47
     
    3.2.2.2 Data Pre-Processing 48
     
    3.2.2.3 Model Selection and Training 50
     
    3.2.2.4 Performance Evaluation 51
     
    3.2.2.5 Visualization 51
     
    3.3 Results and Discussions 52
     
    3.3.1 Selection of Parameters 52
     
    3.3.2 Comparison of Models 53
     
    3.4 Conclusion 57
     
    References 57
     
    4 Res-SE-Net: Boosting Performance of ResNets by Enhancing Bridge Connections 61
    Varshaneya V., S. Balasubramanian and Darshan Gera
     
    4.1 Introduction 61
     
    4.2 Related Work 62
     
    4.3 Preliminaries 63
     
    4.3.1 ResNet 63
     
    4.3.2 Squeeze-and-Excitation Block 64
     
    4.4 Proposed Model 66
     
    4.4.1 Effect of Bridge Connections in ResNet 66
     
    4.4.2 Res-SE-Net: Proposed Architecture 67
     
    4.5 Experiments 68
     
    4.5.1 Datasets 68
     
    4.5.2 Experimental Setup 68
     
    4.6 Results 69
     
    4.7 Conclusion 73
     
    References 74
     
    5 Hitting the Success Notes of Deep Learning 77
    Sakshi Aggarwal, Navjot Singh and K.K. Mishra
     
    5.1 Genesis 78
     
    5.2 The Big Picture: Artificial Neural Network 79
     
    5.3 Delineating the Cornerstones 80
     
    5.3.1 Artificial Neural Network vs. Machine Learning 80
     
    5.3.2 Machine Learning vs. Deep Learning 81
     
    5.3.3 Artificial Neural Network vs. Deep Learning 81
     
    5.4 Deep Learning Architectures 82
     
    5.4.1 Unsupervised Pre-Trained Networks 82
     
    5.4.2 Convolutional Neural Networks 83
     
    5.4.3 Recurrent Neural Networks 84
     
    5.4.4 Recursive Neural Network 85
     
    5.5 Why is CNN Preferred for Computer Vision Applications? 85
     
    5.5.1 Convolutional Layer 86
     
    5.5.2 Nonlinear Layer 86
     
    5.5.3 Pooling Layer 87
     
    5.5.4 Fully Connected Layer 87
     
    5.6 Unravel Deep Learning in Medical Diagnostic Systems 89
     
    5.7 Challenges and Future Expectations 94
     
    5.8 Conclusion 94
     
    References 95
     
    6 Two-Stage Credit Scoring Model Based on Evolutionary Feature Selection and Ensemble Neural Networks 99
    Diwa