Machine Learning Algorithms and Applications

Herausgegeben:Srinivas, Mettu; Sucharitha, G.

• Gebundenes Buch

Produktbeschreibung

Machine Learning Algorithms is for current and ambitious machine learning specialists looking to implement solutions to real-world machine learning problems. It talks entirely about the various applications of machine and deep learning techniques, with each chapter dealing with a novel approach of machine learning architecture for a specific application, and then compares the results with previous algorithms.

The book discusses many methods based in different fields, including statistics, pattern recognition, neural networks, artificial intelligence, sentiment analysis, control, and data mining, in order to present a unified treatment of machine learning problems and solutions. All learning algorithms are explained so that the user can easily move from the equations in the book to a computer program.

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Produktdetails

• Verlag: Wiley & Sons / Wiley-Scrivener
• Artikelnr. des Verlages: 1W119768850
• 1. Auflage
• Seitenzahl: 368
• Erscheinungstermin: 24. August 2021
• Englisch
• Abmessung: 236mm x 161mm x 23mm
• Gewicht: 620g
• ISBN-13: 9781119768852
• ISBN-10: 1119768853
• Artikelnr.: 61855653

Autorenporträt

Mettu Srinivas PhD from the Indian Institute of Technology Hyderabad, and is currently an assistant professor in the Department of Computer Science and Engineering, NIT Warangal, India. G. Sucharitha PhD from KL University, Vijayawada and is currently an assistant professor in the Department of Electronics and Communication Engineering at ICFAI Foundation for Higher Education Hyderabad. Anjanna Matta PhD from the Indian Institute of Technology Hyderabad and is currently an assistant professor in the Department of Mathematics at ICFAI Foundation for Higher Education Hyderabad. Prasenjit Chatterjee PhD is an associate professor in the Mechanical Engineering Department at MCKV Institute of Engineering, India.

Inhaltsangabe

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