Algorithms In Machine Learning Paradigms

✍ Scribed by Jyotsna Kumar Mandal, Somnath Mukhopadhyay, Paramartha Dutta, Kousik Dasgupta

• Publisher: Springer
• Year: 2020
• Tongue: English
• Leaves: 201
• Series: Studies In Computational Intelligence Vol. 870
• Category: Library

✦ Synopsis

This book presents studies involving algorithms in the machine learning paradigms. It discusses a variety of learning problems with diverse applications, including prediction, concept learning, explanation-based learning, case-based (exemplar-based) learning, statistical rule-based learning, feature extraction-based learning, optimization-based learning, quantum-inspired learning, multi-criteria-based learning and hybrid intelligence-based learning.

✦ Table of Contents

Editorial Preface......Page 6
Contents......Page 8
About the Editors......Page 10
1 Introduction......Page 12
2 Preliminaries......Page 14
2.2 Einstein Operations......Page 17
3 Hesitant-Intuitionistic Trapezoidal Fuzzy Number......Page 18
4 Hesitant-Intuitionistic Trapezoidal Fuzzy Prioritized Einstein-Based Aggregation Operators......Page 20
5 An Approach to Multi-criteria Group Decision-Making with H–ITF Information......Page 26
6 A Numerical Illustration......Page 29
7 Conclusions and Scope for Future Studies......Page 33
References......Page 34
1 Introduction......Page 36
2 Related Work......Page 38
3.1 Feature Relevance......Page 39
4 Proposed Approach......Page 40
5 Illustration......Page 44
6 Experiments and Outcome......Page 45
6.1 Summary of Outcome......Page 46
6.2 Overall Comparison of Performance......Page 50
7 Conclusion......Page 51
References......Page 52
1 Introduction......Page 54
2 Related Work......Page 56
3 Development of Expert System......Page 57
3.2 Components of Our Proposed Expert System......Page 58
4.2 Dataset Preparation......Page 60
5.2 Analysis......Page 62
6 Conclusion......Page 65
References......Page 66
1 Introduction......Page 67
2 Related Works......Page 68
3 Proposed Fuzzy Time Series Model......Page 69
4.2 Results and Discussion......Page 74
5 Conclusion and Future Work......Page 77
References......Page 78
Automatic Classification of Fruits and Vegetables: A Texture-Based Approach......Page 80
2 Previous Works......Page 81
3.1 Dataset......Page 83
3.4 Fractal Analysis......Page 84
3.5 Gray-Level Co-occurrence Matrix (GLCM) Analysis......Page 91
3.7 Classification......Page 92
4 Experimentation, Result, and Discussion......Page 93
5 Conclusion......Page 96
References......Page 97
Deep Learning-Based Early Sign Detection Model for Proliferative Diabetic Retinopathy in Neovascularization at the Disc......Page 99
1 Introduction......Page 100
2.1 Overview......Page 103
2.2 Data Preparation......Page 104
2.3 Preprocessing and Vessel Segmentation......Page 106
2.5 Network Architecture for NVD Diagnosis......Page 107
2.6 Observation for NVD Diagnosis......Page 110
3 Result and Discussion......Page 111
References......Page 114
A Linear Regression-Based Resource Utilization Prediction Policy for Live Migration in Cloud Computing......Page 117
1 Introduction......Page 118
1.1 Load Balancing in Cloud Computing......Page 119
2 Prerequisite to the Proposed Work-Linear Regression......Page 121
3 Problem Formulation Using Simulated Annealing (SA) and Linear Regression (LR)......Page 122
3.1 Linear Regression-Based Resource Utilization Procedure for VM Migration......Page 123
4 Overview of Simulation Tool CloudAnalyst......Page 128
5 Simulation with Results and Analysis......Page 129
References......Page 135
1 Introduction......Page 137
2 Proposed Methodology......Page 139
2.1 Facial Feature Points Detection......Page 140
2.2 Facial Feature Extraction......Page 141
2.3 Recognition of Facial Expression......Page 142
3 Experiment and Results......Page 143
3.1 Results on CK+ Database......Page 144
3.2 Results on MUG Database......Page 148
3.3 Results on MMI Database......Page 150
5 Conclusion......Page 152
References......Page 153
1 Introduction......Page 155
3 Motivation and Contribution......Page 157
4.1 Active Appearance Model (AAM)......Page 158
4.4 Circumcenter-Incenter-Centroid Trio......Page 160
4.5 MultiLayer Perceptron......Page 161
6 Results......Page 162
6.2 Japanese Female Facial Expression (JAFFE) Database......Page 163
6.3 MMI Database......Page 164
7 Discussions......Page 167
8 Conclusions......Page 170
References......Page 171
Stable Neighbor-Node Prediction with Multivariate Analysis in Mobile Ad Hoc Network Using RNN Model......Page 173
1 Introduction......Page 174
2 Related Works......Page 176
3.1 Time Lag Selection Methodology......Page 177
3.2 ERNN Configuration......Page 180
5 Results and Discussion......Page 181
6 Conclusion......Page 185
References......Page 186
A New Approach for Optimizing Initial Parameters of Lorenz Attractor and Its Application in PRNG......Page 188
1 Introduction......Page 189
2 Three Dimensional Lorenz System......Page 190
3 Proposed Method for Initial Seed Optimization for the Lorenz Attractor......Page 191
4 Proposed Method for Lorenz System Based PRNG......Page 194
5 Results and Analysis......Page 195
6 Conclusion and Future Scope......Page 198
References......Page 199
Author Index......Page 201

✦ Subjects

Appl. Mathematics: Computational Methods Of Engineering