This cutting-edge new volume covers the hardware architecture implementation, the software implementation approach, and the efficient hardware of machine learning applications.

Machine learning and deep learning modules are now an integral part of many smart and automated systems where signal processing is performed at different levels. Signal processing in the form of text, images, or video needs large data computational operations at the desired data rate and accuracy. Large data requires more use of integrated circuit (IC) area with embedded bulk memories that further lead to more IC area. Trade-offs between power consumption, delay and IC area are always a concern of designers and researchers. New hardware architectures and accelerators are needed to explore and experiment with efficient machine-learning models. Many real-time applications like the processing of biomedical data in healthcare, smart transportation, satellite image analysis, and IoT-enabled systems have a lot of scope for improvements in terms of accuracy, speed, computational powers, and overall power consumption.

This book deals with the efficient machine and deep learning models that support high-speed processors with reconfigurable architectures like graphic processing units (GPUs) and field programmable gate arrays (FPGAs), or any hybrid system. Whether for the veteran engineer or scientist working in the field or laboratory, or the student or academic, this is a must-have for any library.

Suman Lata Tripathi, PhD, is a professor at Lovely Professional University with more than 21 years of experience in academics. She has published more than 103 research papers in refereed journals and conferences. She has organized several workshops, summer internships, and expert lectures for students, and she has worked as a session chair, conference steering committee member, editorial board member, and reviewer for IEEE journals and conferences. She has published three books and currently has multiple volumes scheduled for publication from Wiley-Scrivener.

Mufti Mahmud, PhD, is an associate professor of cognitive computing at the Department of Computer Science of Nottingham Trent University, UK. He is the Coordinator of the Computer Science and Informatics Unit of Assessment of Research Excellence Framework at NTU and the deputy group leader of the Interactive Systems Research Group and the Cognitive Computing & Brain Informatics research group. He is also an active member of the Computing and Informatics Research Centre and the Medical Technologies Innovation Facility. He is a member of numerous societies and research committees.

Preface xiii

Acknowledgements xv

1 A Comprehensive Review of Various Machine Learning Techniques 1
Pooja Pathak and Parul Choudhary

1.1 Introduction 1

1.1.1 Random Forest 2

1.1.2 Decision Tree 3

1.1.3 Support Vector Machine 4

1.1.4 Naive Bayes 5

1.1.5 K-Means Clustering 6

1.1.6 Principal Component Analysis 6

1.1.7 Linear Regression 6

1.1.8 Logistic Regression 7

1.1.9 Semi-Supervised Learning 8

1.1.10 Transductive SVM 9

1.1.11 Generative Models 9

1.1.12 Self-Training 9

1.1.13 Relearning 9

1.2 Conclusions 9

2 Artificial Intelligence and Image Recognition Algorithms 11
Siddharth, Anuranjana and Sanmukh Kaur

2.1 Introduction 12

2.2 Traditional Image Recognition Algorithms 13

2.2.1 Harris Corner Detector (1988) 13

2.2.2 SIFT (2004) 15

2.2.3 ASIFT 16

2.2.4 SURF (2006) 17

2.3 Neural Network-Based Algorithms 21

2.4 Convolutional Neural Network Architecture 22

2.5 Various CNN Architectures 23

2.5.1 LeNet-5 (1998) 23

2.5.2 AlexNet (2012) 24

2.5.3 VGGNet (2014) 24

2.5.4 GoogleNet (2015) 24

3 Efficient Architectures and Trade-Offs for FPGA-Based Real-Time Systems 31
L.M.I. Leo Joseph, J. Ajayan, Sandip Bhattacharya and Sreedhar Kollem

3.1 Overview of FPGA-Based Real-Time System 31

3.1.1 Key Elements of Real-Time System 32

3.1.2 Real-Time System and its Computation 32

3.1.3 FPGA Functionality and Applications 33

3.1.4 FPGA Applications 33

3.1.5 FPGA Architecture 34

3.1.6 Reconfigurable Architectures 35

3.2 Hybrid FPGA Configurations and its Algorithms 38

3.2.1 Hybrid FPGA 38

3.2.2 Hybrid FPGA Architecture 39

3.2.3 Hybrid FPGA Configuration 40

3.3 Hybrid FPGA Algorithms 42

3.3.1 Relevance of Hardware-Accelerated Architecture to FPGA Software Implementation 44

3.4 CNN Hardware Accelerator Architecture Overview 46

3.5 Summary 47

4 A Low-Power Audio Processing Using Machine Learning Module on FPGA and Applications 49
Suman Lata Tripathi, Dasari Lakshmi Prasanna and Mufti Mahmud

4.1 Introduction 49

4.2 Existing Machine Learning Modules and Audio Classifiers 50

4.3 Audio Processing Module Using Machine Learning 56

4.4 Application of Proposed FPGA-Based ML Models 57

4.5 Implementation of a Microphone on FPGA 59

4.6 Conclusion 60

4.7 Future Scope 60

5 Synthesis and Time Analysis of FPGA-Based DIT-FFT Module for Efficient VLSI Signal Processing Applications 65
Siba Kumar Panda, Konasagar Achyut and Dhruba Charan Panda

5.1 Introduction 66

5.2 Implementation of DIT-FFT Algorithm 67

5.2.1 A Quick Overview of DIT-FFT 67

5.2.2 Algorithmic Representation with Example 69

5.2.3 Simulated Output Waveform 69

5.3 Synthesis of Designed Circuit 71

5.4 Static Timing Analysis of Designed Circuit 73

5.5 Result and Discussion 77

5.6 Conclusion 77

6 Artificial Intelligence-Based Active Virtual Voice Assistant 81
Swathi Gowroju, G. Mounika, D. Bhavana, Shaik Abdul Latheef and A. Abhilash

6.1 Introduction 82

6.2 Literature Survey 83

6.3 System Functions 87

6.4 Model Training 88

6.5 Discussion 90

6.5.1 Furnishing Movie Recommendations 91

6.5.2 KNN Algorithm Book Recommendation 92

6.6 Results 93

6.7 Conclusion 102

7 Image Forgery Detection: An Approach with Machine Learning 105
Madhusmita Mishra, Silvia Tittotto and Santos Kumar Das

7.1 Introduction 105

7.2 Historical Background 107

7.3 CNN Architecture 109

7.4 Analysis of Error Level of Image 113

7.5 Proposed Model of Image Forgery Detection, Results and Discussion 115

7.6 Conclusion 118

7.7 Future Research Directions 119

8 Applications of Artificial Neural Networks in Optical Performance Monitoring 123
Isra Imtiyaz, Anuranjana, Sanmukh Kaur and Anubhav Gautam

8.1 Introduction 123

8.2 Algorithms Employed for Performance Monitoring 129

8.2.1 Artificial Neural Networks 129

8.2.2 Deep Neural Networks 130

8.2.3 Convolutional Neural Networks 131

8.2.3.1 Convolutional Layer 131

8.2.3.2 Non-Linear Layer 132

8.2.3.3 Pooling Layer 132

8.2.3.4 Fully Connected Layer 132

8.2.4 Support Vector Regression (SVR) 133

8.2.5 Support Vector Machine (SVM) 133

8.2.6 Kernel Ridge Regression (KRR) 133

8.2.7 Long Short-Term Memory (LSTM) 133

8.3 Artificial Intelligence (AI) Methods, Performance Monitoring and Applications in Optical Networks 134

8.3.1 Performance Monitoring 134

8.3.2 Applications of AI in Optical Networking 135

8.4 Optical Impairments and Fault Management 135

8.4.1 Noise 135

8.4.2 Distortion 135

8.4.3 Timing 136

8.4.4 Component Faults 136

8.4.5 Transmission Impairments 137

8.4.6 Fault Management in Optical Network 137

8.5 Conclusion 138

9 Website Development with Django Web Framework 141
Sanmukh Kaur, Anuranjana and Yashasvi Roy

9.1 Introduction 141

9.2 Salient Features of Django 142

9.2.1 Complete 142

9.2.2 Versatile 142

9.2.3 Secure 142

9.2.4 Scalable 143

9.2.5 Maintainable 143

9.2.6 Portable 143

9.3 UI Design 143

9.3.1 HTML 143

9.3.2 CSS 144

9.3.3 Bootstrap 144

9.4 Methodology 144

9.5 UI Design 144

9.6 Backend Development 148

9.6.1 Login Page 148

9.6.2 Registration Page 149

9.6.3 User Tracking 149

9.7 Ouputs 150

9.8 Conclusion 152

10 Revenue Forecasting Using Machine Learning Models 155
Yashasvi Roy and Sanmukh Kaur

10.1 Introduction 155

10.2 Types of Forecasting 156

10.2.1 Qualitative Forecasting 156

10.2.1.1 Industries That Use Qualitative Forecasting 157

10.2.1.2 Qualitative Forecasting Methods 158

10.2.2 Quantitative Forecasting 158

10.2.2.1 Quantitative Forecasting Methods 159

10.2.3 Artificial Intelligence Forecasting 160

10.2.3.1 Artificial Neural Network (ANN) 160

10.2.3.2 Support Vector Machine (SVM) 161

10.3 Types of ML Models Used in Finance 162

10.3.1 Linear Regression 162

10.3.1.1 Simple Linear Regression 162

10.3.1.2 Multiple Linear Regression 162

10.3.2 Ridge Regression 163

10.3.3 Decision Tree 164

10.3.3.1 Prediction of Continuous Variables 164

10.3.3.2 Prediction of Categorical Variables 165

10.3.4 Random Forest Regressor 165

10.3.5 Gradient Boosting Regression 166

10.3.5.1 Advantages of Gradient Boosting 167

10.4 Model Performance 167

10.4.1 R-Squared Method 167

10.4.2 Mean Squared Error (MSE) 167

10.4.3 Root Mean Square Error (RMSE) 168

10.5 Conclusion 168

11 Application of Machine Learning Optimization Techniques in Wind Resource Assessment 171
Udhayakumar K. and Krishnamoorthy R.

11.1 Introduction 172

11.2 Wind Data Analysis Methods 173

11.2.1 Wind Characteristics Parameters 173

11.2.2 Wind Speed Distribution Methods 173

11.2.3 Weibull Method 174

11.2.4 Goodness of Fit 175

11.3 Wind Site and Measurement Details 175

11.3.1 Seasonal Wind Periods 176

11.3.2 Machine Learning and Optimization Techniques 176

11.3.2.1 Moth Flame Optimization (MFO) Method 176

11.4 Results and Discussions 180

11.4.1 Wind Characteristics 182

11.4.1.1 Kayathar Station (Onshore) 182

11.4.1.2 Gulf of Khambhat (Gujarat Offshore) Station 187

11.4.1.3 Jafrabad (Gujarat-Nearshore) 192

11.4.2 Wind Distribution Fitting 195

11.4.2.1 Kayathar Station (Onshore) 196

11.4.2.2 Bimodal Behaviour 196

11.4.2.3 Gulf of Khambhat (Offshore) Wind Distribution 202

11.4.2.4 Jafrabad Station (Nearshore) Distribution Fitting 203

11.4.3 Optimization Methods for Parameter Estimation 212

11.4.3.1 Optimization Parameters Comparison 212

11.4.4 Wind Power Density Analysis (WPD) 214

11.4.4.1 Comparison of Wind Power Density 215

11.5 Research Summary 221

11.6 Conclusions 222

12 IoT to Scale-Up Smart Infrastructure in Indian Cities: A New Paradigm 227
Indu Bala, Simarpreet Kaur, Lavpreet Kaur and Pavan Thimmavajjala

12.1 Introduction 228

12.2 Technological Progress: A Brief History 229

12.3 What is the Internet of Things (IoT)? 230

12.4 Economic Effects of Internet of Things 230

12.5 Infrastructure and Smart Infrastructure: The Difference 232

12.5.1 What is Smart Infrastructure? 233

12.5.2 What are the Principles of Smart Infrastructure? 234

12.5.3 Components of IoT-Based Smart City Project 235

12.6 Architecture for Smart Cities 236

12.6.1 Networking Technologies 237

12.6.2 Network Topologies 237

12.6.3 Network Architectures 238

12.6.3.1 Home Area Networks (HANs) 238

12.6.3.2 Field/Neighborhood Area Networks (FANs/NANs) 238

12.6.3.3 Wide Area Networks (WANs) 238

12.6.3.4 Network Protocols 238

12.7 IoT Technology in India's Smart Cities: The Current Scenario 239

12.8 Challenges in IoT-Based Smart City Projects 243

12.8.1 Technological Challenges 243

12.8.1.1 Privacy and Security 243

12.8.1.2 Smart Sensors and Infrastructure Essentials 243

12.8.1.3 Networking in IoT Systems 244

12.8.1.4 Big Data Analytics 244

12.8.2 Financial - Economic Challenges 244

12.9 Role of Explainable AI 245

12.10 Conclusion and Future Scope 246

References 246

Index 251