Machine Learning Applications Practical resource on the importance of Machine Learning and Deep Learning applications in various technologies and real-world situations
Machine Learning Applications discusses methodological advancements of machine learning and deep learning, presents applications in image processing, including face and vehicle detection, image classification, object detection, image segmentation, and delivers real-world applications in healthcare to identify diseases and diagnosis, such as creating smart health records and medical imaging diagnosis, and provides real-world examples, case studies, use cases, and techniques to enable the reader’s active learning.
Composed of 13 chapters, this book also introduces real-world applications of machine and deep learning in blockchain technology, cyber security, and climate change. An explanation of AI and robotic applications in mechanical design is also discussed, including robot-assisted surgeries, security, and space exploration. The book describes the importance of each subject area and detail why they are so important to us from a societal and human perspective.
Edited by two highly qualified academics and contributed to by established thought leaders in their respective fields, Machine Learning Applications includes information on:
Content based medical image retrieval (CBMIR), covering face and vehicle detection, multi-resolution and multisource analysis, manifold and image processing, and morphological processing
Smart medicine, including machine learning and artificial intelligence in medicine, risk identification, tailored interventions, and association rules
AI and robotics application for transportation and infrastructure (e.g., autonomous cars and smart cities), along with global warming and climate change
Identifying diseases and diagnosis, drug discovery and manufacturing, medical imaging diagnosis, personalized medicine, and smart health records
With its practical approach to the subject, Machine Learning Applications is an ideal resource for professionals working with smart technologies such as machine and deep learning, AI, IoT, and other wireless communications; it is also highly suitable for professionals working in robotics, computer vision, cyber security and more.
About the Authors xiii Preface xv 1 Statistical Similarity in Machine Learning 1 Dmitriy Klyushin 1.1 Introduction 1 1.2 Featureless Machine Learning 2 1.3 Two-Sample Homogeneity Measure 3 1.4 The Klyushin–Petunin Test 3 1.5 Experiments and Applications 4 1.6 Summary 6 References 6 2 Development of ML-Based Methodologies for Adaptive Intelligent E-Learning Systems and Time Series Analysis Techniques 11 Indra Kumari, Indranath Chatterjee, and Minho Lee 2.1 Introduction 11 2.1.1 Machine Learning 12 2.1.2 Types of Machine Learning 12 2.1.3 Learning Methods 13 2.1.4 E-Learning with Machine Learning 14 2.1.5 Need for Machine Learning 15 2.2 Methodological Advancement of Machine Learning 16 2.2.1 Automatic Learner Profiling Agent 16 2.2.2 Learning Materials’ Content Indexing Agent 17 2.2.3 Adaptive Learning 17 2.2.4 Proposed Research 18 2.2.5 Multi-Perspective Learning 18 2.2.6 Machine Learning Recommender Agent for Customization 19 2.2.6.1 E-Learning 19 2.2.7 Data Creation 19 2.2.8 Naïve Bayes model 19 2.2.9 K-Means Model 20 2.3 Machine Learning on Time Series Analysis 21 2.3.1 Time Series Representation 22 2.3.2 Time Series Classification 24 2.3.3 Time Series Forecasting 25 2.4 Conclusion 26 Acknowledgment 28 Conflict of Interest 28 References 28 3 Time-Series Forecasting for Stock Market Using Convolutional Neural Network 31 Partha Pratim Deb, Diptendu Bhattacharya, Indranath Chatterjee, and Sheetal Zalte 3.1 Introduction 31 3.2 Materials 33 3.3 Methodology 33 3.3.1 The Convolutional Neural Network 34 3.4 Accuracy Measurement 35 3.5 Result and Discussion 35 3.6 Conclusion 47 Acknowledgement 47 References 48 4 Comparative Study for Applicability of Color Histograms for CBIR Used for Crop Leaf Disease Detection 49 Jayamala Kumar Patil, Sampada Abhijit Dhole, Vinay Sampatrao Mandlik, and Sachin B. Jadhav 4.1 Introduction 49 4.2 Literature Review 50 4.3 Methodology 51 4.3.1 Color Features 52 4.3.1.1 RGB Color Model/Space 53 4.3.1.2 HSV Color Space 53 4.3.1.3 YCbCr Color Space 54 4.3.1.4 Color Histogram 54 4.3.2 Database 54 4.3.3 Parameters for Performance Analysis 57 4.3.4 Experimental Procedure for CBIR Using Color Histogram for Detection of Disease 58 4.4 Results and Discussions 60 4.4.1 Results of CBIR Using Color Histogram for Detection of Soybean Alfalfa Mosaic Virus Disease 60 4.4.2 Results of CBIR Using Color Histogram for Detection of Soybean Septoria Brown Spot (SBS) Disease 62 4.4.3 Results of CBIR Using Color Histogram for Detection of Soybean Healthy Leaf 63 4.5 Conclusion 63 References 65 Biographies of Authors 67 5 Stock Index Forecasting Using RNN-Long Short-Term Memory 69 Partha Pratim Deb, Diptendu Bhattacharya, and Sheetal Zalte 5.1 Introduction 69 5.2 Materials 71 5.3 Methodology 71 5.3.1 RNN 71 5.3.2 LSTM 72 5.4 Result and Discussion 73 5.4.1 Comparison Table for the Method TAIEX 80 5.4.2 Comparison Table for Method BSE-SENSEX 80 5.4.3 Comparison Table for Method KOSPI 80 5.5 Conclusion 81 Acknowledgement 83 References 84 6 Study and Analysis of Machine Learning Models for Detection of Phishing URLs 85 Shreyas Desai, Sahil Salunkhe, Rashmi Deshmukh, and Sheetal Zalte 6.1 Introduction 85 6.2 Literature Review 86 6.3 Methodology 87 6.3.1 Proposed Work 87 6.3.2 Traditional Methods 87 6.3.2.1 Blacklist Method 88 6.3.2.2 Heuristic-Based Model 88 6.3.2.3 Visual Similarity 89 6.3.2.4 Machine Learning–Based Approach 89 6.4 Results and Experimentation 89 6.4.1 Dataset Creation 89 6.4.2 Feature Extraction 90 6.4.3 Training Data and Comparison 90 6.4.3.1 XGB (eXtreme Gradient Boosting) 90 6.4.3.2 Logistic Regression (LR) 90 6.4.3.3 RFC (Random Forest Classifier) 91 6.4.3.4 Decision Tree 91 6.4.3.5 SVM (Support Vector Machines) 91 6.4.3.6 KNN (K-Nearest Neighbors) 91 6.5 Model-Metric Analysis 91 6.6 Conclusion 94 References 94 7 Real-World Applications of BC Technology in Internet of Things 97 Pardeep Singh, Ajay Kumar, and Mayank Chopra 7.1 Introduction 97 7.1.1 Relevance and Benefits of Blockchain Technology Applications 98 7.2 Review of Existing Study 100 7.3 Background of Blockchain 101 7.3.1 Blockchain Stakeholders 101 7.3.2 What is Bitcoin? 102 7.3.3 Emergence of Bitcoin 102 7.3.4 Working of Bitcoin 102 7.3.5 Risk in Bitcoin 103 7.3.6 Legal Issues in Bitcoin 103 7.4 Blockchain Technology in Internet of Things 104 7.4.1 Need of Integrating Blockchain with IoT 104 7.4.1.1 IoT Data Traceability and Reliability 105 7.4.1.2 Superior Interoperability 105 7.4.1.3 Increased Security 105 7.4.1.4 IoT System Autonomous Interactions 106 7.4.2 Hyperledger 106 7.4.3 Ethereum 107 7.4.4 Iota 107 7.5 Challenges and Concerns in Integrating Blockchain with the IoT 108 7.5.1 Blockchain Challenges and Concern 108 7.5.1.1 Scalability 108 7.5.1.2 Privacy Infringement 109 7.5.2 Privacy and Security issues with Internet of Things 109 7.6 Blockchain Applications for the Internet of Things (BIoT Applications) 110 7.6.1 BIoT Applications for Smart Agriculture 111 7.6.2 Blockchain for Smart Agriculture 111 7.6.3 Intelligent Irrigation Driven by IoT 111 7.7 Application of BIoT in Healthcare 112 7.7.1 Interoperability 113 7.7.2 Improved Analytics and Data Storage 113 7.7.3 Increased Security 113 7.7.4 Immutability 114 7.7.5 Quicker Services 114 7.7.5.1 Transparency 114 7.8 Application of BIoT in Voting 115 7.9 Application of BIoT in Supply Chain 116 7.10 Summary 116 References 117 8 Advanced Persistent Threat: Korean Cyber Security Knack Model Impost and Applicability 123 Indra Kumari and Minho Lee 8.1 Introduction 123 8.2 Background Study 124 8.3 Literature Review 126 8.4 Research Questions 131 8.5 Research Objectives 131 8.6 Research Hypothesis 131 8.7 Phases of APT Outbreak 131 8.7.1 Gain Access 132 8.7.2 Establish Foothold 132 8.7.3 Deepen Access 133 8.7.4 Move Laterally 133 8.7.5 Look, Learn, and Remain 133 8.8 Research Methodology 134 8.8.1 South Korea Cyber Security Initiatives and Applicability 135 8.8.2 Korea’s Cyber-Security Program Proposals 137 8.8.2.1 Modernized Multi-Negotiator Retreat Arrangement 137 8.8.2.2 Headway of the Realms Exemplary 137 8.8.2.3 Scrutiny of Over apt in Cyber Retreat 137 8.8.2.4 Indiscriminate Inconsistency Revealing 138 8.9 A Deception Exemplary of Counter-Offensive 138 8.10 Conclusion 141 Acknowledgment 142 Conflict of Interest 142 References 142 9 Integration of Blockchain Technology and Internet of Things: Challenges and Solutions 145 Aman Kumar Dhiman and Ajay Kumar 9.1 Introduction 145 9.2 Overview of Blockchain–IoT Integration 146 9.3 How Blockchain–IoT Work Together 146 9.3.1 Network in IoT Devices 147 9.3.2 Network in IoT with Blockchain Technology 148 9.3.3 Data Flow in IoT Devices 148 9.3.4 Data Flow in IoT with Blockchain 149 9.3.5 The Role of Blockchain in IoT 149 9.3.6 The Role of IoT in Blockchain 150 9.4 Blockchain–IoT Applications 151 9.5 Related Studies on Integration of IoT and Blockchain Applications 153 9.6 Challenges of Blockchain–IoT Integration 155 9.7 Solutions of Blockchain-IoT Integration 155 9.8 Future Directions for Blockchain–IoT Integration 156 9.9 Conclusion 157 References 157 10 Machine Learning Techniques for SWOT Analysis of Online Education System 161 Priyanka P. Shinde, Varsha P. Desai, T. Ganesh Kumar, Kavita S. Oza, and Sheetal Zalte 10.1 Introduction 161 10.2 Motivation 162 10.3 Objectives 163 10.4 Methodology 163 10.5 Dataset Preparation 164 10.6 Data Visualization and Analysis 170 10.6.1 Observations 171 10.7 Machine Learning Techniques Implementation 178 10.7.1 K-Nearest Neighbors 178 10.7.2 Decision Tree 178 10.7.3 Random Forest 178 10.7.4 Support Vector Machine 179 10.7.5 Logistic Regression 179 10.8 Conclusion 179 References 180 11 Crop Yield and Soil Moisture Prediction Using Machine Learning Algorithms 183 Debarghya Acharjee, Nibedita Mallik, Dipa Das, Mousumi Aktar, and Parijata Majumdar 11.1 Introduction 183 11.2 Literature Review 185 11.3 Methodology 187 11.4 Result and Discussion 190 11.5 Conclusion 191 References 193 12 Multirate Signal Processing in WSN for Channel Capacity and Energy Efficiency Using Machine Learning 195 Prashant R. Dike, T. S. Vishwanath, V. M. Rohokale, and D. S. Mantri 12.1 Introduction 195 12.2 Energy Management in WSN 197 12.3 Different Strategies to Increase Energy Efficiency 197 12.4 Algorithm Development 198 12.5 Results 202 12.6 Summary 203 References 203 13 Introduction to Mechanical Design of AI-Based Robotic System 207 Mohammad Zubair 13.1 Introduction 207 13.2 Mechanisms in a Robot 209 13.2.1 Serial Manipulator 209 13.2.2 Parallel Manipulator 209 13.3 Kinematics 212 13.3.1 Degree of Freedom 214 13.3.2 Position and Orientation in a Robotic System 215 13.4 Conclusion 216 Acknowledgment 217 Conflict of Interest 217 References 217 Index 219
Indranath Chatterjee is a Professor in the Department of Computer Engineering, at Tongmyong University, South Korea. He received his PhD from University of Delhi, India and has authored several books and numerous, research papers. His areas of research are AI, computer vision, computation neuroscience and medical imaging. Sheetal Zalte is an Assistant Professor in the Department of Computer Science at Shivaji University, India. She earned her PhD from Shivaji University, India, and has published many research papers. Her research area is mobile adhoc networks.
