Krishna Kant Singh is an Associate Professor in Electronics and Communications Engineering in KIET Group of Institutions, Ghaziabad, India. Dr. Singh has acquired BTech, MTech, and PhD (IIT Roorkee) in the area of machine learning and remote sensing. He has authored more than 50 technical books and research papers in international conferences and SCIE journals. Akansha Singh is an Associate Professor in Department of Computer Science Engineering in Amity University, Noida, India. Dr. Singh has acquired BTech, MTech, and PhD (IIT Roorkee) in the area of neural network and remote sensing. She has authored more than 40 technical books and research papers in international conferences and SCIE journals. Her area of interest includes Mobile Computing, Artificial Intelligence, Machine Learning, Digital Image Processing. Korhan Cengiz received his PhD in Electronics Engineering from Kadir Has University, Istanbul, Turkey, in 2016. He has served as keynote speakers at many conferences. His research interests include wireless sensor networks, routing protocols, wireless communications, 5G systems, statistical signal processing, and spatial modulation. Dac-Nhuong Le has a MSc and PhD in computer science from Vietnam National University, Vietnam in 2009 and 2015, respectively. He is Associate Professor in Computer Science, Deputy-Head of Faculty of Information Technology, Haiphong University, Vietnam. He has a total academic teaching experience of 12+ years with many publications in reputed international conferences, journals and online book chapters. His area of research includes: evaluation computing and approximate algorithms, network communication, security and vulnerability, network performance analysis and simulation, cloud computing, IoT and image processing in biomedical.
Inhaltsangabe
Preface xiii
1 Machine Learning Architecture and Framework 1 Nilanjana Pradhan and Ajay Shankar Singh
1.1 Introduction 2
1.2 Machine Learning Algorithms 3
1.2.1 Regression 3
1.2.2 Linear Regression 4
1.2.3 Support Vector Machine 4
1.2.4 Linear Classifiers 5
1.2.5 SVM Applications 8
1.2.6 Naïve Bayes Classification 8
1.2.7 Random Forest 9
1.2.8 K-Nearest Neighbor (KNN) 9
1.2.9 Principal Component Analysis (PCA) 9
1.2.10 K-Means Clustering 10
1.3 Business Use Cases 10
1.4 ML Architecture Data Acquisition 14
1.5 Latest Application of Machine Learning 15
1.5.1 Image Identification 16
1.5.2 Sentiment Analysis 16
1.5.3 News Classification 16
1.5.4 Spam Filtering and Email Classification 17
1.5.5 Speech Recognition 17
1.5.6 Detection of Cyber Crime 17
1.5.7 Classification 17
1.5.8 Author Identification and Prediction 18
1.5.9 Services of Social Media 18
1.5.10 Medical Services 18
1.5.11 Recommendation for Products and Services 18
1.5.11.1 Machine Learning in Education 19
1.5.11.2 Machine Learning in Search Engine 19
1.5.11.3 Machine Learning in Digital Marketing 19
1.5.11.4 Machine Learning in Healthcare 19
1.6 Future of Machine Learning 20
1.7 Conclusion 22
References 23
2 Cognitive Computing: Architecture, Technologies and Intelligent Applications 25 Nilanjana Pradhan, Ajay Shankar Singh and Akansha Singh
2.1 Introduction 26
2.1 The Components of a Cognitive Computing System 27
2.3 Subjective Computing Versus Computerized Reasoning 28
2.4 Cognitive Architectures 29
2.5 Subjective Architectures and HCI 31
2.6 Cognitive Design and Evaluation 32
2.6.1 Architectures Conceived in the 1940s Can't Handle the Data of 2020 41
2.7 Cognitive Technology Mines Wealth in Masses of Information 41
2.7.1 Technology is Only as Strong as Its Flexible, Secure Foundation 42
2.8 Cognitive Computing: Overview 43
2.9 The Future of Cognitive Computing 47
References 49
3 Deep Reinforcement Learning for Wireless Network 51 Bharti Sharma, R.K Saini, Akansha Singh and Krishna Kant Singh
3.1 Introduction 51
3.2 Related Work 54
3.3 Machine Learning to Deep Learning 55
3.3.1 Advance Machine Learning Techniques 56
3.3.1.1 Deep Learning 56
3.3.2 Deep Reinforcement Learning (DRL) 57
3.3.2.1 Q-Learning 58
3.3.2.2 Multi-Armed Bandit Learning (MABL) 58
3.3.2.3 Actor-Critic Learning (ACL) 58
3.3.2.4 Joint Utility and Strategy Estimation Based Learning 59
3.4 Applications of Machine Learning Models in Wireless Communication 59
3.4.1 Regression, KNN and SVM Models for Wireless 60
3.4.2 Bayesian Learning for Cognitive Radio 60
3.4.3 Deep Learning in Wireless Network 61
3.4.4 Deep Reinforcement Learning in Wireless Network 62
3.4.5 Traffic Engineering and Routing 63
3.4.6 Resource Sharing and Scheduling 64
3.4.7 Power Control and Data Collection 64
3.5 Conclusion 65
References 66
4 Cognitive Computing for Smart Communication 73 Poonam Sharma, Akansha Singh and Aman Jatain
