Quantum Computing Models for Cybersecurity and Wireless Communications

The book explores the latest quantum computing research focusing on problems and challenges in the areas of data transmission technology, computer algorithms, artificial intelligence-based devices, computer technology, and their solutions.Future quantum machines will exponentially boost computing power, creating new opportunities for improving cybersecurity. Both classical and quantum-based cyberattacks can be proactively identified and stopped by quantum-based cybersecurity before they harm. Complex math-based problems that support several encryption standards could be quickly solved by using quantum machine learning.This comprehensive book examines how quantum machine learning and quantum computing are reshaping cybersecurity, addressing emerging challenges. It includes in-depth illustrations of real-world scenarios and actionable strategies for integrating quantum-based solutions into existing cybersecurity frameworks. A range of topics are examined, including quantum-secure encryption techniques, quantum key distribution, and the impact of quantum computing algorithms. Additionally, it talks about machine learning models and how to use machine learning to solve problems. Through its in-depth analysis and innovative ideas, each chapter provides a compilation of research on cutting-edge quantum computer techniques, like blockchain, quantum machine learning, and cybersecurity.AudienceThis book serves as a ready reference for researchers and professionals working in the area of quantum computing models in communications, machine learning techniques, IoT-enabled technologies, and various application industries such as finance, healthcare, transportation and utilities.

Autorentext
Budati Anil Kumar, PhD, is an associate professor at the Faculty of Electronics & Communication Engineering, Koneru Lakshmaiah Education Foundation (Deemed University), Aziz Nagar Campus, Hyderabad, Telangana, India. His research interests include cognitive radio networks, software-defined radio networks, artificial intelligence, etc. He has published 53 research articles in highly reputed publishing journals and conferences. Singamaneni Kranthi Kumar, PhD, Faculty of Computer Engineering and Technology, Chaitanya Bharathi Institute of Technology, Gandipet, Hyderabad, Telangana, India. He has authored at least 30 SCI journal articles and received the prestigious "Global Teachers Award" in 2020. Li Xingwang, PhD, is an associate professor at the School of Physics and Electronic Information Engineering, Henan Polytechnic University, Jiaozuo, China. He is on the editorial board of many IEEE journals and his research interests include wireless communication, intelligent transport systems, artificial intelligence, and the Internet of Things.

Inhalt
Preface xv Acknowledgment xvii 1 Performance Evaluation of Avionics System Under Hardware-In- Loop Simulation Framework with Implementation of an AS9100 Quality Management System 1
Rajesh Shankar Karvande and Tatineni Madhavi 1.1 Introduction 2 1.2 HILS Process and Quality Management System 4 1.3 HILS Testing Phase 7 1.4 AS9100 QMS Integrated with HILS Process 8 1.5 Conclusion and Suggestions 10 References 10 2 YouTube Comment Summarizer and Time-Based Analysis 13
Preeti Bailke, Rugved Junghare, Prajakta Kumbhare, Pratik Mandalkar, Pratik Mane and Netra Mohekar 2.1 Introduction 13 2.2 Literature Review 16 2.3 Methodology 18 2.3.1 YouTube Comments Data Collection 18 2.3.1.1 YouTube Data API Integration 18 2.3.1.2 get_video_comments Function 19 2.3.1.3 Comment Processing 19 2.3.1.4 Handling Pagination with get_all_video_ comments 20 2.3.1.5 Excel File Creation with save_to_excel 20 2.3.2 Datasets 20 2.3.3 Extractive Summarization 21 2.4 Result 30 2.5 Performance 30 2.6 Conclusion 31 References 31 3 Enhancing Gait Recognition Using YOLOv8 and Robust Video Matting for Low-Light and Adverse Conditions 33
Premanand Ghadekar, Aadesh Chawla, Sakshi Bodhe, Sharvari Bawane and Dhruv Kshirsagar 3.1 Introduction 34 3.2 Related Works 34 3.3 Methodology 36 3.4 Comparision with Existing Systems 41 3.5 Future Scope 48 3.6 Conclusion 48 Acknowledgment 49 References 49 4 An Ensemble-Based Machine Learning Framework for Breast Cancer Prediction 51
Ramya Palaniappan, Maha Lakshmi, Namitha, Nirmala Devi and Naga Phani 4.1 Introduction 52 4.2 Related Works 53 4.3 Proposed Framework 56 4.3.1 ML Models and Ablation Study 56 4.3.2 Building Ensemble Model Using AdaBoost 57 4.4 Experimental Setup 58 4.4.1 Dataset 58 4.4.2 Data Visualization 59 4.4.3 Data Pre-Processing Phase 60 4.4.4 Proposed Methodology 61 4.4.5 Performance Metrics 62 4.5 Results and Discussion 63 4.5.1 Comparison with Baseline Models 63 4.5.2 Comparison with Existing Literature Works 66 4.6 Existing Works 67 4.7 Conclusion and Future Work 69 Dataset 69 References 69 5 Proactive Fault Detection in Weather Forecast Control Systems Through Heartbeat Monitoring and Cloud-Based Analytics 73
Shelly Prakash and Vaibhav Vyas 5.1 Introduction 74 5.1.1 Cloud Computing 75 5.1.1.1 Fault, Error, Failure 75 5.2 Related Work 77 5.3 Proposed Proactive Fault Detection Architecture 81 5.4 Conclusion 95 References 95 6 FlowGuard: Efficient Traffic Monitoring System 99
Varsha Dange, Atharva Bonde, Om Borse, Harshal Chaudhari and Sanskar Chaudhari 6.1 Introduction 99 6.2 Literature Review 100 6.3 Methodology 113 6.3.1 Theory 113 6.3.2 Requirement 114 6.3.2.1 Hardware Requirements 114 6.3.2.2 Software Requirements 116 6.3.3 Workflow 117 6.3.4 Flowchart 118 6.4 Results and Discussions 118 6.5 Conclusion 121 6.6 Future Scope 121 Acknowledgment 122 References 122 References for Pictures of Components Used 124 7 A Survey on Heart Disease Prediction Using Ensemble Techniques in ml 125
Sudhakar Vecha and M.V.P. Chandra Sekhara Rao 7.1 Introduction 125 7.2 Literature Survey 127 7.3 Datasets 128 7.4 Ensemble Learning in Heart Disease 129 7.5 Challenges and Limitations 134 7.6 Future Directions 134 7.7 Conclusion 135 References 135 8 A Video Surveillance: Crowd Anomaly Detection and Management Alert System 139
Anitha Ponraj, Umasree Mariappan, M. J. Sai Kiran, S. Tejeswar Reddy, N. Vinay and P. Bharath 8.1 Introduction 140 8.2 Related Work 140 8.3 Dataset Description 143 8.4 Problem Definition 143 8.5 Proposed Methodology and System 144 8.5.1 Proposed Methodology 144 8.5.2 Proposed System 146 8.6 Results 148 8.7 Conclusion and Future Scope 150 8.7.1 Conclusion 150 8.7.2 Future Scope 151 References 151 9 Revolutionizing Learning with Qubits: A Review of Quantum Machine Learning Advances 153
Shatakshi Bhusari, Aniket Badakh, Kalyani Daine, Nikita Gagare and Prasad Raghunath Mutkule 9.1 Introduction 154 9.1.1 Parallelism 154 9.1.2 Quantum Speedup 155 9.1.3 Quantum Entanglement 155 9.1.4 Quantum Fourier Transform 155 9.1.5 Quantum Machine Learning Algorithms 155 9.1.6 Quantum Data Representation 155 9.1.7 Quantum Sampling 155 9.1.8 Quantum Annealing 156 9.1.9 Hybrid Quantum-Classical Approaches 156 9.2 Review of Literatu…

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