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.
Audience
This 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.
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 Literature 156 9.2.1 Overview of Key Quantum Computing Principles 156 9.2.1.1 Qubits (Quantum Bits) 157 9.2.1.2 Quantum Gates 157 9.2.1.3 Quantum Parallelism 157 9.2.1.4 Quantum Measurement 157 9.2.1.5 Quantum Fourier Transform 158 9.2.1.6 Quantum Entanglement-Based Algorithms 158 9.3 Basic Quantum Operations, Qubits, and Quantum Gates 158 9.3.1 Basic Quantum Operations 158 9.3.2 Quantum Bits (Qubits) 158 9.3.3 Quantum Gates 159 9.4 Quantum Machine Learning Algorithms 159 9.4.1 Quantum Support Vector Machines (QSVM) 161 9.4.2 Quantum Neural Networks (QNN) 161 9.4.3 Quantum Clustering Algorithms 161 9.4.4 Quantum Principal Component Analysis (QPCA) 162 9.4.5 Quantum Boltzmann Machines 162 9.4.6 Quantum Support Vector Clustering (QSVC) 162 9.5 Quantum Hardware for Machine Learning 162 9.6 Challenges in Building Scalable and Error-Resistant Quantum Hardware 163 9.6.1 Decoherence and Quantum Error Correction 163 9.6.2 Quantum Gate Fidelity 163 9.6.3 Scalability 164 9.6.4 Qubit Connectivity and Crosstalk 164 9.6.5 Material Science and Qubit Implementation 164 9.6.6 Quantum Interconnects 164 9.6.7 Thermal Management 164 9.6.8 Error Mitigation Strategies 164 9.7 Challenges and Limitations in Quantum Machine Learning 165 9.7.1 Quantum Computational Overheads 165 9.7.2 Hybrid Quantum-Classical System Integration 165 9.7.3 Limited Quantum Expressibility 165 9.7.4 Data Preprocessing Challenges 165 9.7.5 Quantum Algorithm Verification 166 9.7.6 Quantum Resource Requirements 166 9.7.7 Adaptation to Quantum Hardware Constraints 166 9.7.8 Limited Quantum Hardware Availability 166 9.7.9 Algorithmic Complexity 166 9.7.10 Quantum Model Interpretability 166 9.8 Future Directions 167 9.9 Conclusion 167 References 167 10 Multi-Band Self-Grounding Antenna for Wireless Technologies 169 Ch. Siva Rama Krishna, P. Livingston, S. Jaya Chandra, J. Hari Babu and K. Sai Babu 10.1 Introduction 170 10.1.1 Literature Review 170 10.2 Design of Antenna 174 10.2.1 Design and Results at Primary Level of Antenna 175 10.2.2 Design and Results at Secondary Level of Antenna 175 10.3 Actual Design of Antenna 176 10.4 Results of Antenna 176 10.4.1 Mathematical Analysis 178 10.4.2 3D Polar Plot 178 10.5 Conclusions 179 References 180 11 Navigating Network Security: A Study on Contemporary Anomaly Detection Technologies 183 Sai Ramya, Smera C. and Sandeep J. 11.1 Introduction 184 11.2 Related Work 186 11.3 Methodology 194 11.4 Conclusion 197 References 197 12 File Fragment Classification: A Comprehensive Survey of Research Advances 201 Teena Mary and Sreeja C.S. 12.1 Introduction 201 12.2 Methodology 203 12.2.1 Selection Criteria 203 12.2.2 Structure of the Paper 204 12.3 Approaches for File Fragment Classification 204 12.3.1 Signature-Based Approaches 204 12.3.2 Content-Based Approaches 206 12.3.3 Deep Learning-Based Approaches 207 12.3.3.1 Convolutional Neural Networks (CNNs) 208 12.3.3.2 Feed Forward Neural Networks (FFNNs) 209 12.3.4 Hierarchical Classification Methods 209 12.4 Survey Findings 210 12.5 Challenges and Future Directions 214 12.6 Conclusion 215 References 216 13 Deepfake Detection and Forensic Precision for Online Harassment 219 K. Gouthami, K. Sunitha, D.U. Durgarani and M. Prathyusha 13.1 Introduction 220 13.2 Literature 221 13.3 Theoretical Analysis and Software Simulation 222 13.3.1 Theoretical Analysis 222 13.3.2 Software Simulation 223 13.3.3 Testing and Optimization 224 References 225 14 Design of Automatic Seed Sowing Machine 227 Chiluka Ramesh, K. Sarada, V. Ajay Shankar and K. Ravi Kumar 14.1 Introduction 228 14.2 Literature Survey 229 14.3 Proposed System 232 14.4 Conclusions 235 References 235 15 In Motion: Exploring Urban Rides Through Data Analytics 237 Rajkumar Sai Varun, Nimmagadda Narayana, Dudam Vipassana and Mohan Dholvan 15.1 Introduction 237 15.2 Literature Survey 238 15.3 Proposed Methodology 240 15.4 Result Analysis 247 15.5 Conclusion 248 References 249 16 Design of Novel Chatbot Using Generative Artificial Intelligence 251 Sk. Khader Zelani, Sk. Gousiya Begum, M. Chandana and N. Lakshmi Tirupatamma 16.1 Introduction 252 16.2 Conclusion and Future Scope 257 References 257 17 The Smart Nebulizer Cap for Enhanced Asthma Management 259 Rossly Netala, Aadi Praharsha and Mohan Dholvan 17.1 Introduction 259 17.2 Literature Survey 261 17.3 Methodology 262 17.4 Conclusions 265 References 265 18 Design of a Digital VLSI Parallel Morphological Reconfigurable Processing Module for Binary and Grayscale Image Processing 267 Y. Bhaskara Rao, K. Rajitha, D. Vijay Harsha Vardhan, N. Naga Raja Kumari and D. Vijaya Saradhi 18.1 Introduction 268 18.2 Literature Survey 269 18.3 Design of a Digital VLSI Parallel Morphological Reconfigurable Processing Module for Binary and Grayscale Image Processing 271 18.4 Result Analysis 274 18.5 Conclusion 276 References 277 19 Intrusion Detection System Using Machine Learning 279 Ballikura Dhanunjay, Earla Sanjay, Aakaram Karthik Raj and Mohan Dholvan 19.1 Introduction 280 19.2 Literature Survey 280 19.3 Methodology 281 19.4 Algorithm 283 19.5 Implementation 285 19.6 Results and Outputs 289 19.6.1 User Interface 289 19.7 Conclusion and Future Scope 290 References 291 20 Prediction of Arrival Delay Time in Freightage Rails 293 Bobbala Shriya, Gudishetty Shrita, Vanga Pragnya Reddy and Nanda Kumar M. 20.1 Introduction 294 20.2 Literature Survey 295 20.3 Methodology 297 20.4 Experimental Results 302 20.5 Conclusions 308 References 309 21 Predicting Flight Delays with Error Calculation Using Machine Learned Classifiers 311 L. Sai Nageswara Raju, T. Naman Krishn Raj, Raipole Manihas Goud and Mohan Dholvan 21.1 Introduction 311 21.2 Literature Survey 312 21.3 Proposed Methodology 314 21.4 Result Analysis 322 21.5 Conclusion 322 References 323 22 Design and Implementation of 8-Bit Ripple Carry Adder and Carry Select Adder at 32-nm CNTFET Technology: A Comparative Study 325 Venkata Rao Tirumalasetty, K. Babulu and G. Appala Naidu 22.1 Introduction 326 22.2 Implementation of RCA & CSA 328 22.3 Simulation Results 333 22.4 Conclusion 335 References 335 23 XGBoost Classifier Based Water Quality Classification Using Machine Learning 337 Nagidi Nikhitha, Sudini Poojitha, Vooturi Arjun, K. Sateesh Kumar and D. Mohan 23.1 Introduction 338 23.2 Related Work 338 23.3 Proposed Methodology 339 23.4 Results and Discussion 342 23.5 Conclusion 345 References 345 Index 347
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.
