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Quantum Computing Models for Cybersecurity and Wireless Communications

Edited by Budati Anil Kumar, Singamaneni Kranthi Kumar and Li Xingwang
Series: Sustainable Computing and Optimization
Copyright: 2025   |   Status: Published
ISBN: 9781394271399  |  Hardcover  |  
370 pages
Price: $195 USD
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One Line Description
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.

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.

Description
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.

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Author / Editor Details
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.

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Table of Contents
Preface
Acknowledgment
1. Performance Evaluation of Avionics System Under Hardware-In-Loop Simulation Framework with Implementation of an AS9100 Quality Management System
Rajesh Shankar Karvande and Tatineni Madhavi
1.1 Introduction
1.2 HILS Process and Quality Management System
1.3 HILS Testing Phase
1.4 AS9100 QMS Integrated with HILS Process
1.5 Conclusion and Suggestions
References
2. YouTube Comment Summarizer and Time-Based Analysis
Preeti Bailke, Rugved Junghare, Prajakta Kumbhare, Pratik Mandalkar, Pratik Mane and Netra Mohekar
2.1 Introduction
2.2 Literature Review
2.3 Methodology
2.3.1 YouTube Comments Data Collection
2.3.1.1 YouTube Data API Integration
2.3.1.2 get_video_comments Function
2.3.1.3 Comment Processing
2.3.1.4 Handling Pagination with get_all_video_comments
2.3.1.5 Excel File Creation with save_to_excel
2.3.2 Datasets
2.3.3 Extractive Summarization
2.4 Result
2.5 Performance
2.6 Conclusion
References
3. Enhancing Gait Recognition Using YOLOv8 and Robust Video Matting for Low-Light and Adverse Conditions
Premanand Ghadekar, Aadesh Chawla, Sakshi Bodhe, Sharvari Bawane and Dhruv Kshirsagar
3.1 Introduction
3.2 Related Works
3.3 Methodology
3.4 Comparision with Existing Systems
3.5 Future Scope
3.6 Conclusion
Acknowledgment
References
4. An Ensemble-Based Machine Learning Framework for Breast Cancer Prediction
Ramya Palaniappan, Maha Lakshmi, Namitha, Nirmala Devi and Naga Phani
4.1 Introduction
4.2 Related Works
4.3 Proposed Framework
4.3.1 ML Models and Ablation Study
4.3.2 Building Ensemble Model Using AdaBoost
4.4 Experimental Setup
4.4.1 Dataset
4.4.2 Data Visualization
4.4.3 Data Pre-Processing Phase
4.4.4 Proposed Methodology
4.4.5 Performance Metrics
4.5 Results and Discussion
4.5.1 Comparison with Baseline Models
4.5.2 Comparison with Existing Literature Works
4.6 Existing Works
4.7 Conclusion and Future Work
Dataset
References
5. Proactive Fault Detection in Weather Forecast Control Systems Through Heartbeat Monitoring and Cloud-Based Analytics
Shelly Prakash and Vaibhav Vyas
5.1 Introduction
5.1.1 Cloud Computing
5.1.1.1 Fault, Error, Failure
5.2 Related Work
5.3 Proposed Proactive Fault Detection Architecture
5.4 Conclusion
References
6. FlowGuard: Efficient Traffic Monitoring System
Varsha Dange, Atharva Bonde, Om Borse, Harshal Chaudhari and Sanskar Chaudhari
6.1 Introduction
6.2 Literature Review
6.3 Methodology
6.3.1 Theory
6.3.2 Requirement
6.3.2.1 Hardware Requirements
6.3.2.2 Software Requirements
6.3.3 Workflow
6.3.4 Flowchart
6.4 Results and Discussions
6.5 Conclusion
6.6 Future Scope
Acknowledgment
References
References for Pictures of Components Used
7. A Survey on Heart Disease Prediction Using Ensemble Techniques in ML
Sudhakar Vecha and M.V.P. Chandra Sekhara Rao
7.1 Introduction
7.2 Literature Survey
7.3 Datasets
7.4 Ensemble Learning in Heart Disease
7.5 Challenges and Limitations
7.6 Future Directions
7.7 Conclusion
References
8. A Video Surveillance: Crowd Anomaly Detection and Management Alert System
Anitha Ponraj, Umasree Mariappan, M. J. Sai Kiran, S. Tejeswar Reddy, N. Vinay and P. Bharath
8.1 Introduction
8.2 Related Work
8.3 Dataset Description
8.4 Problem Definition
8.5 Proposed Methodology and System
8.5.1 Proposed Methodology
8.5.2 Proposed System
8.6 Results
8.7 Conclusion and Future Scope
8.7.1 Conclusion
8.7.2 Future Scope
References
9. Revolutionizing Learning with Qubits: A Review of Quantum Machine Learning Advances
Shatakshi Bhusari, Aniket Badakh, Kalyani Daine, Nikita Gagare and Prasad Raghunath Mutkule
9.1 Introduction
9.1.1 Parallelism
9.1.2 Quantum Speedup
9.1.3 Quantum Entanglement
9.1.4 Quantum Fourier Transform
9.1.5 Quantum Machine Learning Algorithms
9.1.6 Quantum Data Representation
9.1.7 Quantum Sampling
9.1.8 Quantum Annealing
9.1.9 Hybrid Quantum-Classical Approaches
9.2 Review of Literature
9.2.1 Overview of Key Quantum Computing Principles
9.2.1.1 Qubits (Quantum Bits)
9.2.1.2 Quantum Gates
9.2.1.3 Quantum Parallelism
9.2.1.4 Quantum Measurement
9.2.1.5 Quantum Fourier Transform
9.2.1.6 Quantum Entanglement-Based Algorithms
9.3 Basic Quantum Operations, Qubits, and Quantum Gates
9.3.1 Basic Quantum Operations
9.3.2 Quantum Bits (Qubits)
9.3.3 Quantum Gates
9.4 Quantum Machine Learning Algorithms
9.4.1 Quantum Support Vector Machines (QSVM)
9.4.2 Quantum Neural Networks (QNN)
9.4.3 Quantum Clustering Algorithms
9.4.4 Quantum Principal Component Analysis (QPCA)
9.4.5 Quantum Boltzmann Machines
9.4.6 Quantum Support Vector Clustering (QSVC)
9.5 Quantum Hardware for Machine Learning
9.6 Challenges in Building Scalable and Error-Resistant Quantum Hardware
9.6.1 Decoherence and Quantum Error Correction
9.6.2 Quantum Gate Fidelity
9.6.3 Scalability
9.6.4 Qubit Connectivity and Crosstalk
9.6.5 Material Science and Qubit Implementation
9.6.6 Quantum Interconnects
9.6.7 Thermal Management
9.6.8 Error Mitigation Strategies
9.7 Challenges and Limitations in Quantum Machine Learning
9.7.1 Quantum Computational Overheads
9.7.2 Hybrid Quantum-Classical System Integration
9.7.3 Limited Quantum Expressibility
9.7.4 Data Preprocessing Challenges
9.7.5 Quantum Algorithm Verification
9.7.6 Quantum Resource Requirements
9.7.7 Adaptation to Quantum Hardware Constraints
9.7.8 Limited Quantum Hardware Availability
9.7.9 Algorithmic Complexity
9.7.10 Quantum Model Interpretability
9.8 Future Directions
9.9 Conclusion
References
10. Multi-Band Self-Grounding Antenna for Wireless Technologies
Ch. Siva Rama Krishna, P. Livingston, S. Jaya Chandra, J. Hari Babu and K. Sai Babu
10.1 Introduction
10.1.1 Literature Review
10.2 Design of Antenna
10.2.1 Design and Results at Primary Level of Antenna
10.2.2 Design and Results at Secondary Level of Antenna
10.3 Actual Design of Antenna
10.4 Results of Antenna
10.4.1 Mathematical Analysis
10.4.2 3D Polar Plot
10.5 Conclusions
References
11. Navigating Network Security: A Study on Contemporary Anomaly Detection Technologies
Sai Ramya, Smera C. and Sandeep J.
11.1 Introduction
11.2 Related Work
11.3 Methodology
11.4 Conclusion
References
12. File Fragment Classification: A Comprehensive Survey of Research Advances
Teena Mary and Sreeja C.S.
12.1 Introduction
12.2 Methodology
12.2.1 Selection Criteria
12.2.2 Structure of the Paper
12.3 Approaches for File Fragment Classification
12.3.1 Signature-Based Approaches
12.3.2 Content-Based Approaches
12.3.3 Deep Learning-Based Approaches
12.3.3.1 Convolutional Neural Networks (CNNs)
12.3.3.2 Feed Forward Neural Networks (FFNNs)
12.3.4 Hierarchical Classification Methods
12.4 Survey Findings
12.5 Challenges and Future Directions
12.6 Conclusion
References
13. Deepfake Detection and Forensic Precision for Online Harassment
K. Gouthami, K. Sunitha, D.U. Durgarani and M. Prathyusha
13.1 Introduction
13.2 Literature
13.3 Theoretical Analysis and Software Simulation
13.3.1 Theoretical Analysis
13.3.2 Software Simulation
13.3.3 Testing and Optimization
References
14. Design of Automatic Seed Sowing Machine
Chiluka Ramesh, K. Sarada, V. Ajay Shankar and K. Ravi Kumar
14.1 Introduction
14.2 Literature Survey
14.3 Proposed System
14.4 Conclusions
References
15. In Motion: Exploring Urban Rides Through Data Analytics
Rajkumar Sai Varun, Nimmagadda Narayana, Dudam Vipassana and Mohan Dholvan
15.1 Introduction
15.2 Literature Survey
15.3 Proposed Methodology
15.4 Result Analysis
15.5 Conclusion
References
16. Design of Novel Chatbot Using Generative Artificial Intelligence
Sk. Khader Zelani, Sk. Gousiya Begum, M. Chandana and N. Lakshmi Tirupatamma
16.1 Introduction
16.2 Conclusion and Future Scope
References
17. The Smart Nebulizer Cap for Enhanced Asthma Management
Rossly Netala, Aadi Praharsha and Mohan Dholvan
17.1 Introduction
17.2 Literature Survey
17.3 Methodology
17.4 Conclusions
References
18. Design of a Digital VLSI Parallel Morphological Reconfigurable Processing Module for Binary and Grayscale Image Processing
Y. Bhaskara Rao, K. Rajitha, D. Vijay Harsha Vardhan, N. Naga Raja Kumari and D. Vijaya Saradhi
18.1 Introduction
18.2 Literature Survey
18.3 Design of a Digital VLSI Parallel Morphological Reconfigurable Processing Module for Binary and Grayscale Image Processing
18.4 Result Analysis
18.5 Conclusion
References
19. Intrusion Detection System Using Machine Learning
Ballikura Dhanunjay, Earla Sanjay, Aakaram Karthik Raj and Mohan Dholvan
19.1 Introduction
19.2 Literature Survey
19.3 Methodology
19.4 Algorithm
19.5 Implementation
19.6 Results and Outputs
19.6.1 User Interface
19.7 Conclusion and Future Scope
References
20. Prediction of Arrival Delay Time in Freightage Rails
Bobbala Shriya, Gudishetty Shrita, Vanga Pragnya Reddy and Nanda Kumar M.
20.1 Introduction
20.2 Literature Survey
20.3 Methodology
20.4 Experimental Results
20.5 Conclusions
References
21. Predicting Flight Delays with Error Calculation Using Machine Learned Classifiers
L. Sai Nageswara Raju, T. Naman Krishn Raj, Raipole Manihas Goud and Mohan Dholvan
21.1 Introduction
21.2 Literature Survey
21.3 Proposed Methodology
21.4 Result Analysis
21.5 Conclusion
References
22. Design and Implementation of 8-Bit Ripple Carry Adder and Carry Select Adder at 32-nm CNTFET Technology: A Comparative Study
Venkata Rao Tirumalasetty, K. Babulu and G. Appala Naidu
22.1 Introduction
22.2 Implementation of RCA & CSA
22.3 Simulation Results
22.4 Conclusion
References
23. XGBoost Classifier Based Water Quality Classification Using Machine Learning
Nagidi Nikhitha, Sudini Poojitha, Vooturi Arjun, K. Sateesh Kumar and D. Mohan
23.1 Introduction
23.2 Related Work
23.3 Proposed Methodology
23.4 Results and Discussion
23.5 Conclusion
References
Index

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