Frontmatter

Kapitel in diesem Buch

1. Frontmatter I
2. Preface V
3. Contents VII
4. Biographies XI
5. List of contributors XIII
6. 1 Optimizing the traffic flow in VANETs using deep quantum annealing 1
7. 2 Quantum annealing-based routing in UAV network 13
8. 3 Cyberbullying detection of social network tweets using quantum machine learning 25
9. 4 AI-driven cybersecurity modeling using quantum computing for mitigation of attacks in IOT-SDN network 37
10. 5 Machine learning-based quantum modeling to classify the traffic flow in smart cities 49
11. 6 IoT attack detection using quantum deep learning in large-scale networks 67
12. 7 Quantum transfer learning to detect passive attacks in SDN-IOT 79
13. 8 Intrusion detection framework using quantum computing for mobile cloud computing 97
14. 9 Fault-tolerant mechanism using intelligent quantum computing-based error reduction codes 109
15. 10 Study of quantum computing for data analytics of predictive and prescriptive analytics models 121
16. 11 A review of different techniques and challenges of quantum computing in various applications 147
17. 12 Review and significance of cryptography and machine learning in quantum computing 159
18. 13 An improved genetic quantum cryptography model for network communication 177
19. 14 Code-based post-quantum cryptographic technique: digital signature 193
20. 15 Post-quantum cryptography for the detection of injection attacks in small-scale networks 207
21. 16 RSA security implementation in quantum computing for a higher resilience 219
22. 17 Application of quantum computing for digital forensic investigation 231
23. 18 Modern healthcare system: unveiling the possibility of quantum computing in medical and biomedical zones 249
24. 19 Quantum computing-assisted machine learning to improve the prediction of cardiovascular disease in healthcare system 265
25. 20 Mitigating the risk of quantum computing in cyber security era 283
26. 21 IoMT-based data aggregation using quantum learning 301
27. Index 319