ARCHIVES

1. J. Bajwa, U. Munir, A. Nori, and B. Williams, “Artificial intelligence in healthcare: Transforming the practice of medicine,” Future
Healthcare Journal, vol. 8, no. 2, pp. 188–194, Jul. 2021, doi: https://doi.org/10.7861/fhj.2021-0095.
2. L. Pinto-Coelho, “How artificial intelligence is shaping medical imaging technology: A survey of innovations and applications,”
Bioengineering, vol. 10, no. 12, p. 1435, 2023, doi: https://doi.org/10.3390/bioengineering10121435.
3. F. Afroughi et al., “The potential of artificial intelligence in advancing neuroscience: A systematic review of current appli cations and
models,” Intelligence-Based Medicine, vol. 13, p. 100338, Dec. 2025, doi: https://doi.org/10.1016/j.ibmed.2025.100338.
4. M. Kalani and A. Anjankar, “Revolutionizing Neurology: The Role of Artificial Intelligence in Advancing Diagnosis and Treatment,”
Cureus, vol. 16, no. 6, p. e61706, 2024, doi: https://doi.org/10.7759/cureus.61706.
5. M. A. AbuAlrob, A. Itbaisha, and B. Mesraoua, “Unlocking New Frontiers in Epilepsy through AI: from Seizure Prediction to Personalized
Medicine,” Epilepsy & Behavior, vol. 166, p. 110327, Mar. 2025, doi: https://doi.org/10.1016/j.yebeh.2025.110327.
6. P. Magee, Marcello Ienca, and N. Farahany, “Beyond neural data: Cognitive biometrics and mental privacy,” Neuron, vol. 112, no. 18, pp.
3017–3028, Sep. 2024, doi: https://doi.org/10.1016/j.neuron.2024.09.004.
7. A. S. Jwa and R. A. Poldrack, “Addressing privacy risk in neuroscience data: from data protection to harm prevention,” Journal of law and
the biosciences, vol. 9, no. 2, p. lsac025, 2022, doi: https://doi.org/10.1093/jlb/lsac025.
8. D. Whicher, M. Ahmed, S. T. Israni, and M. Matheny, “HEALTH CARE ARTIFICIAL INTELLIGENCE: LAW, REGULATION, AND
POLICY,” Nih.gov, Aug. 02, 2023. https://www.ncbi.nlm.nih.gov/books/NBK605945/
9. D. C. Klonoff et al., “The need for clear medical data ownership laws,” Journal of Translational Medicine, vol. 24, no. 1, Jan. 2026, doi:
https://doi.org/10.1186/s12967-025-07486-z.
10. W. S. Admass, Y. Y. Munaye, and A. A. Diro, “Cyber security: State of the art, Challenges and Future Directions,” Cyber Security and
Applications, vol. 2, no. 2, 2024, doi: https://doi.org/10.1016/j.csa.2023.100031.
11. Katerina Barnova et al., “Implementation of artificial intelligence and machine learning-based methods in brain-computer interaction,”
Computers in Biology and Medicine, vol. 163, pp. 107135–107135, Sep. 2023, doi: https://doi.org/10.1016/j.compbiomed.2023.107135.
12. A. Tarazi, A. Aburrub, and M. Hijah, “Use of artificial intelligence in neurological disorders diagnosis: A scientometric stu dy,” World
Journal of Methodology, vol. 15, no. 3, Mar. 2025, doi: https://doi.org/10.5662/wjm.v15.i3.99403.
13. M. Emish and S. D. Young, “Remote Wearable Neuroimaging Devices for Health Monitoring and Neurophenotyping: A Scoping Review,”
Biomimetics, vol. 9, no. 4, pp. 237–237, Apr. 2024, doi: https://doi.org/10.3390/biomimetics9040237.
14. H. Yang and L. Jiang, “Regulating neural data processing in the age of BCIs: Ethical concerns and legal approaches,” DIGITAL HEALTH,
vol. 11, Mar. 2025, doi: https://doi.org/10.1177/20552076251326123.
15. F. Tong and M. S. Pratte, “Decoding Patterns of Human Brain Activity,” Annual Review of Psychology, vol. 63, no. 1, pp. 483–509, Jan.
2012, doi: https://doi.org/10.1146/annurev-psych-120710-100412.
16. Z. Xue, X. Ling, X. Zhao, and L. Geng, “Neural Mechanisms of Nonauditory Effects of Noise Exposure on Special Populations,” Noise and
Health, vol. 26, no. 121, pp. 70–81, Apr. 2024, doi: https://doi.org/10.4103/nah.nah_78_23.
17. S.-K. Kim and J.-H. Huh, “Artificial Neural Network Blockchain Techniques for Healthcare System: Focusing on the Personal Health
Records,” Electronics, vol. 9, no. 5, p. 763, May 2020, doi: https://doi.org/10.3390/electronics9050763.
18. UNESCO, “The risks and challenges of neurotechnologies for human rights,” Unesco.org, 2023.
https://unesdoc.unesco.org/ark:/48223/pf0000384185
19. Z. Wang, T. Liu, Y. Liu, E. Zio, and X. Guan, “Data Inference: Data Security Threats in the AI Era,” Engineering, vol. 52, pp. 29–33, Aug.
2025, doi: https://doi.org/10.1016/j.eng.2025.08.007.
20. Y. He, A. Aliyu, M. Evans, and C. Luo, “Healthcare cyber security challenges and solutions under the climate of COVID19: A scoping
review,” Journal of Medical Internet Research, vol. 23, no. 4, 2021, doi: https://doi.org/10.2196/21747.
21. S. T. Shah, Z. Ali, Muhammad Waqar, and A. Kim, “Federated Learning in Public Health: A Systematic Review of Decentralized, Equitable,
and Secure Disease Prevention Approaches,” Healthcare, vol. 13, no. 21, pp. 2760–2760, Oct. 2025, doi:
https://doi.org/10.3390/healthcare13212760.
22. J. L. Salmeron and I. Arévalo, “Blind Federated Learning without initial model,” Journal of Big Data, vol. 11, no. 1, Apr. 2024, doi:
https://doi.org/10.1186/s40537-024-00911-y.
23. S. Pati et al., “Privacy preservation for federated learning in health care,” Patterns, vol. 5, no. 7, pp. 100974–100974, Jul. 2024, doi:
https://doi.org/10.1016/j.patter.2024.100974.
24. A. Alshuhail et al., “Machine edge-aware IoT framework for real-time health monitoring: Sensor fusion and AI-driven emergency response
in decentralized networks,” Alexandria Engineering Journal, vol. 129, pp. 1349–1361, Sep. 2025, doi:
https://doi.org/10.1016/j.aej.2025.08.030.
25. M. Rafik et al., “Advances in Federated Learning: Applications and Challenges in Smart Building Environments and Beyond,” Computers,
vol. 14, no. 4, pp. 124–124, Mar. 2025, doi: https://doi.org/10.3390/computers14040124.
26. N. Truong, K. Sun, S. Wang, F. Guitton, and Y. Guo, “Privacy preservation in federated learning: An insightful survey from the GDPR
perspective,” Computers & Security, vol. 110, p. 102402, Nov. 2021, doi: https://doi.org/10.1016/j.cose.2021.102402.
27. M. Asad et al., “Limitations and Future Aspects of Communication Costs in FederatedLearning: A Survey,” Sensors, vol. 23, no. 17, pp.
7358–7358, Aug. 2023, doi: https://doi.org/10.3390/s23177358.
28. J. D. Koen and M. D. Rugg, “Neural Dedifferentiation in the Aging Brain,” Trends in Cognitive Sciences, vol. 23, no. 7, pp. 547–559, Jul.
2019, doi: https://doi.org/10.1016/j.tics.2019.04.012.
29. A. Alabdulatif, “GuardianAI: Privacy-preserving Federated Anomaly Detection with Differential Privacy,” Array, p. 100381, Mar. 2025,doi: https://doi.org/10.1016/j.array.2025.100381.
30. M. Revythi and G. Koukiou, “Quantum Machine Learning and Deep Learning: Fundamentals, Algorithms, Techniques, and Real-World
Applications,” Machine Learning and Knowledge Extraction, vol. 7, no. 3, p. 75, Aug. 2025, doi: https://doi.org/10.3390/make7030075.
31. Q. A. Memon, A. Ahmad, and M. Pecht, “Quantum Computing: Navigating the Future of Computation, Challenges, and Technological
Breakthroughs,” Quantum Reports, vol. 6, no. 4, pp. 627–663, Dec. 2024, doi: https://doi.org/10.3390/quantum6040039.
32. Deepak Ranga, A. Rana, Sunil Prajapat, P. Kumar, K. Kumar, and A. V. Vasilakos, “Quantum Machine Learning: Exploring the Role of
Data Encoding Techniques, Challenges, and Future Directions,” Mathematics, vol. 12, no. 21, pp. 3318–3318, Oct. 2024, doi:
https://doi.org/10.3390/math12213318.
33. Borja Aizpurua, S. Palmer, and Román Orús, “Tensor networks for explainable machine learning in cybersecurity,” Neurocomputing, vol.
639, pp. 130211–130211, Apr. 2025, doi: https://doi.org/10.1016/j.neucom.2025.130211.
34. M. N. da Costa, R. Attux, A. Cichocki, and J. M. T. Romano, “Tensor-Train networks for learning predictive modeling of multidimensional
data,” Neurocomputing, vol. 637, p. 130037, Mar. 2025, doi: https://doi.org/10.1016/j.neucom.2025.130037.
35. D. Guala, S. Zhang, E. Cruz, C. A. Riofrío, J. Klepsch, and J. M. Arrazola, “Practical overview of image classification with tensor-network
quantum circuits,” Scientific Reports, vol. 13, no. 1, p. 4427, Mar. 2023, doi: https://doi.org/10.1038/s41598-023-30258-y.
36. Jörg Rahnenführer et al., “Statistical analysis of high-dimensional biomedical data: a gentle introduction to analytical goals, common
approaches and challenges,” PubMed Central, vol. 21, no. 1, May 2023, doi: https://doi.org/10.1186/s12916-023-02858-y.
37. A. S. Bhatia, M. K. Saggi, and S. Kais, “Application of quantum-inspired tensor networks to optimize federated learning systems,” Quantum
Machine Intelligence, vol. 7, no. 1, Jan. 2025, doi: https://doi.org/10.1007/s42484-025-00243-x.
38. Y. Liu, Y. Li, H. Xu, and Y. Lin, “Quantum-inspired complex spiking neural network for multi-source data joint classification,” Information
Fusion, vol. 126, p. 103526, Feb. 2026, doi: https://doi.org/10.1016/j.inffus.2025.103526.
39. M. L. Hickmann, P. Alves, D. Quero, F. Schwenker, and H.-M. Rieser, “Hybrid quantum tensor networks for aeroelastic applications,”
Quantum Machine Intelligence, vol. 7, no. 2, Nov. 2025, doi: https://doi.org/10.1007/s42484-025-00327-8.
40. A. S. Bhatia and D. E. Bernal, “Federated Learning with tensor networks: a quantum AI framework for healthcare,” Machine Learning
Science and Technology, vol. 5, no. 4, pp. 045035–045035, Oct. 2024, doi: https://doi.org/10.1088/2632-2153/ad8c11.
41. S. Y.-C. Chen and S. Yoo, “Federated Quantum Machine Learning,” Entropy, vol. 23, no. 4, p. 460, Apr. 2021, doi:
https://doi.org/10.3390/e23040460.
42. S. I. Nanayakkara, S. R. Pokhrel, and G. Li, “Understanding global aggregation and optimization of federated learning,” Future Generation
Computer Systems, vol. 159, pp. 114–133, Oct. 2024, doi: https://doi.org/10.1016/j.future.2024.05.009.
43. D. Zhang, S. Yang, M. Chen, L. Zheng, J. Fan, and A. Dong, “Adaptive encryption method of sensitive data in data center database based
on big data cross-mapping fusion algorithm,” Discover Applied Sciences, vol. 7, no. 8, Aug. 2025, doi: https://doi.org/10.1007/s42452-025-
07581-2.
44. Y. Kwak et al., “Quantum distributed deep learning architectures: Models, discussions, and applications,” ICT Express, vol. 9, no. 3, pp.
486–491, Jun. 2023, doi: https://doi.org/10.1016/j.icte.2022.08.004.
45. M. R. Ali, Yashfeen Talpur, U. Nisa, and S. B. Imran, “Brain–computer interfaces: a new horizon in communication for locked-in syndrome,”
Annals of Medicine and Surgery, vol. 87, no. 12, pp. 9159–9160, Oct. 2025, doi: https://doi.org/10.1097/ms9.0000000000004196.
46. B. Wei, S. Cheng, and Y. Feng, “Neural personal information and its legal protection: evidence from China,” Journal of Law and the
Biosciences, vol. 12, no. 1, Jan. 2025, doi: https://doi.org/10.1093/jlb/lsaf006.
47. R. Ballester, J. Cerquides, and L. Artiles, “Quantum federated learning: a comprehensive literature review of foundations, challenges, and
future directions,” Quantum Machine Intelligence, vol. 7, no. 2, Jul. 2025, doi: https://doi.org/10.1007/s42484-025-00292-2.
48. S. Rajesh, Venkata Mani Vakamulla, and T. S. Kumar, “Editorial: AI and machine learning application for neurological disorders and
diagnosis,” Frontiers in Human Neuroscience, vol. 19, Feb. 2025, doi: https://doi.org/10.3389/fnhum.2025.1558584.
49. K. A. S. Kumar, L. Nelson, and B. R. Jibinsingh, “Systematic review of privacy-preserving Federated Learning in decentralized healthcare
systems,” Franklin Open, vol. 13, p. 100440, Nov. 2025, doi: https://doi.org/10.1016/j.fraope.2025.100440.
50. G. Vos, M. Ebrahimpour, L. van Eijk, Z. Sarnyai, and M. Rahimi Azghadi, “Stress monitoring using low-cost electroencephalogram devices:
A systematic literature review,” International journal of medical informatics, vol. 198, p. 105859, Jun. 2025, doi:
https://doi.org/10.1016/j.ijmedinf.2025.105859.
51. D. Gurung and S. R. Pokhrel, “Chained continuous quantum federated learning framework,” Future Generation Computer Systems, vol. 169,
p. 107800, Aug. 2025, doi: https://doi.org/10.1016/j.future.2025.107800.
52. P. Radanliev, “Privacy, ethics, transparency, and accountability in AI systems for wearable devices,” Frontiers in Digital Health, vol. 7, Jun.
2025, doi: https://doi.org/10.3389/fdgth.2025.1431246.
53. M. Li, P. Xu, J. Hu, Z. Tang, and G. Yang, “From challenges and pitfalls to recommendations and opportunities: Implementing federated
learning in healthcare,” Medical Image Analysis, vol. 101, pp. 103497–103497, Feb. 2025, doi: https://doi.org/10.1016/j.media.2025.103497.
54. M. Amadeo, C. Campolo, G. Ruggeri, and A. Molinaro, “Improving communication performance of Federated Learning: A networking
perspective,” Computer Networks, vol. 267, p. 111353, Jul. 2025, doi: https://doi.org/10.1016/j.comnet.2025.111353.
55. M. Leming, K. Kim, R. Bruffaerts, and H. Im, “Strategies for mitigating data heterogeneities in AI-based neuro-disease detection,” Neuron,
Mar. 2025, doi: https://doi.org/10.1016/j.neuron.2025.01.028.
56. S. Singh Gill and R. Buyya, “Transforming Research with Quantum Computing,” Journal of economy and technology, Jul. 2024, doi :
https://doi.org/10.1016/j.ject.2024.07.001.
57. J.-P. A. Yaacoub, H. N. Noura, and O. Salman, “Security of federated learning with IoT systems: Issues, limitations, challenges, and
solutions,” Internet of Things and Cyber-Physical Systems, Apr. 2023, doi: https://doi.org/10.1016/j.iotcps.2023.04.001.
58. S. Z. El Mestari, G. Lenzini, and H. Demirci, “Preserving data privacy in machine learning systems,” Computers & Security, vol. 137, no.
103605, p. 103605, Feb. 2024, doi: https://doi.org/10.1016/j.cose.2023.103605.