A Hybrid Approach Integrating Fuzzy Logic, Neural-Fuzzy Systems, and Quantum Machine Learning for Enhanced Decision-Making in Complex Systems
Keywords:
Hybrid AI, Fuzzy Logic, Neural-Fuzzy Systems, Quantum Machine Learning, Complex Decision-Making, Uncertainty Handling, AI InterpretabilityAbstract
The increasing complexity of modern decision-making systems necessitates the integration of advanced artificial intelligence (AI) techniques. Hybrid AI models that incorporate fuzzy logic, neural-fuzzy systems, and quantum machine learning (QML) offer a promising solution for handling uncertainty, improving interpretability, and accelerating computations. This paper explores how these paradigms complement each other to enhance AI-based decision-making. A systematic review of existing literature highlights advancements in hybrid AI, showcasing applications in medical diagnosis, finance, and cybersecurity. The study presents an experimental analysis comparing hybrid models against traditional machine learning techniques. Finally, we discuss emerging trends and challenges, emphasizing the need for robust, scalable hybrid AI frameworks.
References
Zadeh, L. (1965). Fuzzy sets. Information and Control, 8(3), 338-353.
Castillo, O., Melin, P., & Kacprzyk, J. (2019). Soft computing for intelligent control applications. Springer AI Research, 12(4), 78-89.
Jang, S., Kim, H., & Park, J. (2020). Financial forecasting using adaptive neuro-fuzzy inference systems. IEEE Transactions on Computational Finance, 35(6), 167-178.
Patel, A., Gupta, R., & Sharma, K. (2021). Neural-fuzzy systems for autonomous vehicle control. Journal of Intelligent Transportation Systems, 23(2), 98-112.
Bengio, Y., Marcus, G., & LeCun, Y. (2021). Neuro-symbolic AI: Combining logic with deep learning. Nature Machine Intelligence, 3(7), 550-561.
Liu, X., Zhao, L., & Chen, P. (2022). Hybrid symbolic AI and fuzzy reasoning in healthcare. Expert Systems with Applications, 188, 116529.
Cerezo, M., Sone, A., & McClean, J. (2021). Variational quantum classifiers. Quantum Science and Technology, 6(2), 024005.
Abbas, A., & Zhang, X. (2022). Quantum neural networks for fraud detection. IEEE Quantum Transactions, 5(3), 122-136.
Ren, Y., & Wang, T. (2022). QML-enhanced fuzzy logic classifiers for speech recognition. ACM Transactions on Speech Processing, 18(5), 763-778.
Chen, X., Li, F., & Zhao, Y. (2022). Hybrid AI approaches for medical diagnostics. Journal of Medical AI, 9(1), 213-227.
Das, R., & Sharma, P. (2023). Quantum machine learning for cybersecurity applications. IEEE Security & Privacy, 21(2), 46-58.
Gupta, S., & Kumar, V. (2023). Neural-fuzzy models for industrial automation. International Journal of Advanced Computing, 35(7), 350-362.
Wu, C., & He, X. (2022). Explainable hybrid AI for business decision-making. Business Intelligence Journal, 29(3), 417-432.
Roy, P., & Mitra, S. (2023). AI-driven fuzzy logic in financial risk assessment. Finance AI Quarterly, 11(4), 178-193.
Kim, J., & Choi, L. (2022). Quantum-enhanced hybrid AI for autonomous robotics. Robotics and AI Journal, 14(3), 288-305.
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Copyright (c) 2024 Srivasa venkatraman (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
