Volume 14 Issue 3  ·  ISSN: 2319-4863  ·  Monthly Publication editor@ijdacr.com
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Research Article

Optimization-Driven and Generative-Assisted Adaptive Artificial Intelligence Systems: A Detailed Review of Methods, Architectures, and Governance

Ashutosh kumar singh

IJDACR Vol.14 No.3 (October 2025) ISSN 2319-4863 Open Access Peer Reviewed

Journal

International Journal of Digital Applications and Contemporary Research (IJDACR)

ISSN

2319-4863

Volume / Issue

Vol.14 · Issue 3

Published

October 2025

Access

Open Access

Licence

CC BY-NC-SA 4.0

Authors

Ashutosh kumar singh

Abstract

Artificial Intelligence (AI) systems are being instituted in dynamic, uncertain, and safety-critical systems like financial analytics, healthcare allotment, intelligent traffic, cybersecurity and cyber-physical infrastructures. In these directions, traditional machine learning models with fixed training and accuracy-focused goals tend to fail in supporting performance in changing data distributions, rare events, and uncertainty and adversarial cases. To address these drawbacks, more advanced studies have highlighted optimization based-learning, generative-assisted models as well as adaptive AI designs that can evolve continuously, but can be reliable and ethical. The following review provides a synthesis of the state of optimization-enhancement and generative-assistance AI frameworks intended to work on the increase of the robustness, flexibility, and stability of the decisions taken. The paper focuses on anomaly-conscious learning pipelines, evolutionary and heuristic optimization methods, generative data enhancement and stress testing, adaptive signal processing, and governance conscious computer-aided intelligence. The applications in the fields of finance, health care, intelligent networks, and cyber-physical systems are surveyed in order to find common design principles and research issues. The end of the review is the projection of the future swinging on scalable, self-adaptive and ethically controlled AI systems.

Keywords

Adaptive Artificial Intelligence Optimization-Driven Learning Generative AI Robust Machine Learning Intelligent Systems Ethical AI Governance

How to Cite

Ashutosh kumar singh (2025). Optimization-Driven and Generative-Assisted Adaptive Artificial Intelligence Systems: A Detailed Review of Methods, Architectures, and Governance. International Journal of Digital Applications and Contemporary Research (IJDACR), Vol.14, Issue 3. ISSN: 2319-4863.

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Article Info

Journal IJDACR
Volume Vol. 14
Issue No. 3
Month October
Year 2025
ISSN 2319-4863
Access Open Access

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