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@article{165353,
        author = {Mote Ashwini Gopinath},
        title = {A Review of XAI Models and Applications: Recent Developments and Future Trends},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {11},
        number = {1},
        pages = {849-853},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=165353},
        abstract = {Explainable Artificial Intelligence (XAI) has become increasingly indispensable as AI systems permeate diverse sectors, necessitating transparency and interpretability for user trust and regulatory compliance. This systematic review paper comprehensively examines recent developments and future trends in XAI models and applications. Artificial Intelligence (AI) uses systems and machines to simulate human intelligence and solve common real-world problems. Machine learning and deep learning are Artificial intelligence technologies that use algorithms to predict outcomes more accurately without relying on human intervention. However, the opaque black box model and cumulative model complexity can be used to achieve. Explainable Artificial Intelligence (XAI) is a term that refers to Artificial Intelligence (AI) that can provide explanations for their decision or predictions to human users. XAI aims to increase the transparency, trustworthiness and accountability of AI system, especially when they are used for high-stakes application such as healthcare, finance or security. This paper offers systematic literature review of XAI approaches with different application },
        keywords = {Machine Learning, Deep Learning, Explanation, Explainable AI, Healthcare

},
        month = {},
        }