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@article{195962,
        author = {RIDDHI S and Dr M.RAJU and SRIJA P},
        title = {DATA-CENTRIC AI: IMPROVING MODEL ACCURACY WITHOUT CHANGING ARCHITECTURE},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {2011-2017},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=195962},
        abstract = {Recent advances in Artificial Intelligence (AI) have been largely driven by increasingly complex model architectures and large-scale computation. While this model-centric paradigm has yielded remarkable success, it also faces diminishing returns, high energy consumption, and limited accessibility. Data-centric AI has emerged as a promising alternative, emphasising the systematic improvement of data quality rather than architectural modifications. This paper investigates how model accuracy can be significantly improved without changing the underlying architecture by focusing on data quality dimensions such as labelling accuracy, class balance, noise reduction, data augmentation, and dataset consistency. A structured data-centric workflow is proposed and evaluated using fixed deep learning architectures on standard benchmark datasets. Experimental results demonstrate measurable improvements in accuracy, robustness, and generalisation, confirming that high-quality data is a critical driver of AI performance. The study highlights the practicality of data-centric AI for academic research and industry deployment, particularly in resource-constrained environments.},
        keywords = {Data-centric AI, Data Quality, Model Accuracy, Machine Learning, Dataset Engineering},
        month = {April},
        }