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@article{192747,
        author = {Govind Mulchandani},
        title = {A Learning-Oriented AI Framework for Identifying Basic Galaxy Morphologies from Small-Telescope Images},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {9},
        pages = {3202-3206},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=192747},
        abstract = {Understanding galaxy morphology is a critical part of extragalactic astronomy. However, most students study galaxies only through professional images rather than the data they collect themselves by doing research. This research paper presents a learning-based framework that uses artificial intelligence to help students identify basic galaxy types from images captured with small, low-cost telescopes or from publicly available sky data. This system uses broad categories such as spiral, elliptical, and irregular galaxies and provides simple explanations rather than high-precision scientific labels. The goal isn’t automated discovery, but guided learning that helps students connect real data with galactic structure concepts. This paper describes the system design, classification approach, educational focus, and limitations. The research indicates that simplified AI-assisted analysis can facilitate an early understanding of galaxy morphology without supplanting human interpretation},
        keywords = {},
        month = {February},
        }