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@article{179539,
        author = {Jobeda Khatun and Sahanaj Parvin and Abantika Ghosh and Aritra Ganguly and Nilkanto Das},
        title = {A Framework for Predicting Image Recognition using the JSAAN Teachable Machine.},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {6938-6939},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=179539},
        abstract = {Image identification is a key component of 
today's artificial intelligence applications, which 
significantly affect sectors including healthcare, retail, 
and security. In this study, Google's JSAAN Teachable 
Machine is used to create a predictive model for image 
recognition. The article provides instructions for 
creating a model for photo identification, outlines the 
core concepts of the Teachable Machine, and assesses 
the model's performance on prediction tests. We show 
experimental results based on a generated dataset and 
evaluate the model's accuracy, utility, and real-world 
applicability.},
        keywords = {Applications of artificial intelligence,  machine learning, teachable machines, image  recognition, and prediction models},
        month = {May},
        }