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@article{171709,
        author = {Sushavan Chatterjee and Ferdousi Pervin and Firdous SK and Anjana Das and Tanbir Ahammad Sekh},
        title = {SFFAT Teachable Machine Model for Predictive Image Recognition},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {8},
        pages = {818-819},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=171709},
        abstract = {Image recognition has become an essential part of modern AI applications because of its impact on industries including healthcare, retail, and security. In this study, Google's SFFAT Teachable Machine is used to create a prediction model for image recognition. The article explains the fundamental concepts of the Teachable Machine, demonstrates how to construct a model for image recognition, and evaluates the machine's performance in prediction tasks. We show experimental results on a generated dataset and analyze the model's accuracy, usefulness, and potential for real-world applications.},
        keywords = {Image recognition, Teachable Machine, prediction model, machine learning, AI applications.},
        month = {January},
        }