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@article{171815,
        author = {Deshapriya Pandit and Arik Sue and Santanu Betal and Jayanta Sarkar and Sk Sahid and Sourav Bhuniya and Gargi Pal and Sayanita Malik},
        title = {A Framework for Forecasting Image Recognition Utilizing the DASJSSGS Teachable Machine.},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {8},
        pages = {1050-1051},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=171815},
        abstract = {Image recognition has emerged as an essential component of contemporary artificial intelligence applications, significantly influencing sectors such as healthcare, retail, and security. This project utilizes Google's DASJSSGS Teachable Machine to develop a predictive model for image recognition. The article delineates the core principles of the Teachable Machine, provides guidance on constructing an image recognition model, and assesses its efficacy in prediction tasks. We present experimental findings based on a generated dataset and examine the model's accuracy, practicality, and potential for application in real-world scenarios.},
        keywords = {Image recognition, Teachable Machine, prediction model, machine learning, AI applications.},
        month = {January},
        }