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@article{188553,
        author = {Rahul Singh Rathore},
        title = {Deep Learning Jugaad and Structured Innovation: Constraint Ceativity with Systematic Approach},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {7},
        pages = {1832-1840},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=188553},
        abstract = {Jugaad is an indigenous, frugal, and improvisational approach to innovation that has historically enabled low-cost and resource-efficient solutions in developing economies. Despite its effectiveness, such innovations often remain limited in scale, dependent on individual users, and lack formal structure. The rapid development of artificial intelligence, particularly deep learning, presents an opportunity to enhance Jugaad-driven solutions, making them more scalable, predictive, and sustainable. This conceptual paper examines how deep learning can augment Jugaad by incorporating data-driven accuracy, personalization, automation, and systematic scalability. By synthesizing insights from research on frugal innovation, deep learning technologies, and grassroot innovation practices, the paper proposes a unified framework termed Deep Learning Jugaad. A concise but effective Literature review outlines the findings from scholars on Jugaad, frugal innovation, and AI-supported structured innovation. This integrated approach offers a promising pathway for addressing challenges in healthcare, agriculture, resource management, and other development sectors in underserved regions. The study contributes by presenting a model that bridges grassroots ingenuity with advanced computational intelligence, providing a foundation for sustainable, scalable, and contextually relevant innovations.},
        keywords = {Jugaad, Deep Learning, Frugal Innovation, Structured Innovation, AI for Development, Healthcare Innovation, Scalable Technology},
        month = {December},
        }