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@article{170397,
        author = {Dr.P.Pandia Vadivu and Dr.K.Sudha and Mr.S. Saravanan},
        title = {Digital Learning and Deep Learning for Neuro-Heuristic Brain Analysis: A Guide for High School Students},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {7},
        pages = {611-614},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=170397},
        abstract = {This article introduces high school students to the rapidly developing field of neuro-heuristic brain analysis, a multidisciplinary approach combining neuroscience and artificial intelligence (AI) to better understand the brain's cognitive processes. With the aid of digital learning tools and deep learning, students can now engage in exploring how the human brain functions, how brain-inspired algorithms improve AI, and the role of neuro-heuristic techniques in various fields. Deep learning models, trained on complex brain data, enable researchers to analyze brain imaging, interpret EEG patterns, and create predictive models that mimic human thought and emotion. Digital learning platforms, including online courses, virtual labs, and coding environments, make these advanced concepts accessible to young learners, fostering an interest in neuroscience and AI. This article outlines the applications of neuro-heuristic brain analysis in medicine, mental health, education, and robotics, providing a pathway for high school students to explore and contribute to this exciting field. Additionally, it discusses challenges in AI’s ability to fully replicate human cognition and the potential future impact of neuro-heuristic research. By merging digital learning with deep learning, students are empowered to take part in unlocking the mysteries of the human brain.},
        keywords = {Neuro-heuristic brain analysis, deep learning, artificial intelligence, neuroscience, digital learning, high school education, brain imaging, EEG analysis, predictive modeling, neural networks, cognitive processes, machine learning, neurotechnology, interdisciplinary education, AI in neuroscience.},
        month = {December},
        }