Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
This project explores six methodologies for enhancing course recommendation systems, focusing on personalized learning and academic success. It improves collaborative filtering by analyzing implicit user behavior and employs deep learning techniques like CNNs for personalized recommendations. Predictive modeling methods, including k-nearest neighbors and matrix factorization, help forecast student performance. A skill-based system uses fuzzy clustering to align courses with career goals, while a hybrid approach integrates clustering and association rule mining to generate recommendations based on course history and grades. By combining these techniques, the system aims to optimize accuracy and provide tailored course suggestions.
Keywords:
Collaborative filtering, deep learning, fuzzy clustering, matrix factorization, user implicit behavior analysis, sequential pattern mining, academic performance prediction.
Cite Article:
"Ai Powered Course Selector", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 3, page no.b409-b413, March-2025, Available :http://www.ijrti.org/papers/IJRTI2503159.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator