Enhancing Academic Resource Evaluation in Computer Science and Engineering through Automated Assessment

Navigating the vast amounts of digital academic content on the Internet poses a formidable challenge. Addressing this, we have formulated an academic content evaluator that leverages machine learning algorithms - Decision Tree, SVM, Random Forest and RNN. This machine-learning approach is fueled by citation rates, authorship details, and content analysis. This paper explores the model’s transformative potential, delving into its features, algorithms, and the evolving landscape of academic content assessment.

Keywords:- Academic, Computer Science, Content Evaluation, Resource Evaluation, Quality Assessment.