Authors :
Pranshu Jain; Riya Dubey; Pankhuri Deshmukh; Manas Tiwari; Dr. Mehjabin Khatoon
Volume/Issue :
Volume 8 - 2023, Issue 12 - December
Google Scholar :
http://tinyurl.com/2crt8epk
Scribd :
http://tinyurl.com/yc83694w
DOI :
https://doi.org/10.5281/zenodo.10443888
Abstract :
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.
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.
