Two Researchers from the College of Information Technology Publish a Study on Machine Learning Approaches for Movie Classification and Age Appropriateness Prediction

Duhaa Fadill Abbas
As part of the College of Information Technology’s commitment to advancing specialized research and keeping pace with global developments in artificial intelligence and data analytics, PhD candidate Yaseen Khudair Abbas from the Software Engineering Department, in collaboration with Dr. Ahmed Habib Al-Azzawi from the same department, published a scientific study titled “A Systematic Review of Machine Learning Approaches for Movie Genre Classification and Age Appropriateness Prediction” in the French journal Ingenierie des Systemes d’Information, indexed in Scopus Q3 and published by Lavoisier.

The study provides a systematic review of 78 prior studies following the PRISMA methodology, examining the use of machine learning techniques for classifying movie genres and predicting their age suitability. The results indicate that textual, visual, audio, and hybrid data are the primary sources utilized, with Support Vector Machines (SVM) being the most commonly applied algorithm, while the MovieLens database was the most frequently employed for model training.

The research highlights key challenges in the field, including inconsistencies in databases, cultural variations in age classification standards, and the need for greater reliance on multimodal approaches. It also offers future recommendations to support the development of more accurate and reliable systems for movie classification and content evaluation.

Research Link :
https://iieta.org/journals/isi/paper/10.18280/isi.300914