Course Recommendation System using the integration of Deep Convoluted Neural Network and Harris Hawks Optimization

In today's society, upskilling is important to secure a good career and advance one's career. Various courses available on online and offline platforms help in the process of upskilling. Most of the students are confused about the selection of suitable courses for their levels. Also, some student regrets their choices in future. In this scenario, the selection of an appropriate course based on the learner's past academic history is an important part. We present a course recommendation system based on deep learning in this paper. Our model (DCNN-CRS) is an integration of Deep Convoluted Neural Network (DCNN) and Harris Hawks Optimization (HHO). We have (i) used datasets from school data repositories; (ii) pre-processed removal of anomalies; (iii) extracted features with HHO; (iv) classified using DCN. Various parameters such as accuracy, specificity, sensitivity, recall, and F1-score were evaluated, and our models were compared to CNN, LSTM, RESNET, and RNN models. It was observed that our model has outperformed well compared to others. We achieved an accuracy of 94.4%.