No abstract

No abstract

No abstract

Context-Aware Recommender System (CARS) suggests more relevant services by adapting them to the user’s specific context situation. Nevertheless, the use of many contextual factors can increase data sparsity while few context parameters fail to introduce the contextual effects in recommendations. Moreover, several CARSs are based on similarity algorithms, such as cosine and Pearson correlation coefficients. These methods are not very effective in the sparse datasets. This paper presents a context-aware model to integrate contextual factors into prediction process when there are insufficient co-rated items. The proposed algorithm uses Latent Dirichlet Allocation (LDA) to learn the latent interests of users from the textual descriptions of items. Then, it integrates both the explicit contextual factors and their degree of importance in the prediction process by introducing a weighting function. Indeed, the PSO algorithm is employed to learn and optimize weights of these features. The results on the Movielens 1 M dataset show that the proposed model can achieve an F-measure of 45.51% with precision as 68.64%. Furthermore, the enhancement in MAE and RMSE can respectively reach 41.63% and 39.69% compared with the state-of-the-art techniques.

Various computer-aided diagnosis systems have been expanded and used for diagnosing glaucoma. Since the optic disc and optic cup are the main parameters for the early detection of glaucoma, this study proposes an accurate CAD system that firstly detects the optic disc and cup then classifies them into normal or abnormal. The U-Net architecture is employed. Despite its excellent segmentation performances, this model repeatedly extracts low-level features, which leads to redundant use of computational sources. To address these issues, a two-stage segmentation of the optic disc and cup was proposed. Firstly, a region of interest (ROI) is extracted from the fundus images by localizing and cutting the optic disc zone. Then, a U-Net model was built in order to obtain the refined segmentation. The public REFUGE dataset is adopted to validate proposed system. After a data augmentation step, an average accuracy of 0.97 and 0.96 for predicted OD cut off area and predicted original images respectively are obtained.