Biomedical datasets usually include a large number of features relative to the number of samples. However, some data dimensions may be less relevant or even irrelevant to the output class. Selection of an optimal subset of features is critical, not only to reduce the processing cost but also to improve the classification results. To this end, this paper presents a hybrid method of filter and wr...

image denoising by block matching and threedimensionaltransform filtering (bm3d) is a two steps state-ofthe-art algorithm that uses the redundancy of similar blocks innoisy image for removing noise. similar blocks which can havesome overlap are found by a block matching method and groupedto make 3-d blocks for 3-d transform filtering. in this paper wepropose a new block grouping algorithm in th...

these days, due to growing the e-commerce sites, access to information about items is easier than past. but because of huge amount of information, we need new filtering techniques to find interested information faster and more accurate. therefore recommender systems (rs) introduced for solving this problem. although several recommender approaches have proposed, collaborative filtering (cf) appr...

Memory-based collaborative filtering is the most popular approach to build recommender systems. Despite its success in many applications, it still suffers from several major limitations, including data sparsity. Sparse data affect the quality of the user similarity measurement and consequently the quality of the recommender system. In this paper, we propose a novel user similarity measure based...

Image denoising by block matching and threedimensionaltransform filtering (BM3D) is a two steps state-ofthe-art algorithm that uses the redundancy of similar blocks innoisy image for removing noise. Similar blocks which can havesome overlap are found by a block matching method and groupedto make 3-D blocks for 3-D transform filtering. In this paper wepropose a new block grouping algorithm in th...