Topic modeling is an unsupervised learning task that discovers the hidden topics in a collection of documents. In turn, discovered can be used for summarizing, organizing, and understanding documents collection. Most existing techniques topic are derivatives Latent Dirichlet Allocation which uses bag-of-word assumption However, bag-of-words models completely dismiss relationships between words....

In this work, we adhere to explore a Multi-Tasking learning (MTL) based network perform document attribute classification such as the font type, size, emphasis and scanning resolution of image. To accomplish these tasks, operate on either segmented word level or uniformed size patches randomly cropped out document. Furthermore, hybrid convolution neural (CNN) architecture ”MTL+MI”, which is com...

The article is devoted to the consideration of various grounds for classifying crimes related forgery documents, based on content norms current criminal legislation relevant crimes. In view absence a systematized conceptual apparatus, numerous legal terms and constructions describing documents are analyzed. analysis law allows us conclude that there common object encroachment under consideratio...

sar (synthetic aperture radar) image enhancement and segmentation is purpose of this thesis. sar image segmentation is a primary step before steps such as classification and target recognition. the main obstacle in sar image segmentation is inherent speckle noise. speckle noise is a multiplicative and highly destructive noise which results to intensity inhomogeneity. hence common segmentation m...