In this paper, the comparison between deep learning methods and feature extraction algorithms is presented. The principle of Grey-Level Co-occurrence Matrix (GLCM) and its modifications are used for our research. The main idea was to design a method for the description of combined features and textures. The texture classification process is carried out with the robust support vector machine classifier (SVM). We compare these feature extraction methods with proposed Convolutional Neural Networks (CNN). This proposed network contains 25 layers. Finally, the all evaluation and comparison of color texture retrieval results for all used methods are presented. The all feature extraction algorithms and proposed CNN have been tested on two different color texture datasets (Outex and Vistex datasets).

This paper discusses the tool for the main text and image extraction (extracting and parsing the important data) from a web document. This paper describes our proposed algorithm based on the Document Object Model (DOM) and natural language processing (NLP) techniques and other approaches for extracting information from web pages using various classification techniques such as support vector machine, decision tree techniques, naive Bayes, and K-nearest neighbor. The main aim of the developed algorithm was to identify and extract the main block of a web document that contains the text of the article and the relevant images. The algorithm on a sample of 45 web documents of different types was applied. In addition, the issue of web pages, from the structure of the document to the use of the Document Object Model (DOM) for their processing, was analyzed. The Document Object Model was used to load and navigation of the document. It also plays an important role in the correct identification of the main block of web documents. The paper also discusses the levels of natural language. These methods of automatic natural language processing help to identify the main block of the web document. In this way, the all-textual parts and images from the main content of the web document were extracted. The experimental results show that our method achieved a final classification accuracy of 88.18%.

Intracranial aneurysm (IA) is now a common term closely associated with subarachnoid hemorrhage. IA is the bulging of a blood vessel caused by a weakening of its wall. This bulge can rupture and, in most cases, cause internal bleeding. In most cases, internal bleeding leads to death or other fatal consequences. Therefore, the development of an automated system for detecting IA is needed to help physicians make more accurate diagnoses. For this reason, we have focused on this problem. In this paper, we propose a 2D Convolutional Neural Network (CNN) based on a network commonly used for data classification in medicine. In addition to our proposed network, we also tested ResNet 50, ResNet 101 and ResNet 152 on a publicly available dataset. In this case, ResNet 152 achieved better results than our proposed network, but our network was significantly smaller and the classifications took significantly less time. Our proposed network achieved an overall accuracy of 98%. This result was achieved on a dataset consisting of 611 images. In addition to the mentioned networks, we also experimented with the VGG network, but it was not suitable for this type of data and achieved only 20%. We compare the results in this work with neural networks that have been verified by the scientific community, and we believe that the results obtained by us can help in the creation of an automated system for the detection of IA.

This paper discusses the tool that serve to optimize web pages. The internet has become part of our life. People use different devices to browse web pages, and not everyone is adapted to the device. In this article, we design and implement a tool that allows people to optimize any page in their device. The tool contains an algorithm for identifying the main content of a web page and then extracting text and images from it. The goal of the algorithm is to remove redundant information from the webpage and create a new view that contains only useful information. The document created in this way can be further used in web content viewing applications with minimized data transfer or as direct printing of the main web page content without unnecessary data.

In this paper, the innovative 3D reconstruction method of human skull, based on the patch based technique using Convolutional Neural Network (CNN) is proposed. Firstly, the image filtering to reduce the influence from noise to mode detection was applied. Next, the filtered image was split into segments using proposed CNN (accurate image segmentation). The proposed CNN for image segmentation consists of several layers. Each layer occupies a multi-dimensional array of numbers. The proposed network composed of layers that transform an input image from the original pixel values to the final layer score (layer by layer). The small segments were merged together to the most similar adjacent segments. Finally, the patch based technique (patch extraction) for 3D reconstruction of the skull was used. The experimental results using biomedical data (CT data) indicate the effectiveness of the proposed reconstruction approach.

Assessment of Partially Conductive Cracks from Eddy Current Non-Destructive Testing Signals using Support Vector Machine