The segmentation process represents a first step necessary for any automatic method of extracting information from an image. In the case of X-ray images, through segmentation we can differentiate the bone tissue from the rest of the image. There are nowadays several segmentation techniques, but in general, they all require the human intervention in the segmentation process. Consequently, this article proposes a new segmentation method for the X-ray images using a Convolutional Neural Network (CNN). In present, the convolutional networks are the best techniques for image segmentation. This fact is demonstrated by their wide usage in all the fields, including the medical one. As the X-ray images have large dimensions, for reducing the training time, the method proposed by the present article selects only certain areas (maximum interest areas) from the entire image. The neural network is used as pixel classifier thus causing the label of each pixel (bone or none-bone) from a raw pixel values in a square area. We will also present the method through which the network final configuration was chosen and we will make a comparative analysis with other 3 CNN configurations. The network chosen by us obtained the best results for all the evaluation metrics used, i.e. warping error, rand error and pixel error

No abstract

No abstract

Transport systems have an essential role in modern society because they facilitate access to natural resources and they stimulate trade. Current studies aimed at improving transport networks by developing new methods for optimization. Because of the increase in the global number of cars, one of the most common problems facing the transport network is congestion. By creating traffic models and simulate them, we can avoid this problem and find appropriate solutions. In this paper we propose a new method for modeling traffic. This method considers road intersections as being service centers. A service center represents a set consisting of a queue followed by one or multiple servers. This model was used to simulate real situations in an urban traffic area. Based on this simulation, we have successfully determined the optimal functioning and we have computed the performance measures

The Unified Medical Language System (UMLS) 1 offers the possibility to use annotated medical terms for Computer Aided Diagnoses System (CADS). We present a new semantic fusion system, based on UMLS. This fusion system has applications on a CADS that diagnoses neurodegenerative diseases. Since the UMLS Metathesaurus contains a huge amount of data, classification and extraction of the data we use is necessary. For this purpose, we use a feedforward neural network which is capable of training the negative patterns as well as the positive ones. At the semantic level we generate a three-layered network structure, which gives us the possibility of adding medical knowledge in order to cluster the data and prepare it for the fusion process.