Recurrent neural networks (RNNs) are typically considered as relatively simple architectures, which come along with complicated learning algorithms. This paper has a different view: We start from the fact that RNNs can model any high dimensional, nonlinear dynamical system. Rather than focusing on learning algorithms, we concentrate on the design of network architectures. Unfolding in time is a...

the aim of this study is quantity determining and evaluating of the quality position of teaching and learningprocess. so, the artificial neural networks were used for modeling nonlinear relationships to inspect and evaluate different models of service quality evaluation. the statistical population include 1070 peoples, which were master and phd students of faculty of agriculture of ferdowsiuniv...

A typical growth of cells inside tissue is normally known as a nodular entity. Lung nodule segmentation from computed tomography (CT) images becomes crucial for early lung cancer diagnosis. An issue that pertains to the nodules homogenous modular variants. The resemblance among well neighboring regions very challenging deal with. Here, we propose an end-to-end U-Net-based framework named DA-Net...

Many studies on the semantic segmentation of cracks using machine learning (ML) technique can be found in relevant literature. To date, results obtained are quite good, but often accuracy trained model and evaluated traditional metrics only, most cases, goal is to detect only occurrence cracks. Particular attention should paid thickness segmented crack since, road pavement maintenance, width ma...

Iris segmentation is an important step in the process of iris recognition. images collected under non-cooperative conditions always contain various noise, which a challenge for segmentation. Most U-Net-based methods have made great achievements However, this architecture lacks focusing on target structures varying shapes, and robustness segmenting objects with significant shape variations. In p...

Region of interest (ROI) segmentation is a key step in computer-aided diagnosis (CAD). With the problems blurred tissue edges and imprecise boundaries ROI medical images, it hard to extract satisfactory ROIs from images. In order overcome shortcomings V-Net model or level set method (LSM), we propose this paper new image method, VLSM-Net model, combining these two methods. Specifically, first u...

This paper provides an overview of the different deep convolutional neural network (DCNNs) architectures that have been investigated in past years for generation synthetic computed tomography (CT) or pseudo-CT from magnetic resonance (MR). The U-net, Atrous-net and Residual-net were analyzed, implemented compared. Each was using 2D filters 3D with slices patches respectively as inputs. Two data...

Software architecture is one of the most fundamental products in the process of software development in the areas of behavioral or non- behavioral features like availability or transformability change. There are different ways to evaluate software architecture one of which is the creation of application model. An executable model of software architecture is an official description of architectu...