Artificial neural network has been put into abundant applications in social science research recently. In this study, we investigate the topological structures of organization network, which can possibly account for the different performances of intra-organizational knowledge transfer. We construct two types of networks including hierarchy and scale-free networks, and single-layer perceptron mo...

The purpose of this paper is to demonstrate a new benchmark for comparing the rate of convergence in neural network classification algorithms. The benchmark produces datasets with controllable complexity that can be used to test an algorithm. The dataset generator uses the concept of random numbers and linear normalization to generate the data. In a case of a one-layer perceptron, the output da...

The design, implementation and operation of a low power multilayer perceptron chip (Kakadu) in the framework of a cardiac arrhythmia classiication system is presented in this paper. This system, called MATIC, makes timing decisions using a decision tree, and a neural network is used to identify heartbeats with abnormal morphologies. This classiier was designed to be suitable for use in implanta...

Introduction: Artificial neural networks are a type of systems that use very complex technologies and non-algorithmic solutions for problem solving. These characteristics make them suitable for various medical applications. This study set out to investigate the application of artificial neural networks for differential diagnosis of thalassemia minor and iron-deficiency anemia. Methods: It is...

Abstract Erosion and sedimentation are the most complicated problems in hydrodynamic which are very important in water-related projects of arid and semi-arid basins. For this reason, the presence of suitable methods for good estimation of suspended sediment load of rivers is very valuable. Solving hydrodynamic equations related to these phenomenons and access to a mathematical-conceptual mode...

The liver is one of the most significant organs in human body. We can predict disease a patient at an early stage based on previously predicted values using data from patients with abnormal function. Which helps doctors to make diagnosis. In this paper, function test analyzed for predicting disease, where input patient’s details and output are passed into various classifiers such as Support Vec...

In this paper we propose a novel model for unconditional audio generation based on generating one audio sample at a time. We show that our model, which profits from combining memory-less modules, namely autoregressive multilayer perceptrons, and stateful recurrent neural networks in a hierarchical structure is able to capture underlying sources of variations in the temporal sequences over very ...

This paper describes the dynamic control of a 3 degree of freedom (DOF) finger emulating a human finger for reaching a desired fingertip position in space. The control consists of a neural network (NN) which provides the necessary three torques to the phalanges granting a smooth “natural” speed profile of the fingertip motion. To obtain the results, we face these problems: 1) the elimination of...