Cox regression model serves as a statistical method for analyzing the survival data, which requires some options such as hazard proportionality. In recent decades, artificial neural network model has been increasingly applied to predict survival data. This research was conducted to compare Cox regression and artificial neural network models in prediction of kidney transplant survival. The prese...

This work studies the entity-wise topical behavior from massive network logs. Both the temporal and the spatial relationships of the behavior are explored with the learning architectures combing the recurrent neural network (RNN) and the convolutional neural network (CNN). To make the behavioral data appropriate for the spatial learning in CNN, several reduction steps are taken to form the topi...

in this work, response surface methodology (rsm) and artificial neural network (ann) were used to predict the decolorization efficiency of reactive red 33 (rr 33) by o3/uv process in a bubble column reactor. the effects of four independent variables including time (20-60 min), superficial gas velocity (0.06-0.18 cm/s), initial concentration of dye (50-150 ppm) and ph (3-11) were investigated us...

Solubility data of solid in aqueous and different organic solvents are very important physicochemical properties considered in the design of the industrial processes and the theoretical studies. In this study, experimental solubility data of 666 pharmaceutical compounds in water and 712 pharmaceutical compounds in organic solvents were collected from different sources. Three different artificia...

This paper presents the application of three main Artificial Neural Networks (ANNs) in damage detection of steel bridges. This method has the ability to indicate damage in structural elements due to a localized change of stiffness called damage zone. The changes in structural response is used to identify the states of structural damage. To circumvent the difficulty arising from the non-linear n...

In this paper, a state-of-the-art neuron mathematical model of neural tensor network (NTN) is proposed to RDF knowledge base completion problem. One of the difficulties with the parameter of the network is that representation of its neuron mathematical model is not possible. For this reason, a new representation of this network is suggested that solves this difficulty. In the representation, th...

In this study, colorimetric calibration of scanner has been done via perceptron neural network with three or four layers by back propagation algorithm for colored polyester fabrics. The results obtained for random training samples are not satisfactory but application of selective training samples for L*a*b* or RGB leads to good results, with better results obtained for the L*a*b* method. On the...

This paper presents a fuzzy perceptron as a generic model of multilayer fuzzy neural networks, or neural fuzzy systems, respectively. This model is suggested to ease the comparision of diierent neuro{fuzzy approaches that are known from the literature. A fuzzy perceptron is not a fuzziication of a common neural network architecture, and it is not our intention to enhance neural learning algorit...