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...

Even when neural networks are widely used in a large number of applications, they still considered as black boxes and present some difficulties for dimensioning or evaluating their prediction error. This has led to an increasing interest the overlapping area between more traditional statistical methods, which can help overcome those problems. In this article, mathematical framework relating pol...

Churn prediction is an important factor to consider for Customer Relationship Management (CRM). In this study, statistical and data mining techniques were used for churn prediction. We use linear (logistic regression) and non-linear techniques of Random Forest and Deep Learning architectures including Deep Neural Network, Deep Belief Networks and Recurrent Neural Networks for prediction. This i...

We introduce twin neural network (TNN) regression. This method predicts differences between the target values of two different data points rather than targets themselves. The solution a traditional regression problem is then obtained by averaging over an ensemble all predicted unseen point and training points. Whereas ensembles are normally costly to produce, TNN intrinsically creates predictio...

It is a well known fact that at the beginning of any project, the software industry needs to know, how much will it cost to develop and what would be the time required ? . This paper examines the potential of using a neural network model for estimating the lines of code, once the functional requirements are known. Using the International Software Benchmarking Standards Group (ISBSG) Repository ...

Modelling and forecasting Stock market is a challenging task for economists and engineers since it has a dynamic structure and nonlinear characteristic. This nonlinearity affects the efficiency of the price characteristics. Using an Artificial Neural Network (ANN) is a proper way to model this nonlinearity and it has been used successfully in one-step-ahead and multi-step-ahead prediction of di...

Air Traffic Management (ATM) system is enticing targets of cyber-attacks since 9.11 event, and the security situation of ATM information system is closely related to the safety flight of air transportation. In this paper, an approach of security evaluation for ATM system is proposed based on artificial neural network (ANN). The proposed approach combines neural networks with Bayesian regulariza...

One of the most important processes in the early stages of construction projects is to estimate the cost involved. This process involves a wide range of uncertainties, which make it a challenging task. Because of unknown issues, using the experience of the experts or looking for similar cases are the conventional methods to deal with cost estimation. The current study presents data-driven metho...