We introduce a class of nonstationary covariance functions for Gaussian process (GP) regression. Nonstationary covariance functions allow the model to adapt to functions whose smoothness varies with the inputs. The class includes a nonstationary version of the Matérn stationary covariance, in which the differentiability of the regression function is controlled by a parameter, freeing one from f...

Over the past decade, the Cyberspace has seen an increasing number of attacks coming from botnets using the Peer-to-Peer (P2P) architecture. Peer-to-Peer botnets use a decentralized Command & Control architecture. Moreover, a large number of such botnets already exist, and newer versionswhich significantly differ from their parent botare also discovered practically every year. In this work, the...

infiltration rate is one of the most important soil physical parameters and is a basic input data in irrigation and drainage projects. although, a number of theoretical or experimental based equations are presented to describe this phenomenon but the evaluation of some new sciences such as artificial neural networks, for prediction of the phenomenon can be investigated. generally, the infiltrat...

Bitcoin as the current leader in cryptocurrencies is a new asset class receiving significant attention in the financial and investment community and presents an interesting time series prediction problem. In this paper, some forecasting models based on classical like ARIMA and machine learning approaches including Kriging, Artificial Neural Network (ANN), Bayesian method, Support Vector Machine...

this study investigates the correlations among the tensile strength, mineral composition, and textural features of twenty-ninesandstones from kouzestan province. the regression analyses as well as artificial neural network (ann) are also applied to evaluatethe correlations. the results of simple regression analyses show no correlation between mineralogical features and tensile strength.however,...

background : the aim of this study was to predict the survival rate of iranian gastric cancer patients using the cox proportional hazard and artificial neural network models as well as comparing the ability of these approaches in predicting the survival of these patients. methods: in this historical cohort study, the data gathered from 436 registered gastric cancer patients who have had surgery...

The aim of this paper is to present the development and validation of a neural network model for on-line prediction of coagulant dosage from raw water characteristics. The main parameters influencing the coagulant dosage are firstly determined via a PCA. A brief description of the methodology used for the synthesis of neural models is given and experimental results are included. The training of...

A better approach for training a multi-layered feedforward network for pulse compression is presented. The Bayesian regularization technique used for training the network for pulse radar detection results in superior performance in terms of signal-to-sidelobe ratio compared to the Backpropagation algorithm. The presented method also has better range resolution performance in terms of resistance...

Impairments in gait occur after alcohol consumption, and, if detected in real-time, could guide the delivery of "just-in-time" injury prevention interventions. We aimed to identify the salient features of gait that could be used for estimating blood alcohol content (BAC) level in a typical drinking environment. We recruited 10 young adults with a history of heavy drinking to test our research a...

We develop an algorithm for systematic design of a large artificial neural network using a progression property. We find that some non-linear functions, such as the rectifier linear unit and its derivatives, hold the property. The systematic design addresses the choice of network size and regularization of parameters. The number of nodes and layers in network increases in progression with the o...