Abstract The well-known fact of metallurgy is that the lifetime a metal structure depends on material's corrosion rate. Therefore, applying an appropriate prediction process for manufactured metals or alloys trigger extended life product. At present, current models additive are either complicated built restricted basis towards depletion. This paper presents novel approach to estimate rate and p...

Accuracy and interpretability are two dominant features of successful predictive models. Typically, a choice must be made in favor of complex black box models such as recurrent neural networks (RNN) for accuracy versus less accurate but more interpretable traditional models such as logistic regression. This tradeoff poses challenges in medicine where both accuracy and interpretability are impor...

Modeling with exible models, such as neural networks, requires careful control of the model complexity and generalization ability of the resulting model which nds expression in the ubiquitous bias-variance dilemma [4]. Regularization is a tool for optimizing the model structure reducing variance at the expense of introducing extra bias. The overall objective of adaptive regularization is to tun...

Accuracy and interpretation are two goals of any successful predictive models. Most existing works have to suffer the tradeoff between the two by either picking complex black box models such as recurrent neural networks (RNN) or relying on less accurate traditional models with better interpretation such as logistic regression. To address this dilemma, we present REverse Time AttentIoN model (RE...

The performance of trauma departments is widely audited by applying predictive models that assess probability of survival, and examining the rate of unexpected survivals and deaths. Although the TRISS methodology, a logistic regression modelling technique, is still the de facto standard, it is known that neural network models perform better. A key issue when applying neural network models is th...

natural fire inflicting irreparable damage to rangelands and forest areas is cause changes in landscape ecology. the purpose of this research is comparison of artificial neural network (ann) and line regression (lr) models to predict of forest and rangelands fires to this end, the data consist fire burned area and fire were used weather data over a period of 7 years (2006-2012(.the result indic...

We present a new formulation for binary classification. Instead of relying on convex losses and regularizers such as in SVMs, logistic regression and boosting, or instead non-convex but continuous formulations such as those encountered in neural networks and deep belief networks, our framework entails a non-convex but discrete formulation, where estimation amounts to finding a MAP configuration...

Synonyms Bayesian neural data analysis, Bayesian modelling of neural recordings

In the last decade, high profile financial frauds committed by large companies in both developed and developing countries were discovered and reported. This study compares the performance of five popular statistical and machine learning models in detecting financial statement fraud. The research objects are companies which experienced both fraudulent and non-fraudulent financial statements betw...

In recent years, neural network models have been widely used in the Civil Engineering field. Interesting enhancements may be obtained by re-examining this model from the Bayesian probability logic viewpoint. Using this approach, it will be shown that the conventional regularized learning approach can be derived as a particular approximation of the Bayesian framework. Network training is only a ...