production of highly viscous tar sand bitumen using steam assisted gravity drainage (sagd) with a pair of horizontal wells has advantages over conventional steam flooding. this paper explores the use of artificial neural networks (anns) as an alternative to the traditional sagd simulation approach. feed forward, multi-layered neural network meta-models are trained through the back-error-propaga...

AMPLIA is a multi-agent intelligent learning environment designed to support training of diagnostic reasoning and modelling of domains with complex and uncertain knowledge. AMPLIA focuses on the medical area. It is a system that deals with uncertainty under the Bayesian network approach, where learner-modelling tasks will consist of creating a Bayesian network for a problem the system will pres...

water quality assessment provides a scientific basis for water resources development and management. this case study proposes a factor analysis- hopfield neural network model (fhnn) based on factor analysis method and hopfield neural network method. the results showed that the factor analysis (fa) technique was introduced to identify important water quality parameters. results revealed that bio...

In this paper we propose a method for learning Bayesian belief networks from data. The method uses artificial neural networks as probability estimators, thus avoiding the need for making prior assumptions on the nature of the probability distributions governing the relationships among the participating variables. This new method has the potential for being applied to domains containing both dis...

Over the last decade, Bayesian Networks (BNs) have become a popular tool for modelling many kinds of statistical problems. In this chapter we will discuss the properties of the modelling framework that make BNs particularly well suited for reliability applications. This discussion is closely linked to the analysis of a real-world example.

In this paper, we propose a system that extracts the downbeat times from a beat-synchronous audio feature stream of a music piece. Two recurrent neural networks are used as a front-end: the first one models rhythmic content on multiple frequency bands, while the second one models the harmonic content of the signal. The output activations are then combined and fed into a dynamic Bayesian network...

changes in the physicochemical conditions of process unit, even under control, may lead to what are generically referred to as faults. the cognition of causes is very important, because the system can be diagnosed and fault tolerated. in this article, we discuss and propose an artificial neural network that can detect the incipient and gradual faults either individually or mutually. the main fe...

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...

Aiming at solving the difficulties of interaction synergism and insecurity of the multi-agent system, a trusted evaluation access control model based on dynamic Bayesian network is proposed in this paper. The model establishes trust domains according to the interaction records of agent itself and the history. The influences of time and interactive behavior on trust evaluation are also considere...