We describe an approach to acquire qualitative and quantitative knowledge from verbal statements and dialogues in complex, probabilistic domains. This work is part of the development of an intelligent environment, MEDICUS (M odelling, e xplanation, and di agnostic support for c omplex, u ncertain s ubject matters), that supports modelling and diagnostic reasoning in the domains of environmental...

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Modeling and predicting student knowledge is a fundamental task of an intelligent tutoring system. A popular approach for student modeling is Bayesian Knowledge Tracing (BKT). BKT models, however, lack the ability to describe the hierarchy and relationships between the different skills of a learning domain. In this work, we therefore aim at increasing the representational power of the student m...

This paper presents preliminary research aiming to understand the semantics of bird songs. We investigate relationship between context information and bird song output using dynamic Bayesian networks, and show the potential of our approach for semantics inference.

Intelligent computing methods and knowledge based systems are well known techniques used for the detection of various medical disorders. This paper is based on the review of various intelligent computing methods that are used to detect sleep disorders. The main concern is based on the detection of sleep disorders such as sleep apnea, insomnia, parasomnia and snoring. The most common diagnostic ...

In this paper, a novel hybrid method based on learning algorithmof fuzzy neural network and Newton-Cotesmethods with positive coefficient for the solution of linear Fredholm integro-differential equation of the second kindwith fuzzy initial value is presented. Here neural network isconsidered as a part of large field called neural computing orsoft computing. We propose alearning algorithm from ...