In a teaching and learning environment Bayesian network fits well because it can adjust its structure as per data presented to it. When a Bayesian network learns with a huge number of data, its belief value is updated even if the change in belief is not significant. This causes a problem when the user’s preference changes over time. The learning process cannot catch up rapidly enough to handle ...

We present a novel formalism for introducing deep belief features to Hierarchical Machine Translation Model. The deep features are generated by unsupervised training of a deep belief network built with stacked sets of Restricted Boltzmann Machines. We show that our new deep feature based hierarchical model is better than the baseline hierarchical model with gains for two different languages pai...

This paper investigates lexical stress detection for L2 English speech using Deep Belief Networks (DBNs). The features of the DBN used in this work include the syllable-based prosodic features (assumed to have Gaussian distribution) and their expected lexical stress (assumed to have Bernoulli distribution). As stressed syllables are more prominent than their neighbors, the two preceding and two...

Introduction: This research was meant to provide a model for COPD diagnosis and to classify the cases into phenotypes; General COPD, Chronic bronchitis, Emphysema, and the Asthmatic COPD using a Bayesian Network (BN). Methods: The model was constructed through developing the Bayesian Network structure and instantiating the parameters for each of the variables. In order to validate the achiev...

We took an innovative approach to service level management for network enterprise systems by using integrated monitoring, diagnostics, and adaptation services in a service-oriented architecture. The autonomous diagnosis for trouble-shooting of web service interruptions is based on Bayesian network models. In this paper, we present our methods for building the diagnostic models. We focus on two ...

The basic frameworks and practical schemes of the Bayesian network and the belief propagation to the probabilistic image processing are reviewed. The probabilistic image processing is formulated by means of Bayesian statistics and Markov random fields. The system is regarded as one of Bayesian networks. In general, the Bayesian network has serious computational complexity because the probabilis...

Hidden Markov Models (HMM) are currently widely used in Automatic Speech Recognition (ASR) as being the most effective models. In addition, the HMM are just a special case of graphical models which are dynamic Bayesian Networks (DBN). These are modeling tools more sophisticated because they allow to include several specific variables in the problem of automatic speech recognition other than the...