Abstract: In this contribution a newly developed elearning environment is presented, which incorporates Intelligent Agents and Computational Intelligence Techniques. The new e-learning environment is constituted by three parts, the E-learning platform Front-End, the Student Questioner Reasoning and the Student Model Agent. These parts are distributed geographically in dispersed computer servers, with main focus on the design and development of these subsystems through the use of new and emerging technologies. These parts are interconnected in an interoperable way, using web services for the integration of the subsystems, in order to enhance the user modelling procedure and achieve the goals of the learning process.
Abstract: Nowadays predicting political risk level of country
has become a critical issue for investors who intend to achieve
accurate information concerning stability of the business
environments. Since, most of the times investors are layman and
nonprofessional IT personnel; this paper aims to propose a
framework named GECR in order to help nonexpert persons to
discover political risk stability across time based on the political
news and events.
To achieve this goal, the Bayesian Networks approach was
utilized for 186 political news of Pakistan as sample dataset.
Bayesian Networks as an artificial intelligence approach has been
employed in presented framework, since this is a powerful technique
that can be applied to model uncertain domains. The results showed
that our framework along with Bayesian Networks as decision
support tool, predicted the political risk level with a high degree of
accuracy.
Abstract: There are three approaches to complete Bayesian
Network (BN) model construction: total expert-centred, total datacentred,
and semi data-centred. These three approaches constitute the
basis of the empirical investigation undertaken and reported in this
paper. The objective is to determine, amongst these three
approaches, which is the optimal approach for the construction of a
BN-based model for the performance assessment of students-
laboratory work in a virtual electronic laboratory environment. BN
models were constructed using all three approaches, with respect to
the focus domain, and compared using a set of optimality criteria. In
addition, the impact of the size and source of the training, on the
performance of total data-centred and semi data-centred models was
investigated. The results of the investigation provide additional
insight for BN model constructors and contribute to literature
providing supportive evidence for the conceptual feasibility and
efficiency of structure and parameter learning from data. In addition,
the results highlight other interesting themes.
Abstract: This paper applies Bayesian Networks to support
information extraction from unstructured, ungrammatical, and
incoherent data sources for semantic annotation. A tool has been
developed that combines ontologies, machine learning, and
information extraction and probabilistic reasoning techniques to
support the extraction process. Data acquisition is performed with the
aid of knowledge specified in the form of ontology. Due to the
variable size of information available on different data sources, it is
often the case that the extracted data contains missing values for
certain variables of interest. It is desirable in such situations to
predict the missing values. The methodology, presented in this paper,
first learns a Bayesian network from the training data and then uses it
to predict missing data and to resolve conflicts. Experiments have
been conducted to analyze the performance of the presented
methodology. The results look promising as the methodology
achieves high degree of precision and recall for information
extraction and reasonably good accuracy for predicting missing
values.