An empirical approach that makes use of neuro-fuzzy synergism to evaluate the students in the context of an intelligent tutoring system is presented. In this way, a qualitative model of the student is generated, which is able to evaluate information regarding student's knowledge and cognitive abilities in a domain area. The neuro-fuzzy model has been tested on a prototype tutoring system in the...

this paper studies a new multi-objective fuzzy optimization prob- lem. the objective function of this study has dierent levels. therefore, a suitable optimized solution for this problem would be an optimized solution with preemptive priority. since, the feasible domain is non-convex; the tra- ditional methods cannot be applied. we study this problem and determine some special structures related...

Often, in real-world situations no actual data is available for training neural networks but the domain expert has a good idea of what to expect in terms of input and output parameter values. If the expert can express these relationships in the form of rules, this would provide a resource too valuable to ignore. Fuzzy logic is used to handle the imprecision and vagueness of natural language and...

In a wide variety of physical problems, the complexity of the physics involved is such that it is necessary to develop approximations, because the complete physical model is simply too costly. Sometimes however the complete model is essential to capture all the physics, and often this is only in part of the domain of interest. One can then use heterogeneous domain decomposition techniques: if w...

We present a multistage fuzzy rule-based algorithm for epileptic seizure onset detection. Amplitude, frequency, and entropy-based features were extracted from intracranial electroencephalogram (iEEG) recordings and considered as the inputs for a fuzzy system. These features extracted from multichannel iEEG signals were combined using fuzzy algorithms both in feature domain and in spatial domain...

Ontology is increasingly important in knowledge management and Semantic Web. The problem of it is that the construction of ontology is a time-consuming job and ontology engineers need to spend much time to maintain it. In this paper, we propose an incremental domain ontology learning method. This method can effectively extract new information from new domain documents to update the schema of do...

Developing the expert system (ES) using conventional programming languages is very tedious task. Therefore, it is not surprising that tools have been developed that can support the knowledge engineer. Separate tools now exist to support the knowledge acquisition and to support the implementation. Fuzzy set theory is used to capture imprecision in inputs and outputs of models, and fuzzy expert s...

The Building Information Model (BIM) has become a key tool to achieve communication during the whole building life-cycle. Open standards, such as the Industry Foundation Classes (IFC), have contributed to expand its adoption, but they have limited capabilities for cross-domain information integration and query. To address these challenges, the Linked Building Data initiative promotes the use of...

We propose a fuzzy ontology for human activity representation, which allows us to model and reason about vague, incomplete, and uncertain knowledge. Some relevant subdomains found to be missing in previous proposed ontologies for this domain were modelled as well. The resulting fuzzy OWL 2 ontology is able to model uncertain knowledge and represent temporal relationships between activities usin...

Fuzzy Association Rule Mining Expert-Driven (FARME-D) approach to knowledge acquisition is proposed in this paper as a viable solution to the challenges of rule-based unwieldiness and sharp boundary problem in building a fuzzy rule-based expert system. The fuzzy models were based on domain experts’ opinion about the data description. The proposed approach is committed to modelling of a compact ...