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Activity recognition is an important task which can be applied to many real-life problems in pervasive computing. In this work, we propose concurrent activities recognition system based on two layers of inference. The first layer develops a framework for dealing with fusion system through merging different sources of information using evidence theory. The second layer proposes a decision framework...
Nowadays, we are facing the rapidly growing amount of data being produced in many organizations, social networks and internet. These data are generated in disparate locations and their aggregation into one location is exceedingly time and space consuming. Traditional statistical methods are not sufficient for processing of this massive multi-source data. In this paper, we propose a new fuzzy-based...
Ancillary geodata can supply information to enhance classification accuracy for a variety of remote-sensing applications. To understand the integration of different data into a knowledge-based multisource classification process, this paper evaluates the significance of geodata for the classification accuracy of a very high spatial resolution satellite image for the identification of forest types in...
In this work we deal with the problem of web page clustering from the point of view of document representation. Fuzzy ruled-based systems have been successfully used to represent web documents by means of heuristic combinations of criteria. In these systems, rules were established based on the way humans read documents and have been analyzed in previous works. However, membership functions parameters...
Assessing interpretability of fuzzy systems still remains an open and challenging problem. Defining a good index is extremely difficult mainly due to the inherent subjective nature of interpretability. It strongly depends on the background of the person who makes the assessment according to its own knowledge, but also taking into account its previous experience and preferences. Since looking for fuzzy...
This work presents an application of the novel theory of rule based networks for building models of processes characterised by uncertainty, non-linearity, modular structure and internal interactions. The application of the theory is demonstrated for a flotation process in the context of converting a multiple rule based system into an equivalent single rule based system by linguistic composition of...
Classification is an important data mining task in biomedicine. For easy comprehensibility, rules are preferrable to another functions in the analysis of biomedical data. The aim of this work is to use a new fuzzy immune rule-based classification system for biomedical data. The performance of the proposed approach, in terms of classification accuracy and area under the ROC curve, was compared with...
The performances of conventional crisp and fuzzy K-nearest neighbor (K-NN) algorithms trained using finite samples tends to be poor . With ldquoholesrdquo in the training data, it is unlikely that the decision area formed can actually represent the underlying data distribution. There is a need to capture more useful information from the limited training samples, therefore we propose a new fuzzy rule-based...
This paper presents a fuzzy based Bangla vowel sign recognition system. Vowel and consonant are the main criteria to express any language. Exceptionally, Bangla has some vowel sign which are used to express Bangla word. Therefore, to recognize any Bangla word, at first it is necessary to recognize which vowel sign is used to express that word. Due to its low computational requirement and very high...
An evolutionary system for derivation of fuzzy classification rules is presented. This system uses two populations: one of fuzzy classification rules, and one of membership function definitions. A constrained-syntax genetic programming evolves the first population and a mutation-based evolutionary algorithm evolves the second population. These two populations co-evolve to better classify the underlying...
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