The main objective of this research work is to construct a Fuzzy Temporal Rule Based Classifier that uses fuzzy rough set and temporal logic in order to mine temporal patterns in medical databases. The lower approximation concepts and fuzzy decision table with the fuzzy features are used to obtain fuzzy decision classes for building the classifier. The goals are pre-processing for feature selec...

Fuzzy classification has become of great interest because of its ability to utilize simple linguistically interpretable rules and has overcome the limitations of symbolic or crisp rule based classifiers. This paper introduces an extension to fuzzy classifier: a neutrosophic classifier, which would utilize neutrosophic logic for its working. Neutrosophic logic is a generalized logic that is capa...

the performance of many traffic control strategies depends on how much the traffic flow models are accurately calibrated. one of the most applicable traffic flow model in traffic control and management is lwr or metanet model. practically, key parameters in lwr model, including free flow speed and critical density, are parameterized using flow and speed measurements gathered by inductive loop d...

Cluster analysis is a tool for data analysis. It is a method for finding clusters of a data set with most similarity in the same group and most dissimilarity between different groups. In general, there are two ways, mixture distributions and classification maximum likelihood method, to use probability models for cluster analysis. However, the corresponding probability distributions to most clus...

Bayesian Classifiers Learn joint distribution P(C,F) Assign to f the most probable class label argmaxc′∈C P(c′, f̃) This defines a classifier, i.e., a map: (F1× . . .×Fm)→ C Credal Classifiers Learn joint credal set P(C,F) Set of optimal classes (e.g., according to maximality ) {c′ ∈ C |@c′′ ∈ C ,∀P ∈ P : P(c′′|f̃) > P(c′|f̃)} This defines a credal classifier, i.e., (F1× . . .×Fm)→ 2 May return mo...

potato image segmentation is an important part of image-based potato defect detection. this paper presents a robust potato color image segmentation through a combination of a fuzzy rule based system, an image thresholding based on genetic algorithm (ga) optimization and morphological operators. the proposed potato color image segmentation is robust against variation of background, distance and ...

Over the last few years, support vector machines (SVMs) have shown a great potential as classifiers for remotely sensed data. Generally, these have been used to perform conventional hard classification where each pixel is allocated to only one class. Remote sensing images, particularly at coarse spatial resolutions, are contaminated with mixed pixels that contain more than one class on the grou...

The existence of uncertainty in classified remotely sensed data necessitates the application of enhanced techniques for identifying and visualizing the various degrees of uncertainty. This paper, therefore, applies the multidimensional graphical data analysis technique of parallel coordinate plots (PCP) to visualize the uncertainty in Landsat Thematic Mapper (TM) data classified by the Maximum ...

The aim of this study was to classify Envisat MERIS and Landsat ETM satellite sensor imagery using fuzzy classification techniques such as, linear mixture modelling and artificial neural networks. The images were classified successfully using these two techniques. The fuzzy results were more accurate then hard classification. Landsat ETM imagery was classified using maximum likelihood classifie...