The accurate interpretation of Blood Glucose (BG) values is essential for diabetes care. However, BG monitoring data does not provide complete information about associated meal and moment of measurement, unless patients fulfil it manually. An automatic classification of incomplete BG data helps to a more accurate interpretation, contributing to Knowledge Management (KM) tools that support decis...

In this study, a fuzzy-based multisensor data fusion classifier is developed and applied to land cover classification using ERS-1/JERS-1 SAR composites. This classifier aims at the integration of multisensor and contextual information in a single and a homogeneous framework. Initial Fuzzy Membership Maps (FMM) to different thematic classes are first calculated using classes and sensors a priori...

Classification technique is important tool in data mining for the classification and grouping of data. The techniques of data mining offer various types of classification technique such as binary classifier, statistical classifier; neural network based classifier and also gives rule based classifier. The applicability of classifier depends on the processing of data domain. Now a day’s rule base...

In fuzzy rule-based classification systems (FRBCSs), rule weighting has often been used as a simple mechanism to tune the classifier. In past research, a number of heuristic rule weight specification methods have been proposed for this purpose. A learning algorithm based on reward and punishment has also been proposed to adjust the weights of each fuzzy rule in the rule-base. In this paper, a n...

In this study, we introduce a method for defect classification using relational association rule mining based on fuzzy classifier along with modified artificial bee colony algorithm. Relational association rules are an extension of ordinal association rules, which are a particular type of association rules that describe numerical orderings between attributes that commonly occur in the data. The...

fuzzy newton-cotes method for integration of fuzzy functions that was proposed by ahmady in [1]. in this paper we construct error estimate of fuzzy newton-cotes method such as fuzzy trapezoidal rule and fuzzy simpson rule by using taylor's series. the corresponding error terms are proven by two theorems. we prove that the fuzzy trapezoidal rule is accurate for fuzzy polynomial of degree one and...

Evolutionary Optimization is becoming omnipresent technique in almost every process of intelligent system design. Just to name few, engineering, control, economics and forecasting are some of the scientific fields that take advantage of an evolutionary computational process that aid in engineering systems with intelligent behavior. This special issue of Journal of Universal Computer Science is ...

Granularrules have been extensively used for classification in fuzzy datasets to promote the advancement of artificial intelligence. However, due diversity data types, how improve readability extracted granular rules while ensuring efficiency is always a challenge. Since reduct computing (GrC) can simplify real complex problem and dataset, this article carries out rule learning from perspective...

Fuzzy Newton-Cotes method for integration of fuzzy functions that was proposed by Ahmady in [1]. In this paper we construct error estimate of fuzzy Newton-Cotes method such as fuzzy Trapezoidal rule and fuzzy Simpson rule by using Taylor's series. The corresponding error terms are proven by two theorems. We prove that the fuzzy Trapezoidal rule is accurate for fuzzy polynomial of degree one and...

membership in each class. This viewpoint not only reflects the reality of many applications in which categories have fuzzy boundaries, but also Provides a simple representstion of the potentially complex partition of the feature space. In brief, we use fuzzy i fthen rules to describe a ChsSifier. A typical fuzzy classification rule is like: Fuzzy classification is the task of partitioning a fea...