The introduction of intuitionistic fuzzy sets is due to K. T. Atanassov, who also proposed some problems about this subject. D. Çoker defined the intuitionistic fuzzy topological spaces and, with some coworkers, studied these spaces. In this paper, we define and study the notion of quasicoincidence for intuitionistic fuzzy points and obtain a characterization of continuity for maps between intu...

Detection and segmentation of Brain tumor is very important because it provides anatomical information of normal and abnormal tissues which helps in treatment planning and patient follow-up. There are number of techniques for image segmentation. Proposed research work uses ANFIS (Artificial Neural Network Fuzzy Inference System) for image classification and then compares the results with FCM (F...

This correspondence describes extensions to the fuzzy k-means algorithm for clustering categorical data. By using a simple matching dissimilarity measure for categorical objects and modes instead of means for clusters, a new approach is developed, which allows the use of the k-means paradigm to efficiently cluster large categorical data sets. A fuzzy k-modes algorithm is presented and the effec...

The performance of a fuzzy k-NN rule depends on the number k and a fuzzy membership-array W[I, mR], where l and m R denote the number of classes and the number of elements in the reference set X R respectively. The proposed learning procedure consists in iterative finding such k and W which minimize the error rate estimated by the 'leaving one out' method.

We introduce the notion of interval valued intutionistic (S,T)-fuzzy left h-ideal (k-ideal) of a hemiring (semiring). We describe some characteristic properties and connections. Finally, we construct some equivalence relations induced by interval valued intutionistic (S,T)-fuzzy left h-ideals (k-ideals).

We address the problem of evaluating ranked top-k queries in description logics. The problem occurs whenever we allow queries such as “find cheap hotels close to the conference location” in which fuzzy predicates like cheap and close occur. We show how to efficiently compute the top-k answers of conjunctive queries with fuzzy predicates over DL-LITE like knowledge bases.

This paper presents clustering techniques (K-means, Fuzzy K-means, Subtractive) applied on specific databases (Flower Classification and Mackey-Glass time series) , to automatically process large volumes of raw data, to identify the most relevant and significative patterns in pattern recognition, to extract production rules using Mamdani and Takagi-SugenoKang fuzzy logic inference system types.

Quantitative structural property relations (QSPRs) for boiling points of aliphatic hydrocarbons were derived using a back-propagation neural network and a modified Fuzzy ARTMAP architecture. With the back-propagation model, the selected molecular descriptors were capable of distinguishing between diastereomers. The QSPRs were obtained from four valance molecular connectivity indices (1chiv,2chi...

 In this paper, utilization of clustering algorithms for data fusion in decision level is proposed. The results of automatic isolated word recognition, which are derived from speech spectrograph and Linear Predictive Coding (LPC) analysis, are combined with each other by using fuzzy clustering algorithms, especially fuzzy k-means and fuzzy vector quantization. Experimental results show that the...

More than one third (13 million) of adults aged 65 and above fall each year in the United States. Developing automated systems that detect falls is an important goal for those working in the field of eldercare technology. We developed an acoustic fall detection system (FADE) that automatically recognizes falls using purely acoustic (sound) information. The main challenge of building a fall dete...