Text Categorization (TC) is the automated assignment of text documents to predefined categories based on document contents. For the past few years, TC has become very important essentially in the Information Retrieval area, where information needs have tremendously increased with the rapid growth of textual information sources such as the Internet. In this paper, we compare , for text categoriz...

Drowsiness is a serious problem, which causes a large number of car crashes every year.This paper presents an original drowsiness detection method based on the fuzzy merging of several eye blinking features extracted from an electrooculogram (EOG). These features are computed each second using a sliding window. This method is compared to two supervised learning classifiers: a prototype nearest ...

Image Thresholding is a necessary task in many image processing applications. In this paper we derive fuzzy rules for π-function. We use π-function to fuzzify the original image; this is constructed to locate the intensities of the misclassification regions. Based on information theory, it maximizes the information between image foreground and background. The merit of using fuzzy set is its abi...

The fuzzy c-means (FCM) clustering algorithm is the best known and used method in fuzzy clustering and is generally applied to well defined set of data. In this paper a generalized Probabilistic fuzzy c-means (FCM) algorithm is proposed and applied to clustering fuzzy sets. This technique leads to a fuzzy partition of the fuzzy rules, one for each cluster, which corresponds to a new set of fuzz...

Denoising is the essential step for distant supervision based named entity recognition. Previous denoising methods are mostly on instance-level confidence statistics, which ignore variety of underlying noise distribution different datasets and types. This makes them difficult to be adapted high rate settings. In this paper, we propose Hypergeometric Learning (HGL), a algorithm distantly supervi...

It is found that sub-pixel classifiers for classification of multi-spectral remote sensing data yield a higher accuracy. With this objective, a study has been carried out, where fuzzy set theory based sub-pixel classifiers have been compared with statistical based sub-pixel classifier for classification of multi-spectral remote sensing data.Although, a number of Fuzzy set theory based classifie...

This paper describes our approach for the Chinese clinical named entity recognition (CNER) task organized by 2020 China Conference on Knowledge Graph and Semantic Computing (CCKS) competition. In this task, we need to identify boundary category labels of six entities from electronic medical record (EMR). We constructed a hybrid system composed semi-supervised noisy label learning model based ad...

Fuzzy inference systems based on fuzzy rule bases (FRBs) have been successfully used to model real problems. Some of the limitations exhibited by these traditional fuzzy inference systems are that there is an abundance of fuzzy operations and operators that an expert should identify. In this paper we present an alternate learning and reasoning schema, which use fuzzy functions instead of if...t...