Fuzzy Hedges Research Articles

GimmeHop: A recommender system for mobile devices using ontology reasoners and fuzzy logic

This paper describes GimmeHop, a beer recommender system for Android mobile devices using fuzzy ontologies to represent the relevant knowledge and semantic reasoners to infer implicit knowledge. GimmeHop use fuzzy quantifiers to deal with incomplete data, fuzzy hedges to deal with the user context, and aggregation operators to manage user preferences. The results of our evaluation measure empirically the data traffic and the running time in the case of remote reasoning, the size of the ontologies that can be locally dealt with in a mobile device in the case of local reasoning, and the quality of the automatically computed linguistic values supported in the user queries.

Gujarati character recognition using adaptive neuro fuzzy classifier with fuzzy hedges

Recognition of Indian scripts is a challenging problem and work towards development of an OCR for handwritten Gujarati, an Indian script is still in infancy. This paper implements an Adaptive Neuro Fuzzy Classifier (ANFC) for Gujarati character recognition using fuzzy hedges (FHs). FHs are trained with other network parameters by scaled conjugate gradient training algorithm. The tuned fuzzy hedge values of fuzzy sets improve the flexibility of fuzzy sets; this property of FH improves the distinguishability rates of overlapped classes. This work is further extended for feature selection based on FHs. The values of fuzzy hedges can be used to show the importance of degree of fuzzy sets. According to the FH value, the redundant, noisily features can be eliminated, and significant features can be selected. An FH-based feature selection algorithm is implemented using ANFC. This paper aims to demonstrate recognition of ANFC-FH and improved results of the same with feature selection.

Factor Group of a Fuzzy Soft Group

In this paper we study Molodtsov's notion of Fuzzy Soft Sets considering the fact that the parameters(which are words or sentences) are mostly fuzzy hedges or fuzzy parameters. We discuss and study the properties and structural characteristics of fuzzy soft groups and normal fuzzy soft groups. Then we define the left coset, right coset and middle coset of a fuzzy soft group and prove some important results on them. Finally we present factor group of a fuzzy soft group.

New Construction of Fuzzy Soft Lattices

In this paper, we study M oldtsov notion of soft set and fuzzy soft set considering the fact that the parameters are mostly fuzzy hedges or fuzzy parameters. We also, study the notion of fuzzy soft lattice on groups, homomorphic image, preimage of fuzzy soft lattices, arbitrary family of fuzzy soft lattices and fuzzy normal soft lattices using T-norms. Also the notion of sensible fuzzy soft lattice in groups and some related properties on it are investigated.

Applying fuzzy logic to measure completeness of a conceptual model

In a computing environment, the success of an information system depends upon the quality of its conceptual model. The importance of measuring quality of a conceptual model in quantitative terms has been emphasized in the research but still the quantitative measures are very scarce in the literature. A new Fuzzy Completeness Index (FCI) is introduced in this paper as a quantitative measure for the quality of a conceptual model. It takes into account completeness of a conceptual model based upon the concept of functional dependencies. For a given conceptual model the incorporation of functional dependencies is mapped onto a TAS Graph and is then measured using the fuzzy membership values and fuzzy hedges. The FCI has been calculated for different conceptual models. It has been illustrated that the quality in terms of completeness can effectively be measured through the FCI based approach. The higher the value of FCI the closer is the conceptual model to the real world in representing functional constraints.