Interval type-2 fuzzy logic systems (IT2 FLSs) have demonstrated better abilities to handle
uncertainties than their type-1 (T1) counterparts in many applications; however, the high …

Data, as being the vital input of system modelling, contain dissimilar level of imprecision that
necessitates different modelling approaches for proper analysis of the systems. Numbers …

The number of people with a chronic disease is rapidly increasing, giving the healthcare
industry more challenging problems. To date, there exist several ontology and IoT-based …

The need to deal with vague information in Semantic Web languages is rising in importance
and, thus, calls for a standard way to represent such information. We may address this issue …

Abstract World Wide Web (WWW) constitutes fuzzy information and requires soft computing
techniques to deal context of the query. It works on the principle of keyword matching …

An increasing number of decision support systems based on domain knowledge are
adopted to diagnose medical conditions such as diabetes and heart disease. It is widely …

Recommender systems evaluate and filter the vast amount of information available on the
Web, so they can be used to assist users in the process of accessing to relevant information …

With the rapidly growing number of available Cloud services, to fulfill the need for ordinary
users to select accurate services has become a significant challenge. However, as a Cloud …

In this paper, a Takagi-Sugeno-Kang (TSK)-type-based self-evolving compensatory interval
type-2 fuzzy neural network (FNN)(TSCIT2FNN) is proposed for system modeling and noise …

Classical, two-valued, ontologies have been successfully applied to represent the
knowledge in many domains. However, it has been pointed out that they are not suitable in …