An Intelligent-Mamdani Inference Scheme for Healthcare Applications Based on Fuzzy Markup Language

Abstract

In recent years, personal health management has been interested to researchers and healthcare practitioners. Recording and analyzing physiological variations in ordinary life could be especially useful to manage health problems and to care individuals. It is widely pointed out that various vital signs are important indicators used to evaluate the wellness of physical bodies. In this study, an Intelligent-Mamdani Inference Scheme (IMIS) based on fuzzy markup language (FML) is proposed to apply to the semantic decision-making for personal health in healthcare applications. The IMIS could provide semantic analysis of personal health status by using the knowledge base and fuzzy inference rules, which are preestablished by domain experts. This scheme is a well-defined composition, including a FML editor, a FML parser, a fuzzy inference mechanism and a semantic decision-making mechanism. The experimental results show that the proposed scheme is feasible for semantic decision of personal health. A person can understand his physical conditions via the generated semantic decision-marking mechanism by the input of vital signs.

Human Identification after Cosmetic Surgery

“…A Mamdani fuzzy inference system is established for edge detection because Mamdani fuzzy systems are more spontaneous and easier to realize w.r.t to rule bases and are also well-matched to applications where the rules are created from human knowledge, such as medical problem solving [27]. Fuzzy logic methodology uses membership functions to delineate the grade to which a pixel ts to an edge/undeviating region.…”