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Health Effects of Electromagnetic Pollution Modeling Using Fuzzy Inference System
Author(s) -
BOUMAIZA Souad,
BOUHARATI Saddek
Publication year - 2015
Publication title -
the faseb journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.709
H-Index - 277
eISSN - 1530-6860
pISSN - 0892-6638
DOI - 10.1096/fasebj.29.1_supplement.762.1
Subject(s) - fuzzy logic , defuzzification , fuzzy set , computer science , fuzzy control system , data mining , artificial intelligence , mathematics , fuzzy number , control theory (sociology) , control (management)
Electromagnetic radiations has some negative effects on public health. The main sources of these radiations are mobile phones, base stations, Wi‐Fi,medical equipment and domestic appliances. Hundreds of studies on the health effects of electromagnetic fields have very mixed results. This requires us to use an analytical technique based on the principles of artificial intelligence, including the principles of fuzzy logic. This work aims to predict the health effects on exposed subjects. Because these reactions are characterized by uncertainty and imprecision, we found it useful to analyze these data by the techniques of fuzzy logic. The structure of the block diagram of our analysis system consists of seven inputs (Age, Sex,Distance, Frequency, Induction, Duration and Type of exposure) and one output (health effects).These are inspired from epidemiological studies that aim to find the link between exposures to electromagnetic field on the one hand and biological or pathological effects on the other. After fuzzification of inputs and output, several rules of inference, connecting the fuzzy input variables to the fuzzy output variable were developed in the form: IF “predicate” THEN “conclusion”. The next step is the combination of all the rules and defuzzification of parameters. Finally, we can assume the effects of electromagnetic fields in other cases not investigated. Fuzzy logic deals with uncertainty is perfectly appropriate in our case in which a fuzzy algorithm is proposed to predict the health effects on exposed subjects from the input variables.