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Examination of static and 50 Hz electric field effects on tissues by using a hybrid genetic algorithm and neural network
Author(s) -
Hardalaç Fιrat,
Güler Göknur
Publication year - 2008
Publication title -
expert systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.365
H-Index - 38
eISSN - 1468-0394
pISSN - 0266-4720
DOI - 10.1111/j.1468-0394.2008.00447.x
Subject(s) - electric field , artificial neural network , biological system , computer science , genetic algorithm , ion , feedforward neural network , materials science , algorithm , biomedical engineering , chemistry , artificial intelligence , biology , machine learning , physics , medicine , organic chemistry , quantum mechanics
The effects of electric fields on tissue are the main subject of many investigations. The importance of this subject comes from the electrical properties of the cell membrane and its sensitivity to changes in electrical conditions. Permeability of membranes to various ions can change by the effect of an electric field depending on their conductivity. The performances of cells and tissues change due to differences between the membrane's permeability to various ions and molecules. The aim of this study was to determine lipid peroxidation and superoxide dismutase (SOD) levels in spleen and testis tissues exposed to different intensities and exposure periods of static and 50 Hz alternating electric fields. The increase in SOD and thiobarbituric acid reactive substance levels of spleen and testis tissues was found to depend significantly on the type of electric field and the exposure period. The experimental results are applied to a hybrid genetic algorithm and neural network as learning data and the training of the feedforward neural network is realized. At the end of this training, without applying electric field to tissues, the determination of the effects of the electric field on tissues by using a computer is predicted by the neural network. After the experiments, the prediction of the hybrid genetic algorithm and neural network approach is on average 99.25%–99.99%.