G. J. Tsekouras

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The present research investigates the relationship between the central corneal thickness (CCT), Heidelberg Retina Tomograph II (HRTII) structural measurements and intraocular pressure (IOP) using an innovative non-linear multivariable regression method in order to define the risk factors in future glaucoma development and patient management. The method is(More)
Several empirical and analytical relations exist between different tunnel characteristics and surface and subsurface deformation, while numerical analyses (mainly using finite difference programs) have also been applied with satisfactory results. In the last years, the solution of some soil mechanics problems has been derived using the approach of the(More)
Several empirical and analytical relations exist between different tunnel characteristics and surface and subsurface deformation, while numerical analyses (mainly using finite difference programs) have also been applied with satisfactory results. In the last years, the solution of some soil mechanics problems has been derived using the approach of the(More)
There is limited evidence to suggest an optimal biometric method in order to achieve an enhanced level of information security as well as recognition accuracy. Recently, novel approaches for the development of practical biometric identification systems have shown that body motion analysis seems to overcome most of the risks and vulnerabilities related to(More)
PURPOSE In this paper a new nonlinear multivariable regression method is presented in order to investigate the relationship between the central corneal thickness (CCT) and the Heidelberg Retina Tomograph (HRTII) optic nerve head (ONH) topographic measurements, in patients with established glaucoma. METHODS Forty nine eyes of 49 patients with glaucoma were(More)
In this paper a new pattern recognition methodology is described for the classification of the daily chronological load curves of power systems, in order to estimate their respective representative daily load profiles, which can be mainly used for load forecasting and feasibility studies of demand side management programs. It is based on pattern recognition(More)
The objective of this paper is to compare the performance of different Artificial Neural Network (ANN) training algorithms regarding the prediction of the hourly load demand of the next day in intercontinental Greek power system. These techniques are: (a) stochastic training process and (b) batch process with (i) constant learning rate, (ii) decreasing(More)
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