Ghazi Bousaleh

Prediction of blood transfusion donation

The goal of the present study was to develop and evaluate machine learning algorithms for the prediction of blood transfusion donation. The machine learning algorithms studied included multilayer perceptrons (MLPs) and support vector machines (SVMs). The methods were evaluated retrospectively in a group of 600 patients and validated prospectively in a group of 148 patients. We reach a sensitivity of 65.8% and a specificity of 78.2% in the prospective group. This discrimination is very interesting because it could allow to propose to the patients, classified as non-donators, to give their blood in the future. Furthermore, the blood transfusion donation UCI corpus used, has been processed in a different manner than the initial marketing one. Therefore, this recent corpus could give a new training set for testing and improving machine learning methods in the future.

Pattern recognition techniques applied to electric power signal processing

We propose in this paper an approach whose main objective is to detect disturbances that affect an electric power signal. The method allows us to locate the time occurrence of these disturbances. The signal processing consists of determining two distributions that are based on the energy of the wavelet signal decomposition: the deviation distribution and the deformation distribution. Theses distributions are a signature of the disturbance and are able to provide an identification of the type of the problem. The method has been developed using the analysis by the Discrete Wavelet Transform (DWT). The electrical signal is decomposed into several levels by DWT. The different waveforms resolution levels allows us to detect any deviations from the sane signal. The energy distributions data obtained in the first step will be used as feature vectors for training an artificial neural network (ANN) with multilayer perceptrons (MLPs) and support vector machines (SVMs) to classify the Power Quality Disturbance (PQD).

Reduction of power field radiation for PLC applications

In this article, we present our investigations on the radiation of the power-line communication (PLC) system. Power line communication or power line carrier is a technology for carrying data on a conductor usually dedicated to electric power transmission. At high frequency, the power lines, whose are dimensioned and adjusted for electrical energy transfer, not to carry high frequency (HF) data signal, act then as radiating antennas.

Creep test material rupture prediction by neural networks

This work focuses on acoustic emission analysis of mechanisms damage in fiber composite materials, subjected to heavy loads during a creep test. The goal of the present study was to develop and evaluate machine learning algorithms for the prediction of material rupture with creep test by traction method. This study aimed to predict if a tensile specimen will break in 30 seconds or not. Multilayer Perceptrons were trained retrospectively in a group of 80 samples moments and tested prospectively in a group of 16 tensile specimens. During the 5-cross validations we reached a sensitivity of 88% and a specificity of 88% in the prospective group. The mean area under the ROC (Receiver Operating Curves) was equal to 0.86. Those results are very interesting because they are a first important step in the lifetime prediction of material rupture before significant damages can occur.

Study of current distribution over a power cable presenting non-uniform geometry using the partial differential equations approach.

This paper presents a theoretical study of the currents and voltages characteristics transmission over an energy cable (Power Line Transmission PLT). The geometry of line is non-uniform. The proposed approach is based on the Distributed Network model, where the lumped parameters vary as the lines geometry.

New method for analyzing the quality of a telephone network

The development of new broadband services over the telephone network requires a good quality of the installation.