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Skikda, Algeria

The Université of 20 août 1955 of Skikda is a university located in Skikda, Algeria.The University of Skikda was created by Executive Decree No. 01/272 of 18 September 2001. She went through the following phases: . 1987 - 1998 Ecole Normale Supérieure of Technical Education . 1998 - 2001 University Center . September 18, 2001 University of Skikda . Launched August 20, 2005 and christened University August 20, 1955. Educational Organization: The University is organized into six schools: - Faculty of science. - Faculty of Technology. - Faculty of Economics, Trade and Management Science. - Faculty of Social science and Humanities. - Faculty of Arts and Languages. - Faculty of Law and Political science. Domicile: The university is domiciled in three sites: 1. El-haddaik main site is spread over 246 hectares and is home to five first faculties 2. The site Merdj-Eddib: home ENSET and the Department of Petrochemical and Process Engineering. 3. The site AZZABA: located in the city of Azzaba distant 39 km houses the Faculty of Law and Political science. Wikipedia.

In this study, a comparison between generalized regression neural network (GRNN) and multiple linear regression (MLR) models is given on the effectiveness of modelling dissolved oxygen (DO) concentration in a river. The two models are developed using hourly experimental data collected from the United States Geological Survey (USGS Station No: 421209121463000 [top]) station at the Klamath River at Railroad Bridge at Lake Ewauna. The input variables used for the two models are water, pH, temperature, electrical conductivity, and sensor depth. The performances of the models are evaluated using root mean square errors (RMSE), the mean absolute error (MAE), Willmott's index of agreement (d), and correlation coefficient (CC) statistics. Of the two approaches employed, the best fit was obtained using the GRNN model with the four input variables used. © 2014 Taylor & Francis.

Ferdi Y.,Skikda University
Journal of Mechanics in Medicine and Biology | Year: 2012

The goal of this paper is to describe some applications of fractional order calculus to biomedical signal processing with emphasis on the ability of this mathematical tool to remove noise, enhance useful information, and generate fractal signals. Three types of digital filters are considered, namely, lowpass differentiation filter, smoothing filter, and 1/f β-noise generation filter. The filter impulse responses are functions of the fractional order and the sampling period only, and thus can be computed easily. Application examples are presented for illustrations. © World Scientific Publishing Company.

Boucheham B.,Skikda University
International Journal of Machine Learning and Cybernetics | Year: 2013

We propose a novel method (FANSEA) that performs very complex time series matching. The matching here includes comparison and alignment of time series, for diverse needs: diagnosis, clustering, retrieval, mining, etc. The complexity stands in the fact that the method is able to match quasi-periodic time series, that are eventually phase shifted, of different lengths, composed of different number of periods, characterized by local morphological changes and that might be shifted/scaled on the time/magnitude axis. This is the most complex case that can occur in time series matching. The efficiency stands in the fact that the newly developed FANSEA method produces alignments that are comparable to those of the previously published SEA method. However and as a result of data reduction, FANSEA consumes much less time and data; hence, allowing for faster matching and lower storage space. Basically, FANSEA is composed of two main steps: Data reduction by curve simplification of the time series traces and matching through exchange of extracted signatures between the time series under process. Due to the quasi-periodic nature of the electrocardiogram (ECG), the tests were conducted on records selected from the Massachusetts Institute of Technology-Beth Israel Hospital database (MIT-BIH). Numerically, the new method data reduction was up to 80 % and the time reduction was up to 95 %. Accordingly and among many possible applications, the new method is very suitable for searching, querying and mining of large time series databases. © 2012 Springer-Verlag.

Khelili F.,Skikda University
Physical Review D - Particles, Fields, Gravitation and Cosmology | Year: 2012

Using the noncommutative deformed canonical commutation relations proposed by Carmona et al., a model describing the dynamics of the noncommutative complex scalar field is proposed. The noncommutative field equations are solved, and the vacuum energy is calculated to the second order in the parameter of noncommutativity. As an application to this model, the Casimir effect, due to the zero-point fluctuations of the noncommutative complex scalar field, is considered. It turns out that in spite of its smallness, the noncommutativity gives rise to a repulsive force at the microscopic level, leading to a modified Casimir potential with a minimum at the point a min= √584πθ. © 2012 American Physical Society.

Boucheham B.,Skikda University
International Arab Journal of Information Technology | Year: 2012

We consider comparison of two Piecewise Linear Approximation (PLA) data reduction methods, a recursive PLA segmentation technique (Douglas-Peucker Algorithm) and a sequential PLA-segmentation technique (FAN) when applied in prior of our previously developed time series alignment technique SEA, which was established as a very effective method. The outcome of these two combination are two new time series alignment methods: RecSEA and SeqSEA. The study shows that both RecSEA and SeqSEA perform alignments as good as those of SEA with important reductions in data (RecSEA: up to 60%, SeqSEA up to 80% samples reduction) and processing time(RecSEA: up to 85%, SeqSEA up to 95% time reduction) with respect to the SEA method. This makes both the two new methods more suitable for time series databases querying, searching and retrieval. Particularly, SeqSEA is significantly much faster than RecSEA for long time series.

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