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Accurate detection of cyber-attacks plays a central role in safeguarding computer networks and information systems. This paper addresses the problem of detecting SYN flood attacks, which are the most popular Denial of Service (DoS) attacks. Here, we compare the detection capacity of three commonly monitoring charts namely, a Shewhart chart, a Cumulative Sum (CUSUM) control chart and exponentially...
Document image binarization is a central problem in many document analysis systems. Indeed, it represents one of the basic challenges, especially in case of historical documents analysis. In this paper, we propose a novel robust multi stage framework that combines different existing document image thresholding methods for the purpose of getting a better binarization result. CLAHE technique is introduced...
Skew detection is a crucial step for document analysis systems. Indeed, it represents one of the basic challenges, especially in case of historical documents analysis. In this paper, we propose a novel robust skew angle detection and correction technique. Morphological Skeleton is introduced to significantly reduce the amount of data to treat by removing the redundant pixels and keeping only the central...
This paper addresses the parameter identification problem of a fractional order system with a known structure. Thus, based on the variational iteration method, its shown that the identification of the parameters can be formulated as an optimization problem. The objective function is the L2-norm of the error between the measured and the model outputs, and the unknown model parameters are the decision...
The block oriented structure such as Hammerstein, Wiener, etc… became very popular for the non-linear system modelling due to its simplicity and parsimony. In this paper, fractional hammerstein system is considered; it consists of a static non-linear block followed by a fractional linear dynamical block. The particle swarm optimisation is used to estimate the system parameters as well as the fractional...
In this paper, we present an efficient approach to investigate data of EEG-based Brain-Machine Interface (BMI) using a bagging Support Vector Machines (SVMs) for collected data classification from a P3-speller paradigm. The combination of SVMs allows to handle the problem of EEG data variability between the different sessions of the acquisition process. This variability is caused by temporal non-stationarity...
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