Adel M. Alimi

An evolutionary neuro-fuzzy approach to recognize on-line Arabic handwriting

The author describes a system that recognizes on-line Arabic cursive handwriting. In this system, a genetic algorithm is used to select the best combination of characters recognized by a fuzzy neural network. The handwritten words used in this system are modelled by a theory of movement generation. Based on this motor theory, the features extracted from each character are the neuro-physiological and biomechanical parameters of the equation describing the curvilinear velocity of the script. The evolutionary approach proposed permits the recognition of cursive handwriting with a segmentation procedure allowing overlapped strokes having neuro-physiological meaning.

A neuro-fuzzy approach to recognize Arabic handwritten characters

In this paper we describe a system that recognizes online Arabic handwritten characters. In this system, a fuzzy neural network is used to classify characters. The characters used in this system were segmented from cursive handwriting that are modelled by a theory of movement generation. Based on this theory, the features extracted from each character are the neuro-physiological parameters of the equation describing the curvilinear velocity of the script. For each character presented to the system, a fuzzy membership is assigned to each output of the neural network.

ICDAR 2009 Online Arabic Handwriting Recognition Competition

This paper describes the Online Arabic handwriting recognition competition held at ICDAR 2009. This first competition uses the ADAB-database with Arabic online handwritten words. This year, 3 groups with 7 systems are participating in the competition. The systems were tested on known data (sets 1 to 3) and on one test dataset which is unknown to all participants (set 4). The systems are compared on the most important characteristic of classification systems, the recognition rate. Additionally, the relative speed of the different systems were compared. A short description of the participating groups, their systems, the experimental setup, and the performed results are presented.

The analysis of error in an on-line recognition system of Arabic handwritten characters

In this paper we describe a system that recognizes on-line Arabic handwriting characters. In this system, a dynamic programming algorithm is implemented. We present here the effects of some parameters of the system on its performances and on its recognition errors. This study shows a way to select the optimum values for some key parameters of the system to obtain minimum recognition error rates.

Analytical plug-in method for kernel density estimator applied to genetic neutrality study

The plug-in method enables optimization of the bandwidth of the kernel density estimator in order to estimate probability density functions (pdfs). Here, a faster procedure than that of the common plug-in method is proposed. The mean integrated square error (MISE) depends directly upon which is linked to the second-order derivative of the pdf. As we intend to introduce an analytical approximation of , the pdf is estimated only once, at the end of iterations. These two kinds of algorithm are tested on different random variables having distributions known for their difficult estimation. Finally, they are applied to genetic data in order to provide a better characterisation in the mean of neutrality of Tunisian Berber populations.

FROM GAITS TO ROBOT, A HYBRID METHODOLOGY FOR A BIPED WALKER

This paper presents a methodology allowing simple and effective biped robot prototyping and validation using a multi-agent architecture for gait generation and a self assembled biped walker. The gait generator, GG, is based on PSO, particle swarm optimization, which is an evolutionary algorithm; the Obtained gaits are fitted to a robot in order to evaluate the real performances of the approach. To control the Center of Mass, COM, a fuzzy approach, FGG, is introduced allowing fast and effective stability control.

Fast Plug-in Method for Parzen Probability Density Estimator Applied to Genetic Neutrality Study

In order to compute an optimal Kernel probability density function estimator (KDE), the plug-in method is considered in this paper. Such algorithm gives a way to optimize both the kernel and the bandwidth. Here, we propose a new procedure witch is faster than the common plug-in one. For each time, a factor J(f), which is linked to the second order derivative of the pdf, is analytically approximated. The pdf is estimated only once at the end of iterations. Different random variables with difficult distributions are generated in order to prove the efficiency of the proposed optimal estimator. These algorithms are then, applied to genetics data in order to give a better characterisation in the mean of neutrality of a given population by using the fast MISE optimal pdf estimation of the Tajimas D criterion. This criterion is a statistic allowing the estimation of the genetic neutrality of the sample population.

Speech emotion recognition based on Arabic features

This paper presents the principal phase of extraction and recognition of the basic emotions in the Arabic speech applied to five emotional states were taken into effect; neutral, sadness, fear, anger and happiness. Emotional speech database REGIM_TES [1] was created and evaluated to provide all practical experiences of extraction. The selected descriptors in our study are; Pitch of voice, Energy, MFCCs, Formant, LPC and the spectrogram. Descriptors showed the importance of the Arabic language on the physiological events and the influence of culture on emotional behavior. A comparative study between the kernel functions has enabled us to promote the RBF kernel SVMs multiclass classifier [15] performing the classification phase.

Fuzzy feature selection based on interval type-2 fuzzy sets

When dealing with real world data; noise, complexity, dimensionality, uncertainty and irrelevance can lead to low performance and insignificant judgment. Fuzzy logic is a powerful tool for controlling conflicting attributes which can have similar effects and close meanings. In this paper, an interval type-2 fuzzy feature selection is presented as a new approach for removing irrelevant features and reducing complexity. We demonstrate how can Feature Selection be joined with Interval Type-2 Fuzzy Logic for keeping significant features and hence reducing time complexity. The proposed method is compared with some other approaches. The results show that the number of attributes is proportionally small.

Building and analysing emotion corpus of the Arabic speech

This paper presents the principal phase of extraction and recognition of the basic emotions in the Arabic speech applied to five emotional states were taken into effect; neutral, sadness, fear, anger and happiness. Emotional speech database REGIM TES [1] [23] was created and evaluated to provide all practical experiences of extraction. The selected descriptors in our study are; Pitch of voice, Energy, MFCCs, Formant, LPC and the spectrogram. Descriptors showed the importance of the Arabic language on the physiological events and the influence of culture on emotional behavior.