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Ariana, Tunisia

Naoui F.B.,Manouba University | Zaiem I.,Tunis Carthage University
International Journal of Pharmaceutical and Healthcare Marketing | Year: 2010

Purpose: The purpose of this paper is to study the theoretical foundation of the relationship quality concept and loyalty, and to study the relationship between relationship quality, its antecedents and loyalty. Design/methodology/approach: An empirical study was conducted in the parapharmaceutical sector. Data collection was carried out through the direct administration of a questionnaire to a sample of 300 pharmacists dealing with sales representatives of the parapharmaceutical products. Findings: The results show that there is a significant relation between the antecedents of relationship quality, namely, interpersonal communication, relational contact, conflict resolution and client-oriented behavior, and relationship quality itself. Relationship quality has also an impact on loyalty which is accounted for positively by satisfaction, and negatively by affective conflict. Research limitations/implications: The authors limited the scope of the study to purifying measurement scales and testing the links between the various concepts. Practical implications: The paper sheds light on the action leverages that suppliers have to work on so as to improve the quality of their relation with their clients. Originality/value: Compared to most previous research works which did not go beyond the probing of antecedents of relationship quality separately, the most important contribution of the study lies in taking into account some important antecedents in the study of the influence of antecedents of relationship quality on each dimension of relationship quality. © Emerald Group Publishing Limited. Source


Valderrama P.,French National Institute for Agricultural Research | Marco P.H.,French National Institute for Agricultural Research | Locquet N.,French National Institute for Agricultural Research | Ammari F.,Tunis Carthage University | Rutledge D.N.,French National Institute for Agricultural Research
Chemometrics and Intelligent Laboratory Systems | Year: 2011

Multi-way data analysis techniques are becoming ever more widely used to extract information from data, such as 3D excitation-emission fluorescence spectra, that are structured in (hyper-) cubic arrays. Parallel Factor Analysis (PARAFAC) is very commonly applied to resolve 3D-fluorescence data and to recover the signals corresponding to the various fluorescent constituents of the sample. The choice of the appropriate number of factors to use in PARAFAC is one of the crucial steps in the analysis. When the signals in the data come from a relatively small number of easily distinguished constituents, the choice of the appropriate number of factors is usually easy and the mathematical diagnostic tools such as the Core Consistency, in general give good results. However, when the data is from a set of natural samples, the core consistency may not be a good indicator for the choice of the appropriate number of factors.In this work, Multi-way Principal Component Analysis (MPCA) and the Durbin-Watson criterion (DW) are utilized to choose the number of factors to use in PARAFAC decomposition. This is demonstrated in a case where 3D-front-face fluorescence spectroscopy is used to monitor of the evolution of naturally occurring and neo-formed fluorescent components in oils during thermal treatment. © 2010 Elsevier B.V. Source


Jebara S.B.,Tunis Carthage University
BIOSIGNALS 2013 - Proceedings of the International Conference on Bio-Inspired Systems and Signal Processing | Year: 2013

This paper focuses on the classification of speech sequences into two classes: healthy speech and esophageal speech. Two kinds of features are selected: those based on speaker speech production mechanism and those using listener auditory system properties. Two classification strategies are used: the Discriminant Analysis and the GMM based bayesian classifier. Experiments, conducted with a large database, show classification accuracy using both features. Moreover, auditory based features are the best since error rates tend to be null. Source


Ben Jebara S.,Tunis Carthage University
European Signal Processing Conference | Year: 2014

This paper deals with noise removal in ElectroMyoGram (EMG) signals acquired in the hostile noisy environment of functional Magnetic Resonance Imaging (fMRI). The noise due to magnetic fields and radio frequencies corrupts significantly the EMG signal which render its extraction very difficult. The proposed approach operes in the frequency domain to estimate the noise spectrum to subtract it from noisy observation spectrum. The noise estimation is based on spectral minima tracking in each frequency bin without any distinction between muscle activity and muscle rest. But it looks for connected time-frequency regions of muscle activity presence to estimate a bias compensation factor. The method is tested with a simulated noisy observation in order to evaluate its performance using objective criteria. It is also validated for real noisy observations where no clean is available. © 2014 EURASIP. Source


Ben Jebara S.,Tunis Carthage University
European Signal Processing Conference | Year: 2014

The purpose of this work is to discriminate between smoker and non-smoker speakers by analyzing their voice. In fact, the vocal folds, the main organ responsible of producing voice, is damaged by smoke so that its structure and its vibration are altered. Some bio-mechanical features, describing vocals folds behavior and status are used. They are based on the two-mass model which characterizes vocal folds by the mass, the stiffness and the losses of their cover and body parts. Bio-mechanical features of smokers and non-smokers are analyzed and compared to select relevant features permitting to discriminate between the two categories of speakers. The Quadratic Discriminant Analysis is used as a tool of classification and shows a relatively good rate of detection of smokers. © 2014 EURASIP. Source

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