Most group decision meetings are perceived to be extremely unproductive in terms of efficiently utilizing the participants' time and effectively achieving the group decision meeting objectives. Indeed, group decision meetings consume a great deal of time and effort in organizations. These problems occur frequently because effective guidelines or procedures are not used. To overcome these problems, many group decision support systems (GDSS) imbed some facilitation mechanisms and are currently being used with the help of a human facilitator who guides the group members through the decision process. We propose in this paper a toolkit for GDSS facilitators that we integrate in our proposed architecture for distributed GDSS. Based on a model of the decision making processes group facilitation tasks are automated, at least partially in order to increase the ability of inexperienced facilitator to monitor and control the group decision meeting process.

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In Multi-agents systems, the cognitive capability present in an agent can be deployed to realize effective problem-solving by the combined effort of the system and the user. It offers the potential to automate a far wider part of the problem solving task than was possible with classical DSS. In this paper, we propose to integrate agents in a group decision support system. The resulting system, MADS is designed to support operators during contingencies. We experiment our system on a case of boiler breakdown to detect a functioning defect of the boiler (GLZ: Gas Liquefying Zone) to diagnose the defect and to suggest one or several appropriate cure actions. In MADS the communication support enhances communication and coordination capabilities of participants. A simple scenario is given, to illustrate the feasibility of the proposal.

Minimal Enclosing Ball (MEB) has a limitation for dealing with a large dataset in which computational load drastically increases as training data size becomes large. To handle this problem in huge dataset used for speaker recognition and identification system, we propose two algorithms using Fuzzy C-Mean clustering method. Our method uses divide-and-conquer strategy; trains each decomposed sub-problems to get support vectors and retrains with the support vectors to find a global data description of a whole target class. Our study is experimented on Universal Background Model (UBM) architectures in speech recognition and identification system to eliminate all noise features and reducing time training. For this, the training data, learned by Support Vector Machines (SVMs), is partitioned among several data sources. Computation of such SVMs can be efficiently achieved by finding a core-set for the image of the data.

Several selection methods in the literature are essentially based on an evaluation function that determines whether a model M contributes positively to boost the performances of the whole ensemble. In this paper, we propose a method called DIversity and ACcuracy for Ensemble Selection (DIACES) using an evaluation function based on both diversity and accuracy. The method is applied on homogenous ensembles composed of C4.5 decision trees and based on a hill climbing strategy. This allows selecting ensembles with the best compromise between maximum diversity and minimum error rate. Comparative studies show that in most cases the proposed method generates reduced size ensembles with better performances than usual ensemble simplification methods.

Traditional Decision Support Systems (DSS) give not enough possibilities of intervention to the user. These systems are reduced to an insular and very technical state in which the objective is not support decision but to dump data on the screen in the hope that the user will know what to do with. In complex situations, decision is not structured and it becomes primordial to design intelligent and cooperative systems allowing a joint resolution of problem based on dynamic sharing of the tasks between the user and the system and according to problems to be solved. In this perspective, we propose a cooperative architecture for intelligent decision support system. The framework embeds expert knowledge within the DSS to provide intelligent DSS using collaboration technologies by putting the decision maker effectively in the loop of the decision process. To this end, we used a structure based on domain and task conceptual modelling. Applicability and relevance of this model are illustrated through a case study where the system and the operator cooperate in decision problem which consists of identifying boiler defects, diagnosing and suggesting actions of cure.

While Modern Standard Arabic is the formal spoken and written language of the Arab world; dialects are the major communication mode for everyday life. Therefore, identifying a speaker's dialect is critical in the Arabic-speaking world for speech processing tasks, such as automatic speech recognition or identification. In this paper, we examine two approaches that reduce the Universal Background Model (UBM) in the automatic dialect identification system across the five following Arabic Maghreb dialects: Moroccan, Tunisian, and 3 dialects of the western (Oranian), central (Algiersian), and eastern (Constantinian) regions of Algeria. We applied our approaches to the Maghreb dialect detection domain that contains a collection of 10-second utterances and we compared the performance precision gained against the dialect samples from a baseline GMM-UBM system and the ones from our own improved GMM-UBM system that uses a Reduced UBM algorithm. Our experiments show that our approaches significantly improve identification performance over purely acoustic features with an identification rate of 80.49%.