Predicting attitudes towards people with tuberculosis is a solution for preserving public health and a means of strengthening social ties to improve resilience to health threats. The assessment of attitudes towards the sick in general is essential to understand the educational level of a given population and to measure its resilience in contributing to the health of all within the framework of community life. The case of tuberculosis is chosen in this study to highlight the need for a change in attitudes, particularly due to the preponderance of this disease in Africa. While it is clear that attitudes influence the organization of individuals and community life, it remains a challenge to put in place an effective mechanism for evaluating the metrics that contribute to determining the attitude towards people with tuberculosis. Knowledge of attitudes towards any disease is very important to understanding collective values on this disease, hence the need to predict attitudes in the case of tuberculosis in favor of health education for all social strata while targeting areas of training not yet explored or requiring capacity building among populations. Changing attitudes towards tuberculosis patients will contribute to preserving public health and will help reduce stigma, improve understanding of the disease and encourage supportive and preventive behaviors. Achieving these changes involves dismantling stereotypes, improving access to care, mobilizing the media and social networks, including people with TB in society and strengthening the commitment of public authorities. The approach adopted consists of assessing the state of attitude towards tuberculosis patients at a given time and in a specific space based on the characteristics of the different social strata living there. An analysis of several metrics provided by machine learning algorithms makes it possible to identify differences in attitudes and serve as a decision-making aid on the strategies to be implemented. This work also relies on the investigation and analysis of historical trends using machine learning algorithms to understand population attitudes towards tuberculosis patients.

The work in this article focuses on the modelling of an intelligent digital ecosystem for educational and career guidance for students and young people seeking their first job or retraining. To do so, the multi-expert system paradigm was used to aggregate the different expertises required for a good guidance, the multi-agent system principle was used to have a modular and easily scalable ecosystem. Indeed, the agents of the system communicate with each other using the FIPA-ACL language, in a collaborative vision, throughout the orientation assistance process to perform tasks such as proposing business sectors, occupations, training, and training paths. The ontologies of the Semantic Web have been used to have a complete semantic description of the shared information and to promote communication between the different software agents of the ecosystem. Big Data principles have also been deployed to manage and exploit structured and unstructured data from different data sources related to the guidance ecosystem.
The ecosystem modeled in this way has several innovative and powerful technological and scientific aspects. Thus, in terms of design and modelling, the proposed ecosystem considers all the actors and factors involved in the guidance process, including labor market trends. In technological/scientific terms, it is based on methods that allow it to be modular and scalable.

This paper presents the use of the FAHPmethod (Fuzzy Analytic Hierarchy Process) to help young people choose the most appropriate activity sectors for their profile. This choice is based on three criteria: Professional interests, professional sub-interests and personality traits. This work is a part of a global context aiming to apply the Multi-criteria Decision-Making (MCDM) methods in the vocational guidance according to the process schematized in Figure 3.

This work focuses on the use of multi-criteria decision-making method AHP for using in educational and vocational guidance. Analytical Hierarchy Process (AHP), proposed by the mathematician Thomas Saaty in 1980, is a method of analysis greatly used in the context of a multi-criteria analysis; it allows the comparison and the choice between the preset options. To achieve this goal, a vital work, preceded the use of the AHP method, which consists in doing a prototyping of trades according to the guidance criteria and sub-criteria. The IT system based on this method allows the student to find, firstly, the activities' sectors which are the most appropriate to his/her profile, to choose subsequently the trades and finally, to identify, the potential training paths.

The computer system has become one of the centerpieces in the functioning of organizations hence the importance of an IT (Information Technology) master plan to manage its development. To find a provider for the IT master plan's realization, organizations are increasingly using tendering as the mode of awarding contracts.
This article focuses on the use of multi-criteria decision-making method AHP for analysis and evaluation of tenders during the awarding of contracts of IT master plan's realization. To achieve this goal, a painstaking work was realized, on the one hand, for making an inventory of criteria and sub-criteria involved in the evaluation of bids and on the other hand for specifying the degrees of preference for each pair of criteria and each pair of sub-criteria. Finally, a test was performed by using fictitious tenders.
The goals of this work are to make available to members of tenders committee a decision support tool for evaluating tenders of IT master plan's realization submitted by bidders and endow the organizations with effective IT master plans in order to increase the performance of their information systems.