Navegando por Autor "LEITE, Lourdes Daniele Câmara"

Agora exibindo 1 - 3 de 3

Application Of Fuzzy Logic To Assess The Degree Of Aptitude Of Professionals In The Areas Of Management
(Instituto de Tecnologia e Educação Galileo da Amazônia, 2024) LEITE, Lourdes Daniele Câmara; LEITE, Jandecy Cabral
The study proposes the creation of a Fuzzy inference model to assess the performance of professionals in the management area, based on quantitative criteria such as ability, knowledge, and attitude. The aim is to assist the Human Resources sector in the recruitment and selection process, identifying the most suitable profile for management positions. The model was developed in three phases: identification of aptitude indicators, Fuzzy system modeling, and experimentation of the proposed model. The results showed that the Fuzzy system is effective in analyzing and classifying candidate performance, optimizing decision-making in recruitment.
Sistema fuzzy para avaliação do grau de aptidão de gestores
(Instituto Nacional da Propriedade Industrial (INPI), 2024-07-30) LEITE, Lourdes Daniele Câmara; NASCIMENTO, Manoel Henrique Reis do; LEITE, Hellen Lima; SANTOS, Alyson de Jesus dos
Document for the registration of computer software by the National Institute of Industrial Property (INPI), validating the software "Fuzzy system for evaluating the aptitude level of managers", developed in MATLAB, valid for 50 years.
Sistema fuzzy para avaliação do grau de aptidão de profissionais das áreas de gestão
(Instituto de Tecnologia e Educação Galileo da Amazônia, 2024-07-30) LEITE, Lourdes Daniele Câmara; NASCIMENTO, Manoel Henrique Reis
Companies are increasingly specializing in the Human Resources sector, especially in the area of recruitment and selection of people. This work developed a fuzzy inference model to evaluate the competencies of operational level managers. Through multiple qualitative criteria, the model was designed to be used by the Human Resources sector of organizations, helping to determine if the candidate's profile fits the requirements of the position. The model was tested and resulted in 27 inference rules, offering an effective way to assess candidates' competencies during the recruitment and selection process.