Browsing by Author "Lima, Juan Fernando"

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Application of the LSA technique to determine the priority of alerts from a command and control center
(Institute of Electrical and Electronics Engineers, 2021) Prado Cabrera, Katerine Daniela
It is essential to determine the alert level in a command-and-control center when someone calls an operator for an emergency since life may be in danger. The alert level is determined based on the evaluation that the operator can perceive during the call. Sometimes, cases can be similar to those previously attended. Therefore, it is helpful that this knowledge can be rescued and applied to new cases. In this context, the Latent Semantic Analysis (LSA) technique can determine the alert level and find the most representative words in each case. Thus, when a new alarm is triggered, the system can recommend the alert level with which it is rated. Consequently, a solution based on previous knowledge has been stated. This solution leads to the following methodological process: i) data preprocessing, ii) topic analysis and iii) classification. When this proposal was applied, the results revealed an accuracy greater than 60% in predicting the type of alert based on the text
Finding insights between active aging Variables: towards a data mining approach
(Universidad de Toulous, Universidad de Limoges, Laboratorio de Investigación de Robótica, 2022) Orellana Cordero, Marcos; Lima, Juan Fernando; Bueno Pacheco, Alexandra; Prado Cabrera, Katerine Daniela; Acosta Urigüen, María Inés; Cedillo Orellana, Irene Priscila
Several proposals on active aging have been addressed within the psychological field, conceptualizing it satisfactorily as a perspective of aging. Those proposals generate indicators that assess the level of physical health, psychological wellbeing, adequate social adaptation. Physical, cognitive, and functional faculties, interpersonal relationships, and productive activities have been evaluated. Although several technological approaches have been proposed to promote active aging, they have not included a deep understanding of the results obtained from solution implementations. Then, this paper presents the first step towards an approach that uses variables proposed by active aging models (e.g., health, cognition, activity, affection, fitness aspects) to generate knowledge through patterns. These patterns are identified using data obtained through several instruments (i.e., psychological evaluations, health studies, and human experts' contributions). Thus, selecting those variables and evaluating them as future models is necessary. Domain experts perform this evaluation. The evaluation of this proposal has been completed with participants belonging to the health area through a case study. This evaluation generates input data for engineers to apply data mining techniques to reveal strategic knowledge. Finally, from the psychologist's point of view, the results showed that the contribution results are appropriate for achieving healthy aging indicators.