No abstract

This paper presents a literary review of facial recognition in 2D, which plays an important role in the life of the human being in terms of safety, work activity, etc. The focus is on the results obtained by some researchers with the application of feature extraction techniques, pattern classifiers, databases and their respective percentage of efficiency obtained. The objective is to determine efficient techniques that allow an optimal 2D facial recognition process, based on the quality of databases, feature extractors and pattern classifiers.

This article presented in the context of 2D global facial recognition, using Gabor Wavelet's feature extraction algorithms, and facial recognition Support Vector Machines (SVM), the latter incorporating the kernel functions: linear, cubic and Gaussian. The models generated by these kernels were validated by the cross validation technique through the Matlab application. The objective is to observe the results of facial recognition in each case. An efficient technique is proposed that includes the mentioned algorithms for a database of 2D images. The technique has been processed in its training and testing phases, for the facial image databases FERET [1] and MUCT [2], and the models generated by the technique allowed to perform the tests, whose results achieved a facial recognition of individuals over 96%.

The development of information technology has generated a large number of investigation areas, with data mining being one of them. Investigation in databases and information technology has led to an approach to store and manipulate these precise data for greater decision making. Data mining is a process of extracting useful information, deriving patterns and trends from huge amounts of data. These patterns and trends are known as a data mining model and can be applied in the Business. Data mining is also called knowledge discovery process, knowledge extraction from data, knowledge extraction or data analysis / patterns. This article presents a first step towards the unification of the framework for the discovery of knowledge in databases. The elements between milfing data, knowledge discovery, and other related fields are described. Processes of knowledge discovery are defined in database and basic data extraction algorithms, application problems are discussed and it concludes with a focus on the application of data mining in the business.

El presente artículo muestra la utilización de inteligencia artificial (IA) para ser más específico redes neuronales, en el análisis del estado de los suelos que poseen los invernaderos del sector norte de la provincia de Cotopaxi, esto con el uso de un sistema informático y sensores con los cuales se obtuvieron datos para verificar si el suelo donde se producen las rosas es apto para su respectivo sembrío. El objetivo de este trabajo es obtener datos del estado del suelo dentro de invernaderos para su análisis con la utilización de redes neuronales. Enfocándose en los resultados obtenidos en (Segovia, Rojas, & Quishpe, 2021), se evidencia la variación de resultados con respecto a los rangos de un suelo óptimo. Siendo redes neuronales más estable para la medición de estado de los suelos. Este análisis permite sacar conclusiones y toma de decisiones acertadas para el mantenimiento y control del suelo. La técnica utilizada resultó muy eficiente por cuanto coincide con el rango establecido por el sector agrícola.