Redes neuronais artificiais na produção de tecnologia educativa para o ensino da diagonalização

This research has been generated to solve a deficit of linear algebra for higher education students of technical sciences. The solution was based on artificial neural networks and incorporated as an educational technology to support students during their autonomous tasks, accumulating knowledge and simulating the teacher role. This paper provides a guide involving three stages for those who wish to develop intelligent systems based on artificial neural networks for such a subject. La presente investigación surgió para resolver el déficit de promoción en la asignatura de álgebra lineal de los estudiantes de las carreras de ciencias técnicas de la Educación Superior. La solución se basó en incorporar redes neuronales artificiales como tecnología educativa para apoyar al estudiante durante su estudio independiente, acumulando conocimiento y simulando el rol de un profesor. Este artículo ofrece una guía que comprende tres etapas para aquellas personas que deseen desarrollar sistemas inteligentes basados en redes neuronales artificiales para dicha asignaturaA presente pesquisa apareceu para resolver o déficit de promoção na matéria de álgebra lineal dos estudantes das carreiras de ciências técnicas da Educação Superior. A solução baseou-se na incorporação de redes neuronais artificiais como tecnologia educativa para o apoio do estudante durante o seu estudo independente, onde de um lado acumula conhecimento e de outro simula o role de um professor. Este artigo oferece uma guia que compreende três etapas para aquelas pessoas que desejarem controlar sistemas inteligentes baseados em redes neuronais artificiais para a dita matéria

Online and Non-Parametric Drift Detection Methods Based on Hoeffding’s Bounds

I. Frías-Blanco, J. d. Campo-Ávila, G. Ramos-Jiménez, R. Morales-Bueno, A. Ortiz-Díaz and Y. Caballero-Mota, "Online and Non-Parametric Drift Detection Methods Based on Hoeffding’s Bounds," in IEEE Transactions on Knowledge and Data Engineering, vol. 27, no. 3, pp. 810-823, 1 March 2015 doi: 10.1109/TKDE.2014.2345382. © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Incremental and online learning algorithms are more relevant in the data mining context because of the increasing necessity to process data streams. In this context, the target function may change over time, an inherent problem of online learning (known as concept drift). In order to handle concept drift regardless of the learning model, we propose new methods to monitor the performance metrics measured during the learning process, to trigger drift signals when a significant variation has been detected. To monitor this performance, we apply some probability inequalities that assume only independent, univariate and bounded random variables to obtain theoretical guarantees for the detection of such distributional changes. Some common restrictions for the online change detection as well as relevant types of change (abrupt and gradual) are considered. Two main approaches are proposed, the first one involves moving averages and is more suitable to detect abrupt changes. The second one follows a widespread intuitive idea to deal with gradual changes using weighted moving averages. The simplicity of the proposed methods, together with the computational efficiency make them very advantageous. We use a Naïve Bayes classifier and a Perceptron to evaluate the performance of the methods over synthetic and real data.Supported in part by the SESAAME project number TIN2008-06582-C03-03 of the MICINN, Spain. Supported in part by the AUIP (Asociación Universitaria Iberoamericana de Postgrado)

Aplicación de la investigación de operaciones a la distribución de recursos relacionados con la COVID-19

Aim: To apply the vehicle routing model based on optimized decision-making for the distribution of medical resources to hospitalized patients, and patients with a possible COVID-19 diagnosis, in Camagüey, Cuba. Methods: Heterogeneous vehicle routing problems with time windows were used in combination with optimization algorithms to cope with the distribution of supplies. Main results: A total of 15 models were used in the experiment to study the behavior of the algorithms applied to the problem. The CVRP library was run in Matlab. Three metaheuristic models were utilized: EDA, SA, VNS. FSMVRPTW was solved according to the information modeled, through the EDA and VNS algorithms. The latter was included in the study for its open source code, in Excel. Conclusions: Studies of vehicle routing problems have shown their usefulness in different complex scenarios, such as pandemics, to optimize the distribution of resources. The existence of optimum organization of transportation to distribute medical resources in COVID-19 times is a vital tool for decision-making in the province of Camagüey, which can be extended to the whole country.Objetivo: Aplicar el modelo de enrutamiento de vehículos combinado con algoritmos de optimización para la toma de decisiones en la distribución de insumos relacionados con el servicio asistencial a pacientes hospitalizados y sospechosos de la COVID-19 en Camagüey, Cuba. Métodos: Se utilizaron los problemas de enrutamiento de vehículos heterogéneos con ventanas de tiempo, en combinación con algoritmos de optimización para solucionar la distribución de estos recursos. Principales resultados: Se experimentó con un total de 15 modelos para el estudio del comportamiento de los algoritmos aplicados al problema, donde se utilizó la biblioteca CVRP, implementada en Matlab. Se implementaron tres de metaheurísticas: EDA, SA, VNS. A partir de la información modelada se procedió a la solución del problema FSMVRPTW a través de algoritmos EDA y VNS, utilizado este último por contar con una implementación de código abierto en Excel. Conclusiones: Los estudios acerca del problema de enrutamiento de vehículos han demostrado su utilidad en diferentes situaciones complejas, como las pandemias, para optimizar la distribución de recursos. En tiempos de COVID-19, contar con una organización del transporte óptima para distribuir los recursos médicos, es una herramienta vital para la toma de decisiones en la provincia Camagüey, extensible a toda Cuba

Diseño de una escala predictiva de mortalidad en pacientes con enfermedad renal crónica

Introducción: La predicción de mortalidad en pacientes con enfermedad renal crónica, mediante escalas o índices pronósticos presenta limitaciones reales. Objetivo: Diseñar una escala predictiva de mortalidad en pacientes con enfermedad renal crónica. Métodos: Se realizó un estudio observacional, analítico, longitudinal prospectivo en 169 pacientes con enfermedad renal crónica desde el 1 de enero de 2022 al 31 de diciembre de 2022. La investigación se desarrolló en 2 etapas: Durante los primeros 6 meses del año se analizaron las variables de estudio para el diseño de la escala predictiva. En los próximos 6 meses, los pacientes fueron seguidos para identificar la ocurrencia o no de la variable dependiente mortalidad. Se determinó la capacidad discriminatoria de la escala predictiva y se evaluaron curvas de supervivencia. Resultados: Las variables que conformaron la escala predictiva fueron edad > 65 años, enfermedad cardiovascular, albúmina 390 mmol/L. El poder discriminatorio para predecir mortalidad fue bueno, índice C: 0,856 (IC 95 %: 0,783-0,929; p< 0,001). Los pacientes con valores menores a 4 puntos presentaron media de supervivencia de 149,438 ± 7,296 días. En cambio, los que tenían valores superiores presentaron media de supervivencia de 93,128 ± 8,545 días. Conclusiones: La escala predictiva contribuyó a la estratificación del riesgo de mortalidad de los pacientes. Las variables incluidas son de fácil determinación e interpretación por lo que es un modelo útil en la toma de decisiones médicas en el ámbito clínico actual

A Recommender System for Programming Online Judges Using Fuzzy Information Modeling

Programming online judges (POJs) are an emerging application scenario in e-learning recommendation areas. Specifically, they are e-learning tools usually used in programming practices for the automatic evaluation of source code developed by students when they are solving programming problems. Usually, they contain a large collection of such problems, to be solved by students at their own personalized pace. The more problems in the POJ the harder the selection of the right problem to solve according to previous users performance, causing information overload and a widespread discouragement. This paper presents a recommendation framework to mitigate this issue by suggesting problems to solve in programming online judges, through the use of fuzzy tools which manage the uncertainty related to this scenario. The evaluation of the proposal uses real data obtained from a programming online judge, and shows that the new approach improves previous recommendation strategies which do not consider uncertainty management in the programming online judge scenarios. Specifically, the best results were obtained for short recommendation lists

Algoritmo para el aprendizaje de reglas de clasificación basado en la teoría de los conjuntos aproximados extendida

Rough sets have allowed developing several machine learning techniques, among them methods to discover rules of classification. In this paper, we present an algorithm to generate rules of classification based on similarity relations, this allows to apply this method in the case of features with discrete or real domains. The experimental results show a satisfactory performance of this algorithm in comparison with other such as C4.5 and MODLELos conjuntos aproximados han demostrado ser efectivos para desarrollar técnicas de aprendizaje automático, entre ellos métodos para el descubrimiento de reglas de clasificación. En este trabajo se presenta un algoritmo para generar reglas de clasificación basado en relaciones de similaridad, lo que permite que sea aplicable en casos donde los rasgos tienen dominio discreto o continuo. Los resultados experimentales muestran un desempeño satisfactorio en comparación con otros algoritmos conocidos como C4.5 y MODLE

Effects of using reducts in the performance of the irbasir algorithm

Feature selection is a preprocessing technique with the objective of finding a subset of attributes that improve the classifier performance. In this paper, a new algorithm (IRBASIRRED) is presented for the generation of learning rules that uses feature selection to obtain the knowledge model. Also a new method (REDUCTSIM) is presented for the reduct's calculation using the optimization technique, Particle Swarm Optimization (PSO). The proposed algorithm was tested on data sets from the UCI Repository and compared with the algorithms: C4.5, LEM2, MODLEM, EXPLORE and IRBASIR. The results obtained showed that IRBASIRRED is a method that generates classification rules using subsets of attributes, obtaining better results than the algorithm where all attributes are usedLa selección de atributos es una técnica de preprocesado cuyo objetivo es buscar un subconjunto de atributos que mejore el rendimiento del clasificador. Basándonos en este concepto en este trabajo se presenta un nuevo algoritmo para la generación de reglas de aprendizaje que utiliza la selección de atributos para obtener el modelo de conocimiento (IRBASIRRED). Se presenta también un nuevo método (REDUCTSIM) para el cálculo de reductos utilizando la técnica de optimización PSO (Particle Swarm Optimization). El algoritmo propuesto fue probado en conjuntos de datos de la UCI Repository y se comparo con los algoritmos C4.5, MODLEM, EXPLORE e IRBASIR. Los resultados obtenidos demuestran que IRBASIRRED es un método que genera reglas de clasificación utilizando subconjuntos de atributos reducidos, obteniendo mejores resultados que con el algoritmo donde se utilizaban todos los atributos

Optimización del stock de piezas de repuesto para equipos médicos

One of the most common problems for warehouse management is the spare parts stock planning to cover the repair and maintenance needs of medical equipment. At the Center for Clinical and Electro Engineering, the planning of spare parts for maintenance and replacement is carried out through reports issued by electromedical who work the health areas of the country. Nowadays the recorded information, plus expert judgment is not enough for carrying out an optimum planning that would supply the correct amount of parts demanded, in an adequate period of time. Taking into account these insufficiencies this paper presents an algorithm that allows the optimization of the stock of spare parts for medical equipment. For its conformation, statistics estimation techniques were used: Stratified Random Sampling, Correlation and Simple Linear Regression

Exploring content-based group recommendation for suggesting restaurants in Havana City

Recommender systems (RSs) are a relevant kind of artificial intelligence-based systems focused on providing users with the information that best fit their preferences and needs in a search space overloaded of possible options. Specifically, group recommender systems (GRSs) are a special type of RS centered on recommending items that are consumed in groups and not individually, being TV program and touristic packages key examples of such items. The current work is focused on proposing a content-based group recommendation approach (CB-GRS) contextualized to the restaurant recommendation domain. In contrast to previous content-based group recommendation models, the proposal incorporates novel stages such as restaurants feature imputation, the generation of a virtual group profile, the use of feature weighting, and the automatic selection of the most appropriate aggregation approach for composing group recommendations. The proposal is evaluated in an original recommendation scenario, related to restaurant from Havana City in Cuba, where several restaurant attributes are identified for applying the proposed CB-GRS approach. The experimental protocol evaluates individually each component of the proposal, evidencing their importance as part of the whole framework. Furthermore, the comparison with previous works has been also developed. The proposed approach can be applied in other recommendation scenarios, and in addition, the developed experimental protocol is generalizable for the evaluation of further content-based individual and group recommendation approaches in the tourism domain