Influencia de un entorno virtual de enseñanza aprendizaje en la afectividad hacia las matemáticas de estudiantes de secundaria: estudio de casos

Presentamos un estudio de casos sobre cómo afecta el uso de un Entorno Virtual de Enseñanza y Aprendizaje (EVEA) en la afectividad hacia las matemáticas de un grupo de alumnos de secundaria. Para esta investigación se ha diseñado un EVEA basado en la programación, cuya estructura básica la constituyen Scratch, Edmodo, PowToon y actividades específicas correspondientes al currículo de Matemáticas de E.S.O. La metodología es experimental, con un grupo de contraste, un pre-test, un post-test y análisis de las respuestas obtenidas con un cuestionario diseñado ad-oc. Podemos concluir que se produjo un aumento de la afectividad y como consecuencia un cambio semi positivo en los resultados académicos

Classification of Signals by Means of Genetic Programming

[Abstract] This paper describes a new technique for signal classification by means of Genetic Programming (GP). The novelty of this technique is that no prior knowledge of the signals is needed to extract the features. Instead of it, GP is able to extract the most relevant features needed for classification. This technique has been applied for the solution of a well-known problem: the classification of EEG signals in epileptic and healthy patients. In this problem, signals obtained from EEG recordings must be correctly classified into their corresponding class. The aim is to show that the technique described here, with the automatic extraction of features, can return better results than the classical techniques based on manual extraction of features. For this purpose, a final comparison between the results obtained with this technique and other results found in the literature with the same database can be found. This comparison shows how this technique can improve the ones found.Instituto de Salud Carlos III; RD07/0067/0005Xunta de Galicia; 10SIN105004P

Using Genetic Algorithms for Automatic Recurrent ANN Development: an Application to EEG Signal Classification

[Abstract] ANNs are one of the most successful learning systems. For this reason, many techniques have been published that allow the obtaining of feed-forward networks. However, fe w works describe techniques for developing recurrent networks. This work uses a genetic algorithm for automatic recurrent ANN devel opment. This system has been applied to solve a well-known problem: classi fication of EEG signals from epileptic patients. Results show the high performance of this system, and its ability to develop simple networks, with a low number of neurons and connections.Red Gallega de Investigación sobre Cáncer Colorrectal; ref. 2009/58Programa Ibeoramericano de Ciencia y Tecnología para el Desarrollo; 209RT0366Ministerio de Industria, Turismo y Comercio; TSI-020110-2009-53Xunta de Galicia; 10SIN105004PRInstituto de Salud Carlos III; PIO52048Instituto de Salud Carlos III; RD07/0067/000

Approach of Genetic Algorithms With Grouping Into Species Optimized With Predator-Prey Method for Solving Multimodal Problems

[Abstract] Over recent years, Genetic Algorithms have proven to be an appropriate tool for solving certain problems. However, it does not matter if the search space has several valid solutions, as their classic approach is insufficient. To this end, the idea of dividing the individuals into species has been successfully raised. However, this solution is not free of drawbacks, such as the emergence of redundant species, overlapping or performance degradation by significantly increasing the number of individuals to be evaluated. This paper presents the implementation of a method based on the predator-prey technique, with the aim of providing a solution to the problem, as well as a number of examples to prove its effectiveness

Aplicando flipped classroom para el aprendizaje basado en problemas (ABP) en secundaria

La experiencia que exponemos a continuación muestra la aplicación del método de enseñanza flipped classroom combinado con aprendizaje basado en problemas ABP. La experiencia ha sido realizada en aulas de secundaria

Applied Computational Techniques on Schizophrenia Using Genetic Mutations

[Abstract] Schizophrenia is a complex disease, with both genetic and environmental influence. Machine learning techniques can be used to associate different genetic variations at different genes with a (schizophrenic or non-schizophrenic) phenotype. Several machine learning techniques were applied to schizophrenia data to obtain the results presented in this study. Considering these data, Quantitative Genotype – Disease Relationships (QDGRs) can be used for disease prediction. One of the best machine learning-based models obtained after this exhaustive comparative study was implemented online; this model is an artificial neural network (ANN). Thus, the tool offers the possibility to introduce Single Nucleotide Polymorphism (SNP) sequences in order to classify a patient with schizophrenia. Besides this comparative study, a method for variable selection, based on ANNs and evolutionary computation (EC), is also presented. This method uses half the number of variables as the original ANN and the variables obtained are among those found in other publications. In the future, QDGR models based on nucleic acid information could be expanded to other diseases.Programa Iberoamericano de Ciencia y Tecnología para el Desarrollo; 209RT-0366Xunta de Galicia; 10SIN105004PRInstituto de Salud Carlos III; RD07/0067/0005Xunta de Galicia; Ref. 2009/5

Predicting Inflow Flow in Hydraulic Dams Using Artificial Neural Networks

Cursos e Congresos, C-155[Abstract] Accurate prediction of inflow in dams plays a crucial role in water resource management Kim et al. (2019); Vargas-Garay et al. (2018); Zhong et al. (2018) and risk mitigation Costabile et al. (2020); Rabuñal et al. (2007). This study focuses on the Portodemouros dam (located between the provinces of A Coruña and Pontevedra), where a model based on a Long Short-Term Memory (LSTM) artificial neural network has been implemented to predict dam inflow. The results demonstrate the well-established effectiveness of the LSTM network in flow prediction Dongkyuna and Seokkoob (2021); Jo and Jung (2023); Li et al. (2020) applied to the Portodemouros dam compared to other models. This comparison has already been performed in other studies with both mathematical models Amirreza et al. (2022); Ansori and Anwar (2022); A.R1 et al. (2018); Beck et al. (2017); Ciabatta et al. (2016); Costabile et al. (2020); Fan et al. (2013); Hermanovsky et al. (2017); Kim et al. (2019); Vargas-Garay et al. (2018); Zhong et al. (2018), genetic programming Aytek et al. (2008); Havl´ıˇcek et al. (2013); Heˇrmanovsk´y et al. (2017); Rabuñal et al. (2007) and other machine learning algorithms Jo and Jung (2023). Combining precipitation data from multiple regions and meteorological forecasts significantly enhances the model’s ability to anticipate variations in dam inflow. This improved accuracy is essential for early flood detection and informed decision-making in dam operation. This study forms part of the Marine Science programme (ThinkInAzul) supported by Ministerio de Ciencia e Innovación and Xunta de Galicia with funding from European Union NextGenerationEU (PRTR-C17.I1) and European Maritime and Fisheries Fun

Using Genetic Algorithms to Improve Support Vector Regression in the Analysis of Atomic Spectra of Lubricant Oils

[Abstract] Purpose – The purpose of this paper is to assess the quality of commercial lubricant oils. A spectroscopic method was used in combination with multivariate regression techniques (ordinary multivariate multiple regression, principal components analysis, partial least squares, and support vector regression (SVR)). Design/methodology/approach – The rationale behind the use of SVR was the fuzzy characteristics of the signal and its inherent ability to find nonlinear, global solutions in highly complex dimensional input spaces. Thus, SVR allows extracting useful information from calibration samples that makes it possible to characterize physical-chemical properties of the lubricant oils. Findings – A dataset of 42 spectra measured from oil standards was studied to assess the concentration of copper into the oils and, thus, evaluate the wearing of the machinery. It was found that the use of SVR was very advantageous to get a regression model. Originality/value – The use of genetic algorithms coupled to SVR was considered in order to reduce the time needed to find the optimal parameters required to get a suitable prediction model

Afectividad hacia las matemáticas en un EVEA

Presentamos un estudio de casos sobre la mejora de la afectividad por parte de los alumnos de secundaria hacia la asignatura de matemáticas utilizando un EVEA (Entorno Virtual de Enseñanza y Aprendizaje) cuya estructura básica la constituyen Scratch, Edmodo, PowToon y actividades específicas correspondientes al currículo de Matemáticas de E.S.O. Comenzamos con una breve descripción de los elementos del EVEA y las principales características de las actividades específicas diseñadas, todas ellas enmarcadas en el bloque 1 de Aritmética de la asignatura de matemáticas de primero de la E.S.O. según el Decreto 19/2015, de 12 de junio, de la Comunidad Autónoma de La Rioja. A continuación describimos el análisis de resultados obtenidos por tres alumnos, seleccionados en función de sus resultados académicos anteriores y la información aportada por un test sobre la afectividad creado al efecto. Como conclusión podemos decir que en al menos dos de los tres casos la afectividad hacia las matemáticas ha aumentado considerablemente, en el otro caso no ha aumentado al mismo nivel que los otros dos. Respecto a los resultados académicos, estos han mejorado, pero no han llegado a suponer un aumento tan considerable como puede llegar a ser en la afectividad