Modelo de red neuronal artificial para predecir resultados académicos en la asignatura Matemática II

Flores, Fernando Alaín Incio; Sanchez, Dulce Lucero Capuñay; Urbina, Ronald Omar Estela

doi:10.15359/ree.27-1.14516

Cited by 4 publications

(5 citation statements)

References 25 publications

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“…The metrics and the selected algorithm indicate that the model is quite robust because it updates the weight and bias values at the culmination of each epoch, as stated by Incio et al [39]. In addition, since it combines the gradient descent method and the Gauss-Newton method, it becomes an efficient optimization tool that reduces the sum of squared errors [40].…”

Section: Neural Net Fitting Turbidity Prediction Modelmentioning

confidence: 99%

“…curve (Figure 2) also displays the efficiency of the neural network model observing a good fit between predicted and experimental values, as mentioned by Noor et al [30] and Sahin et al [38], which is also explained for the upper value of R 2 , justifying the selection. The metrics and the selected algorithm indicate that the model is quite robust because it updates the weight and bias values at the culmination of each epoch, as stated by Incio et al [39]. In addition, since it combines the gradient descent method and the Gauss-Newton method, it becomes an efficient optimization tool that reduces the sum of squared errors [40].…”

Section: Neural Net Fitting Turbidity Prediction Modelmentioning

confidence: 99%

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An Artificial Intelligence Approach for Estimating the Turbidity of Artisanal Wine and Dosage of Clarifying Agents

De La Cruz Rojas,

Nuñez-Pérez,

Lara-Fiallos

et al. 2024

Applied Sciences

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Red wine is a beverage consumed worldwide and contains suspended solids that cause turbidity. The study’s purpose was to mathematically model estimated turbidity in artisanal wines concerning the dosage and types of fining agents based on previous studies presenting positive results. Burgundy grape wine (Vitis lambrusca) was made and clarified with ‘yausabara’ (Pavonia sepium) and bentonite at different concentrations. The system was modelled using several machine learning models, including MATLAB’s Neural Net Fitting and Regression Learner applications. The results showed that the validation of the neural network trained with the Levenberg–Marquardt algorithm obtained significant statistical indicators, such as the coefficient of determination (R2) of 0.985, mean square error (MSE) of 0.004, normalized root mean square error (NRSME) of 6.01 and Akaike information criterion (AIC) of −160.12, selecting it as the representative model of the system. It presents an objective and simple alternative for measuring wine turbidity that is useful for artisanal winemakers who can improve quality and consistency.

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Section: Neural Net Fitting Turbidity Prediction Modelmentioning

confidence: 99%

Section: Neural Net Fitting Turbidity Prediction Modelmentioning

confidence: 99%

An Artificial Intelligence Approach for Estimating the Turbidity of Artisanal Wine and Dosage of Clarifying Agents

De La Cruz Rojas,

Nuñez-Pérez,

Lara-Fiallos

et al. 2024

Applied Sciences

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show abstract

“…Una red neuronal es un modelo matemático computacional que tiene como objetivo un aprendizaje mediante experiencias, funcionando como las neuronas de un cerebro humano (Incio-Flores et al, 2023). Para Incio-Flores et al, (2023), una red neuronal está formada por: una estructura interna, determinada por los pesos o sinapsis, que tienen funciones inhibidoras o excitadoras; un sumador, que tiene como finalidad ponderar la suma de todas las entradas multiplicadas por la sinapsis; una función de activación, no línea; y un umbral de exterior. La unidad principal de una red neuronal es la neurona, dispositivo que, a partir de un conjunto de datos de entrada, puede generar una salida (Elman, 1991).…”

Section: La Inteligencia Artificial Y Las Redes Neuronalesunclassified

Análisis mediante inteligencia artificial de las emociones del alumnado autista en la interacción social con el robot NAO

Lorenzo Lledó,

Lorenzo-Lledó,

Rodríguez-Quevedo

2024

Red

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Currently, technology is the most widely used tool in the development of daily life activities. The number of fields of knowledge that benefit from its versatility and application in the development of their activities is increasing. In the educational environment, it allows the generation of activities adapted to the needs of students. In recent years, robotics and artificial intelligence are the most widespread. The characteristics of these tools favour their application with students with autism spectrum disorder. Therefore, the objective of the research is the application of robotics to promote communication and social interaction in students with autism by analysing the emotions they show throughout the different activities. For this purpose, a pilot study was implemented with the NAO robot and four autistic children who developed imitation, game and social interaction activities. An automatic system based on convolutional neural networks was used to detect mood states in the interaction process. The results show that sadness, happiness and anger are the emotions most likely to occur in the participants. Therefore, it is concluded that the robot and the artificial intelligence system are a fundamental element to help express emotions in social interaction. Actualmente, la tecnología es la herramienta más utilizada en el desarrollo de las actividades de la vida diaria. Cada vez es mayor, el número de campos de conocimiento que se benefician de su versatilidad y la aplicación en el desarrollo de sus actividades. En el entorno educativo, permite generar actividades adaptadas a las necesidades del alumnado. En los últimos años, la robótica y la inteligencia artificial son las que mayor difusión están teniendo. Las características de estas herramientas favorecen su aplicación con el alumnado con Trastorno del Espectro Autista. Por tanto, el objetivo de la investigación es la aplicación de la robótica para favorecer la comunicación e interacción social en el alumnado con autismo analizando las emociones que manifiestan a lo largo de las distintas actividades. Para ello, se implementó un estudio piloto con el robot NAO y cuatro niños autistas que desarrollaron actividades de imitación, juego e interacción social. Durante su realización se utilizó un sistema automático basado en redes neuronales convolucionales para detectar los estados de ánimo en el proceso de interacción. Los resultados muestran que tristeza, felicidad y enfado son las emociones que tiene una mayor probabilidad de producirse en los participantes. Por tanto, se concluye que el robot y el sistema de inteligencia artificial son un elemento fundamental para ayudar a expresar sus emociones en las interacciones sociales.

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“…When background data is considered, it often focuses on specific academic programs (Saire, 2023;García, 2021). Furthermore, most studies predict course or year success, not overall academic performance (Puga & Torres, 2023;Incio et al, 2023;Menacho, 2017), limiting policy applications. Studies that consider data at a single point in time also face the problem of "concept drift", namely, a high risk of disconnection between the training data and new rounds of real-life data in a changing environment (Mathrani et al, 2021).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Studies that consider data at a single point in time also face the problem of "concept drift", namely, a high risk of disconnection between the training data and new rounds of real-life data in a changing environment (Mathrani et al, 2021). Some studies incorporate ad hoc student surveys with small datasets (<100 observations), affecting model generalizability (Incio et al, 2023;García, 2021).…”

Section: Literature Reviewmentioning

confidence: 99%

Predicting undergraduate academic performance in a leading Peruvian university: A machine learning approach

Salas,

Caldas

2024

EDUCA

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Despite improved higher education accessibility in low and middle-income countries (LMICs), challenges persist in student drop-out, especially for socio-economically disadvantaged students. While machine learning models have enhanced our understanding of this challenge by predicting academic performance, many studies overlook LMIC-specific institutional factors or focus on specific courses, limiting their generalizability and policy uses. To address these issues, the authors compiled a comprehensive database using administrative and census data to predict undergraduate academic performance at the Pontifical Catholic University of Peru (PUCP). The study found that the most effective models were tree-based ensembles, particularly Random Forest, with key predictors including prior secondary school performance and university admission test scores. They present a high-performing model using only ten features that can predict future academic performance and potentially aid in reducing student drop-out at PUCP.

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Modelo de red neuronal artificial para predecir resultados académicos en la asignatura Matemática II

Cited by 4 publications

References 25 publications

An Artificial Intelligence Approach for Estimating the Turbidity of Artisanal Wine and Dosage of Clarifying Agents

An Artificial Intelligence Approach for Estimating the Turbidity of Artisanal Wine and Dosage of Clarifying Agents

Análisis mediante inteligencia artificial de las emociones del alumnado autista en la interacción social con el robot NAO

Predicting undergraduate academic performance in a leading Peruvian university: A machine learning approach

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