RESUMEN Introducción: la evaluación del modelo homeostático es una prueba muy cercana al estándar de oro (pinza euglucémica). Objetivo: entrenar una red neuronal tipo perceptrón multicapa para determinar el modelo homeostático de resistencia a la insulina. Metodología: estudio analítico y transversal. El aprendizaje de la red neuronal se realizó a partir de una base de datos de 2004 adultos venezolanos. Posteriormente se añadió 4363 adultos mexicanos de la base de datos de la Encuesta Nacional de Salud y Nutrición (ENSANUT). Las variables fueron modelo homeostático de resistencia a la insulina (HOMA2-IR), insulina basal, glucosa basal. Se empleó redes neuronales tipo perceptrón multicapa. Resultados: el entrenamiento del modelo de redes neuronales tuvo un error relativo de 0,003, mientras que en la prueba fue 0,005. Para HOMA2-IR cualitativo, el porcentaje de pronósticos incorrectos fue 0,60 % en el entrenamiento, y 0,70 % en la prueba. Después del aprendizaje del modelo, se añadió valores de insulina y glucosa basal de 4363 adultos mexicanos, observándose que los valores de HOMA2-IR generados mediante perceptrón multicapa mantuvieron la eficiencia del modelo, obteniéndose un coeficiente de determinación R2 de 0,983, lo que implica que el 98 % de la variación en los valores de HOMA2-IR pueden ser explicados mediante valores de HOMA2-IR obtenidos mediante perceptrón multicapa. Conclusiones: la red neuronal tipo perceptrón multicapa, da resultados virtualmente idénticos a los obtenidos mediante la calculadora HOMA2-IR. La implementación de este algoritmo puede ser beneficioso como herramienta de fácil implementación en los sistemas de atención primaria, especializada y en entornos hospitalarios.

ABSTRACT Introduction: the evaluation of the homeostatic model is a test very close to the gold standard (euglycemic clamp). Objective: train a multilayer perceptron-type neural network to determine the homeostatic model of insulin resistance. Methodology: analytical and cross-sectional study. The learning of the neural network was carried out from a database of 2004 Venezuelan adults. Subsequently, 4,363 Mexican adults were added to the database of the National Health and Nutrition Survey (ENSANUT). The variables were homeostatic model of insulin resistance (HOMA2-IR), basal insulin, and basal glucose. Multilayer perceptron-type neural networks were used. Results: the training of the neural network model had a relative error of 0.003, while in the test it was 0.005. For qualitative HOMA2-IR, the percentage of incorrect predictions was 0.60 % in training, and 0.70 % in testing. After learning the model, insulin and basal glucose values from 4363 Mexican adults were added, observing that the HOMA2-IR values generated by multilayer perceptron maintained the efficiency of the model, obtaining a coefficient of determination R2 of 0.983, which implies that 98 % of the variation in HOMA2-IR values can be explained by HOMA2-IR values obtained using multilayer perceptron. Conclusions: the multilayer perceptron-type neural network gives results virtually identical to those obtained using the HOMA2-IR calculator. The implementation of this algorithm can be beneficial as a tool that is easy to implement in primary and specialized care systems and in hospital environments.

RESUMEN El propósito de esta investigación es proponer un modelo para el diseño de ambientes laborales en las Pymes Bogotá Área - Metropolitana tomando como guía la metodología de la Ingeniera Kansei, con el apoyo de instrumentos como la encuesta y la prueba de diferencial semántico, las redes neuronales (perceptrón multicapa) para su validación. Se elabora un cuestionario con cuestionario con el apoyo de expertos, al que se le realiza una prueba piloto para mejorar y evaluar la pertinencia de las preguntas. Haciendo uso del Software SPSS se realizó el análisis estadístico, aplicando las pruebas de normalidad, realizando correlaciones de Spearman, análisis de componentes principales, regresión lineal a las ecuaciones resultantes para entender las relaciones entre las percepciones estudiadas y el ambiente laboral en general. Posteriormente, hizo uso del perceptrón multicapa como modelo de redes neuronales para validar las relaciones entre las variables y percepciones propuestas permitiendo a su vez determinar la importancia de las dimensiones emocionales en el diseño de ambientes laborales, a partir de lo cual se pudo concluir que es posible la aplicación de la Ingeniería Kansei para el diseño de ambientes laborales.

ABSTRACT The purpose of this research is to propose a model for the design of work environments in the SMEs Bogotá Metropolitan Area, taking as a guide the methodology of Kansei Engineering, with the support of instruments such as the survey and the semantic differential test, the neural networks (multilayer perceptron) for its validation. A questionnaire is elaborated with the support of experts, and a pilot test is carried out to improve and evaluate the relevance of the questions. Using the SPSS Software, the statistical analysis was performed, applying the normality tests, performing Spearman correlations, principal component analysis, and linear regression to the resulting equations to understand the relationships between the studied perceptions and the work environment in general. Subsequently the multilayer perceptron was used as a neural network model to validate the relationships between the proposed variables and perceptions, allowing at the same time to determine the importance of the emotional dimensions in the design of work environments, from which it was possible to conclude that the application of Kansei Engineering for the design of work environments is possible.

RESUMO O objetivo desta pesquisa é propor um modelo para o projeto de ambientes de trabalho em PMEs da Região Metropolitana de Bogotá, usando a metodologia da Engenharia Kansei como guia, com o apoio de instrumentos como a pesquisa e o teste de diferencial semântico, e redes neurais (perceptron multicamadas) para validação. É elaborado um questionário com o apoio de especialistas, que é testado como piloto para melhorar e avaliar a relevância das perguntas. A análise estatística foi realizada com o software SPSS, aplicando testes de normalidade, correlações de Spearman, análise de componentes principais e regressão linear às equações resultantes para entender as relações entre as percepções estudadas e o ambiente de trabalho em geral. Posteriormente, utilizou-se o perceptron de múltiplas camadas como modelo de rede neural para validar as relações entre as variáveis e as percepções propostas, permitindo, por sua vez, determinar a importância das dimensões emocionais no projeto de ambientes de trabalho, a partir do qual foi possível concluir que é possível aplicar a Engenharia Kansei ao projeto de ambientes de trabalho.

Abstract — Artificial neural networks (ANNs) have become indispensable tools for solving classification tasks across various domains. This systematic literature review explores the landscape of ANN utilization in classification, addressing three key research questions: the types of architectures employed, their accuracy, and the data utilized. The review encompasses 30 studies published between 2019 and 2024, revealing Convolutional Neural Networks (CNNs) as the predominant architecture in image-related tasks, followed by Multilayer Perceptron (MLP) architectures for general classification tasks. Feed Forward Neural Networks (FFNN) exhibited the highest average accuracy with a 97.12%, with specific studies achieving exceptional results across diverse classification tasks. Moreover, the review identifies digitized images as a commonly utilized data source, reflecting the broad applicability of ANNs in tasks such as medical diagnosis and remote sensing. The findings underscore the importance of machine learning approaches, highlight the robustness of ANNs in achieving high accuracy, and suggest avenues for future research to enhance interpretability, efficiency, and generalization capabilities, as well as address challenges related to data quality.

Resumen — Las redes neuronales artificiales (ANNs) se han convertido en herramientas indispensables para resolver tareas de clasificación en diversos dominios. Esta revisión sistemática de la literatura explora el panorama de la utilización de ANN en la clasificación, abordando tres preguntas clave de investigación: los tipos de arquitecturas empleadas, su precisión y los datos utilizados. La revisión abarca 30 estudios publicados entre 2019 y 2024, revelando las Redes Neuronales Convolucionales (CNNs) como la arquitectura predominante en tareas relacionadas con imágenes, seguidas por las arquitecturas de Perceptrón Multicapa (MLP) para tareas de clasificación en general. Las Redes Neuronales de Propagación Hacia Adelante (FFNN) exhibieron la mayor precisión promedio con un 97.12 %, con estudios específicos logrando resultados excepcionales en diversas tareas de clasificación. Además, la revisión identifica las imágenes digitalizadas como una fuente de datos comúnmente utilizada, reflejando la amplia aplicabilidad de las ANN en tareas como el diagnóstico médico y la teledetección. Los hallazgos subrayan la importancia de los enfoques de aprendizaje automático, destacan la robustez de las ANN en lograr una alta precisión y sugieren caminos para investigaciones futuras para mejorar la interpretabilidad, eficiencia y capacidades de generalización, así como abordar desafíos relacionados con la calidad de los datos.

Abstract Corrosion is a recurrent problem in the natural gas industry, due to the presence of corrosive gases such as CO2 and H2S that in the presence of water can attack steel and produce damages and leaks that generate accidents and contamination, that is why the prediction of the tendency of natural gas towards corrosion is essential to develop strategies to inhibit and mitigate this process. The objective of the study was to develop an Artificial Neural Network (ANN) trained with composition data of natural gas samples to predict the corrosive tendency, as an alternative method that can be used as an efficient predictive tool. The ANN was trained with a sample of 46 natural gases and 11 components for each one, in addition to pressure and temperature as operational conditions, representing a total of 598 training data of the ANN, also 8 additional samples were used for external validation of the model. The ANN that developed was within the principles of Bayesian statistics, with a Multilayer Perceptron architecture. It was obtained that the ANN is able to correctly classify 95.65% of the corrosive tendency of the samples, being those with non-corrosive tendency the ones that presented the lowest prediction percentage (75%). The prediction of the external samples followed the expected trend, with lower capacity to classify the non-corrosive ones, and the samples with possible corrosion and corrosive were predicted in 80%. It is concluded that ANN is an efficient tool for predicting the corrosive tendency of natural gas in the presence of CO2 and its effectiveness depends on using samples with all the necessary components to train it, and it can be improved by introducing other parameters such as the amount of water present.

Resumen La corrosión es un problema recurrente en la industria del gas natural, debido a la presencia de gases corrosivos como el CO2 y el H2S que en presencia de agua pueden atacar el acero y producir daños y fugas que generen accidentes y contaminación, es por ello que la predicción de la tendencia del gas natural hacia la corrosión es fundamental para desarrollar estrategias de inhibición y mitigación de este proceso. El objetivo del estudio fue desarrollar una Red Neuronal Artificial (RNA) entrenada con datos de composición de muestras de gas natural para predecir la tendencia corrosiva, como método alternativo que pueda ser utilizado como una herramienta predictiva eficiente. La RNA se entrenó con una muestra de 46 gases naturales y 11 componentes por cada una, además de la presión y la temperatura como condiciones operacionales, lo que representa un total de 598 datos de entrenamiento de la RNA, además se utilizaron 8 muestras adicionales para la validación externa del modelo. La RNA que desarrolló fue dentro de los principios de la estadística Bayesiana, con una arquitectura de Perceptrón Multicapa. Se obtuvo que la RNA es capaz de clasificar correctamente el 95,65% de la tendencia corrosiva de las muestras, siendo aquellas con tendencia no corrosiva las que presentaron el menor porcentaje de predicción (75%). La predicción de las muestras externas siguió la tendencia esperada, con menor capacidad para clasificar a las no corrosivas, y las muestras con posible corrosión y corrosivas fueron predichas en 80%. Se concluye que la RNA es una herramienta eficiente para la predicción de la tendencia corrosiva del gas natural en presencia de CO2 y su efectividad depende de que se utilicen muestras con todos los componentes necesarios para entrenar la misma, además puede ser mejorada al introducir otros parámetros como la cantidad de agua presente.

SUMMARY: The determination of sex differences in anatomical structures is critical in establishing gold standard morphometric data in basic medical sciences, and in surgical and internal sciences in selecting the right area during invasive intervention and applying the correct intervention methodology appropriate to the area. The aim of this study is to determine the sex difference using Machine learning (ML) algorithms and Artificial neural networks (ANN) with parameters obtained from basilar artery. The study was performed on computed tomography angiography images of 63 women and 94 men. The following parameters were measured on the images: initial width of the right vertebral artery, initial width of the left vertebral artery, termination width of the right vertebral artery, termination width of the left vertebral artery, basilar artery width, and basilar artery length. The measurements were used in ML algorithms and ANN input to determine sex differences. As a result of the study, a sex difference rate of 0.84 was determined with the ML algorithms Random Forest (RF), Quadratic Discriminant Analysis (QDA), Extra Tree Classifier (ETC) and 0.84 with the Multilayer Perceptron Classifier (MLCP) algorithm of ANN. As a result of the study, sex difference was found with an accuracy rate of 0.84 using ML algorithms and ANN with parameters obtained from basilar artery. In this context, we think that this study will shed light on basic and clinical medical sciences.

RESUMEN: La determinación de las diferencias de sexo en las estructuras anatómicas es fundamental para establecer datos morfométricos de gold estándar, en las ciencias médicas básicas y en las ciencias quirúrgicas e internas para seleccionar el área correcta durante la intervención invasiva y aplicar la metodología de intervención correcta y apropiada. El objetivo de este estudio fue determinar la diferencia de sexo utilizando algoritmos de aprendizaje automático (ML) y redes neuronales artificiales (ANN) con parámetros obtenidos de la arteria basilar. El estudio se realizó sobre imágenes de angiografía por tomografía computarizada de 63 mujeres y 94 hombres. Se midieron en las imágenes los siguientes parámetros: ancho inicial de la arteria vertebral derecha, ancho inicial de la arteria vertebral izquierda, ancho de terminación de la arteria vertebral derecha, ancho de terminación de la arteria vertebral izquierda, ancho de la arteria basilar y longitud de la arteria basilar. Las mediciones se utilizaron en algoritmos de ML y entradas de ANN para determinar las diferencias de sexo. Como resultado del estudio, se determinó una tasa de diferencia de sexo de 0,84 con los algoritmos ML Random Forest (RF), Análisis Discriminante Cuadrático (QDA), Extra Tree Classifier (ETC) y 0,84 con el algoritmo Multilayer Perceptron Classifier (MLCP) de ANA. Como resultado del estudio, se encontró una diferencia de sexo con una tasa de precisión de 0,84 utilizando algoritmos ML y ANN con parámetros obtenidos de la arteria basilar. En este contexto, pensamos que este estudio será de utilidad en las ciencias médicas básicas y clínicas.

ABSTRACT Rainfall forecasts are of great importance to prevent disasters, economic losses in crops and several important aspects for the country's hydroeconomy, hence the need to develop rainfall prediction models. At the Villa Clara Provincial Meteorological Center (CMPVC), mathematical models with a high level of accuracy are used, but they require a lot of processing time. Hence, the need to look for more current and accurate, ways to make accurate rainfall forecasts but in a faster way. The progress of artificial intelligence and especially artificial neural networks have allowed the development of models to solve the problem of rainfall prediction; using the metrics provided by the World Meteorological Organization (WMO) from 1975 to 2016. In this work, an analysis of the different regression models with multiple outputs to predict rainfall is carry out in the months of May, June, July and August. The regression models that best fit this problem were linear regression, regression tree, k-nearest neighbors, direct regression, chained regression and multilayer perceptron. After running the models, the multilayer perceptron was the regression model with the best results, with a high degree of efficiency for rainfall forecasting.

RESUMEN Los pronósticos de las precipitaciones tienen gran importancia para prevenir desastres, pérdidas económicas en cultivos y varios aspectos importantes para la hidroeconomía del país, de ahí la necesidad de desarrollar modelos de predicción de precipitaciones. En el Centro Meteorológico Provincial de Villa Clara (CMPVC) se emplean modelos matemáticos con un nivel de precisión alto, pero requieren mucho tiempo de procesamiento. De ahí la necesidad de buscar maneras más actuales y precisas para realizar pronósticos de lluvia acertados, pero de manera más rápida. El progreso de la inteligencia artificial y en especial, las redes neuronales artificiales han permitido desarrollar modelos para dar solución al problema de predicción de precipitaciones; mediante las métricas proporcionadas por la Organización Meteorológica Mundial (OMM) comprendidas entre los años 1975 hasta el 2016. En este trabajo se realiza un análisis de los diferentes modelos de regresión con múltiples salidas para predecir las precipitaciones en los meses mayo, junio, julio y agosto. Los modelos de regresión que más se ajustaron a esta problemática fueron la regresión lineal, el árbol de regresión, el k-vecinos más cercanos, la regresión directa, la regresión encadenada y el perceptrón multicapa. Luego de realizar las corridas de los modelos resultó el perceptrón multicapa, el modelo de regresión con mejores resultados, con un alto grado de eficacia para el pronóstico de las precipitaciones.

Resumen En la exploración geofísica, el análisis del terreno es fundamental para planificar la adquisición de datos de gravimetría y magnetometría. No obstante, en la actualidad dichos análisis requieren el uso supervisado de información secundaria extensa. Por este motivo, en este estudio se propone una metodología que utiliza el aprendizaje profundo para evaluar el impacto de la cobertura vegetal y la topografía en la adquisición geofísica. Se usó una red neuronal artificial perceptrón multicapa para considerar cinco variables terrestres, y una red neural convolucional para la clasificación automatizada de coberturas vegetales sobre imágenes satelitales. A partir de esto, se obtuvieron puntos para la localización de estaciones de levantamiento geofísico que cumplen con un criterio de favorabilidad por accesibilidad, distancias, restricciones por cuerpos de agua y coberturas boscosas. La geometría obtenida se evaluó en la exploración del Granito de Durania en Colombia, y los resultados se analizaron utilizando transectas de adquisición y modelado computacional de anomalías magnéticas y de gravedad. Finalmente, se aplicaron técnicas de interpolación donde el método Ponderación de Distancia Inversa (IDW) mostró la mejor imagen para interpretar la delimitación del cuerpo intrusivo.

Abstract In geophysical exploration, terrain analysis is fundamental for planning the gravity and magnetic surveys. However, current analyses necessitate supervised use of extensive secondary information. Consequently, this study proposes a methodology that leverages deep learning to assess the impact of vegetation coverage and topography in geophysical acquisition. An artificial neural network multilayer perceptron was used to consider five terrain-related variables, while a convolutional neural network is used for automated classification of vegetation cover on satellite imagery. From this, geophysical acquisition points are obtained, adhering to criteria involving accessibility favourability, distances, restrictions imposed by bodies of water, and forested cover. The obtained geometry was tested in the exploration of the Granito de Durania in Colombia, and the results were analysed using acquisition transects and computational modelling of gravity and magnetic anomalies. Interpolation techniques were applied, with the Inverse Distance Weighting (IDW) method yielding the most informative map for interpreting the delineation of the intrusive body.

Resumen La inteligencia artificial ha permitido desarrollar herramientas para el reconocimiento automático de frutos y hortalizas con mayor precisión y rapidez. El desarrollo de nuevos genotipos de árboles frutales requiere del uso de herramientas tecnológicas para la identificación de variedades con mayor robustez que los métodos convencionales. En esta investigación se aplicaron algoritmos de aprendizaje automático para identificar seis variedades de durazno (Prunus persica L.) CP-03-06, Oro Azteca, Oro San Juan, Cardenal, Colegio y Robin a partir de imágenes digitales de hojas. Los modelos máquina de soporte vectorial (SVM), bosque aleatorio (RF) y perceptrón multicapa (MLP) se entrenaron y evaluaron con base en tres descriptores cromáticos y 14 morfológicos extraídos de imágenes digitales. La evaluación del desempeño en predicción de los modelos se realizó con base en métricas globales y específicas para cada clase objetivo (variedad de durazno). Los cinco descriptores más importantes para identificar las variedades de durazno fueron los tres canales de color HSV (hue, saturation, value), la redondez y la excentricidad de las hojas. SVM obtuvo la mayor precisión global de clasificación con Acc de 98.7 % y F1macro de 98 %. SVM obtuvo el mayor puntaje F1 (99.2 %) para identificar la variedad de durazno CP-03-06 y el menor puntaje F1 (96.1 %) para identificar la variedad Cardenal. La utilización conjunta de descriptores cromáticos y morfológicos mejoró el desempeño de los algoritmos de aprendizaje para identificar las seis variedades de durazno. Los modelos SVM, RF y MLP obtuvieron un Acc de 98.7, 98.6 y 97 %, respectivamente. Este estudio muestra el potencial de las técnicas de aprendizaje automático para su aplicación en el reconocimiento de descriptores de interés en cultivos agrícolas y su aplicación a procesos automatizados en la agricultura.

Summary Artificial intelligence has allowed the development of tools for automatic recognition of fruits and vegetables with greater precision and speed. The development of new genotypes of fruit trees requires the use of technological tools to identify varieties with greater robustness than conventional methods. In this research, machine learning algorithms were applied to identify six peach varieties (Prunus persica L.) CP-03-06, Oro Azteca, Oro San Juan, Cardenal, Colegio and Robin from digital leaf images. The support vector machine (SVM), random forest (RF) and multilayer perceptron (MLP) models were trained and evaluated based on three chromatic and 14 morphological descriptors extracted from digital images. The evaluation of the prediction performance of the models was based on global metrics and specific for each target class (peach variety). The five most important descriptors to identify peach varieties were three HSV color channels (hue, saturation, value), roundness and eccentricity of the leaves. SVM achieved the highest overall classification accuracy with Acc of 98.7 % and F1macro of 98 %. SVM obtained the highest F1 score (99.2 %) to identify the peach variety CP-03-06 and the lowest F1 score (96.1 %) to identify the Cardenal variety. The joint use of chromatic and morphological descriptors improved the performance of learning algorithms to identify the six peach varieties. The SVM, RF and MLP models obtained an Acc of 98.7, 98.6 and 97 %, respectively. This study shows the potential of machine learning methods for their application in recognizing descriptors of interest in agricultural crops and their application to automated processes in agriculture.

RESUMEN Objetivos: evaluar el rendimiento predictivo de diferentes algoritmos de inteligencia artificial para estimar el tiempo de ejecución del baño en cama en pacientes críticos. Métodos: estudio metodológico, que utilizó algoritmos de inteligencia artificial para predecir el tiempo de baño en cama en pacientes críticos. Se analizaron los resultados de modelos de regresión múltiple, redes neuronales perceptrón multicapa y función de base radial, árbol de decisión y random forest. Resultados: entre los modelos evaluados, el modelo de red neuronal con función de base radial, que contiene 13 neuronas en la capa oculta, presentó el mejor desempeño predictivo para estimar el tiempo de ejecución del baño en cama. En la validación de datos, la correlación al cuadrado entre los valores predichos y los valores originales fue del 62,3%. Conclusiones: el modelo de red neuronal con función de base radial mostró mejor rendimiento predictivo para estimar el tiempo de ejecución del baño en cama en pacientes críticos.

RESUMO Objetivos: avaliar a performance preditiva de diferentes algoritmos de inteligência artificial para estimar o tempo de execução do banho no leito em pacientes críticos. Métodos: estudo metodológico, que utilizou algoritmos de inteligência artificial para predizer o tempo de banho no leito em pacientes críticos. Foram analisados os resultados dos modelos de regressão múltipla, redes neurais perceptron multicamadas e função de base radial, árvore de decisão e random forest. Resultados: entre os modelos avaliados, o modelo de rede neural com função de base radial, contendo 13 neurônios na camada oculta, apresentou melhor performance preditiva para estimar o tempo de execução do banho no leito. Na validação dos dados, o quadrado da correlação entre os valores preditos e os valores originais foi de 62,3%. Conclusões: o modelo de rede neural com função de base radial apresentou melhor performance preditiva para estimar o tempo de execução do banho no leito em pacientes críticos.

ABSTRACT Objectives: to assess the predictive performance of different artificial intelligence algorithms to estimate bed bath execution time in critically ill patients. Methods: a methodological study, which used artificial intelligence algorithms to predict bed bath time in critically ill patients. The results of multiple regression models, multilayer perceptron neural networks and radial basis function, decision tree and random forest were analyzed. Results: among the models assessed, the neural network model with a radial basis function, containing 13 neurons in the hidden layer, presented the best predictive performance to estimate the bed bath execution time. In data validation, the squared correlation between the predicted values and the original values was 62.3%. Conclusions: the neural network model with radial basis function showed better predictive performance to estimate bed bath execution time in critically ill patients.

RESUMEN La energía eólica es una importante fuente de energía renovable que se ha desarrollado considerablemente en los últimos años. Con el fin de obtener pronósticos de energía eólica de alta precisión con 24 horas de anticipación para usos operativos y comerciales, se necesita una combinación de modelos físicos y estadísticos. En este artículo se propone y evalúa una metodología híbrida que emplea un modelo numérico (Weather Research and Forecasting) y un algoritmo de red neuronal (NN). La metodología se aplica a un parque eólico en el noroeste de México, una región con alto potencial eólico donde la compleja geografía agrega gran incertidumbre a los pronósticos de energía eólica. Los pronósticos de energía se evalúan usando la generación de energía real en el sitio de interés durante un año y se comparan con dos modelos de referencia: árboles de decisión (DT, por su sigla en inglés) y regresión de vectores de soporte (SVR). El método propuesto muestra un mejor desempeño respecto a los métodos de referencia, mostrando un error absoluto medio porcentual horario de 6.97%, lo que representa 6 y 13 puntos porcentuales menos de error en los pronósticos de energía eólica que con los métodos DT y SVR, respectivamente. El pronóstico de energía eólica NN no es muy preciso cuando tiene un fuerte forzamiento sinóptico, por lo que en su lugar deben emplearse enfoques novedosos como los algoritmos jerárquicos. En general, el modelo propuesto tiene la capacidad de producir pronósticos de energía eólica de alta calidad para la mayoría de las condiciones climáticas que prevalecen en esta región, y de- muestra un buen desempeño respecto a modelos similares para pronósticos de energía eólica a mediano plazo.

ABSTRACT Wind energy is an important renewable source that has been considerably developed recently. In order to obtain successful 24-h lead-time wind power forecasts for operational and commercial uses, a combination of physical and statistical models is desirable. In this paper, a hybrid methodology that employs a numerical weather prediction model (Weather Research and Forecasting) and a neural network (NN) algorithm is proposed and assessed. The methodology is applied to a wind farm in northwestern Mexico, a region with high wind potential where complex geography adds large uncertainty to wind energy forecasts. The energy forecasts are then evaluated against actual on-site power generation over one year and compared with two reference models: decision trees (DT) and support vector regression (SVR). The proposed method exhibits a better performance with respect to the reference methods, showing an hourly normalized mean absolute percentage error of 6.97%, which represents 6 and 13 percentage points less error in wind power forecasts than with DT and SVR methods, respectively. Under strong synoptic forcing, the NN wind power forecast is not very accurate, and novel approaches such as hierarchical algorithms should be employed instead. Overall, the proposed model is capable of producing high-quality wind power forecasts for most weather conditions prevailing in this region and demonstrates a good performance with respect to similar models for medium-term wind power forecasts.

RESUMO Dados de área foliar de plantas de café são importantes para estudos e análises de produtividade, fisiologia, adaptação às condições ambientais e manejos culturais. O objetivo deste trabalho foi predizer a área foliar de plantas de café por meio de redes neurais artificiais e avaliar a eficiência dessa metodologia por meio de comparação com modelos de regressão múltipla. Foram avaliados 43 genótipos de reprodução e idade semelhantes e testados 14 tipos de equações de regressão múltipla a partir de combinações de comprimento e largura de folhas O algoritmo backpropagation foi utilizado para desenvolver redes neurais do tipo perceptron multicamadas, e foram testadas possíveis combinações entre duas funções de ativação da camada intermediária e o número de neurônios na camada intermediária. Na modelagem de redes neurais artificiais, os melhores resultados de ajuste foram encontrados com a função de ativação sigmoide e três neurônios na camada oculta (R² = 0,990; RMSE = 2,855 na fase de treinamento). Considerando os erros (RMSE, MAE e MAPE) e coeficientes de determinação como parâmetros estatísticos comparando os dois métodos utilizados, os modelos que utilizaram redes neurais artificiais apresentaram as melhores estimativas de área foliar nas fases de treinamento e validação. O método de redes neurais artificiais pode ser utilizado como alternativa ou modelo de apoio para estimativa de área foliar de cafeeiros. produtividade fisiologia culturais 4 1 multicamadas R² R (R 0,990 0990 0 990 2855 2 855 2,85 treinamento. . treinamento) RMSE, (RMSE MAPE utilizados validação cafeeiros 0,99 099 99 285 85 2,8 0,9 09 9 28 8 2, 0,

ABSTRACT Leaf area data from coffee plants are important for studies and analyses of grain yield, physiology, adaptation to environmental conditions, and cultural management. The objective of this study was to predict leaf area of coffee plants using artificial neural networks and compare the efficiency of this methodology with multiple regression models. Forty-three genotypes of similar reproduction and age were evaluated, testing 14 types of multiple regression equations from combinations of leaf length and width. The backpropagation algorithm was used to develop multilayer perceptron neural networks; several combinations were tested between two activation functions of the intermediate layer (hidden layer) and the number of neurons in this layer. The best fitting results in the artificial neural network modeling were found with the sigmoid activation function and three neurons in the hidden layer (R² = 0.990 and RMSE = 2.855 in the training phase). Considering the errors (RMSE, MAE, and MAPE) and the coefficient of determination as criteria for best fit, the artificial neural network models better estimated the leaf area in the training and validation phases. Therefore, the artificial neural network methodology can be used as alternative for estimating leaf area of coffee plants. yield physiology conditions management Fortythree Forty evaluated 1 width R² R (R 0990 0 990 0.99 2855 2 855 2.85 phase. phase . phase) RMSE, (RMSE MAE MAPE fit phases Therefore 099 99 0.9 285 85 2.8 09 9 0. 28 8 2.

SUMMARY OBJECTIVE: The study used machine learning models to predict the clinical outcome with various attributes or when the models chose features based on their algorithms. METHODS: Patients who presented to an orthopedic outpatient department with joint swelling or myalgia were included in the study. A proforma collected clinical information on age, gender, uric acid, C-reactive protein, and complete blood count/liver function test/renal function test parameters. Machine learning decision models (Random Forest and Gradient Boosted) were evaluated with the selected features/attributes. To categorize input data into outputs of indications of joint discomfort, multilayer perceptron and radial basis function-neural networks were used. RESULTS: The random forest decision model outperformed with 97% accuracy and minimum errors to anticipate joint pain from input attributes. For predicted classifications, the multilayer perceptron fared better with an accuracy of 98% as compared to the radial basis function. Multilayer perceptron achieved the following normalized relevance: 100% (uric acid), 10.3% (creatinine), 9.8% (AST), 5.4% (lymphocytes), and 5% (C-reactive protein) for having joint pain. Uric acid has the highest normalized relevance for predicting joint pain. CONCLUSION: The earliest artificial intelligence-based detection of joint pain will aid in the prevention of more serious orthopedic complications. OBJECTIVE algorithms METHODS age gender Creactive C reactive protein countliver count liver testrenal renal parameters Random Boosted featuresattributes features/attributes discomfort functionneural neural RESULTS 97 classifications 98 100 , acid) 103 10 3 10.3 creatinine, creatinine (creatinine) 9 8 9.8 AST, AST (AST) 54 5 4 5.4 lymphocytes, lymphocytes (lymphocytes) CONCLUSION intelligencebased intelligence complications 1 10. (creatinine 9. (AST 5. (lymphocytes

ABSTRACT Eggs are a widely consumed source of protein, with consumers often preferring free-range eggs due to their higher nutritive value and prices. However, dishonest traders sometimes mislabel cage eggs as free-range eggs for unjustified profits. Biochemical methods are currently used to differentiate between caged and free-range eggs, which could involve chemical reagents, sample preparation, and costly instruments. In this study, physical traits measurements were combined with machine learning to identify eggs according to their farming system. Measurements of 27 physical traits for 480 eggs were conducted using simple tools, and the multicollinearity was reduced by comparing correlation coefficients, resulting in 16 physical traits. Multi-layer Perceptron Neural Network, Naive Bayes, Linear Support Vector Classifier, Radial Basis Functions Support Vector Classifier and Random Forest were used to create recognition models, and the leave-one-out cross-validation method was used for training and evaluation. The Multi-layer Perceptron Neural Network achieved the best classification performance with an accuracy of 0.94167, a F1 score of 0.94118. The result demonstrates that the physical traits of eggs provide sufficient features for the Multi-layer Perceptron Neural Network classifier. Compared to mainstream biochemical methods, we proposed a novel approach to differentiate between caged and free-range eggs using only physical trait measurements, thereby avoiding the need for chemical reagents, sample preparation, and expensive instruments. protein freerange free range prices However profits reagents preparation instruments study system 2 48 tools coefficients 1 Multilayer Multi layer Bayes models leaveoneout leave one out crossvalidation cross validation evaluation 094167 0 94167 0.94167 F 094118 94118 0.94118 classifier 4 09416 9416 0.9416 09411 9411 0.9411 0941 941 0.941 094 94 0.94 09 9 0.9 0.

Resumo O objetivo deste estudo é analisar os importantes fatores que influenciam a satisfação com a vida dos coreanos e identificar a sua importância. Para tanto, utilizamos artigos acadêmicos relacionados aos fatores que influenciam a satisfação com a vida e dados de questionários da pesquisa sobre integração social realizada anualmente pelo governo coreano. Um método de análise de modelagem de tópicos foi usado para derivar fatores de influência importantes, e um método de análise de rede neural, um dos métodos de aprendizado de máquina, foi usado para analisar a importância relativa dos fatores de influência. A análise mostrou que o fator que teve maior impacto na satisfação com a vida dos coreanos foi a satisfação com o trabalho. Outros fatores incluíram autoestima, nível de preocupação, ansiedade e nível de satisfação com o estado de saúde. O estudo utilizou métodos como modelagem de tópicos e análise de redes neurais para derivar os principais fatores que afetam a satisfação com a vida e analisar a importância relativa dos fatores envolvidos nela. Os resultados do estudo sugerem que, em reconhecimento da importância da satisfação no trabalho, futuras pesquisas nesta área devem ser expandidas e que o governo coreano deve introduzir várias políticas para promover esse aspecto. tanto neural máquina trabalho autoestima preocupação saúde nela aspecto

Abtract This study aims analyze the important influencing factors that affect the life satisfaction of Koreans, and to identify the relative importance of these factors. For this purpose, we utilize academic papers on what influences life satisfaction, and questionnaire data from the survey on social integration conducted annually by the Korean Government. A topic modelling analysis method was used to derive important influencing factors, and a neural network analysis method, one of the machine learning methods, was used to analyze the relative importance of influencing factors. The analysis showed that the factor that had the greatest impact on Koreans’ life satisfaction was satisfaction with work. Other factors included self-esteem, level of worry and anxiety, and level of satisfaction with health status. The study used methods such as topic modeling and neural network analysis to derive the main factors affecting life satisfaction and analyze the relative importance of the factor involved. The study results suggest that in recognition of the importance of job satisfaction, future research should be expanded, and that the Korean Government should introduce various policies to increase job satisfaction. Koreans purpose work selfesteem, selfesteem self esteem, esteem self-esteem anxiety status involved expanded

ABSTRACT With the increase in image production in recent years, there has been significant progress in the application of deep learning algorithms across various domains. Convolutional neural networks (CNNs) have been increasingly employed in remote sensing, covering all stages of target discrimination according to Johnson’s criteria (detection, recognition, and identification). These CNNs are applied in many conditions and imagery from many types of sensors. In this study, we explored the use of the YOLO-v8 method, the latest version of the You Only Look Once (YOLO) family of object detection models, in conjunction with CNN architectures and supervised learning algorithms. This approach was applied to detect, recognize, and identify targets in videos captured by optical sensors, considering varying resolutions and conditions. Additionally, our research investigated the use of two CNN architectures, Inception-v3 and VGG-16, to extract relevant information from the images. The attributes obtained from the CNNs were used as input for three classification algorithms: multilayer perceptron (MLP), logistic regression, and support vector machine (SVM), thereby completing the target discrimination process. It is worth noting that in the combination of Inception-v3 and MLP, we achieved an average accuracy of 90.67%, thus completing the target discrimination process. years domains (CNNs sensing Johnsons Johnson s detection, (detection recognition identification. identification . identification) sensors study YOLOv8 YOLOv YOLO v8 v YOLO-v method (YOLO models detect recognize Additionally Inceptionv3 Inceptionv Inception v3 Inception-v VGG16, VGG16 VGG 16, 16 VGG-16 images MLP , (MLP) regression SVM, SVM (SVM) process 9067 90 67 90.67% VGG1 1 VGG-1 (MLP (SVM 906 9 6 90.67 VGG- 90.6 90.