Browsing by Subject "Regression"

Now showing 1 - 4 of 4
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    Open Access
    Assessment of the Educational Environment Using the Dundee Ready Education Environment Measure (DREEM) Among Physical Therapy Students at Taif University
    (Universidad de Murcia. Servicio de Publicaciones, 2025) Mona Hassan ElLaithy; Ibrahim Saeed Aljulaymi; Hisham Mohamed Hussein; Mostafa S. Abdel-fattah; Ahmed Abdelmoniem Ibrahim; Dewir, Ibrahim; Sin departamento asociado
    Background: The educational environment significantly influences students' learning experiences, academic performance, and satisfaction. The Dundee Ready Education Environment Measure (DREEM) is widely used to assess students' perceptions of their educational climate. Objectives: To evaluate the learning environment of Physical Therapy students at Taif University using the DREEM questionnaire and to examine how gender, academic level, and age predict students' perceptions. Methods: A cross-sectional study was conducted among undergraduate Physical Therapy students using the validated Arabic version of the DREEM questionnaire. Descriptive statistics, bivariate analyses (t-test, ANOVA), and multiple linear regression were employed to analyze the data. Results were reported with 95% confidence intervals, and significance was set at p < 0.05. Results: A total of 234 students completed the survey. The mean overall DREEM score was 125.1 out of 200, indicating a "more positive than negative" perception. Male students had significantly higher DREEM scores than female students (2.65 vs. 2.51; p = 0.014). While differences across academic years were not statistically significant (p = 0.138), second-year students reported the highest scores. Multiple linear regression showed that female gender (β = -0.14, p = 0.012) and being in the fourth year (β = -0.25, p = 0.042) were associated with lower overall DREEM scores. Conclusion: The educational environment for Physical Therapy students at Taif University is generally perceived as positive. However, differences based on gender and academic year highlight the need for targeted interventions, particularly for female and fourth-year students. Enhancing peer support, stress management, and inclusive teaching strategies may improve the overall student experience.
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    Estimation of nitrogen content in cucumber plant (Cucumis sativus L.) leaves using hyperspectral imaging data with neural network and partial least squares regressions
    (Elsevier, 2021-10-15) Sabzi, Sajad; Pourdarbani, Razieh; Rohban, Mohammad H.; García Mateos, Ginés; Arribas, J. I.; Informática y Sistemas; Facultades de la UMU::Facultad de Informática
    In recent years, farmers have often mistakenly resorted to overuse of chemical fertilizers to increase crop yield. However, excessive consumption of fertilizers might lead to severe food poisoning. If nutritional deficiencies are detected early, it can help farmers to design better fertigation practices before the problem becomes unsolvable. The aim of this study is to predict the amount of nitrogen (N) content in cucumber (Cucumis sativus L., var. Super Arshiya-F1) plant leaves using hyperspectral imaging (HSI) techniques and three different regression methods: a hybrid artificial neural networks-particle swarm optimization (ANN-PSO); partial least squares regression (PLSR); and unidimensional deep learning convolutional neural networks (CNN). Cucumber plant seeds were planted in 20 different pots. After growing the plants, pots were categorized and three levels of nitrogen overdose were applied to each category: 30%, 60% and 90% excesses, called N30%, N60%, N90%, respectively. HSI images of plant leaves were captured before and after the application of nitrogen excess. A prediction regression model was developed for each individual category. Results showed that mean regression coefficients (R) for ANN-PSO were inside 0.937–0.965, PLSR 0.975–0.997, and CNN 0.965–0.985 ranges, test set. We conclude that regression models have a remarkable ability to accurately predict the amount of nitrogen content in cucumber plants from hyperspectral leaf images in a non-destructive way, being PLSR slightly ahead of CNN and ANN-PSO methods.
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    Open Access
    Inducing Non-Orthogonal and Non-Linear Decision Boundaries in Decision Trees via Interactive Basis Functions
    (Elsevier, 2019-05-15) Camacho, Maximo; Lopez, Fernando; Paez, Antonio; Ruiz, Manuel; Métodos Cuantitativos para la Economía y la Empresa
    We use a local projection approach to analyze the effect of economic recessions on income inequality in a comprehensive sample of 43 countries from 1960 to 2016. Although we consider both business-cycle and growth-cycle recessions, we fail to find evidence of significant positive impacts of economic downturns on income distribution, once controls are added to the model. However, we do find important differences across countries, which mainly depend on the degree of economic development.
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    Multi-surrogate assisted multi-objective evolutionary algorithms for feature selection in regression and classification problems with time series data
    (Elsevier, 2022-12-10) Espinosa, Raquel; Jiménez Barrionuevo, Fernando; Palma Méndez, José Tomás; Ingeniería de la Información y las Comunicaciones; Facultades de la UMU::Facultad de Informática
    Feature selection wrapper methods are powerful mechanisms for reducing the complexity of prediction models while preserving and even improving their precision. Meta-heuristic methods, such as multi-objective evolutionary algorithms, are commonly used as search strategies in feature selection wrapper methods since they allow minimizing the cardinality of the attribute subset and simultaneously maximizing the predictive capacity of the model. However, in high-dimensional problems, multi-objective evolutionary algorithms for wrapper-type feature selection may require excessive computational time, sometimes impractical, especially when the learning algorithm has a high computational cost, such as deep learning. To address this drawback, in this paper we propose a multi-surrogate assisted multi-objective evolutionary algorithm for feature selection, specially designed to improve generalization error. The proposed method has been compared with conventional feature selection wrapper methods that use random forest, support vector machine and long short-term memory learning algorithms to evaluate subsets of attributes. The experiments have been carried out with regression and classification problems with time series data for air quality forecasting in the south-east of Spain and for indoor temperature forecasting in a domotic house. The results demonstrate the superiority of the proposed multi-surrogate assisted method over conventional wrapper methods using the same run times.