Examinando por Autor "Salas, Rodrigo"
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Ítem A Step Forward to Formalize Tailored to Problem Specificity Mathematical Transforms(Frontiers Media S.A., 2022-06) Glaría, Antonio; Salas, Rodrigo; Chabert, Stéren; Roncagliolo, Pablo; Arriola, Alexis; Tapia, Gonzalo; Salinas, Matías; Zepeda, Herman; Taramasco, Carla; Oshinubi, Kayode; Demongeot, JacquesLinear functional analysis historically founded by Fourier and Legendre played a significant role to provide a unified vision of mathematical transformations between vector spaces. The possibility of extending this approach is explored when basis of vector spaces is built Tailored to the Problem Specificity (TPS) and not from the convenience or effectiveness of mathematical calculations. Standardized mathematical transformations, such as Fourier or polynomial transforms, could be extended toward TPS methods, on a basis, which properly encodes specific knowledge about a problem. Transition between methods is illustrated by comparing what happens in conventional Fourier transform with what happened during the development of Jewett Transform, reported in previous articles. The proper use of computational intelligence tools to perform Jewett Transform allowed complexity algorithm optimization, which encourages the search for a general TPS methodology. Copyright © 2022 Glaría, Salas, Chabert, Roncagliolo, Arriola, Tapia, Salinas, Zepeda, Taramasco, Oshinubi and Demongeot.Ítem Air quality assessment and pollution forecasting using artificial neural networks in Metropolitan Lima-Peru(Nature Research, 2021-12) Hoyos Cordova, Chardin; Lopez Portocarrero, Manuel Niño; Salas, Rodrigo; Torres, Romina; Canas Rodrigues, Paulo; López-Gonzales, Javier LinkolkThe prediction of air pollution is of great importance in highly populated areas because it directly impacts both the management of the city’s economic activity and the health of its inhabitants. This work evaluates and predicts the Spatio-temporal behavior of air quality in Metropolitan Lima, Peru, using artifcial neural networks. The conventional feedforward backpropagation known as Multilayer Perceptron (MLP) and the Recurrent Artifcial Neural network known as Long Short-Term Memory networks (LSTM) were implemented for the hourly prediction of PM10 based on the past values of this pollutant and three meteorological variables obtained from fve monitoring stations. The models were validated using two schemes: The Hold-Out and the Blocked-Nested Cross-Validation (BNCV). The simulation results show that periods of moderate PM10 concentration are predicted with high precision. Whereas, for periods of high contamination, the performance of both models, the MLP and LSTM, were diminished. On the other hand, the prediction performance improved slightly when the models were trained and validated with the BNCV scheme. The simulation results showed that the models obtained a good performance for the CDM, CRB, and SMP monitoring stations, characterized by a moderate to low level of contamination. However, the results show the difculty of predicting this contaminant in those stations that present critical contamination episodes, such as ATE and HCH. In conclusion, the LSTM recurrent artifcial neural networks with BNCV adapt more precisely to critical pollution episodes and have better predictability performance for this type of environmental data.Ítem An architecture based on computing with words to support runtime reconfiguration decisions of service-based systems(Atlantis Press, 2018) Torres, Romina; Salas, Rodrigo; Bencomo, Nelly; Astudillo, HernánService-based systems (SBSs) need to be reconfigured when there is evidence that the selected Web services configurations no further satisfy the specifications models and, thus the decision-related models will need to be updated accordingly. However, such updates need to be performed at the right pace. On the one hand, if the updates are not quickly enough, the reconfigurations that are required may not be detected due to the obsolescence of the specification models used at runtime, which were specified at design-time. On the other hand, the other extreme is to promote premature reconfiguration decisions that are based on models that may be highly sensitive to environmental fluctuations and which may affect the stability of these systems. To deal with the required trade-offs of this situation, this paper proposes the use of linguistic decision-making (LDM) models to represent specification models of SBSs and a dynamic computing-with-words (CWW) architecture to dynamically assess the models by using a multi-period multi-attribute decision making (MP-MADM) approach. The proposed solution allows systems under dynamic environments to offer improved system stability by better managing the trade-off between the potential obsolescence of the specification models, and the required dynamic sensitivity and update of these models. © 2018, the Authors.Ítem Detection of variables for the diagnosis of overweight and obesity in young Chileans using machine learning techniques(Elsevier B.V., 2023-03) Calderon-Diaz, Mailyn; Serey-Castillo, Leonardo J.; Vallejos-Cuevas, Esperanza A.; Espinoza, Alexis; Salas, Rodrigo; Macias-Jimenez, Mayra A.Overweight and obesity are considered epidemic problems. The number of factors involved in developing extra body fat makes harder the detection of this problem. Therefore, among the several variables and their levels presented in overweight and obese people, there is a need to improve the classification of people with these conditions. To this aim, in this paper, we conducted a variable analysis from biochemical and lipid profiles in young Chileans with normal weight, overweight, and obesity using machine learning techniques. XGBoost library was selected as the classifier. 21 variables (13 from biochemical and 8 from lipid profiles) were chosen as features. 100 iterations were conducted, and an 80% cross-validation was obtained. The variables with greater relevance in the classification task were total cholesterol, glycemia, LDH enzyme, bilirubin, and VLDL cholesterol. All of these, except bilirubin, are consistent with previous research in which these features have been used to assess risk factors for developing overweight or obesity. Then, further research must include a deep study regarding bilirubin's influence over these conditions. © 2023 Elsevier B.V.. All rights reserved.Ítem Explainable Machine Learning Techniques to Predict Muscle Injuries in Professional Soccer Players through Biomechanical Analysis(MDPI, 2024-01) Calderón-Díaz, Mailyn; Silvestre Aguirre, Rony; Vásconez, Juan P.; Yáñez, Roberto; Roby, Matías; Querales, Marvin; Salas, RodrigoThere is a significant risk of injury in sports and intense competition due to the demanding physical and psychological requirements. Hamstring strain injuries (HSIs) are the most prevalent type of injury among professional soccer players and are the leading cause of missed days in the sport. These injuries stem from a combination of factors, making it challenging to pinpoint the most crucial risk factors and their interactions, let alone find effective prevention strategies. Recently, there has been growing recognition of the potential of tools provided by artificial intelligence (AI). However, current studies primarily concentrate on enhancing the performance of complex machine learning models, often overlooking their explanatory capabilities. Consequently, medical teams have difficulty interpreting these models and are hesitant to trust them fully. In light of this, there is an increasing need for advanced injury detection and prediction models that can aid doctors in diagnosing or detecting injuries earlier and with greater accuracy. Accordingly, this study aims to identify the biomarkers of muscle injuries in professional soccer players through biomechanical analysis, employing several ML algorithms such as decision tree (DT) methods, discriminant methods, logistic regression, naive Bayes, support vector machine (SVM), K-nearest neighbor (KNN), ensemble methods, boosted and bagged trees, artificial neural networks (ANNs), and XGBoost. In particular, XGBoost is also used to obtain the most important features. The findings highlight that the variables that most effectively differentiate the groups and could serve as reliable predictors for injury prevention are the maximum muscle strength of the hamstrings and the stiffness of the same muscle. With regard to the 35 techniques employed, a precision of up to 78% was achieved with XGBoost, indicating that by considering scientific evidence, suggestions based on various data sources, and expert opinions, it is possible to attain good precision, thus enhancing the reliability of the results for doctors and trainers. Furthermore, the obtained results strongly align with the existing literature, although further specific studies about this sport are necessary to draw a definitive conclusion.Ítem Femicide and Attempted Femicide before and during the COVID-19 Pandemic in Chile(MDPI, 2022-07-01) Cantor, Erika; Salas, Rodrigo; Torres, RominaExperts and international organizations hypothesize that the number of cases of fatal intimate partner violence against women increased during the COVID-19 pandemic, primarily due to social distancing strategies and the implementation of lockdowns to reduce the spread of the virus. We described cases of attempted femicide and femicide in Chile before (January 2014 to February 2020) and during (March 2020 to June 2021) the pandemic. The attempted-femicide rate increased during the pandemic (incidence rate ratio: 1.22 [95% confidence interval: 1.04 to 1.43], p value: 0.016), while the rate of femicide cases remained unchanged. When a comparison between attempted-femicide and femicide cases was performed, being a foreigner, having an intimate partner relationship with a perpetrator aged 40 years or more, and the use of firearms during the assault were identified as factors associated independently with a higher probability of being a fatal victim in Chile. In conclusion, this study emphasizes that attempted femicide and femicide continued to occur frequently in family contexts both before and during the COVID-19 pandemic. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.Ítem Impact of Remote Monitoring Technologies for Assisting Patients With Gestational Diabetes Mellitus: A Systematic Review(Frontiers in Bioengineering and Biotechnology, 2022-03) Bertini, Ayleena; Gárate, Bárbarac; Pardo, Fabiána; Pelicand, Juliea; Sobrevia, Luise; Torres, Rominal; Sterenc, Chabert; Salas, RodrigoIntroduction: In Chile, 1 in 8 pregnant women of middle socioeconomic level has gestational diabetes mellitus (GDM), and in general, 5–10% of women with GDM develop type 2 diabetes after giving birth. Recently, various technological tools have emerged to assist patients with GDM to meet glycemic goals and facilitate constant glucose monitoring, making these tasks more straightforward and comfortable. Objective: To evaluate the impact of remote monitoring technologies in assisting patients with GDM to achieve glycemic goals, and know the respective advantages and disadvantages when it comes to reducing risk during pregnancy, both for the mother and her child. Methods: A total of 188 articles were obtained with the keywords “gestational diabetes mellitus,” “GDM,” “gestational diabetes,” added to the evaluation levels associated with “glucose level,” “glycemia,” “glycemic index,” “blood sugar,” and the technological proposal to evaluate with “glucometerm” “mobile application,” “mobile applications,” “technological tools,” “telemedicine,” “technovigilance,” “wearable” published during the period 2016–2021, excluding postpartum studies, from three scientific databases: PUBMED, Scopus and Web of Science. These were managed in the Mendeley platform and classified using the PRISMA method. Results: A total of 28 articles were selected after elimination according to inclusion and exclusion criteria. The main measurement was glycemia and 4 medical devices were found (glucometer: conventional, with an infrared port, with Bluetooth, Smart type and continuous glucose monitor), which together with digital technology allow specific functions through 2 identified digital platforms (mobile applications and online systems). In four articles, the postprandial glucose was lower in the Tele-GDM groups than in the control group. Benefits such as improved glycemic control, increased satisfaction and acceptability, maternal confidence, decreased gestational weight gain, knowledge of GDM, and other relevant aspects were observed. There were also positive comments regarding the optimization of the medical team’s time. Conclusion: The present review offers the opportunity to know about the respective advantages and disadvantages of remote monitoring technologies when it comes to reducing risk during pregnancy. GDM centered technology may help to evaluate outcomes and tailor personalized solutions to contribute to women’s health. More studies are needed to know the impact on a healthcare system. Copyright © 2022 Bertini, Gárate, Pardo, Pelicand, Sobrevia, Torres, Chabert and Salas.Ítem Predicting Cardiovascular Rehabilitation of Patients with Coronary Artery Disease Using Transfer Feature Learning(Multidisciplinary Digital Publishing Institute (MDPI), 2023-02) Torres, Romina; Zurita, Christopher; Mellado, Diego; Nicolis, Orietta; Saavedra, Carolina; Tuesta, Marcelo; Salinas, Matías; Bertini, Ayleen; Pedemonte, Oneglio; Querales, Marvin; Salas, RodrigoCardiovascular diseases represent the leading cause of death worldwide. Thus, cardiovascular rehabilitation programs are crucial to mitigate the deaths caused by this condition each year, mainly in patients with coronary artery disease. COVID-19 was not only a challenge in this area but also an opportunity to open remote or hybrid versions of these programs, potentially reducing the number of patients who leave rehabilitation programs due to geographical/time barriers. This paper presents a method for building a cardiovascular rehabilitation prediction model using retrospective and prospective data with different features using stacked machine learning, transfer feature learning, and the joint distribution adaptation tool to address this problem. We illustrate the method over a Chilean rehabilitation center, where the prediction performance results obtained for 10-fold cross-validation achieved error levels with an NMSE of (Formula presented.) and an (Formula presented.) of (Formula presented.), where the best-achieved performance was an error level with a normalized mean squared error of 0.008 and an (Formula presented.) up to (Formula presented.). The results are encouraging for remote cardiovascular rehabilitation programs because these models could support the prioritization of remote patients needing more help to succeed in the current rehabilitation phase. © 2023 by the authors.Ítem Prediction of intensity and location of seismic events using deep learning(Elsevier B.V., 2021-04) Nicolis, Orietta; Plaza, Francisco; Salas, RodrigoThe object of this work is to predict the seismic rate in Chile by using two Deep Neural Network (DNN) architectures, Long Short Term Memory (LSTM) and Convolutional Neural Networks (CNN). For this, we propose a methodology based on a three-module approach: a pre-processing module, a spatial and temporal estimation module, and a prediction module. The first module considers the Epidemic-Type Aftershock Sequences (ETAS) model for estimating the intensity function, which will be used for estimating the seismic rate on a 1 × 1 degree grid providing a sequence of daily images covering all the seismic area of Chile. The spatial and temporal estimation module uses the LSTM and CNN for predicting the intensity and the location of earthquakes. The last module integrates the information provided by the DNNs for predicting future values of the maximum seismic rate and their location. In particular, the LSTM will be trained using the maximum intensity of the last 30 days as input for predicting the maximum intensity of the next day, and the CNN will be trained on the last 30 images provided by the application of the ETAS model for predicting the probability that the next day the maximum event will be in certain area of Chile. Some performance indexes (such as R2 and accuracy) will be used for validating the proposed models. © 2020 Elsevier B.V.Ítem Self-improving generative artificial neural network for pseudorehearsal incremental class learning(Algorithms, 2019) Mellado, Diego; Saavedra, Carolina; Chabert, Sterena; Torres, Romina; Salas, RodrigoDeep learning models are part of the family of artificial neural networks and, as such, they suffer catastrophic interference when learning sequentially. In addition, the greater number of these models have a rigid architecture which prevents the incremental learning of new classes. To overcome these drawbacks, we propose the Self-Improving Generative Artificial Neural Network (SIGANN), an end-to-end deep neural network system which can ease the catastrophic forgetting problem when learning new classes. In this method, we introduce a novel detection model that automatically detects samples of new classes, and an adversarial autoencoder is used to produce samples of previous classes. This system consists of three main modules: a classifier module implemented using a Deep Convolutional Neural Network, a generator module based on an adversarial autoencoder, and a novelty-detection module implemented using an OpenMax activation function. Using the EMNIST data set, the model was trained incrementally, starting with a small set of classes. The results of the simulation show that SIGANN can retain previous knowledge while incorporating gradual forgetfulness of each learning sequence at a rate of about 7% per training step. Moreover, SIGANN can detect new classes that are hidden in the data with a median accuracy of 43% and, therefore, proceed with incremental class learning.Ítem Visual-Predictive Data Analysis Approach for the Academic Performance of Students from a Peruvian University(MDPI, 2022-11) Orrego Granados, David; Ugalde, Jonathan; Salas, Rodrigo; Torres, Romina; López Gonzales, Javier LinkolkThe academic success of university students is a problem that depends in a multi-factorial way on the aspects related to the student and the career itself. A problem with this level of complexity needs to be faced with integral approaches, which involves the complement of numerical quantitative analysis with other types of analysis. This study uses a novel visual-predictive data analysis approach to obtain relevant information regarding the academic performance of students from a Peruvian university. This approach joins together domain understanding and data-visualization analysis, with the construction of machine learning models in order to provide a visual-predictive model of the students’ academic success. Specifically, a trained XGBoost Machine Learning model achieved a performance of up to 91.5% Accuracy. The results obtained alongside a visual data analysis allow us to identify the relevant variables associated with the students’ academic performances. In this study, this novel approach was found to be a valuable tool for developing and targeting policies to support students with lower academic performance or to stimulate advanced students. Moreover, we were able to give some insight into the academic situation of the different careers of the university. © 2022 by the authors.