21 results on '"RANDOM forest algorithms"'
Search Results
2. Revisión de alcance: evaluación de técnicas de aprendizaje automático en el mantenimiento predictivo.
- Author
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Campos-Olivares, Daniel, Carrasco-Muñoz, Alejandro, Mazzoleni, Mirko, Ferramosca, Antonio, and Luque-Sendra, Amalia
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DATABASES ,MACHINE learning ,LITERATURE reviews ,ARTIFICIAL intelligence ,RANDOM forest algorithms ,DEEP learning - Abstract
Copyright of DYNA - Ingeniería e Industria is the property of Publicaciones Dyna SL and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Published
- 2024
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3. PREDICCIÓN DE LA EROSIÓN DEL SUELO MEDIANTE RANDOM FOREST: CASO DE ESTUDIO CUENCA RÍO GRANDE, ANTIOQUIA.
- Author
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Isabel Arango-Carvajal, Laura
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MACHINE learning , *SUPERVISED learning , *RANDOM forest algorithms , *NATURAL capital , *PHENOMENOLOGICAL theory (Physics) - Abstract
Contextualization: Currently, the knowledge of natural phenomena associated with the preservation of the systems is of interest both for researchers in the natural sciences, and for the environmental authorities in charge of decision- making on resource management. In this sense, work has been carried out on the interpretation and prediction of different physical phenomena such as erosion, to create scenarios that allow strengthening the response criteria for the conservation of the natural capital of the soil. Knowledge gap: The ability to predict the phenomenon of erosion is limited on many occasions due to the quantity and variability of the parameters and variables that are related to erosion; besides that, in many cases, a high computational processing is required to achieve that they are associated with each other. Purpose: The aim is to implement a machine learning model as an alternative tool for complex modeling and erosion prediction. Methodology: In this study, a model is developed from the training of the non-parametric Random Forest method through supervised learning, to predict erosion occurrences in the Rio Grande basin, considering the variables that have previously been used in other methods to model this phenomenon. Results and conclusions: The results showed a capacity to predict erosion in the basin with an approximate precision of 77%, so this method can be applied to obtain fast and reliable predictions. In addition, it was found that the variables used in the RUSLE model mainly explain the occurrence or not of erosion. The great importance of the temperature variable introduced in the model is also surprising. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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4. Incumplimiento en el pago de impuestos: Intervención con el empleo de nudges y normas sociales.
- Author
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Bevilacqua, Solon and Fonseca de Miranda, Edivan do Socorro
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SOCIAL norms , *RANDOM forest algorithms , *TAXATION , *BUSINESSPEOPLE , *SMALL business , *MACHINE learning , *TEXT messages , *LOGISTIC regression analysis - Abstract
In this research, a group of charging messages for the payment of taxes was investigated. Altogether 12 variations of billing messages (social norms, simplification, disclosure, previous engagement, reminders and previous choices) were evaluated, and their effectiveness was tested. The messages were transmitted to defaulting microentrepreneurs in four Brazilian states. From a database containing information about defaulting micro entrepreneurs, 250 thousand text messages were sent making charges. The data were obtained from the Secretaria Especial da Micro e Pequena Empresa (Sempe). Tests were used to analyse the difference between means and Logistic Regression was used in sequence. The Random Forest, Logistic Regression and Naïve Bayes widgets were used to indicate the robustness of the Machine Learning predictive model. The research findings indicated that the formats "simplification", "previous choices" and "alert", employees, did not have an effect in combating default. However, when aligned with social norms, messages in the form of "past options" and "reminders" increase the payment of debts. The widgets used indicated an excellent fit to the machine learning model. The Random Forest tool attested with superiority that the model is robust and suitable for the predictive function. The results of the research provide a contribution to public policies when they present an effective action to reduce defaults in the payment of taxes. The use of messages in the social norm format can be adapted to other situations, constituting a suggestion for future research. [ABSTRACT FROM AUTHOR]
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- 2024
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5. Métodos de valoración de jugadores de futbol profesional.
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Amat, Oriol and Gómez, Pere
- Subjects
DISCOUNTED cash flow ,SOCCER players ,NONLINEAR regression ,VALUATION ,INSURANCE premiums ,SOCCER teams ,MACHINE learning ,RANDOM forest algorithms ,CASH flow ,INCOME ,STATISTICS - Abstract
This article describes the methods used to assess the transfer rights of professional football players. The methods include discounted cash flows, comparables, linear regression, non-linear regression, random forest, and machine learning. The valuation is based on variables such as personal characteristics, performance, income contributed to the club, and the club and market context. Other methods such as expert opinions and value estimation through crowdvaluation are also mentioned. The importance of considering multiple variables in player valuation is emphasized, and it is concluded that these methods are useful in processes such as transfer negotiations and determining insurance premiums. [Extracted from the article]
- Published
- 2024
6. Análisis del desempeño de técnicas de aprendizaje automático para identificar vegetación acuática con bandas de Sentinel-2.
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VENTURINI, VIRGINIA, MARCHETTI, ZULEICA Y., WALKER, ELISABET, and FAGIOLI, GIANFRANCO
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MACHINE learning , *LAND cover , *WATERSHEDS , *REMOTE-sensing images , *RANDOM forest algorithms - Abstract
Natural disasters, such as river overflows, extreme droughts, natural forest fires are more frequently observed in Argentina. Faced with these catastrophes, efficient management is essential to make quick decisions to minimize damage, which is a latent concern in local and regional governments and in the scientific community. In Argentina, the Paraná River basin represents a strategic resource in itself, as it encompasses the greatest fluvial, ecological wealth and large urban centers. However, the extreme events that characterize the dynamics of the wetlands affect the urban centers located near them. The presence of aquatic vegetation (free or rooted) masks the flooded areas, hiding the first signs of flooding, making the monitoring and rapid detection of these areas difficult. In this work, optical satellite images and machine learning models were used to classify the different land covers in wetlands of the Paraná river system. The focus was on environments where free water and aquatic marsh vegetation coexist, characteristic of the metropolitan region of the city of Santa Fe, and considering the technical limitations of decision-making agencies. Therefore, the Sentinel-2 (S2) mission images were used to train and evaluate different machine learning algorithms. All bands of S2 images were used, unifying the spatial resolution to 10 m. The results indicated that the coastal aerosol bands (B1) and two mid-infrared bands (B11 and B12) provide the most information for the identification of the samples. Moreover, the random forest method showed the best performance for the aquatic vegetation class, which was of primary interest for this work. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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7. Sistema para la toma de decisiones en el riego de cultivos protegidos basado en aprendizaje de máquina.
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Villavicencio_Quintero, Dennis, Cabrera_Hernández, Emilio, Godo_Alonso, Alain, and Santana Ching, Ivan
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WATER shortages , *IRRIGATION water , *PLANT protection , *MACHINE learning , *RANDOM forest algorithms , *AGRICULTURAL industries , *WATER use , *ARTIFICIAL intelligence , *REGRESSION trees , *EVAPOTRANSPIRATION , *MICROIRRIGATION , *GREENHOUSES - Abstract
The water scarcity is a concern of the agricultural industry as it uses four fifths of the of the total fresh water consumed for irrigation and two thirds of the total used for human consumption. For this reason, the development of systems that optimize the use of water in irrigation is essential. In the greenhouses of the UEB "Valle del Yabú" of the Santa Clara municipality, irrigation is carried out using a drip-based system that requires the presence of an operator for decisionmaking who does not have information about some of the hydrometeorological variables that govern the crop. This paper focused on to design a support system for decision-making in irrigation based on machine learning. As an important parameter of the system, the evapotranspiration coefficient of the crop is calculated using the Turc formula. The collected environmental data is conditioned and linear regression, regressive random forests, and gradient-boosted trees regression models are trained with them to determine future evapotranspiration values using the Apache Spark framework. The model that obtained the best results was the regressive random forest with a coefficient of determination (r2) of 0,79 and with it the volume of water lost by the crop is calculated. Finally, the system was able to provide the estimates of both variables, which favour decision-making by specialists. [ABSTRACT FROM AUTHOR]
- Published
- 2023
8. Modelo para la detección de ataques de phishing contra el servicio de correo electrónico.
- Author
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Dominguez, Antonio Hernández and Baluja García, Walter
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NAIVE Bayes classification ,SUPPORT vector machines ,MACHINE learning ,RANDOM forest algorithms ,FEATURE extraction ,DATA mining - Abstract
Copyright of Revista Cubana de Ciencias Informáticas is the property of Universidad de las Ciencias Informaticas (UCI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Published
- 2023
9. Análisis de variables asociadas al rendimiento académico en cursos universitarios virtuales.
- Author
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Gil-Vera, Víctor D. and Quintero-López, Catalina
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MACHINE learning ,ONLINE databases ,ONLINE education ,RANDOM forest algorithms ,UNIVERSITIES & colleges ,VIRTUAL universities & colleges - Abstract
Copyright of Formación Universitaria is the property of Centro de Informacion Tecnologica (CIT) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Published
- 2023
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10. Hacia una metodología de evaluación del rendimiento del alumno en entornos de aprendizaje iVR utilizando eye-tracking y aprendizaje automático.
- Author
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Serrano-Mamolar, Ana, Miguel-Alonso, Ines, Checa, David, and Pardo-Aguilar, Carlos
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HEAD-mounted displays , *CRANES (Machinery) , *MACHINE learning , *RANDOM forest algorithms , *CLASSROOM environment - Abstract
At present, the use of eye-tracking data in immersive Virtual Reality (iVR) learning environments is set to become a powerful tool for maximizing learning outcomes, due to the low-intrusiveness of eye-tracking technology and its integration in commercial iVR Head Mounted Displays. However, the most suitable technologies for data processing should first be identified before their use in learning environments can be generalized. In this research, the use of machine-learning techniques is proposed for that purpose, evaluating their capabilities to classify the quality of the learning environment and to predict user learning performance. To do so, an iVR learning experience simulating the operation of a bridge crane was developed. Through this experience, the performance of 63 students was evaluated, both under optimum learning conditions and under stressful conditions. The final dataset included 25 features, mostly temporal series, with a dataset size of up to 50M data points. The results showed that different classifiers (KNN, SVM and Random Forest) provided the highest accuracy when predicting learning performance variations, while the accuracy of user learning performance was still far from optimized, opening a new line of future research. This study has the objective of serving as a baseline for future improvements to model accuracy using complex machine-learning techniques. [ABSTRACT FROM AUTHOR]
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- 2023
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11. Algoritmos de aprendizaje automático para clasificar zonas de inundación a partir de imágenes de radar de apertura sintética.
- Author
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Pablo Ambrosio-Ambrosio, Juan and Manuel González-Camacho, Juan
- Subjects
MACHINE learning ,SYNTHETIC aperture radar ,BODIES of water ,REMOTE-sensing images ,RANDOM forest algorithms ,SUPPORT vector machines ,PYTHON programming language ,BOOSTING algorithms ,SODIC soils - Abstract
Copyright of Tecnología y Ciencias del Agua is the property of Instituto Mexicano de Tecnologia del Agua (IMTA) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Published
- 2023
- Full Text
- View/download PDF
12. Armed conflict; pragmatism; justices of the peace; judicial decisions; land restitution.
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Andrade-Girón, Daniel, Sandivar-Rosas, Juana, and Carreño-Cisneros, Edgardo
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MACHINE learning ,HEALTH facilities ,COVID-19 pandemic ,RANDOM forest algorithms ,CLINICAL deterioration - Abstract
Copyright of Revista de Ciencias Sociales (13159518) is the property of Revista de Ciencias Sociales de la Universidad del Zulia Venezuela and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Published
- 2023
13. Aprendizaje automático aplicado a la predicción de diabetes mellitus, utilizando información socioeconómica y ambiental de usuarios del sistema de salud.
- Author
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Alexander Mejía, Jessner, Andrés Oviedo-Benalcázar, Mario, Armando Ordoñez, José, and Fernando Valencia, José
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MACHINE learning , *K-nearest neighbor classification , *RANDOM forest algorithms , *DECISION trees , *DATABASES - Abstract
Objective: The objective was to apply models based on machine learning techniques to support the early diagnosis of diabetes mellitus, using environmental, social, economic and health data variables, without dependence on clinical sample collection. Methodology: Data from 10,889 users affiliated with the subsidized health system in the southwestern area of Colombia, diagnosed with hypertension and grouped into users without (74.3%) and with (25.7%) diabetes mellitus, were used. Supervised models were trained using k-nearest neighbors, decision trees, and random forests, as well as ensemble-based models, applied to the database before and after balancing the number of cases in each diagnostic group. The performance of the algorithms was evaluated by dividing the database into training and test data (70/30, respectively), and metrics of accuracy, sensitivity, specificity, and area under the curve were used. Results: Sensitivity values increased significantly when using balanced data, going from maximum values of 17.1% (unbalanced data) to values as high as 57.4% (balanced data). The highest value of area under the curve (0.61) was obtained with the ensemble models, by applying a balance in the amount of data for each group and by coding the categorical variables. The variables with the greatest weight were associated with hereditary aspects (24.65%) and with the ethnic group (5.59%), in addition to visual difficulty, low water consumption, a diet low in fruits and vegetables, and the consumption of salt and sugar. Conclusions: Although predictive models, using people's socioeconomic and environmental information, emerge as a tool for the early diagnosis of diabetes mellitus, their predictive capacity still needs to be improved. [ABSTRACT FROM AUTHOR]
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- 2023
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14. Aplicación de modelos de aprendizaje supervisados para la prevención sobre fallos de maquinaria.
- Author
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Pan Celestino, Angel Jian, Guillen Bravo, Kevin Raul, and Roca Becerra, Jorge Luis
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MACHINE learning ,SUPERVISED learning ,ARTIFICIAL intelligence ,SUPPORT vector machines ,RANDOM forest algorithms ,OPERATIONS management - Abstract
Copyright of UCV Hacer is the property of UCV Hacer and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Published
- 2023
- Full Text
- View/download PDF
15. Interpretación de Gases Disueltos en Aceite Dieléctrico Mediante Bosques Aleatorios Para la Detección de Anomalías en Transformadores de Potencia.
- Author
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Freire, A. S., Astudillo, J. C., Quinatoa, C. I., and Arias, F. R.
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ARTIFICIAL neural networks ,RANDOM forest algorithms ,POWER transformers ,GAS analysis ,SUPPORT vector machines - Abstract
Copyright of Revista Técnica Energía is the property of Centro Nacional de Control de Energia CENACE and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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- 2023
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16. Aplicaciones del Machine Learning en el turismo - estudio en Colombia y sus zonas de posconflicto.
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GÓMEZ MUÑOZ, DANIEL MAURICIO, CASALLAS BERNAL, JUAN DIEGO, and RODRÍGUEZ MOLANO, JOSÉ IGNACIO
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MACHINE learning ,RANDOM forest algorithms ,TOURIST attractions ,TOURISM ,ARTIFICIAL intelligence ,DEVELOPING countries ,TOURISM websites ,FOOD tourism - Abstract
Copyright of Revista Turismo & Desenvolvimento (RT&D) / Journal of Tourism & Development is the property of Associacao de Gestao e Planeamento em Turismo da Universidade de Aveiro (AGPTUA) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Published
- 2023
- Full Text
- View/download PDF
17. Inteligencia artificial aplicada al método Backward Seismic Analysis.
- Author
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Möller-Acuña, Patricia-Andrea and Pineda-Nalli, Patricio-Andrés
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STORAGE tanks , *SEISMIC response , *CONDITIONED response , *RANDOM forest algorithms , *EARTHQUAKES , *PREDICTION models , *STEEL tanks - Abstract
This work presents applications of the Backward Seismic Analysis (BSA) method for steel storage tanks using a data base of more than 382 steel storage tanks in operation during large subductive earthquakes: Valdivia 1960, Central Chile 1985, Tocopilla 2007, El Maule 2010, Alaska 1964, and others in the United States between 1933 and 1995 (subductive and cortical). It has been recorded that most of the steel storage tanks without anchor systems have failed during large earthquakes. These have been designed with the standards API 650-E, AWWA-D100, and NZSEE, which propose similar procedures for estimating seismic forces, but with different design methods. During different conferences, the causes of the failures were evaluated, concluding that the tanks were designed mainly with the API 650-E code and were unanchored. Moreover, the design codes employed do not consider relevant aspects that condition the seismic response of steel storage tanks. This work develops a prediction model based on the historical information already described, which is capable of efficiently predicting if a steel storage tank will suffer any failures during an earthquake. Various algorithms were evaluated, finding that the Random Forest method exhibits the best results. The results obtained in the prediction of steel storage tank failures reach more than 90% efficiency in most of the evaluated scenarios. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
18. Reconocimiento de rutas biosintéticas para semioquímicos mediante técnicas de aprendizaje de máquina.
- Author
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Valencia-Colman, Laura S. and C., Édgar E. Daza
- Subjects
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MULTILAYER perceptrons , *RANDOM forest algorithms , *PRINCIPAL components analysis , *SEMIOCHEMICALS , *MACHINE learning , *SELF-organizing maps - Abstract
In this work we consider 148 semiochemicals reported for the family Scarabaeidae, whose chemical structure was characterized using a set of 200 molecular descriptors from five different classes. The selection of the most discriminating descriptors was carried out with three different techniques: Principal Component Analysis, for each class of descriptors, Random Forests and Boruta-Shap, applied to the total of descriptors. Although the three techniques are conceptually different, they select a similar number of descriptors from each class. We proposed a combination of machine learning techniques to search for a structural pattern in the set of semiochemicals and then perform their classification. The pattern was established from the high belonging of a subset of these metabolites to the groups that were obtained by a grouping method based on fuzzy C-means logic; the discovered pattern corresponds to the biosynthetic pathway by which they are obtained biologically. This first classification was corroborated with Kohonen's self-organizing maps. To classify those semiochemicals whose belonging to a biosynthetic pathway was not clearly defined, we built two models of Multilayer Perceptrons which had an acceptable performance. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
19. Evaluación de métodos de clasificación supervisada para la estimación de cambios espacio-temporales de cobertura en los páramos de Merchán y Telecom, Cordillera Oriental de Colombia.
- Author
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Poveda-Sotelo, Yoan, Bermúdez-Cella, Mauricio A., and Gil-Leguizamón, Pablo
- Subjects
REMOTE-sensing images ,NATURAL resources management ,LAND use planning ,LANDSAT satellites ,RANDOM forest algorithms ,MOORS (Wetlands) ,GEOGRAPHIC information systems ,ECOSYSTEM services - Abstract
Copyright of Boletin de Geologia is the property of Universidad Industrial de Santander and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Published
- 2022
- Full Text
- View/download PDF
20. Servicio de clasificación documental multi cliente basado en técnicas de aprendizaje de máquina y Elasticsearch.
- Author
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García-Chicangana, David-Santiago, Cobos-Lozada, Carlos-Alberto, Mendoza-Becerra, Martha-Eliana, Niño-Zambrano, Miguel-Ángel, and Martínez-Figueroa, James-Mauricio
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RANDOM forest algorithms , *DECISION trees , *RECORDS management , *MACHINE learning , *K-nearest neighbor classification , *UPLOADING of data - Abstract
This paper presents a document classification service that allows multiple client (multi-tenant) document management systems to provide greater confidence and credibility regarding the document types assigned to documents uploaded by users. The research was carried out through the phases of CRISP-DM, where two document representation models were evaluated (bags of words with cumulative n-grams and BERT, which was recently proposed by Google) and five machine learning techniques (multilayer perceptron, random forests, k-nearest neighbors, decision trees, and naïve bayes). The experiments were carried out with data from two organizations, and the best results were obtained by multilayer perceptron, random forests, and k-nearest neighbors, which showed very similar results regarding general accuracy and recall by class. The results are not conclusive with respect to the ability to offer the service to multiple clients with a single model, since this also depends on their documents and document types. Therefore, a service is offered which is based on a microservices architecture that allows each organization to create its own model, monitor its performance in production, and update it when performance is not adequate. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
21. MACHINE LEARNING APLICADO AL ANÁLISIS DEL RENDIMIENTO DE DESARROLLOS DE SOFTWARE.
- Author
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Daniel Gil-Vera, Víctor and Seguro-Gallego, Cristian
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COMPUTER software development , *COMPUTER software quality control , *RANDOM forest algorithms , *CUSTOMER satisfaction , *WEB services , *MACHINE learning - Abstract
Performance tests are crucial to measure the quality of software developments, since they allow identifying aspects to be improved in order to achieve customer satisfaction. The objective of this research was to identify the optimal Machine Learning technique to predict whether or not a software development meets the customer's acceptance criteria. A dataset with information obtained from web services performance tests and the F1-score quality metric were used. This paper concludes that, although the Random Forest technique obtained the best score, it is not correct to state that it is the best Machine Learning technique; the quantity and quality of the data used in the training play a very important role, as well as an adequate processing of the information. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
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