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dc.creatorsilva d, jesus g
dc.creatorSenior Naveda, Alexa
dc.creatorHernández Palma, Hugo
dc.creatorNiebles Núñez, William
dc.creatorJiménez - Rodríguez, Luis Miguel
dc.date.accessioned2020-01-30T13:45:56Z
dc.date.available2020-01-30T13:45:56Z
dc.date.issued2020
dc.identifier.issn1742-6588
dc.identifier.issn1742-6596
dc.identifier.urihttp://hdl.handle.net/11323/5956
dc.description.abstractIndicators are the most important management tool for environmental monitoring. Environmental indicators condense the information and simplify the approach to environmental phenomena, which are often complex, and makes them very useful for communication. The usefulness of these indicators consists of providing relevant information, summarized in the form of concise and illustrative statements for decision making, both for the organization's management and for the rest of the members. The prediction of limit values, together with the potentialities offered by the recommendation system based on ontology make this system a powerful tool for supporting decision-making in the Environmental Management process with a wide possibility of generalization in the business sector.es_ES
dc.language.isoenges_ES
dc.publisherJournal of Physics: Conference Serieses_ES
dc.relation.ispartof10.1088/1742-6596/1432/1/012049/pdfes_ES
dc.rightsCC0 1.0 Universal*
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/*
dc.subjectArtificial neural networkses_ES
dc.subjectEnvironmental indicatorses_ES
dc.subjectEnvironmental monitoringes_ES
dc.titleEnvironmental indicators through artificial neural networkses_ES
dc.typeArticlees_ES
dcterms.references[1] Cios, K. J., & Kurgan, L. A. (2000). Trends in Data Mining and Knowledge Discovery. (Dm), 1- 26.es_ES
dcterms.references[2] Viloria A., Lis-Gutiérrez JP., Gaitán-Angulo M., Godoy A.R.M., Moreno G.C., Kamatkar S.J. (2018) Methodology for the Design of a Student Pattern Recognition Tool to Facilitate the Teaching - Learning Process Through Knowledge Data Discovery (Big Data). In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943. Springer, Chames_ES
dcterms.references[3] Demsar, J. (2006). Comparison of Classifiers over Multiple Data Sets. Journal of Machine Learning Research, vol. 7: 31.es_ES
dcterms.references[4] Hutt, S.; Gardener, M.; Kamentz, D.; Duckworth, A.; D'Mello, S.: Prospectively Predicting 4- year College Graduation from Student Applications. Proceedings of the 8th International Conference on Learning Analytics and Knowledge, pp. 280-289 (2018)es_ES
dcterms.references[5] Ahuja, R.; Kankane, Y.: Predicting the probability of student's degree completion by using different data mining techniques. Fourth International Conference on Image Information Processing (ICIIP), pp. 1-4 (2017)es_ES
dcterms.references[6] Martins, L.; Carvalho, R.; Victorino, C.; Holanda, M.: Early Prediction of College Attrition Using Data Mining. 16th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 1075-1078 (2017)es_ES
dcterms.references[7] IHOBE. (1999). Guía de Indicadores Medioambientales para la Empresa. Berlin: Ministerio Federal para el Medio Ambiente, la Conservación de la Naturaleza y la Seguridad Nuclear.es_ES
dcterms.references[8] Russell, S.; Norvig, P.: Artificial Intelligence A Modern Approach. Pearson Education 3rd Ed, pp. 705 (2010)es_ES
dcterms.references[9] Makhabel, B.: Learning Data Mining with R. Packt Publishing 1st Ed, pp. 143 (2015)es_ES
dcterms.references[10] Witten, I.; Frank, E.; Hall, M.; Pal, C.: Data Mining Practical Machine Learning Tools and Techniques. Elsevier 4th Ed, pp. 167-169 (2016).es_ES
dcterms.references[11] Bucci, N., Luna, M., Viloria, A., García, J. H., Parody, A., Varela, N., & López, L. A. B. (2018, June). Factor analysis of the psychosocial risk assessment instrument. In International Conference on Data Mining and Big Data (pp. 149-158). Springer, Cham.es_ES
dcterms.references[12] Gaitán-Angulo, M., Viloria, A., & Abril, J. E. S. (2018, June). Hierarchical Ascending Classification: An Application to Contraband Apprehensions in Colombia (2015–2016). In Data Mining and Big Data: Third International Conference, DMBD 2018, Shanghai, China, June 17– 22, 2018, Proceedings (Vol. 10943, p. 168). Springer.es_ES
dcterms.references[13] Viloria, A., & Lezama, O. B. P. (2019). An intelligent approach for the design and development of a personalized system of knowledge representation. Procedia Computer Science , 151 , 1225- 1230.es_ES
dcterms.references[14] Viloria A., Lis-Gutiérrez JP., Gaitán-Angulo M., Godoy A.R.M., Moreno G.C., Kamatkar S.J. (2018) Methodology for the Design of a Student Pattern Recognition Tool to Facilitate the Teaching - Learning Process Through Knowledge Data Discovery (Big Data). In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943. Springer, Chames_ES
dcterms.references[15] Viloria, A., Bucci, N., Luna, M., Lis-Gutiérrez, J. P., Parody, A., Bent, D. E. S., & López, L. A. B. (2018, June). Determination of dimensionality of the psychosocial risk assessment of internal, individual, double presence and external factors in work environments. In International Conference on Data Mining and Big Data (pp. 304-313). Springer, Cham.es_ES
dcterms.references[16] Bishop, C. (1995). Extremely well-written, up-to-date. Requires a good mathematical background, but rewards careful reading, putting neural networks firmly into a statistical context. Neural Networks for Pattern Recognition.es_ES
dcterms.references[17] Pretnar, A. The Mystery of Test & Score. Ljubljana: University of Ljubljana. Retrieved from: https://orange.biolab.si/blog/2019/1/28/the-mystery-of-test-and-score/ (2019).es_ES
dcterms.references[18] Castellanos Domíngez, M. I., Quevedo Castro, C. M., Vega Ramírez, A., Grangel González, I., & Moreno Rodríguez, R. (2016). Sistema basado en ontología para el apoyo a la toma de decisiones en el proceso de gestión ambiental empresarial. Paper presented at the II International Workshop of Semantic Web, La Habana, Cuba. http://ceur-ws.org/Vol-1797/es_ES
dcterms.references[19] Yasser, A. M., Clawson, K., & Bowerman, C.: Saving cultural heritage with digital make-believe: machine learning and digital techniques to the rescue. In Proceedings of the 31st British Computer Society Human Computer Interaction Conference (p. 97). BCS Learning & Development Ltd. (2017).es_ES
dcterms.references[20] Castellanos Domínguez, M. I., & Grangel González, I. (2013). Las ontologías, su uso para la gestión del conocimiento medioambiental. Paper presented at the III Taller Internacional la Matemática, la Informática y la Física en el Siglo XXI, Holguín.es_ES
dcterms.references[21] Khelifi, F. J., J. (2011). K-NN Regression to Improve Statistical Feature Extraction for Texture Retrieval. IEEE Transactions on Image Processing, 20, 293-298.es_ES
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones_ES
dc.rights.accessrightsinfo:eu-repo/semantics/openAccesses_ES


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