| dc.creator | silva d, jesus g | |
| dc.creator | Senior Naveda, Alexa | |
| dc.creator | Hernández Palma, Hugo | |
| dc.creator | Niebles Núñez, William | |
| dc.creator | Jiménez - Rodríguez, Luis Miguel | |
| dc.date | 2020-01-30T13:45:56Z | |
| dc.date | 2020-01-30T13:45:56Z | |
| dc.date | 2020 | |
| dc.date.accessioned | 2023-10-03T19:49:58Z | |
| dc.date.available | 2023-10-03T19:49:58Z | |
| dc.identifier | 1742-6588 | |
| dc.identifier | 1742-6596 | |
| dc.identifier | http://hdl.handle.net/11323/5956 | |
| dc.identifier | Corporación Universidad de la Costa | |
| dc.identifier | REDICUC - Repositorio CUC | |
| dc.identifier | https://repositorio.cuc.edu.co/ | |
| dc.identifier.uri | https://repositorioslatinoamericanos.uchile.cl/handle/2250/9172452 | |
| dc.description | Indicators 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. | |
| dc.format | application/pdf | |
| dc.format | application/pdf | |
| dc.language | eng | |
| dc.publisher | Journal of Physics: Conference Series | |
| dc.relation | 10.1088/1742-6596/1432/1/012049/pdf | |
| dc.relation | [1] Cios, K. J., & Kurgan, L. A. (2000). Trends in Data Mining and Knowledge Discovery. (Dm), 1-
26. | |
| dc.relation | [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, Cham | |
| dc.relation | [3] Demsar, J. (2006). Comparison of Classifiers over Multiple Data Sets. Journal of Machine
Learning Research, vol. 7: 31. | |
| dc.relation | [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) | |
| dc.relation | [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) | |
| dc.relation | [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) | |
| dc.relation | [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. | |
| dc.relation | [8] Russell, S.; Norvig, P.: Artificial Intelligence A Modern Approach. Pearson Education 3rd Ed,
pp. 705 (2010) | |
| dc.relation | [9] Makhabel, B.: Learning Data Mining with R. Packt Publishing 1st Ed, pp. 143 (2015) | |
| dc.relation | [10] Witten, I.; Frank, E.; Hall, M.; Pal, C.: Data Mining Practical Machine Learning Tools and
Techniques. Elsevier 4th Ed, pp. 167-169 (2016). | |
| dc.relation | [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. | |
| dc.relation | [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. | |
| dc.relation | [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. | |
| dc.relation | [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, Cham | |
| dc.relation | [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. | |
| dc.relation | [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. | |
| dc.relation | [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). | |
| dc.relation | [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/ | |
| dc.relation | [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). | |
| dc.relation | [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. | |
| dc.relation | [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. | |
| dc.rights | CC0 1.0 Universal | |
| dc.rights | http://creativecommons.org/publicdomain/zero/1.0/ | |
| dc.rights | info:eu-repo/semantics/openAccess | |
| dc.rights | http://purl.org/coar/access_right/c_abf2 | |
| dc.subject | Artificial neural networks | |
| dc.subject | Environmental indicators | |
| dc.subject | Environmental monitoring | |
| dc.title | Environmental indicators through artificial neural networks | |
| dc.type | Artículo de revista | |
| dc.type | http://purl.org/coar/resource_type/c_6501 | |
| dc.type | Text | |
| dc.type | info:eu-repo/semantics/article | |
| dc.type | info:eu-repo/semantics/publishedVersion | |
| dc.type | http://purl.org/redcol/resource_type/ART | |
| dc.type | info:eu-repo/semantics/acceptedVersion | |
| dc.type | http://purl.org/coar/version/c_ab4af688f83e57aa | |
