| dc.relation | 1. Amelec, V.: Increased efficiency in a company of development of technological solutions in
the areas commercial and of consultancy. Adv. Sci. Lett. 21(5), 1406–1408 (2015)
2. Varela, I.N., Cabrera, H.R., Lopez, C.G., Viloria, A., Gaitán, A.M., Henry, M.A.:
Methodology for the reduction and integration of data in the performance measurement of
industries cement plants. In: Tan, Y., Shi, Y., Tang, Q. (eds.) DMBD 2018. LNCS, vol.
10943, pp. 33–42. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93803-5_4
3. Lis-Gutiérrez, M., Gaitán-Angulo, M., Balaguera, M.I., Viloria, A., Santander-Abril, J.E.:
Use of the industrial property system for new creations in colombia: a departmental analysis
(2000–2016). In: Tan, Y., Shi, Y., Tang, Q. (eds.) Data Mining and Big Data. Lecture Notes
in Computer Science, vol. 10943, pp. 786–796. Springer, Cham (2018). https://doi.org/10.
1007/978-3-319-93803-5_74
4. Anuradha, K., Kumar, K.A.: An E-commerce application for presuming missing items. Int.
J. Comput. Trends Technol. 4, 2636–2640 (2013)
5. Larose, D.T., Larose, C.D.: Discovering Knowledge in Data (2014). https://doi.org/10.1002/
9781118874059
6. Pickrahn, I., et al.: Contamination incidents in the pre-analytical phase of forensic DNA
analysis in Austria—Statistics of 17 years. Forensic Sci. Int. Genet. 31, 12–18 (2017).
https://doi.org/10.1016/j.fsigen.2017.07.012
7. Barrios-Hernández, K.D.C., Contreras-Salinas, J.A., Olivero-Vega, E.: La Gestión por
Procesos en las Pymes de Barranquilla: Factor Diferenciador de la Competitividad
Organizacional. Información tecnológica 30(2), 103–114 (2019)
8. Prajapati, D.J., Garg, S., Chauhan, N.C.: Interesting association rule mining with consistent
and inconsistent rule detection from big sales data in distributed environment. Futur.
Comput. Inform. J. 2, 19–30 (2017). https://doi.org/10.1016/j.fcij.2017.04.003
9. Abdullah, M., Al-Hagery, H.: Classifiers’ accuracy based on breast cancer medical data and
data mining techniques. Int. J. Adv. Biotechnol. Res. 7, 976–2612 (2016)
10. Khanali, H.: A survey on improved algorithms for mining association rules. Int. J. Comput.
Appl. 165, 8887 (2017)
11. Ban, T., Eto, M., Guo, S., Inoue, D., Nakao, K., Huang, R.: A study on association rule
mining of darknet big data. In: International Joint Conference on Neural Networks, pp. 1–7
(2015). https://doi.org/10.1109/IJCNN.2015.7280818
12. Vo, B., Le, B.: Fast algorithm for mining generalized association rules. Int. J. Database
Theory Appl. 2, 1–12 (2009)
13. Al-hagery, M.A.: Knowledge discovery in the data sets of hepatitis disease for diagnosis and
prediction to support and serve community. Int. J. Comput. Electron. Res. 4, 118–125 (2015)
14. Amelec, V., Alexander, P.: Improvements in the automatic distribution process of finished
product for pet food category in multinational company. Adv. Sci. Lett. 21(5), 1419–1421
(2015)
15. Cabarcas, J., Paternina, C.: Aplicación del análisis discriminante para identificar diferencias
en el perfil productivo de las empresas exportadoras y no exportadoras del Departamento del
Atlántico de Colombia. Revista Ingeniare 6(10), 33–48 (2011)
16. Caridad, J.M., Ceular, N.: Un análisis del mercado de la vivienda a través de redes
neuronales artificiales. Estudios de economía aplicada (18), 67–81 (2001)
17. Correia, A., Barandas, H., Pires, P.: Applying artificial neural networks to evaluate export
performance: a relational approach. Rev. Onternational Comp. Manag. 10(4), 713–734
(2009)
18. De La Hoz, E., González, Á., Santana, A.: Metodología de Medición del Potencial
Exportador de las Organizaciones Empresariales. Información Tecnológica 27(6), 11–18
(2016)
19. De La Hoz, E., López, P.: Aplicación de Técnicas de Análisis de Conglomerados y Redes
Neuronales Artificiales en la Evaluación del Potencial Exportador de una Empresa.
Información Tecnológica 28(4), 67–74 (2017)
20. Escandón, D., Hurtado, A.: Los determinantes de la orientación exportadora y los resultados
en las pymes exportadoras en Colombia. Estudios Gerenciales 30(133), 430–440 (2014)
21. Kumar, G., Malik, H.: Generalized regression neural network based wind speed prediction
model for western region of India. Procedia Comput. Sci. 93(September), 26–32 (2016).
https://doi.org/10.1016/j.procs.07.177
22. Obschatko, E., Blaio, M.: El perfil exportador del sector agroalimentario argentino. Las
profucciones de alto valor. Estudio 1. EG.33.7. Ministerio de Economía de Argentina (2003)
23. Olmedo, E., Velasco, F., Valderas, J.M.: Caracterización no lineal y predicción no
paramétrica en el IBEX35. Estudios de Economía Aplicada 25(3), 1–3 (2007)
24. Paredes, D.: Elaboración del plan de negocios de exportación. Programa de Plan de Negocio,
Exportador- PLANEX (2016). https://goo.gl/oTnARL
25. Qazi, N.: Effectof Feature Selection, Synthetic Minority Over-sampling (SMOTE) And
Under-sampling on Class imbalance Classification (2012). https://doi.org/10.1109/UKSim.
116
26. Smith, D.: A neural network classification of export success in Japanese service firms. Serv.
Mark. Q. 26(4), 95–108 (2005)
27. Sharmila, S., Kumar, M.: An optimized farthest first clustering algorithm. In: Nirma
University International Conference on Engineering, NUiCONE 2013, pp. 1–5 (2013).
https://doi.org/10.1109/NUiCONE.2013.6780070
28. Sun, G., Hoff, S., Zelle, B., Nelson, M.: Development and Comparison of Backpropagation
and Generalized Regression Neural Network Models to Predict Diurnal and Seasonal Gas
and PM 10 Concentrations and Emissions from Swine Buildings, vol. 0300, no. 08 (2008)
29. Uberbacher, E.C., Mural, R.J.: Locating protein-coding regions in human DNA sequences
by a multiple sensor-neural network approach. Proc. Natl. Acad. Sci. 88(24), 11261–11265
(1991) | |
