La revista Investigación e Innovación en Ingenierías respeta los derechos morales de cada autor, sin embargo los autores ceden los derechos patrimoniales de sus artículos, y al mismo tiempo certifican a través de una carta que su trabajo es inédito y no ha sido publicado anteriormente.
Algoritmo Novedoso Para la Detección de Tareas Repetitivas en el Teclado
Corresponding Author(s) : Bairon Londoño González
blondono@unisimonbolivar.edu.co
Investigación e Innovación en Ingenierías,
Vol. 3 Núm. 2 (2015): Julio - Diciembre
Resumen
En este artículo una herramienta para la detección de tareas repetitivas con secuencias lógicas realizadas a través de comandos del teclado, mediante el diseño e implementación de un algoritmo basado en el uso de autómatas finitos determinísticos y agentes de búsqueda de patrones. La novedad del algoritmo desarrollado radica en que está orientado a la detección de tareas repetitivas cuyas actividades tienen una secuencia lógica y que actualmente no se encuentran automatizadas por lo complejo que es esta labor
Palabras clave
Automatización de tareas repetitivas, algoritmos para la detección de patrones, comandos de teclado, autómatas.
[1]
B. Londoño González y P. A. Sánchez, «Algoritmo Novedoso Para la Detección de Tareas Repetitivas en el Teclado», Investigación e Innovación en Ingenierías, vol. 3, n.º 2, jul. 2015.
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Referencias
-
1. Coronato, A., d'Acierno, A., & De Pietro, G. (2005). Automatic implementation of constraints in component based applications. Information and Software Technology , 47 (7), 497-509.
2. Borràs, J., Moreno, A., & Valls, A. (2014). Intelligent tourism recommender systems: A survey. Expert Systems with Applications , 41 (16), 7370-7389
3. Li, G., Lian, H., Feng, S., & Zhu, L. (2013). Automatic variable selection for longitudinal generalized linear models. Computational Statistics & Data Analysis , 61, 174-186.
4. Alshalabi, H., Tiun, S., Omar, N., & Albared, M. (2013). Experiments on the Use of Feature Selection and Machine Learning Methods in Automatic Malay Text Categorization. Procedia Technology , 11, 748-754
5. Joo, M., & Zhou, Y. (2008). A novel framework for automatic generation of fuzzy neural networks. Neurocomputing , 71 (4-6), 584-591.
6. Chen, T., Zhang, X.-S., Guo, S.-Z., Li, H.-Y., & Wu, Y. (2013). State of the art: Dynamic symbolic execution for automated test generation. Future Generation Computer Systems , 29 (7), 1758-1773.
7. Ruvini, J., & Dony, C. (2000). APE: Learning User’s Habits to Automate Repetitive Tasks. Proceedings of the 2000 Conference on Intelligent User Interfaces.
8. Kaber, D., & Prinzel, L. Adaptive and Adaptable Automation Design: A Critical Review of the Literature and Recommendations for Future Research. Hanover: NASA. 2006
9. Sarter, N., Woods, D., & Billings, C. (1997). Automation Surprises. En G. Salvendry, Handbook of Human Factors & Ergonomics. Wiley.
10. Parasuraman, R., Sheridan, T., & Wickens, C. (2000). A Model for Types and Levels of Human Interaction. IEEE Transactions on systems, man, and cybernetics - Part A: Systems and Humans , 286-297.
11. Sagarna, R., Mendiburu, A., Inza, I., & Lozano, J. (2014). Assisting in search heuristics selection through multidimensional supervised classification: A case study on software testing. Information Sciences , 258, 122-139.
12. Sheridan, T., & Verplank, W. (1987). Human and computer control of undersea teleoperators. Cambridge, MA: MIT Man-Machine Laboratory.
13. Sah, M., & Wade, V. (2012). Automatic metadata mining from multilingual enterprise content. Web Semantics: Science, Services and Agents on the World Wide Web , 11, 41-62.
14. Kuo, R., Huang, Y., Lin, C., Wu, Y., & Zulvia, F. (2014). Automatic kernel clustering with bee colony optimization algorithm. Information Sciences , 283 (1), 107-122.
15. Les, T., Kruk, M., & Osowski, S. (2013). Automatic recognition of industrial tools using artificial intelligence approach. Expert Systems with Applications , 40 (12), 4777-4784.
16. Flanagan, C. (2004). Automatic software model checking via constraint logic. Science of Computer Programming , 50 (1-3), 253-270.
17. Gómez, A., Penadés, C., Canós, J., Borges, M., & Llavador, M. (2014). A framework for variable content document generation with multiple actors. Information and Software Technology , 56 (9), 1101-1121.
18. Darragh, J., & Witten, I. (1991). Adaptive predictive text generation and the reactive keyboard. Interacting with Computers , 3 (1), 27-50.
19. Osasan, K., & Stacey, T. (2014). Automatic prediction of time to failure of open pit mine slopes based on radar monitoring and inverse velocity method. International Journal of Mining Science and Technology , 24 (2), 275-280.
20. Palomo-Duarte, M., García-Domínguez, A., & Medina-Bulo, I. (2014). Automatic dynamic generation of likely invariants for WS-BPEL compositions. Expert Systems with Applications , 41 (11), 5014-5055.
21. Guo, Y., Wang, Y., & Liu, X. (2014). Real-time optical detection system for monitoring roller condition with automatic error compensation. Optics and Lasers in Engineering , 53, 69-78.
22. Cypher, A. (1993). Watch what I do: Programming by demonstration. Cambridge, Mass.: MIT Press.
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24. Maes, P. (1994). Agents that reduce workand information overload. Communications of the ACM , 37 (7), 31-40.
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26. Holling, H., Bertling, J., & Zeuch, N. (2009). Automatic item generation of probability word problems. Studies in Educational Evaluation , 35 (2-3), 71-76.
27. Myer, T. (2009). Apple Automator with AppleScript Bible. Indianapolis: Jhon Wiley & Sons.
28. Mitchell, T., Caruana, R., Freitag, D., McDermott, J., & Zabowski, D. (1994). Experience with a learning personal assistant. Communications of the ACM , 37 (7), 81-91.
29. Motoda, H. (1997). Machine learning techniques to make computers easier to use. Proceedings of the 15th International Joint Conference on Artificial Intelligence (IJCAI-97). San Francisco: Morgan Kaufmann.
30. Caglayan, A., Snorrason, M., Jacoby, J., Mazzu, J., Jones, R., & Kumar, K. (1997). Learn sesame: a learning agent engine. Applied Artificial Intelligence , 11, 393-412.
31. Armstrong, R., Freitag, D., Joachims, T., & Mitchell, T. (1995). WebWatcher: A learning apprentice for the World Wide Web. AAAI spring symposium on information gathering.
32. . Debroy, V., & Wong, W. (2014). Combining mutation and fault localization for automated program debugging. Journal of Systems and Software , 90, 45-60.
33. Ku, N., Jo, A., Ha, S., Rho, M., & Lee, K.-Y. (2012). Automatic generation of equations of motion for multibody system in discrete event simulation framework. Procedia Technology , 1, 55-64.
34. Guevara, C. (2012). Reconocimiento de patrones para identificación de usuarios en accesos informáticos. Madrid, España: Universidad Complutense de Madrid, Tesis de Maestría.
35. Argall, B., Chernova, S., Veloso, M., & Browning, B. (2009). A survey of robot learning from demonstration. Robot. Auton. Syst. , 469-483.
36. Christl, A., Koschke, R., & Storey, M. (2007). Automated clustering to support the reflexion method. Information and Software Technology , 49 (3), 255-274.
37. Yigit, T., Isik, A., & Ince, M. (2014). Multi Criteria Decision Making System for Learning Object Repository. Procedia - Social and Behavioral Sciences , 141, 813-816.
38. Zhan, Y., & Clark, J. (2008). A search-based framework for automatic testing of MATLAB/Simulink models. Journal of Systems and Software , 81 (2), 262-285.
39. Derrode, S., & Pieczynski, W. (2013). Unsupervised data classification using pairwise Markov chains with automatic copulas selection. Computational Statistics & Data Analysis , 63, 81-98.
40. Hopcroft, J., Motwani, R., & Ullman, J. (2001). Introduction to Automata Theory, Languages, and Computation. Massachusetts, USA: Addison-Wesley.
41. Liu, D., Cui, B., Liu, Y., & Zhong, D. (2013). Automatic control and real-time monitoring system for earth–rock dam material truck watering. Automation in Construction , 30, 70-80.
42. Pérez, B., & Polo, M. (2009). Generación automática de casos de prueba para Líneas de Producto de Software. Revista Española de Innovación, Calidad e Ingeniería del Software , 5 (2), 17-27.
43. Dunn, k. (2004). Automatic update risks: can patching let a hacker in? Network Security , 2004 (7), 5-8.
44. Farjoodi, J., & Soroushian, A. (2001). Efficient Automatic Selection of Tolerances in Nonlinear Dynamic Analysis. En A. Zingoni, Structural Engineering, Mechanics and Computation (págs. 853-859). Oxford: Elsevier Science.
45. Fernández, A., Gómez, A., Lecumberry, F., Pardo, A., & Ramírez, I. (2014). Pattern Recognition in Latin America in the “Big Data” Era. Pattern Recognition , In press.
46. Yajun, Z., & Qian, Q. (2012). A New Type of Automatic Monitoring System of Static and Dynamic Displacement on Dam and Slope. Procedia Engineering , 43, 387-392.
47. Zhang, Y., Dang, Y., Chen, H., Thurmond, M., & Larson, C. (2009). Automatic online news monitoring and classification for syndromic surveillance. Decision Support Systems , 47 (4), 508-517.
48. Zhang, Y., Li, Y., & Zheng, W. (2013). Automatic software deployment using user-level virtualization for cloud-computing. Future Generation Computer Systems , 29 (1), 323-329.
49. Superalumnos. (07 de 11 de 2007). Superalumnos.net. Recuperado el 11 de 08 de 2014, de Base de datos de ejemplo: Inmobiliaria: http://superalumnos.net/base-de-datos-de-ejemplo-inmobiliaria
50. Sutton, R., & Barto, A. (1998). Reinforcement Learning: An Introduction. Cambridge: Cambridge University Press.
51. Van Royen, K., Poels, K., Daelema, W., & Vandebosch, H. (2014). Kathleen Van Royen, Karolien Poels, Walter Daelemans, Heidi Vandebosch, Automatic monitoring of cyberbullying on social networking sites: From technological feasibility to desirability. Telematics and Informatics , in Press.
52. Zhou, P., Li, D., Wu, H., & Cheng, F. (2011). The automatic model selection and variable kernel width for RBF neural networks. Neurocomputing , 74 (17), 3628-3637.
53. Arcuri, A., & Yao, X. (2008). Search based software testing of object-oriented container. Information Sciences , 178 (15), 3075-3095.
54. Xu, J. (2012). Rule-based automatic software performance diagnosis and improvement. Performance Evaluation , 69 (11), 525-550.
55. Zhang, Y., Zhang, L., & Alamgir, M. (2014). Adaptive 3D Facial Action Intensity Estimation and Emotion Recognition. Expert Systems with Applications , In press.
56. Alonso, D., Pastor, J., Sánchez, P., Álvarez, B., & Vicente-Chicote, C. (2012). Generación Automática de Software para Sistemas de Tiempo Real: Un Enfoque basado en Componentes, Modelos y Frameworks. Revista Iberoamericana de Automática e Informática Industrial RIAI , 9 (2), 170-181.
57. Wang, Y., Wang, D., & Fang, W. (2014). Automatic node selection and target tracking in wireless camera sensor networks. Computers & Electrical Engineering , 40 (2), 484-493.
58. Zienkiewicz, O., Taylor, R., & Zhu, J. (2013). Automatic Mesh Generation. En O. Zienkiewicz, R. Taylor, & J. Zhu, The Finite Element Method: its Basis and Fundamentals (Septima edición ed., págs. 573-640). Oxford: Butterworth-Heinemann.
59. Mokhtarian, F., & Abbasi, S. (2005). Robust automatic selection of optimal views in multi-view free-form object recognition. Pattern Recognition , 38 (7), 1021-1031.
60. Moral, S. (2006). Modelos de Computación I. Granada, España: Guias de asignatura. Universidad de Granada.
61. Seok, J., & Seong, H. (2015). Automatic generation algorithm of expected results for testing of component-based software system. Information and Software Technology , 57, 1-20.
62. Shih, H. (2014). A robust occupancy detection and tracking algorithm for the automatic monitoring and commissioning of a building. Energy and Buildings , 77, 270-280.
63. Varela-Vaca, A., & Gasca, R. (2013). Towards the automatic and optimal selection of risk treatments for business processes using a constraint programming approach. Information and Software Technology , 55 (11), 1948-1973.
64. Wang, Q., & Yu, X. (2014). Ontology based automatic feature recognition framework. Computers in Industry , 65 (7), 1041-1052.
65. Shahriar, H., & Zulkernine, M. (2011). Taxonomy and classification of automatic monitoring of program security vulnerability exploitations. Journal of Systems and Software , 84 (2), 250-269.
66. Arcuri, A. (2011). Evolutionary repair of faulty software. Applied Soft Computing , 11 (4), 3494-3514.
67. Cobo, L., Subramanian, K., Isbell, C., Lanterman, A., & Thomaz, A. (2014). Abstraction from demonstration for efficient reinforcement learning in high-dimensional domains. Artificial Intelligence , 103-128.
68. Dehua, W., Pan, L., Bo, L., & Zeng, G. (2012). Water Quality Automatic Monitoring System Based on GPRS Data Communications. Procedia Engineering , 28, 840-843.
69. Dominguez, A., Tojo, J., & Castier, M. (2002). Automatic implementation of thermodynamic models for reliable parameter estimation using computer algebra. Computers & Chemical Engineering , 26 (10), 1473-1479.
70. Liu, D., Yang, Z., Tang, C., Wang, J., & Liu, Y. (2004). An automatic monitoring system for the shiplock slope of Wuqiangxi Hydropower Station. Engineering Geology , 76 (1-2), 79-91.
71. Lu, C., & Lu, Z. (2008). Local feature extraction for iris recognition with automatic scale selection. Image and Vision Computing , 26 (7), 935-940.
72. Lucey, P., Cohn, J., Prkachin, K., Solomon, P., Chew, S., & Matthews, I. (2012). Painful monitoring: Automatic pain monitoring using the UNBC-McMaster shoulder pain expression archive database. Image and Vision Computing , 30 (3), 197-205.
73. Messelis, T., & De Causmaecker, P. (2014). An automatic algorithm selection approach for the multi-mode resource-constrained project scheduling problem. European Journal of Operational Research , 233 (3), 511-528.
Referencias
1. Coronato, A., d'Acierno, A., & De Pietro, G. (2005). Automatic implementation of constraints in component based applications. Information and Software Technology , 47 (7), 497-509.
2. Borràs, J., Moreno, A., & Valls, A. (2014). Intelligent tourism recommender systems: A survey. Expert Systems with Applications , 41 (16), 7370-7389
3. Li, G., Lian, H., Feng, S., & Zhu, L. (2013). Automatic variable selection for longitudinal generalized linear models. Computational Statistics & Data Analysis , 61, 174-186.
4. Alshalabi, H., Tiun, S., Omar, N., & Albared, M. (2013). Experiments on the Use of Feature Selection and Machine Learning Methods in Automatic Malay Text Categorization. Procedia Technology , 11, 748-754
5. Joo, M., & Zhou, Y. (2008). A novel framework for automatic generation of fuzzy neural networks. Neurocomputing , 71 (4-6), 584-591.
6. Chen, T., Zhang, X.-S., Guo, S.-Z., Li, H.-Y., & Wu, Y. (2013). State of the art: Dynamic symbolic execution for automated test generation. Future Generation Computer Systems , 29 (7), 1758-1773.
7. Ruvini, J., & Dony, C. (2000). APE: Learning User’s Habits to Automate Repetitive Tasks. Proceedings of the 2000 Conference on Intelligent User Interfaces.
8. Kaber, D., & Prinzel, L. Adaptive and Adaptable Automation Design: A Critical Review of the Literature and Recommendations for Future Research. Hanover: NASA. 2006
9. Sarter, N., Woods, D., & Billings, C. (1997). Automation Surprises. En G. Salvendry, Handbook of Human Factors & Ergonomics. Wiley.
10. Parasuraman, R., Sheridan, T., & Wickens, C. (2000). A Model for Types and Levels of Human Interaction. IEEE Transactions on systems, man, and cybernetics - Part A: Systems and Humans , 286-297.
11. Sagarna, R., Mendiburu, A., Inza, I., & Lozano, J. (2014). Assisting in search heuristics selection through multidimensional supervised classification: A case study on software testing. Information Sciences , 258, 122-139.
12. Sheridan, T., & Verplank, W. (1987). Human and computer control of undersea teleoperators. Cambridge, MA: MIT Man-Machine Laboratory.
13. Sah, M., & Wade, V. (2012). Automatic metadata mining from multilingual enterprise content. Web Semantics: Science, Services and Agents on the World Wide Web , 11, 41-62.
14. Kuo, R., Huang, Y., Lin, C., Wu, Y., & Zulvia, F. (2014). Automatic kernel clustering with bee colony optimization algorithm. Information Sciences , 283 (1), 107-122.
15. Les, T., Kruk, M., & Osowski, S. (2013). Automatic recognition of industrial tools using artificial intelligence approach. Expert Systems with Applications , 40 (12), 4777-4784.
16. Flanagan, C. (2004). Automatic software model checking via constraint logic. Science of Computer Programming , 50 (1-3), 253-270.
17. Gómez, A., Penadés, C., Canós, J., Borges, M., & Llavador, M. (2014). A framework for variable content document generation with multiple actors. Information and Software Technology , 56 (9), 1101-1121.
18. Darragh, J., & Witten, I. (1991). Adaptive predictive text generation and the reactive keyboard. Interacting with Computers , 3 (1), 27-50.
19. Osasan, K., & Stacey, T. (2014). Automatic prediction of time to failure of open pit mine slopes based on radar monitoring and inverse velocity method. International Journal of Mining Science and Technology , 24 (2), 275-280.
20. Palomo-Duarte, M., García-Domínguez, A., & Medina-Bulo, I. (2014). Automatic dynamic generation of likely invariants for WS-BPEL compositions. Expert Systems with Applications , 41 (11), 5014-5055.
21. Guo, Y., Wang, Y., & Liu, X. (2014). Real-time optical detection system for monitoring roller condition with automatic error compensation. Optics and Lasers in Engineering , 53, 69-78.
22. Cypher, A. (1993). Watch what I do: Programming by demonstration. Cambridge, Mass.: MIT Press.
23. Lieberman, H. (1993). Mondrian: A teachable graphical editor. En D. Cypher, In Watch what I do: Programming by demonstration. Cambridge, Mass.: MIT Press.
24. Maes, P. (1994). Agents that reduce workand information overload. Communications of the ACM , 37 (7), 31-40.
25. Maes, P., & Kozierok, R. (1993). Learning: Interface Agents. AAAI-93 Proceedings, (págs. 459-465).
26. Holling, H., Bertling, J., & Zeuch, N. (2009). Automatic item generation of probability word problems. Studies in Educational Evaluation , 35 (2-3), 71-76.
27. Myer, T. (2009). Apple Automator with AppleScript Bible. Indianapolis: Jhon Wiley & Sons.
28. Mitchell, T., Caruana, R., Freitag, D., McDermott, J., & Zabowski, D. (1994). Experience with a learning personal assistant. Communications of the ACM , 37 (7), 81-91.
29. Motoda, H. (1997). Machine learning techniques to make computers easier to use. Proceedings of the 15th International Joint Conference on Artificial Intelligence (IJCAI-97). San Francisco: Morgan Kaufmann.
30. Caglayan, A., Snorrason, M., Jacoby, J., Mazzu, J., Jones, R., & Kumar, K. (1997). Learn sesame: a learning agent engine. Applied Artificial Intelligence , 11, 393-412.
31. Armstrong, R., Freitag, D., Joachims, T., & Mitchell, T. (1995). WebWatcher: A learning apprentice for the World Wide Web. AAAI spring symposium on information gathering.
32. . Debroy, V., & Wong, W. (2014). Combining mutation and fault localization for automated program debugging. Journal of Systems and Software , 90, 45-60.
33. Ku, N., Jo, A., Ha, S., Rho, M., & Lee, K.-Y. (2012). Automatic generation of equations of motion for multibody system in discrete event simulation framework. Procedia Technology , 1, 55-64.
34. Guevara, C. (2012). Reconocimiento de patrones para identificación de usuarios en accesos informáticos. Madrid, España: Universidad Complutense de Madrid, Tesis de Maestría.
35. Argall, B., Chernova, S., Veloso, M., & Browning, B. (2009). A survey of robot learning from demonstration. Robot. Auton. Syst. , 469-483.
36. Christl, A., Koschke, R., & Storey, M. (2007). Automated clustering to support the reflexion method. Information and Software Technology , 49 (3), 255-274.
37. Yigit, T., Isik, A., & Ince, M. (2014). Multi Criteria Decision Making System for Learning Object Repository. Procedia - Social and Behavioral Sciences , 141, 813-816.
38. Zhan, Y., & Clark, J. (2008). A search-based framework for automatic testing of MATLAB/Simulink models. Journal of Systems and Software , 81 (2), 262-285.
39. Derrode, S., & Pieczynski, W. (2013). Unsupervised data classification using pairwise Markov chains with automatic copulas selection. Computational Statistics & Data Analysis , 63, 81-98.
40. Hopcroft, J., Motwani, R., & Ullman, J. (2001). Introduction to Automata Theory, Languages, and Computation. Massachusetts, USA: Addison-Wesley.
41. Liu, D., Cui, B., Liu, Y., & Zhong, D. (2013). Automatic control and real-time monitoring system for earth–rock dam material truck watering. Automation in Construction , 30, 70-80.
42. Pérez, B., & Polo, M. (2009). Generación automática de casos de prueba para Líneas de Producto de Software. Revista Española de Innovación, Calidad e Ingeniería del Software , 5 (2), 17-27.
43. Dunn, k. (2004). Automatic update risks: can patching let a hacker in? Network Security , 2004 (7), 5-8.
44. Farjoodi, J., & Soroushian, A. (2001). Efficient Automatic Selection of Tolerances in Nonlinear Dynamic Analysis. En A. Zingoni, Structural Engineering, Mechanics and Computation (págs. 853-859). Oxford: Elsevier Science.
45. Fernández, A., Gómez, A., Lecumberry, F., Pardo, A., & Ramírez, I. (2014). Pattern Recognition in Latin America in the “Big Data” Era. Pattern Recognition , In press.
46. Yajun, Z., & Qian, Q. (2012). A New Type of Automatic Monitoring System of Static and Dynamic Displacement on Dam and Slope. Procedia Engineering , 43, 387-392.
47. Zhang, Y., Dang, Y., Chen, H., Thurmond, M., & Larson, C. (2009). Automatic online news monitoring and classification for syndromic surveillance. Decision Support Systems , 47 (4), 508-517.
48. Zhang, Y., Li, Y., & Zheng, W. (2013). Automatic software deployment using user-level virtualization for cloud-computing. Future Generation Computer Systems , 29 (1), 323-329.
49. Superalumnos. (07 de 11 de 2007). Superalumnos.net. Recuperado el 11 de 08 de 2014, de Base de datos de ejemplo: Inmobiliaria: http://superalumnos.net/base-de-datos-de-ejemplo-inmobiliaria
50. Sutton, R., & Barto, A. (1998). Reinforcement Learning: An Introduction. Cambridge: Cambridge University Press.
51. Van Royen, K., Poels, K., Daelema, W., & Vandebosch, H. (2014). Kathleen Van Royen, Karolien Poels, Walter Daelemans, Heidi Vandebosch, Automatic monitoring of cyberbullying on social networking sites: From technological feasibility to desirability. Telematics and Informatics , in Press.
52. Zhou, P., Li, D., Wu, H., & Cheng, F. (2011). The automatic model selection and variable kernel width for RBF neural networks. Neurocomputing , 74 (17), 3628-3637.
53. Arcuri, A., & Yao, X. (2008). Search based software testing of object-oriented container. Information Sciences , 178 (15), 3075-3095.
54. Xu, J. (2012). Rule-based automatic software performance diagnosis and improvement. Performance Evaluation , 69 (11), 525-550.
55. Zhang, Y., Zhang, L., & Alamgir, M. (2014). Adaptive 3D Facial Action Intensity Estimation and Emotion Recognition. Expert Systems with Applications , In press.
56. Alonso, D., Pastor, J., Sánchez, P., Álvarez, B., & Vicente-Chicote, C. (2012). Generación Automática de Software para Sistemas de Tiempo Real: Un Enfoque basado en Componentes, Modelos y Frameworks. Revista Iberoamericana de Automática e Informática Industrial RIAI , 9 (2), 170-181.
57. Wang, Y., Wang, D., & Fang, W. (2014). Automatic node selection and target tracking in wireless camera sensor networks. Computers & Electrical Engineering , 40 (2), 484-493.
58. Zienkiewicz, O., Taylor, R., & Zhu, J. (2013). Automatic Mesh Generation. En O. Zienkiewicz, R. Taylor, & J. Zhu, The Finite Element Method: its Basis and Fundamentals (Septima edición ed., págs. 573-640). Oxford: Butterworth-Heinemann.
59. Mokhtarian, F., & Abbasi, S. (2005). Robust automatic selection of optimal views in multi-view free-form object recognition. Pattern Recognition , 38 (7), 1021-1031.
60. Moral, S. (2006). Modelos de Computación I. Granada, España: Guias de asignatura. Universidad de Granada.
61. Seok, J., & Seong, H. (2015). Automatic generation algorithm of expected results for testing of component-based software system. Information and Software Technology , 57, 1-20.
62. Shih, H. (2014). A robust occupancy detection and tracking algorithm for the automatic monitoring and commissioning of a building. Energy and Buildings , 77, 270-280.
63. Varela-Vaca, A., & Gasca, R. (2013). Towards the automatic and optimal selection of risk treatments for business processes using a constraint programming approach. Information and Software Technology , 55 (11), 1948-1973.
64. Wang, Q., & Yu, X. (2014). Ontology based automatic feature recognition framework. Computers in Industry , 65 (7), 1041-1052.
65. Shahriar, H., & Zulkernine, M. (2011). Taxonomy and classification of automatic monitoring of program security vulnerability exploitations. Journal of Systems and Software , 84 (2), 250-269.
66. Arcuri, A. (2011). Evolutionary repair of faulty software. Applied Soft Computing , 11 (4), 3494-3514.
67. Cobo, L., Subramanian, K., Isbell, C., Lanterman, A., & Thomaz, A. (2014). Abstraction from demonstration for efficient reinforcement learning in high-dimensional domains. Artificial Intelligence , 103-128.
68. Dehua, W., Pan, L., Bo, L., & Zeng, G. (2012). Water Quality Automatic Monitoring System Based on GPRS Data Communications. Procedia Engineering , 28, 840-843.
69. Dominguez, A., Tojo, J., & Castier, M. (2002). Automatic implementation of thermodynamic models for reliable parameter estimation using computer algebra. Computers & Chemical Engineering , 26 (10), 1473-1479.
70. Liu, D., Yang, Z., Tang, C., Wang, J., & Liu, Y. (2004). An automatic monitoring system for the shiplock slope of Wuqiangxi Hydropower Station. Engineering Geology , 76 (1-2), 79-91.
71. Lu, C., & Lu, Z. (2008). Local feature extraction for iris recognition with automatic scale selection. Image and Vision Computing , 26 (7), 935-940.
72. Lucey, P., Cohn, J., Prkachin, K., Solomon, P., Chew, S., & Matthews, I. (2012). Painful monitoring: Automatic pain monitoring using the UNBC-McMaster shoulder pain expression archive database. Image and Vision Computing , 30 (3), 197-205.
73. Messelis, T., & De Causmaecker, P. (2014). An automatic algorithm selection approach for the multi-mode resource-constrained project scheduling problem. European Journal of Operational Research , 233 (3), 511-528.
2. Borràs, J., Moreno, A., & Valls, A. (2014). Intelligent tourism recommender systems: A survey. Expert Systems with Applications , 41 (16), 7370-7389
3. Li, G., Lian, H., Feng, S., & Zhu, L. (2013). Automatic variable selection for longitudinal generalized linear models. Computational Statistics & Data Analysis , 61, 174-186.
4. Alshalabi, H., Tiun, S., Omar, N., & Albared, M. (2013). Experiments on the Use of Feature Selection and Machine Learning Methods in Automatic Malay Text Categorization. Procedia Technology , 11, 748-754
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