Neural networks for the web services classification
This article introduces a n-gram-based approach to automatic classification of Web services using a multilayer perceptron-type artificial neural network. Web services contain information that is useful for achieving a classification based on its functionality. The approach relies on word n-grams ext...
- Autores:
-
silva d, jesus g
Senior Naveda, Alexa
Solórzano Movilla, José
Niebles Núñez, William
Hernández Palma, Hugo
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2020
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/5959
- Acceso en línea:
- https://hdl.handle.net/11323/5959
https://repositorio.cuc.edu.co/
- Palabra clave:
- Neural networks
Web services
Artificial neural network
- Rights
- openAccess
- License
- CC0 1.0 Universal
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| dc.title.spa.fl_str_mv |
Neural networks for the web services classification |
| title |
Neural networks for the web services classification |
| spellingShingle |
Neural networks for the web services classification Neural networks Web services Artificial neural network |
| title_short |
Neural networks for the web services classification |
| title_full |
Neural networks for the web services classification |
| title_fullStr |
Neural networks for the web services classification |
| title_full_unstemmed |
Neural networks for the web services classification |
| title_sort |
Neural networks for the web services classification |
| dc.creator.fl_str_mv |
silva d, jesus g Senior Naveda, Alexa Solórzano Movilla, José Niebles Núñez, William Hernández Palma, Hugo |
| dc.contributor.author.spa.fl_str_mv |
silva d, jesus g Senior Naveda, Alexa Solórzano Movilla, José Niebles Núñez, William Hernández Palma, Hugo |
| dc.subject.spa.fl_str_mv |
Neural networks Web services Artificial neural network |
| topic |
Neural networks Web services Artificial neural network |
| description |
This article introduces a n-gram-based approach to automatic classification of Web services using a multilayer perceptron-type artificial neural network. Web services contain information that is useful for achieving a classification based on its functionality. The approach relies on word n-grams extracted from the web service description to determine its membership in a category. The experimentation carried out shows promising results, achieving a classification with a measure F=0.995 using unigrams (2-grams) of words (characteristics composed of a lexical unit) and a TF-IDF weight. |
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2020 |
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2020-01-30T13:47:46Z |
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2020-01-30T13:47:46Z |
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2020 |
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Artículo de revista |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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https://hdl.handle.net/11323/5959 |
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Corporación Universidad de la Costa |
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| dc.language.iso.none.fl_str_mv |
eng |
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eng |
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10.1088/1742-6596/1432/1/012076/pdf |
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[1] Sean Marston, Zhi Li, Subhajyoti Bandyopadhyay, Juheng Zhang , Anand Ghalsas (2010) “Cloud computing — The business perspective”, Decision support systems, Volume 51, Issue 1, April 2011, Pages 176–189, Elsevier. [2] Bifet, A., & De Francisci Morales, G. (2014). Big data stream learning with Samoa. Recuperado dehttps://www.researchgate.net/publication/282303881_Big_data_stream_learning_with_SAM OA. [3] Mell, Grance. "The NIST definition of cloud computing." NIST Special Publication 800 – 145, 2011Valarie Zeithaml A, A. Parasuraman, Leonard L. Berry (1999.5). Total, quality Management services. Bogota: Diaz de Santos. [4] Sitto, K., M. Presser, 2015. Field Guide to Hadoop. California: O’REILLY, 31-33 pp. [5] Sosinsky, B., 2011. Cloud Computing Bible. Indiana: Wiley Publishing Inc., 3 pp. [6] Bravo, M., Alvarado, M.: Similarity measures for substituting Web services. International Journal of Web Services Research, 7 (3), pp. 1–29 (2010). [7] Chen, L., Zhang, Y., Song, Z.L., Miao, Z.: Automatic web services classification based on rough set theory. Journal of Central South University, 20, pp. 2708–2714 (2013). [8] Pineda Lezama, O., & Gómez Dorta, R. (2017). Techniques of multivariate statistical analysis: An application for the Honduran banking sector. Innovare: Journal of Science and Technology, 5 (2), 61-75 [9] 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 [10] Zhu, Fang et al. "IBM Cloud Computing Powering a Smarter Planet", Libro Cloud Computing, Volumen 599.51/2009, Páginas 621- 625. [11] Mohanty, R., Ravi, V., Patra, M.R.: Web-services classification using intelligent techniques. Expert Systems with Applications, 37(7), pp. 5484–5490 (2010). [12] L. Thames and D. Schaefer, “Softwaredefined Cloud Manufacturing for Industry 4.0,” Procedía CIRP, vol. 52, p p .12-17, 2016. [13] Amelec Viloria, Dionicio Neira-Rodado, Omar Bonerge Pineda Lezama. Recovery of scientific data using Intelligent Distributed Data Warehouse. ANT/EDI40 2019: 1249-1254. [14] Schweidel, D. A., & Knox, G. (2013). Incorporating direct marketing activity into latent attrition models. Marke¬ting Science, 31(3), pp. 471-487. [15] Setnes, M., & Kaymak, U. (2001). Fuzzy modeling of client preference from large data sets: an application to target selection in direct marketing. Fuzzy Systems, IEEE Transactions on, 9(1), pp. 153-163. [16] Amelec Viloria, Omar Bonerge Pineda Lezama. Improvements for Determining the Number of Clusters in k-Means for Innovation Databases in SMEs. ANT/EDI40 2019: 1201-1206 [17] Nisa, R., Qamar, U.: A text mining based approach for web service classification. Information Systems and e-Business Management, pp. 1–18 (2014). [18] Wu, J., Chen, L., Zheng, Z., Lyu, M. R., Wu, Z.: Clustering web services to facilitate service discovery. Knowledge and information systems, 38(1), pp. 207–229 (2014) [22] Alderson, J. (2015). A markerless motion capture technique for sport performance analysis and injury prevention: Toward a big data, machine learning future. Journal of Science and Medicine in Sport, 19, e79. doi: 10.1016/j.jsams.2015.12.192. |
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silva d, jesus gSenior Naveda, AlexaSolórzano Movilla, JoséNiebles Núñez, WilliamHernández Palma, Hugo2020-01-30T13:47:46Z2020-01-30T13:47:46Z20201742-65881742-6596https://hdl.handle.net/11323/5959Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/This article introduces a n-gram-based approach to automatic classification of Web services using a multilayer perceptron-type artificial neural network. Web services contain information that is useful for achieving a classification based on its functionality. The approach relies on word n-grams extracted from the web service description to determine its membership in a category. The experimentation carried out shows promising results, achieving a classification with a measure F=0.995 using unigrams (2-grams) of words (characteristics composed of a lexical unit) and a TF-IDF weight.silva d, jesus g-will be generated-orcid-0000-0003-3555-9149-600Senior Naveda, AlexaSolórzano Movilla, JoséNiebles Núñez, WilliamHernández Palma, HugoengJournal of Physics: Conference Series10.1088/1742-6596/1432/1/012076/pdf[1] Sean Marston, Zhi Li, Subhajyoti Bandyopadhyay, Juheng Zhang , Anand Ghalsas (2010) “Cloud computing — The business perspective”, Decision support systems, Volume 51, Issue 1, April 2011, Pages 176–189, Elsevier.[2] Bifet, A., & De Francisci Morales, G. (2014). Big data stream learning with Samoa. Recuperado dehttps://www.researchgate.net/publication/282303881_Big_data_stream_learning_with_SAM OA.[3] Mell, Grance. "The NIST definition of cloud computing." NIST Special Publication 800 – 145, 2011Valarie Zeithaml A, A. Parasuraman, Leonard L. Berry (1999.5). Total, quality Management services. Bogota: Diaz de Santos.[4] Sitto, K., M. Presser, 2015. Field Guide to Hadoop. California: O’REILLY, 31-33 pp.[5] Sosinsky, B., 2011. Cloud Computing Bible. Indiana: Wiley Publishing Inc., 3 pp.[6] Bravo, M., Alvarado, M.: Similarity measures for substituting Web services. International Journal of Web Services Research, 7 (3), pp. 1–29 (2010).[7] Chen, L., Zhang, Y., Song, Z.L., Miao, Z.: Automatic web services classification based on rough set theory. Journal of Central South University, 20, pp. 2708–2714 (2013).[8] Pineda Lezama, O., & Gómez Dorta, R. (2017). Techniques of multivariate statistical analysis: An application for the Honduran banking sector. Innovare: Journal of Science and Technology, 5 (2), 61-75[9] 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[10] Zhu, Fang et al. "IBM Cloud Computing Powering a Smarter Planet", Libro Cloud Computing, Volumen 599.51/2009, Páginas 621- 625.[11] Mohanty, R., Ravi, V., Patra, M.R.: Web-services classification using intelligent techniques. Expert Systems with Applications, 37(7), pp. 5484–5490 (2010).[12] L. Thames and D. Schaefer, “Softwaredefined Cloud Manufacturing for Industry 4.0,” Procedía CIRP, vol. 52, p p .12-17, 2016.[13] Amelec Viloria, Dionicio Neira-Rodado, Omar Bonerge Pineda Lezama. Recovery of scientific data using Intelligent Distributed Data Warehouse. ANT/EDI40 2019: 1249-1254.[14] Schweidel, D. A., & Knox, G. (2013). Incorporating direct marketing activity into latent attrition models. Marke¬ting Science, 31(3), pp. 471-487.[15] Setnes, M., & Kaymak, U. (2001). Fuzzy modeling of client preference from large data sets: an application to target selection in direct marketing. Fuzzy Systems, IEEE Transactions on, 9(1), pp. 153-163.[16] Amelec Viloria, Omar Bonerge Pineda Lezama. Improvements for Determining the Number of Clusters in k-Means for Innovation Databases in SMEs. ANT/EDI40 2019: 1201-1206[17] Nisa, R., Qamar, U.: A text mining based approach for web service classification. Information Systems and e-Business Management, pp. 1–18 (2014).[18] Wu, J., Chen, L., Zheng, Z., Lyu, M. R., Wu, Z.: Clustering web services to facilitate service discovery. Knowledge and information systems, 38(1), pp. 207–229 (2014) [22] Alderson, J. (2015). A markerless motion capture technique for sport performance analysis and injury prevention: Toward a big data, machine learning future. 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