Keywords
web accessibility
machine learning
unsupervised learning
statistics
machine learning
unsupervised learning
statistics
How to Cite
Zambrano, F., & Munoz, E. (2023). Statistical machine learning methods applied in the study of web accessibility: a literature review. Minerva, 2023(Special), 97-105. https://doi.org/10.47460/minerva.v2023iSpecial.121
Abstract
Technological development in solid-state chemistry, nanotechnology, and new materials is advancing at an accelerated pace; studies of methods to generate thin films of conductive and semiconductor materials are of great interest; however, current methods tend to be very expensive and inaccessible to developing countries. This work seeks to present viable and economical alternatives for teaching laboratories to investigate chemical deposition processes in aqueous solutions and produce thin layers of materials of interest in solid-state chemistry and new materials. The metals studied were copper, cobalt and, nickel in different salts and reducing agents, hydrazine hydrochloride, phenylhydrazine and, sodium borohydride. The main results show that it is possible to use cheaper chemicals to study depositions in an aqueous solution, a viable alternative for laboratories.
References
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doi: 10.1145/3058555.3058559.
[12] S. Bahram, D. Sen, and R. S. Amant, “Prediction of Web page accessibility based on structural and textual features,” W4A 2011 - Int. Cross-Disciplinary Conf. Web Access., pp. 5–8, 2011, doi: 10.1145/1969289.1969329.
[13] I. Hernández, C. R. Rivero, D. Ruiz, and R. Corchuelo, “A statistical approach to URL-based web page clustering,” WWW’12 - Proc. 21st Annu. Conf. World Wide Web Companion, pp. 525–526, 2012, doi: 10.1145/2187980.2188109.
[14] M. Usama et al., “Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges,” IEEE Access, vol. 7, pp. 65579–65615, 2019, doi: 10.1109/ACCESS.2019.2916648.
[15] J. P. Bigham, “Increasing web accessibility by automatically judging alternative text quality,” Int. Conf. Intell. User Interfaces, Proc. IUI, pp. 349–352, 2007, doi: 10.1145/1216295.1216364.
[16] P. Taylor, C. Fraley, and A. E. Raftery, “Journal of the American Statistical Association and Density Estimation Model-Based Clustering, Discriminant Analysis, and Density Estimation,” no. May, pp. 37–41, 2012.
[17] T. Gowda and C. Mattmann, “Clustering web pages based on structure and style similarity,” Proc. - 2016 IEEE 17th Int. Conf. Inf. Reuse Integr. IRI 2016, pp. 175–180, 2016, doi: 10.1109/IRI.2016.30.
[18] J. Mucha, M. Snaprud, and A. Nietzio, “Web page clustering for more efficient website accessibility evaluations,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 9758, pp. 259–266, 2016, doi: 10.1007/978-3-319-41264-1_35.
[19] M. D. Olvera Lobo, M. Aguilar Soto, and E. Ruíz de Osma, “Evaluación de sitios web de postgrados biomédicos en España Evaluation of websites for biomedical,” vol. 24, no. 1, pp. 47–60, 2012.
[20] Servicio Ecuatoriano de Normalización (INEN), “Reglamento Técnico Ecuatoriano RTE INEN 288 ‘Accesibilidad para el Contenido Web,’” pp. 1–6, 2016, [Online]. Available: http://www.normalizacion.gob.ec/wp-content/uploads/downloads/2016/02/RTE-288.pdf.
[2] A. Ismail and K. S. Kuppusamy, “Web accessibility investigation and identification of major issues of higher education websites with statistical measures: A case study of college websites,” J. King Saud Univ. - Comput. Inf. Sci., vol. 34, no. 3, pp. 901–911, 2022.
doi: 10.1016/j.jksuci.2019.03.011.
[3] The World Wide Web Consortium, “Home | Web Accessibility Initiative (WAI) | W3C.” https://www.w3.org/WAI/ (accessed jul. 31, 2022).
[4] D. Naranjo-Villota, J. Guaña-Moya, P. Acosta-Vargas, and V. Muirragui-Irrazábal, “Evaluación de la accesibilidad web en institutos acreditados de educación superior del Ecuador,” Rev. Espac., vol. 41, no. 4, p. 5, 2020, [Online]. Available: http://revistaespacios.com/a20v41n04/20410405.html
[5] T. Acosta, P. Acosta-Vargas, J. Zambrano-Miranda, and S. Lujan-Mora, “Web Accessibility Evaluation of Videos Published on YouTube by Worldwide Top-Ranking Universities,” IEEE Access, vol. 8, pp. 110994–111011, 2020, doi: 10.1109/ACCESS.2020.3002175.
[6] N. K. Esparza Cruz, Z. Merino Acosta, and H. Guerrero Torres, “Accesibilidad Web en las Instituciones de Educación Superior del Ecuador: Año 2016,” J. Sci. Res. Rev. Cienc. e Investig., vol. 1, no. CITT2016, pp. 44–48, 2016, doi: 10.26910/issn.2528-8083vol1isscitt2016.2016pp44-48.
[7] Ó. R. G. López, T. M. B. Palacios, and M. B. Mateos, “El índice cuantitativo de calidad web como instrumento objetivo de medición de la calidad de sitios web corporativos,” Investig. Eur. Dir. y Econ. la Empresa., vol. 19, no. 1, pp. 16–30, 2013, doi: 10.1016/j.iedee.2012.07.004.
[8] Y. S. Rodríguez, E. Á. Calderón, L. B. Pérez, and C. A. S. Anlas, “State of web accessibility of e-government portals in Latin America,” Bibl. An. Investig., vol. 16, no. 1, pp. 7–22, 2020.
[9] L. Olsina and M. Bérnabe, “Técnicas Estadísticas para el Análisis de la Calidad de Sitios Web,” no. July, 2019.
[10] D. Sato, H. Takagi, and C. Asakawa, “Accessibility evaluation based on machine learning technique,” Eighth Int. ACM SIGACCESS Conf. Comput. Access. ASSETS 2006, vol. 2006, pp. 253–254, 2006, doi: 10.1145/1168987.1169041.
[11] M. Zhang, C. Wang, Z. Yu, C. Shen, and J. Bu, “Active learning for web accessibility evaluation,” Proc. 14th Web All Conf. W4A 2017, 2017.
doi: 10.1145/3058555.3058559.
[12] S. Bahram, D. Sen, and R. S. Amant, “Prediction of Web page accessibility based on structural and textual features,” W4A 2011 - Int. Cross-Disciplinary Conf. Web Access., pp. 5–8, 2011, doi: 10.1145/1969289.1969329.
[13] I. Hernández, C. R. Rivero, D. Ruiz, and R. Corchuelo, “A statistical approach to URL-based web page clustering,” WWW’12 - Proc. 21st Annu. Conf. World Wide Web Companion, pp. 525–526, 2012, doi: 10.1145/2187980.2188109.
[14] M. Usama et al., “Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges,” IEEE Access, vol. 7, pp. 65579–65615, 2019, doi: 10.1109/ACCESS.2019.2916648.
[15] J. P. Bigham, “Increasing web accessibility by automatically judging alternative text quality,” Int. Conf. Intell. User Interfaces, Proc. IUI, pp. 349–352, 2007, doi: 10.1145/1216295.1216364.
[16] P. Taylor, C. Fraley, and A. E. Raftery, “Journal of the American Statistical Association and Density Estimation Model-Based Clustering, Discriminant Analysis, and Density Estimation,” no. May, pp. 37–41, 2012.
[17] T. Gowda and C. Mattmann, “Clustering web pages based on structure and style similarity,” Proc. - 2016 IEEE 17th Int. Conf. Inf. Reuse Integr. IRI 2016, pp. 175–180, 2016, doi: 10.1109/IRI.2016.30.
[18] J. Mucha, M. Snaprud, and A. Nietzio, “Web page clustering for more efficient website accessibility evaluations,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 9758, pp. 259–266, 2016, doi: 10.1007/978-3-319-41264-1_35.
[19] M. D. Olvera Lobo, M. Aguilar Soto, and E. Ruíz de Osma, “Evaluación de sitios web de postgrados biomédicos en España Evaluation of websites for biomedical,” vol. 24, no. 1, pp. 47–60, 2012.
[20] Servicio Ecuatoriano de Normalización (INEN), “Reglamento Técnico Ecuatoriano RTE INEN 288 ‘Accesibilidad para el Contenido Web,’” pp. 1–6, 2016, [Online]. Available: http://www.normalizacion.gob.ec/wp-content/uploads/downloads/2016/02/RTE-288.pdf.
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