نوع مقاله : مقاله پژوهشی
نویسندگان
1 دانشیار گروه فرش دانشکده هنر و علوم انسانی دانشگاه شهرکرد
2 مدرس گروه کامپیوترآموزشکده فنی و حرفهای دختران جونقان/ دانشگاه فنی و حرفهای استان چهارمحال و بختیاری
چکیده
کلیدواژهها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
Abstract:
Lozenge rug chaleshtor is in terms of artistic and aesthetic aspects, it is considered one of the consumer and capital goods that have international fame. From the past until now, this native art has been traditionally produced with the same design, pattern, and colour without paying attention to the tastes of its audience, which can be one of the reasons for the failure of this original art in the global rug markets shortly. Therefore, knowledge and awareness of consumers' taste, which is considered as one of the steps before production, and using modern science, can help to produce according to the taste of the audience and, accordingly, to be more successful in terms of sales and providing high export statistics. To achieve this, in this article, sub-branches of artificial intelligence were used to achieve the taste of Lozenge rug chaleshtor audiences. Three algorithms more related to the subject, 1. artificial neural networks, 2. decision tree learning and 3. support vector machine are compared and finally, the most suitable algorithm for this subject is the artificial neural networkss for receiving the taste of the audience of the Lozenge rug chaleshtor and providing suitable patterns in the field of design. , role, colour, dimensions, texture and price according to the audience's taste, and it was tried to answer the question of whether that artificial neural networkss can introduce the principles of the audience's taste of lozenge rug chaleshtor. in structural fields. For this purpose, primary data in the field of design and role, colour, raw materials, texture, dyeing, dimensions and price were collected through the contact questionnaire and then, using an artificial neural networkss, an algorithm was designed, the results showed that traditional lozenge designs with half motifs curved and quiet, with bright, limited, soft colours, natural dyeing, dimensions under 6 meters and preferably square with a price per meter of up to 10 million tomans is the final taste of the audience of this type of rugs.
Keywords: Taste,Lozenge Rug, Chaleshtor, Artificial Neural Networks
کلیدواژهها [English]
4.Famoye and Wang. Censored generalized poisson regression model. Computational Statistics & Data Analysis. 2004, 46(3). Pp: 547-560. Available at: https://doi.org/10.1016/j.csda.2003.08.007.
13.Marques AI, García V, Sanchez JS. A literature review on the application of evolutionary computing to credit scoring. Journal of the Operational Research Society, 2012, 64(9), 1384-1399.
15.Hagan MT, Demuth HB, Beale MH, De Jesu´s O, Neural network design, 2nd edition, Martin Hagan Publisher. 2014.
16.Sunday et.al. Dynamic failure analysis of process systems using neural networks. Process Safety and Environmental Protection, 111, pp: 529-543. Available at: https://doi.org/10.1016/j.psep.2017.08.005.
17.Wen-Zheng Xu et.al, Corroded pipeline failure analysis using Artificial Neural Networkss scheme. Advanced in Engineering Software, 112, pp: 255-266. Available at: https://doi.org/10.1016/j.advengsoft.2017.05.006.
18.Ghaemi Z, Alimohammadi A, Farnaghi M. LaSVM-based big data learning system for dynamic prediction of air pollution in. Tehran Environmental monitoring and assessment 2018;190:300.
19.Akkas ̧ E, Akin L, Çubukçu HE, Artuner H. Application of decision tree algorithm for classifcation and identification of natural minerals using SEM–EDS. Comput Geosci 2015;80:38–48.