Towards a Quantitative Identification of Mobile Social Media UIDPs’ Visual Features Using a Combination of Digital Image Processing and Machine Learning Techniques
| Autor: | Marcela Quiroz-Castellanos, Efrén Mezura-Montes, Laura Nely Sánchez-Morales, Giner Alor-Hernández, Nicandro Cruz-Ramírez, Viviana Yarel Rosales-Morales |
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| Rok vydání: | 2020 |
| Předmět: | |
| Zdroj: | Intuitionistic and Type-2 Fuzzy Logic Enhancements in Neural and Optimization Algorithms: Theory and Applications ISBN: 9783030354442 Intuitionistic and Type-2 Fuzzy Logic Enhancements in Neural and Optimization Algorithms |
| DOI: | 10.1007/978-3-030-35445-9_45 |
| Popis: | User Interface Design Patterns (UIDPs) improve the interaction between users and e-applications through the use of interfaces with a suitable and intuitive navigability without restrictions on the size of the screen to show the content. Nowadays, UIDPs are frequently used in the development of new mobile apps. In fact, mobile apps are ubiquitous: in education through learning platforms; in medicine through health self-care apps and in a social dimension, of course, through social networks. Social media networks have become one of the main channels of communication and dissemination of content; however, surprisingly, UIDPs have not been deeply analyzed in the design and development process of social media apps. In this sense, we propose the use of a combination of digital image processing and machine learning techniques to both quantitatively identify the main visual features of UIDPs in social media apps and assess the goodness of those features for building highly accurate classifiers. Our results suggest that such a combination seems sensible not only for explicitly unveiling patterns shared by different users but also for constructing such kind of classifiers. |
| Databáze: | OpenAIRE |
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