Tactile demographics : predicting demographic information using touch data from mobile devices / by Baylea Williams.
| Author/creator | Williams, Baylea author. |
| Other author | Tabrizi, M. H. N., degree supervisor. |
| Other author | East Carolina University. Department of Computer Science. |
| Format | Theses and dissertations |
| Publication | [Greenville, N.C.] : [East Carolina University], 2021. |
| Description | 69 pages : illustrations (chiefly color) |
| Supplemental Content | Access via ScholarShip |
| Subjects |
| Variant title | Tactile demographics predicting demographic information using touch data from mobile devices |
| Summary | The research conducted in this thesis was to serve as a baseline on which human demographics are most likely to be able to be predicted through touch screen interactions. In addition, it served as a way of finding which machine learning models are best suited to be applied to a larger scale experiment of this phenomena. We were able to reliably predict both age and race of participants and in the meantime show that the best machine learning models used was Random Forest Decision Trees and Na̐ve Bayes producing a higher classifier of accuracy than other classifiers tested. While the sample size used during this study was small, due to the ongoing Covid-19 pandemic, the results of this study indicate that research in this area is worthy of significant exploration. |
| General note | Presented to the faculty of the Department of Computer Science |
| General note | Advisor: Nasseh Tabrizi |
| General note | Title from PDF t.p. (viewed September 22, 2021). |
| Dissertation note | M.S. East Carolina University 2021. |
| Bibliography note | Includes bibliographical references. |
| Technical details | System requirements: Adobe Reader. |
| Technical details | Mode of access: World Wide Web. |
Availability
| Library | Location | Call Number | Status | Item Actions |
|---|---|---|---|---|
| Electronic Resources | Access Content Online | ✔ Available |