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pro vyhledávání: '"İncel A"'
Autor:
İşgüder, Egemen, İncel, Özlem Durmaz
Motion sensors integrated into wearable and mobile devices provide valuable information about the device users. Machine learning and, recently, deep learning techniques have been used to characterize sensor data. Mostly, a single task, such as recogn
Externí odkaz:
http://arxiv.org/abs/2311.07765
Autor:
Saylam, Berrenur, İncel, Özlem Durmaz
The ability to monitor ambient characteristics, interact with them, and derive information about the surroundings has been made possible by the rapid proliferation of edge sensing devices like IoT, mobile, and wearable devices and their measuring cap
Externí odkaz:
http://arxiv.org/abs/2311.01201
Autor:
Saylam, Berrenur, İncel, Özlem Durmaz
With the development of wearable technologies, a new kind of healthcare data has become valuable as medical information. These data provide meaningful information regarding an individual's physiological and psychological states, such as activity leve
Externí odkaz:
http://arxiv.org/abs/2303.04484
Sleep is among the most important factors affecting one's daily performance, well-being, and life quality. Nevertheless, it became possible to measure it in daily life in an unobtrusive manner with wearable devices. Rather than camera recordings and
Externí odkaz:
http://arxiv.org/abs/2303.06028
Autor:
Agac, Sumeyye, Incel, Ozlem Durmaz
Publikováno v:
In Computers and Electrical Engineering July 2024 117
Publikováno v:
Geochemistry, Geophysics, Geosystems, Vol 25, Iss 2, Pp n/a-n/a (2024)
Abstract In natural lower crustal rocks, we observe that plagioclase breakdown is often partial as evidenced by the presence of epidote‐group minerals and the absence of the remaining reaction products for example, kyanite and quartz. Due to the la
Externí odkaz:
https://doaj.org/article/f2743d4a67a94e61ba685cf680fc0808
Publikováno v:
In Computers and Electrical Engineering July 2023 109 Part A
Autor:
Saylam, Berrenur1 (AUTHOR) berrenur.saylam@bogazici.edu.tr, İncel, Özlem Durmaz1 (AUTHOR) berrenur.saylam@bogazici.edu.tr
Publikováno v:
Diagnostics (2075-4418). Mar2024, Vol. 14 Issue 5, p501. 18p.
Publikováno v:
In Procedia Computer Science 2023 225:1272-1281
Autor:
Berrenur Saylam, Özlem Durmaz İncel
Publikováno v:
Diagnostics, Vol 14, Iss 5, p 501 (2024)
This study investigates the prediction of mental well-being factors—depression, stress, and anxiety—using the NetHealth dataset from college students. The research addresses four key questions, exploring the impact of digital biomarkers on these
Externí odkaz:
https://doaj.org/article/bf7800559fdf481e87b58877533e39e3