Zobrazeno 1 - 10
of 437
pro vyhledávání: '"Elke A. Rundensteiner"'
Publikováno v:
The VLDB Journal. 32:665-688
Publikováno v:
International Journal of Data Science and Analytics. 14:407-438
Publikováno v:
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies. 6:1-32
The rates of mental illness, especially anxiety and depression, have increased greatly since the start of the COVID-19 pandemic. Traditional mental illness screening instruments are too cumbersome and biased to screen an entire population. In contras
Autor:
ML Tlachac, Ricardo Flores, Miranda Reisch, Rimsha Kayastha, Nina Taurich, Veronica Melican, Connor Bruneau, Hunter Caouette, Joshua Lovering, Ermal Toto, Elke A. Rundensteiner
Publikováno v:
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies. 6:1-32
The growing prevalence of depression and suicidal ideation among college students further exacerbated by the Coronavirus pandemic is alarming, highlighting the need for universal mental illness screening technology. With traditional screening questio
Autor:
ML Tlachac, Walter Gerych, Kratika Agrawal, Benjamin Litterer, Nicholas Jurovich, Saitheeraj Thatigotla, Jidapa Thadajarassiri, Elke A. Rundensteiner
Publikováno v:
2022 IEEE International Conference on Big Data (Big Data).
Autor:
Emmanuel Agu, Walter Gerych, Hamid Mansoor, Luke Buquicchio, Kavin Chandrasekaran, Abdulaziz Alajaji, Elke A. Rundensteiner
Publikováno v:
Visual Informatics, Vol 5, Iss 3, Pp 39-53 (2021)
Human Bio-Behavioral Rhythms (HBRs) such as sleep-wake cycles (Circadian Rhythms), and the degree of regularity of sleep and physical activity have important health ramifications. Ubiquitous devices such as smartphones can sense HBRs by continuously
Publikováno v:
Proceedings of the 2022 ACM International Joint Conference on Pervasive and Ubiquitous Computing.
Publikováno v:
Conference on Information Technology for Social Good.
Publikováno v:
Proceedings of the VLDB Endowment. 14:2154-2166
Cutting-edge machine learning techniques often require millions of labeled data objects to train a robust model. Because relying on humans to supply such a huge number of labels is rarely practical, automated methods for label generation are needed.
Autor:
Elke A. Rundensteiner, Suranjan De, Lane Harrison, Sanjay K. Sahoo, Thang La, Tabassum Kakar, Xiao Qin
Publikováno v:
Computer Graphics Forum. 40:263-274