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pro vyhledávání: '"Senarath, Yasas"'
Autor:
Ara, Zinat, Salemi, Hossein, Hong, Sungsoo Ray, Senarath, Yasas, Peterson, Steve, Hughes, Amanda Lee, Purohit, Hemant
Data annotation interfaces predominantly leverage ground truth labels to guide annotators toward accurate responses. With the growing adoption of Artificial Intelligence (AI) in domain-specific professional tasks, it has become increasingly important
Externí odkaz:
http://arxiv.org/abs/2403.01722
Autor:
Senarath, Yasas, Mukhopadhyay, Ayan, Vazirizade, Sayyed Mohsen, Purohit, Hemant, Nannapaneni, Saideep, Dubey, Abhishek
Emergency response is highly dependent on the time of incident reporting. Unfortunately, the traditional approach to receiving incident reports (e.g., calling 911 in the USA) has time delays. Crowdsourcing platforms such as Waze provide an opportunit
Externí odkaz:
http://arxiv.org/abs/2112.02012
In this paper, we present an experiment on using deep learning and transfer learning techniques for emotion analysis in tweets and suggest a method to interpret our deep learning models. The proposed approach for emotion analysis combines a Long Shor
Externí odkaz:
http://arxiv.org/abs/2012.06025
The number of emergencies have increased over the years with the growth in urbanization. This pattern has overwhelmed the emergency services with limited resources and demands the optimization of response processes. It is partly due to traditional `r
Externí odkaz:
http://arxiv.org/abs/2011.05440
Publikováno v:
Digital Government: Research & Practice; Mar2024, Vol. 5 Issue 1, p1-19, 19p