Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Yılmaz, Selim Fırat"'
Autor:
Ngô, Minh, Mandira, Burak, Yılmaz, Selim Fırat, Heij, Ward, Karaoglu, Sezer, Bouma, Henri, Dibeklioglu, Hamdi, Gevers, Theo
Lies and deception are common phenomena in society, both in our private and professional lives. However, humans are notoriously bad at accurate deception detection. Based on the literature, human accuracy of distinguishing between lies and truthful s
Externí odkaz:
http://arxiv.org/abs/1812.10558
Akademický článek
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Akademický článek
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Autor:
Mandıra, Burak, Dersu Giritlioglu, Yılmaz, Selim Fırat, Ertenli, Can Ufuk, Berhan Faruk Akgür, Kınıklıoglu, Merve, Aslı Gül Kurt, Doganlı, Merve Nur, Mutlu, Emre, Seref Can Gürel, Dibeklioglu, Hamdi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9180dded7c85bbc190e8ec0ba64b42b4
Autor:
Dersu Giritlioğlu, Can Ufuk Ertenli, Emre Mutlu, Burak Mandira, Hamdi Dibeklioglu, Selim Firat Yilmaz, Aslı Gül Kurt, Merve Kiniklioglu, Şeref Can Gürel, Berhan Faruk Akgür
Publikováno v:
Journal on Multimodal User Interfaces, 15(4), 337-358. Springer Verlag
Journal on Multimodal User Interfaces
Journal on Multimodal User Interfaces
Personality analysis is an important area of research in several fields, including psychology, psychiatry, and neuroscience. With the recent dramatic improvements in machine learning, it has also become a popular research area in computer science. Wh
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1c437a72adcc528b2b7c1d0668e2b02d
https://doi.org/10.1007/s12193-020-00347-7
https://doi.org/10.1007/s12193-020-00347-7
Publikováno v:
IEEE Transactions on Neural Networks and Learning Systems
We investigate cross-lingual sentiment analysis, which has attracted significant attention due to its applications in various areas including market research, politics and social sciences. In particular, we introduce a sentiment analysis framework in
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::119bee6fa9b0dc258306dd849344ddf2
https://hdl.handle.net/11693/77683
https://hdl.handle.net/11693/77683
Publikováno v:
IEEE Transactions on Neural Networks and Learning Systems
IEEE Transactions on Neural Networks and Learning Systems.
IEEE Transactions on Neural Networks and Learning Systems.
Recurrent Neural Networks (RNNs) are widely used for online regression due to their ability to generalize nonlinear temporal dependencies. As an RNN model, Long-Short-Term-Memory Networks (LSTMs) are commonly preferred in practice, as these networks
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f8a97dfc5313576cd032b515110561c2
https://hdl.handle.net/11693/77682
https://hdl.handle.net/11693/77682