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1
Classification networks for continuous automatic pain intensity monitoring in video using facial expression on the X-ITE Pain Database
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Classification networks for continuous automatic pain intensity monitoring in video using facial expression on the X-ITE Pain Database

Othman, Ehsan ; Werner, Philipp ; Saxen, Frerk ; Al-Hamadi, Ayoub ; Gruss, Sascha ; Walter, Steffen

Journal of visual communication and image representation, 2023-03, Vol.91, p.103743, Article 103743 [Periódico revisado por pares]

Elsevier Inc

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2
An interpretable machine learning approach to multimodal stress detection in a simulated office environment
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An interpretable machine learning approach to multimodal stress detection in a simulated office environment

Naegelin, Mara ; Weibel, Raphael P. ; Kerr, Jasmine I. ; Schinazi, Victor R. ; La Marca, Roberto ; von Wangenheim, Florian ; Hoelscher, Christoph ; Ferrario, Andrea

Journal of biomedical informatics, 2023-03, Vol.139, p.104299-104299, Article 104299 [Periódico revisado por pares]

United States: Elsevier Inc

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3
Möglichkeiten und Perspektiven zum Einsatz der künstlichen Intelligenz in der Sportorthopädie
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Möglichkeiten und Perspektiven zum Einsatz der künstlichen Intelligenz in der Sportorthopädie

Oronowicz, Jakub ; Ley, Christophe ; Pachowsky, Milena ; Seil, Romain ; Tischer, Thomas

Sportorthopädie-Sporttraumatologie, 2023-03, Vol.39 (1), p.4-10 [Periódico revisado por pares]

Elsevier GmbH

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4
Advanced brain ageing in adult psychopathology: A systematic review and meta-analysis of structural MRI studies
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Advanced brain ageing in adult psychopathology: A systematic review and meta-analysis of structural MRI studies

Blake, Kimberly V. ; Ntwatwa, Ziphozihle ; Kaufmann, Tobias ; Stein, Dan J. ; Ipser, Jonathan C. ; Groenewold, Nynke A.

Journal of psychiatric research, 2023-01, Vol.157, p.180-191 [Periódico revisado por pares]

England: Elsevier Ltd

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5
Predicting early dropout in online versus face-to-face guided self-help: A machine learning approach
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Predicting early dropout in online versus face-to-face guided self-help: A machine learning approach

Gonzalez Salas Duhne, Paulina ; Delgadillo, Jaime ; Lutz, Wolfgang

Behaviour research and therapy, 2022-12, Vol.159, p.104200-104200, Article 104200 [Periódico revisado por pares]

England: Elsevier Ltd

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6
A large and rich EEG dataset for modeling human visual object recognition
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A large and rich EEG dataset for modeling human visual object recognition

Gifford, Alessandro T. ; Dwivedi, Kshitij ; Roig, Gemma ; Cichy, Radoslaw M.

NeuroImage (Orlando, Fla.), 2022-12, Vol.264, p.119754-119754, Article 119754 [Periódico revisado por pares]

United States: Elsevier Inc

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7
Identifying the psychological processes delineating non-harmful from problematic binge-watching: A machine learning analytical approach
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Identifying the psychological processes delineating non-harmful from problematic binge-watching: A machine learning analytical approach

Flayelle, Maèva ; Elhai, Jon D. ; Maurage, Pierre ; Vögele, Claus ; Brevers, Damien ; Baggio, Stéphanie ; Billieux, Joël

Telematics and informatics, 2022-11, Vol.74, p.101880, Article 101880 [Periódico revisado por pares]

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8
Disentangling the impact of childhood abuse and neglect on depressive affect in adulthood: A machine learning approach in a general population sample
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Disentangling the impact of childhood abuse and neglect on depressive affect in adulthood: A machine learning approach in a general population sample

Betz, Linda T. ; Rosen, Marlene ; Salokangas, Raimo K.R. ; Kambeitz, Joseph

Journal of affective disorders, 2022-10, Vol.315, p.17-26 [Periódico revisado por pares]

Elsevier B.V

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9
Leadership in the digital era: A review of who, what, when, where, and why
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Leadership in the digital era: A review of who, what, when, where, and why

Banks, George C. ; Dionne, Shelley D. ; Mast, Marianne Schmid ; Sayama, Hiroki

The Leadership quarterly, 2022-10, Vol.33 (5), p.101634, Article 101634 [Periódico revisado por pares]

Elsevier Inc

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10
Machine learning advances the classification and prediction of responding from psychophysiological reactions
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Machine learning advances the classification and prediction of responding from psychophysiological reactions

Karekla, Maria ; Demosthenous, Giorgos ; Georgiou, Chryssis ; Konstantinou, Pinelopi ; Trigiorgi, Andria ; Koushiou, Maria ; Panayiotou, Georgia ; Gloster, Andrew T.

Journal of contextual behavioral science, 2022-10, Vol.26, p.36-43 [Periódico revisado por pares]

Elsevier Inc

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