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Uncertainty-Aware Semi-supervised Method using Large Unlabelled and Limited Labeled COVID-19 Data.

R. Alizadehsani, D. Sharifrazi, N. Izadi, J. Joloudari, A. Shoeibi, J. Górriz, S. Hussain, J. Arco, Z. Sani, F. Khozeimeh, A. Khosravi, S. Nahavandi, S. Islam, and U. Acharya. CoRR, (2021)

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Deep Learning for Neuroimaging-based Diagnosis and Rehabilitation of Autism Spectrum Disorder: A Review.M. Khodatars, A. Shoeibi, N. Ghassemi, M. Jafari, A. Khadem, D. Sadeghi, P. Moridian, S. Hussain, R. Alizadehsani, A. Zare and 4 other author(s). CoRR, (2020)Exerting Cost-Sensitive and Feature Creation Algorithms for Coronary Artery Disease Diagnosis.R. Alizadehsani, M. Hosseini, R. Boghrati, A. Ghandeharioun, F. Khozeimeh, and Z. Sani. Int. J. Knowl. Discov. Bioinform., 3 (1): 59-79 (2012)Identification of Clinical Features Associated with Mortality in COVID-19 Patients.R. Eskandarian, R. Alizadehsani, M. Behjati, M. Zahmatkesh, Z. Sani, A. Haddadi, K. Kakhi, M. Roshanzamir, A. Shoeibi, S. Hussain and 7 other author(s). Oper. Res. Forum, (March 2023)Uncertainty Aware Neural Network from Similarity and Sensitivity.H. Kabir, S. Mondal, S. Khanam, A. Khosravi, S. Rahman, M. Qazani, R. Alizadehsani, H. Asadi, S. Mohamed, S. Nahavandi and 1 other author(s). CoRR, (2023)Automatic Diagnosis of Schizophrenia using EEG Signals and CNN-LSTM Models.A. Shoeibi, D. Sadeghi, P. Moridian, N. Ghassemi, J. Heras, R. Alizadehsani, A. Khadem, Y. Kong, S. Nahavandi, and J. Górriz. CoRR, (2021)Material Recognition for Automated Progress Monitoring using Deep Learning Methods.N. Ghassemi, H. Mahami, M. Darbandi, A. Shoeibi, S. Hussain, F. Nasirzadeh, R. Alizadehsani, D. Nahavandi, A. Khosravi, and S. Nahavandi. CoRR, (2020)Coronary artery disease detection using computational intelligence methods.R. Alizadehsani, M. Zangooei, M. Hosseini, J. Habibi, A. Khosravi, M. Roshanzamir, F. Khozeimeh, N. Sarrafzadegan, and S. Nahavandi. Knowl. Based Syst., (2016)Coronary artery disease detection using artificial intelligence techniques: A survey of trends, geographical differences and diagnostic features 1991-2020.R. Alizadehsani, A. Khosravi, M. Roshanzamir, M. Abdar, N. Sarrafzadegan, D. Shafie, F. Khozeimeh, A. Shoeibi, S. Nahavandi, M. Panahiazar and 6 other author(s). Comput. Biol. Medicine, (2021)A data mining approach for diagnosis of coronary artery disease.R. Alizadehsani, J. Habibi, M. Hosseini, H. Mashayekhi, R. Boghrati, A. Ghandeharioun, B. Bahadorian, and Z. Sani. Comput. Methods Programs Biomed., 111 (1): 52-61 (2013)BERT-deep CNN: state of the art for sentiment analysis of COVID-19 tweets.J. Joloudari, S. Hussain, M. Nematollahi, R. Bagheri, F. Fazl, R. Alizadehsani, R. Lashgari, and A. Talukder. Soc. Netw. Anal. Min., 13 (1): 99 (December 2023)

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Uncertainty-Aware Semi-supervised Method using Large Unlabelled and Limited Labeled COVID-19 Data.

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copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Uncertainty-Aware Semi-supervised Method using Large Unlabelled and Limited Labeled COVID-19 Data.

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Uncertainty-Aware Semi-supervised Method using Large Unlabelled and Limited Labeled COVID-19 Data.