المؤلفون: Owais, M., Lee, Y.W., Mahmood, T., Haider, A., Sultan, H., Park, K.R.

المصدر: IEEE Journal of Biomedical and Health Informatics IEEE J. Biomed. Health Inform. Biomedical and Health Informatics, IEEE Journal of. 25(6):1881-1891 Jun, 2021

المؤلفون: Yamac, M., Ahishali, M., Degerli, A., Kiranyaz, S., Chowdhury, M.E.H., Gabbouj, M.

المصدر: IEEE Transactions on Neural Networks and Learning Systems IEEE Trans. Neural Netw. Learning Syst. Neural Networks and Learning Systems, IEEE Transactions on. 32(5):1810-1820 May, 2021

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المؤلفون: Lazhar Khriji, Seifeddine Messaoud, Soulef Bouaafia, Amna Maraoui, Ahmed Ammari

المصدر: Proceedings of the XXth Conference of Open Innovations Association FRUCT, Vol 29, Iss 1, Pp 185-191 (2021)

مصطلحات موضوعية: deep learning, covid-19 recognition, coughing and breathing patterns analysis, Telecommunication, TK5101-6720

وصف الملف: electronic resource

Relation: https://www.fruct.org/publications/fruct29/files/Khr.pdf; https://doaj.org/toc/2305-7254; https://doaj.org/toc/2343-0737

URL الوصول: https://doaj.org/article/f85b0433c0e04f5f8df0b99eafdaf761

المؤلفون: Olusola O. Abayomi-Alli, Robertas Damaševičius, Aaqif Afzaal Abbasi, Rytis Maskeliūnas

المساهمون: MDPI AG (Basel, Switzerland)

المصدر: Electronics; Volume 11; Issue 16; Pages: 2520

مصطلحات موضوعية: small data, COVID-19 recognition, Computer Networks and Communications, Hardware and Architecture, Control and Systems Engineering, Signal Processing, sound classification, audio processing, data augmentation, transfer learning, deep learning, Electrical and Electronic Engineering

وصف الملف: application/pdf

URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::88bc9bd236274a4f654d2240b00b8bde

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المؤلفون: Soulef Bouaafia, Mohsen Machhout, Lazhar Khriji, Ahmed Chiheb Ammari, Seifeddine Messaoud, Amna Maraoui

المصدر: FRUCT
Proceedings of the XXth Conference of Open Innovations Association FRUCT, Vol 29, Iss 1, Pp 185-191 (2021)

مصطلحات موضوعية: Audio signal, coughing and breathing patterns analysis, Computer science, business.industry, Speech recognition, Deep learning, Feature extraction, deep learning, Short-term memory, Wearable computer, TK5101-6720, medicine.disease, covid-19 recognition, Breath gas analysis, Atypical pneumonia, Telecommunication, medicine, Artificial intelligence, Noise (video), business

URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e5694f7933f2dd0127ed7c905f1e6fee
https://doi.org/10.23919/fruct52173.2021.9435454

المؤلفون: Aysen Degerli, Muhammad E. H. Chowdhury, Mehmet Yamac, Moncef Gabbouj, Serkan Kiranyaz, Mete Ahishali

المساهمون: Tampere University, Computing Sciences

المصدر: Yamaç, M, Ahishali, M, Degerli, A, Kiranyaz, S, Chowdhury, M E H & Gabbouj, M 2021, ' Convolutional Sparse Support Estimator-Based COVID-19 Recognition From X-Ray Images ', IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 5, 9408240, pp. 1810-1820 . https://doi.org/10.1109/TNNLS.2021.3070467

مصطلحات موضوعية: Classification tasks, Computer science, 02 engineering and technology, transfer learning, virus pneumonia, Diagnosis, differential diagnosis, 0202 electrical engineering, electronic engineering, information engineering, Learning techniques, Artificial neural network, Classification (of information), Sparse approximation, Computer Science Applications, X ray, classification, Benchmark (computing), 020201 artificial intelligence & image processing, State-of-the-art performance, Computer Networks and Communications, diagnostic imaging, Feature extraction, Pneumonia, Viral, Cross-validation, Diagnosis and prognosis, representation-based classification, Diagnosis, Differential, Deep Learning, bacterial pneumonia, Artificial Intelligence, Coronavirus disease (COVID-19) recognition, x-ray computed tomography, Pneumonia, Bacterial, Humans, human, Representation (mathematics), Sparse representation, business.industry, Deep learning, X-Rays, COVID-19, Pattern recognition, Neural network (nn), 113 Computer and information sciences, Convolution, Data set, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)~virus, Classification scheme, Diagnosis performance, Artificial intelligence, Neural Networks, Computer, business, Tomography, X-Ray Computed, Software

وصف الملف: fulltext

URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8555a0cd6ce95d26b74b336e2950fcef
https://pubmed.ncbi.nlm.nih.gov/33872157