His paper, “Approximation of the factorization of the sparse semi-negative matrix for the classification of COVID-19 X-ray images,” co-authored with researchers from the University of Tunisia, who are grappling with the health crisis, has been selected for the 14th International Conference on Computational Collective Intelligence, to be held in Hammamet, in Tunisia, from September 28 to 30.
\n\n\n\nAmel Mhamdi, a tenured professor at aivancity, is developing an analysis of the digital data generated by medical imaging—which is widely used to help radiologists accurately diagnose COVID-19—by proposing a new semi-NMF (non-negative matrix factorization) algorithm capable of identifying COVID-19 patients based on chest X-ray images.
\n\n\n\nThis small team of researchers is proud to remain true to its mathematical approach and to continue working toward responsible AI.
\n\n\n\n\n","protected":false},"excerpt":{"rendered":"His article, “Approximation of the factorization of the sparse semi-negative matrix for the classification of COVID-19 X-ray images,” co-authored with researchers from the University of Tunisia, who are grappling with the health crisis, has been selected for…
\n","protected":false},"author":3,"featured_media":1153,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"content-type":"","footnotes":""},"categories":[28],"tags":[],"class_list":{"0":"post-1152","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-articles"},"yoast_head":"\n