H
Hanadi Hassen
Researcher at Qatar University
Publications - 8
Citations - 91
Hanadi Hassen is an academic researcher from Qatar University. The author has contributed to research in topics: Deep learning & Handwriting recognition. The author has an hindex of 4, co-authored 8 publications receiving 44 citations.
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An early warning tool for predicting mortality risk of COVID-19 patients using machine learning
Muhammad E. H. Chowdhury,Tawsifur Rahman,Amith Khandakar,Somaya Al-Madeed,Susu M. Zughaier,Suhail A.R. Doi,Hanadi Hassen,Mohammad Tariqul Islam +7 more
TL;DR: The prognostic model, nomogram, and LNLCA score can help in early detection of high mortality risk of COVID-19 patients, which will help doctors to improve the management of patient stratification.
Journal ArticleDOI
An Early Warning Tool for Predicting Mortality Risk of COVID-19 Patients Using Machine Learning.
Muhammad E. H. Chowdhury,Tawsifur Rahman,Amith Khandakar,Somaya Al-Madeed,Susu M. Zughaier,Suhail A.R. Doi,Hanadi Hassen,Mohammad Tariqul Islam +7 more
TL;DR: Wang et al. as discussed by the authors used a multi-tree XGBoost model to identify key biomarkers to predict the mortality of individual patient and developed a nomogram for predicting the mortality risk among COVID-19 patients.
Proceedings ArticleDOI
Arabic handwriting recognition using sequential minimal optimization
Hanadi Hassen,Somaya Al-Maadeed +1 more
TL;DR: A holistic approach which handles the whole word image without any segmentation step is adopted which resulted in 91.5928 % correct classification of Arabic words written by one hundred different writers.
Proceedings ArticleDOI
Arabic Bank Cheque Words Recognition Using Gabor Features
TL;DR: Gabor features are investigated with ELM andSMO classifiers and the results from Gabor features with SMO classifier outperform previous studies.
Proceedings ArticleDOI
Unsupervised Technique for Anomaly Detection in Qatar Stock Market
TL;DR: This research investigates the use of unsupervised learning for detecting stock market manipulation and introduces a new preprocessing step for improving the recall of the anomaly detection system without hurting the precision.