| On the interpretability of machine learning-based model for predicting hypertension R Elshawi, MH Al-Mallah, S Sakr BMC medical informatics and decision making 19, 1-32, 2019 | 277 | 2019 |
| Automated machine learning: State-of-the-art and open challenges R Elshawi, M Maher, S Sakr arXiv preprint arXiv:1906.02287, 2019 | 259 | 2019 |
| Interpretability in healthcare: A comparative study of local machine learning interpretability techniques R ElShawi, Y Sherif, M Al‐Mallah, S Sakr Computational Intelligence 37 (4), 1633-1650, 2021 | 236 | 2021 |
| Predictors of in-hospital length of stay among cardiac patients: a machine learning approach TA Daghistani, R Elshawi, S Sakr, AM Ahmed, A Al-Thwayee, ... International journal of cardiology 288, 140-147, 2019 | 184 | 2019 |
| Big data systems meet machine learning challenges: towards big data science as a service R Elshawi, S Sakr, D Talia, P Trunfio Big data research 14, 1-11, 2018 | 182 | 2018 |
| Using machine learning on cardiorespiratory fitness data for predicting hypertension: The Henry Ford ExercIse Testing (FIT) Project S Sakr, R Elshawi, A Ahmed, WT Qureshi, C Brawner, S Keteyian, ... PloS one 13 (4), e0195344, 2018 | 127 | 2018 |
| Large scale graph processing systems: survey and an experimental evaluation O Batarfi, RE Shawi, AG Fayoumi, R Nouri, SMR Beheshti, A Barnawi, ... Cluster Computing 18, 1189-1213, 2015 | 121 | 2015 |
| Comparison of machine learning techniques to predict all-cause mortality using fitness data: the Henry ford exercIse testing (FIT) project S Sakr, R Elshawi, AM Ahmed, WT Qureshi, CA Brawner, SJ Keteyian, ... BMC medical informatics and decision making 17, 1-15, 2017 | 99 | 2017 |
| Big data 2.0 processing systems: Taxonomy and open challenges F Bajaber, R Elshawi, O Batarfi, A Altalhi, A Barnawi, S Sakr Journal of Grid Computing 14, 379-405, 2016 | 84 | 2016 |
| DLBench: a comprehensive experimental evaluation of deep learning frameworks R Elshawi, A Wahab, A Barnawi, S Sakr Cluster Computing 24, 2017-2038, 2021 | 62 | 2021 |
| ILIME: Local and Global Interpretable Model-Agnostic Explainer of Black-Box Decision R ElShawi, Y Sherif, M Al-Mallah, S Sakr Advances in Databases and Information Systems: 23rd European Conference …, 2019 | 41 | 2019 |
| Runtime detection of business process compliance violations: an approach based on anti patterns A Awad, A Barnawi, A Elgammal, R Elshawi, A Almalaise, S Sakr Proceedings of the 30th Annual ACM Symposium on Applied Computing, 1203-1210, 2015 | 37 | 2015 |
| Using machine learning to define the association between cardiorespiratory fitness and all-cause mortality (from the henry ford exercise testing project) MH Al-Mallah, R Elshawi, AM Ahmed, WT Qureshi, CA Brawner, MJ Blaha, ... The American journal of cardiology 120 (11), 2078-2084, 2017 | 32 | 2017 |
| Automated machine learning: Techniques and frameworks R Elshawi, S Sakr Big Data Management and Analytics: 9th European Summer School, eBISS 2019 …, 2020 | 29 | 2020 |
| Exploiting time series of Sentinel-1 and Sentinel-2 to detect grassland mowing events using deep learning with reject region V Komisarenko, K Voormansik, R Elshawi, S Sakr Scientific Reports 12 (1), 983, 2022 | 28 | 2022 |
| On characterizing the performance of distributed graph computation platforms A Barnawi, O Batarfi, SMR Behteshi, R Elshawi, A Fayoumi, R Nouri, ... Performance Characterization and Benchmarking. Traditional to Big Data: 6th …, 2015 | 28 | 2015 |
| A decision support framework for automl systems: A meta-learning approach S Dyrmishi, R Elshawi, S Sakr 2019 International Conference on Data Mining Workshops (ICDMW), 97-106, 2019 | 27 | 2019 |
| The impact of digoxin on mortality in patients with chronic systolic heart failure: a propensity-matched cohort study M Al-Khateeb, WT Qureshi, R Odeh, AM Ahmed, S Sakr, R Elshawi, ... International Journal of Cardiology 228, 214-218, 2017 | 23 | 2017 |
| Radwa El Shawi, Ayman G. Fayoumi, Reza Nouri, Seyed-Mehdi-Reza Beheshti, Ahmed Barnawi, and Sherif Sakr. Large scale graph processing systems: survey and an experimental evaluation O Batarfi Cluster Computing 18 (3), 1189-1213, 2015 | 22 | 2015 |
| Automated machine learning: State-of-the-art and open challenges. arXiv 2019 R Elshawi, M Maher, S Sakr arXiv preprint arXiv:1906.02287, 1906 | 21 | 1906 |
Sherif SakrUT, KSAU-HS, UNSWVerified email at ut.ee
