H
Haytham H. Elmousalami
Researcher at Zagazig University
Publications - 16
Citations - 428
Haytham H. Elmousalami is an academic researcher from Zagazig University. The author has contributed to research in topics: Fuzzy logic & Ensemble learning. The author has an hindex of 9, co-authored 16 publications receiving 254 citations. Previous affiliations of Haytham H. Elmousalami include Cairo University.
Papers
More filters
Posted ContentDOI
Automatic X-ray COVID-19 Lung Image Classification System based on Multi-Level Thresholding and Support Vector Machine
Lamia Nabil Mahdy,Kadry Ali Ezzat,Haytham H. Elmousalami,Hassan Aboul Ella,Aboul Ella Hassanien +4 more
TL;DR: The suggested system of multi-level thresholding plus SVM presented high accuracy in classification of the infected lung with Covid-19, and the deep studying based totally methodology is usually recommended for the detection of COVID-19 infected patients using X-ray images.
Posted Content
Day Level Forecasting for Coronavirus Disease (COVID-19) Spread: Analysis, Modeling and Recommendations
TL;DR: The usage of support vector machine (SVM) in the prediction of coronavirus infected and death cases in Egypt which help in decision-making process is explored and the proposed method is shown to achieve good accuracy and precision results.
Journal ArticleDOI
Artificial Intelligence and Parametric Construction Cost Estimate Modeling: State-of-the-Art Review
TL;DR: This study reviews the common practices and procedures conducted to identify the cost drivers that the past literature has classified into two main categories: qualitative and quantitative drivers.
Journal ArticleDOI
Comparison of Artificial Intelligence Techniques for Project Conceptual Cost Prediction: A Case Study and Comparative Analysis
TL;DR: A publicly open dataset for FCIPs is presented to be used for future models’ validation and analysis and shows that the most accurate and suitable method is XGBoost with 9.091% and 0.929 based on mean absolute percentage error and adjusted R2, respectively.
Journal ArticleDOI
Drilling stuck pipe classification and mitigation in the Gulf of Suez oil fields using artificial intelligence
TL;DR: A public open dataset for the drilled wells at the Gulf of Suez to be used for the future experiments, algorithms’ validation, and analysis and presented that the most reliable algorithm was extremely randomized trees (extra trees) with 100% classification accuracy based on testing dataset.