Mohamed Abd Elaziz
Zagazig University
336 Papers
341 Citations
Mohamed Abd Elaziz is an academic researcher from Zagazig University. The author has contributed to research in topics: Computer science & Feature selection. The author has an hindex of 35, co-authored 189 publications. Previous affiliations of Mohamed Abd Elaziz include Ajman University of Science and Technology & Huazhong University of Science and Technology.
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Papers
Robust Machine Learning Technique to Classify COVID-19 Using Fusion of Texture and Vesselness of X-Ray Images
Shaik Mahaboob Basha,Victor Hugo C. de Albuquerque,Samia Allaoua Chelloug,Mohamed Abd Elaziz,Shaik Hashmitha Mohisin,Suhail Parvaze Pathan +5 more
TL;DR: A robust machine learning technique is proposed to classify COVID-19 using fusion of texture and vesselness features from X-ray images, achieving 91.8% accuracy with Random Forest-based classifier and 97% true positive rate for COVID-19 diagnosis.
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Improved Approach Based on Fuzzy Rough Set and Sine-Cosine Algorithm: A Case Study on Prediction of Osteoporosis
TL;DR: A new method to predict osteoporosis is presented, which starts by pre-processing the data to avoid an imbalanced dataset, then the sine–cosine algorithm, based on the information gain fuzzy-rough set, is applied to select the most discriminative descriptors.
Multithreshold Segmentation and Machine Learning Based Approach to Differentiate COVID-19 from Viral Pneumonia
Shaik Mahaboob Basha,Aloisio Vieira Lira Neto,Samah Alshathri,Mohamed Abd Elaziz,Shaik Hashmitha Mohisin,Victor Hugo C. de Albuquerque +5 more
TL;DR: The main objective is to probe a simple threshold-based segmentation approach to identify possible infection regions in CXR images and investigate intensity-based, wavelet transform (WT)-based, and Laws based texture features with statistical measures.
Modified whale optimization algorithm for solving unrelated parallel machine scheduling problems
Mohammed A. A. Al-qaness,Ahmed A. Ewees,Ahmed A. Ewees,Mohamed Abd Elaziz,Mohamed Abd Elaziz +4 more
- 01 Jul 2021
TL;DR: In this article, a new method is introduced to address UPMSP problems with sequence-dependent and machine-dependent setup time, which utilizes two meta-heuristic techniques, the whale optimization algorithm (WOA) and the firefly algorithm (FA), by combining their features to perform this task.
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Quantum Mutation Reptile Search Algorithm for Global Optimization and Data Clustering
Rolla Almodfer,Mohammed Mudhsh,Samia Allaoua Chelloug,Mohammad Shehab,Laith Abualigah,Mohamed Abd Elaziz +5 more
TL;DR: The proposed quantum mutation-based search strategy is used to enhance the performance of the RSA to solve various optimization problems and show the QMRSA ’ s superiority in dealing with the mathematical benchmark functions and real-world problems like data clustering.
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