R
Rania M. Ghoniem
Researcher at Princess Nora bint Abdul Rahman University
Publications - 63
Citations - 395
Rania M. Ghoniem is an academic researcher from Princess Nora bint Abdul Rahman University. The author has contributed to research in topics: Computer science & Engineering. The author has an hindex of 4, co-authored 16 publications receiving 46 citations. Previous affiliations of Rania M. Ghoniem include Mansoura University.
Papers
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Boosting Arithmetic Optimization Algorithm with Genetic Algorithm Operators for Feature Selection: Case Study on Cox Proportional Hazards Model
Ahmed A. Ewees,Mohammed A. A. Al-qaness,Laith Abualigah,Diego Oliva,Zakariya Yahya Algamal,Ahmed M. Anter,Rehab Ali Ibrahim,Rania M. Ghoniem,Mohamed Abd Elaziz +8 more
TL;DR: The findings of this paper illustrated that the proposed AOAGA method finds new best solutions for several test cases, and it got promising results compared to other comparative methods published in the literature.
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Improved seagull optimization algorithm using Lévy flight and mutation operator for feature selection
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Self-adaptive Equilibrium Optimizer for solving global, combinatorial, engineering, and Multi-Objective problems
TL;DR: In this paper , a self-adaptive equilibrium optimizer (self-EO) is proposed to solve global, combinatorial, engineering, and multi-objective optimization problems.
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A Novel Bio-Inspired Deep Learning Approach for Liver Cancer Diagnosis
TL;DR: A novel hybrid segmentation algorithm is proposed to extract liver lesions from computed tomography images using SegNet network, UNet network, and artificial bee colony optimization (ABC), namely, SegNet-UNet-ABC, which outperforms other algorithms regarding specificity, F1-score, accuracy, and computational time.
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Modeling and Optimization of a Compression Ignition Engine Fueled with Biodiesel Blends for Performance Improvement
Ali Alahmer,Hegazy Rezk,Wail Aladayleh,Ahmad Mostafa,M. Abu-Zaid,Hussein Alahmer,Mohamed Gomaa,Amel Alhussan,Rania M. Ghoniem +8 more
TL;DR: In this article , a robust fuzzy model is created, depending on the experimental results, to simulate the output performance of the compression ignition engine, and the particle swarm optimization (PSO) algorithm is used in the second stage to determine the optimal operating parameters.