Nora El-Rashidy

1.7k total citations
47 papers, 876 citations indexed

About

Nora El-Rashidy is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging and Health Information Management. According to data from OpenAlex, Nora El-Rashidy has authored 47 papers receiving a total of 876 indexed citations (citations by other indexed papers that have themselves been cited), including 26 papers in Artificial Intelligence, 11 papers in Radiology, Nuclear Medicine and Imaging and 9 papers in Health Information Management. Recurrent topics in Nora El-Rashidy's work include Artificial Intelligence in Healthcare (9 papers), Machine Learning in Healthcare (9 papers) and AI in cancer detection (8 papers). Nora El-Rashidy is often cited by papers focused on Artificial Intelligence in Healthcare (9 papers), Machine Learning in Healthcare (9 papers) and AI in cancer detection (8 papers). Nora El-Rashidy collaborates with scholars based in Egypt, Saudi Arabia and South Korea. Nora El-Rashidy's co-authors include Shaker El–Sappagh, Samir Abdelrazek, Hazem M. El‐Bakry, S. M. Riazul Islam, Tamer Abuhmed, Mahmoud Y. Shams, Esraa Hassan, Ahmed M. Elshewey, Hager Saleh and Zahraa Tarek and has published in prestigious journals such as Scientific Reports, IEEE Access and Sensors.

In The Last Decade

Nora El-Rashidy

43 papers receiving 836 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Nora El-Rashidy Egypt 15 312 209 129 96 88 47 876
Abdullah Alqahtani Saudi Arabia 16 317 1.0× 167 0.8× 77 0.6× 118 1.2× 94 1.1× 95 951
Mohammed Alzahrani Saudi Arabia 15 310 1.0× 121 0.6× 222 1.7× 121 1.3× 53 0.6× 59 1.0k
Mustafa Ghaderzadeh Iran 17 343 1.1× 296 1.4× 88 0.7× 46 0.5× 48 0.5× 28 810
Jimmy Singla India 14 274 0.9× 83 0.4× 107 0.8× 103 1.1× 59 0.7× 63 809
Ali Raza Pakistan 18 265 0.8× 70 0.3× 96 0.7× 121 1.3× 79 0.9× 70 870
Subrato Bharati Bangladesh 17 411 1.3× 290 1.4× 80 0.6× 99 1.0× 35 0.4× 43 887
Sandeep Kumar Mathivanan India 17 310 1.0× 216 1.0× 62 0.5× 55 0.6× 69 0.8× 99 929
Shagun Sharma India 16 281 0.9× 250 1.2× 80 0.6× 87 0.9× 59 0.7× 121 1.2k
Pronab Ghosh Bangladesh 15 437 1.4× 266 1.3× 395 3.1× 46 0.5× 53 0.6× 25 967
Firdaus Firdaus Indonesia 18 199 0.6× 99 0.5× 50 0.4× 78 0.8× 143 1.6× 123 1.0k

Countries citing papers authored by Nora El-Rashidy

Since Specialization
Citations

This map shows the geographic impact of Nora El-Rashidy's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Nora El-Rashidy with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nora El-Rashidy more than expected).

Fields of papers citing papers by Nora El-Rashidy

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Nora El-Rashidy. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Nora El-Rashidy. The network helps show where Nora El-Rashidy may publish in the future.

Co-authorship network of co-authors of Nora El-Rashidy

This figure shows the co-authorship network connecting the top 25 collaborators of Nora El-Rashidy. A scholar is included among the top collaborators of Nora El-Rashidy based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Nora El-Rashidy. Nora El-Rashidy is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Hassan, Esraa, et al.. (2025). An innovative approach to advanced voice classification of sacred Quranic recitations through multimodal fusion. Egyptian Informatics Journal. 30. 100640–100640.
2.
El-Rashidy, Nora, et al.. (2025). Explainable self-supervised learning for medical image diagnosis based on DINO V2 model and semantic search. Scientific Reports. 15(1). 32174–32174.
3.
El-Rashidy, Nora, et al.. (2025). A predictive machine learning approach for early fault identification in solar photovoltaic systems. Electric Power Systems Research. 247. 111856–111856. 1 indexed citations
4.
Alosaimi, Wael, et al.. (2024). ArabBert-LSTM: improving Arabic sentiment analysis based on transformer model and Long Short-Term Memory. Frontiers in Artificial Intelligence. 7. 1408845–1408845. 2 indexed citations
5.
Ali, Zainab H., et al.. (2024). SDN-based reliable emergency message routing schema using Digital Twins for adjusting beacon transmission in VANET. Journal of Network and Computer Applications. 230. 103944–103944. 4 indexed citations
6.
7.
El-Rashidy, Nora, et al.. (2024). Enhancing the Diagnosis of Depression and Anxiety Through Explainable Machine Learning Methods. International Journal of Advanced Computer Science and Applications. 15(4).
8.
Hassan, Esraa, et al.. (2024). Optimizing poultry audio signal classification with deep learning and burn layer fusion. Journal Of Big Data. 11(1). 21 indexed citations
9.
Saleh, Hager, Nora El-Rashidy, Mohamed Abd Elaziz, Ahmad O. Aseeri, & Shaker El–Sappagh. (2024). Genetic algorithm-based hybrid deep learning model for explainable Alzheimer’s disease prediction using temporal multimodal cognitive data. International Journal of Data Science and Analytics. 20(2). 1073–1103. 6 indexed citations
10.
Saleh, Hager, et al.. (2023). Diagnosis of COVID-19 Using Chest X-ray Images and Disease Symptoms Based on Stacking Ensemble Deep Learning. Diagnostics. 13(11). 1968–1968. 13 indexed citations
11.
Ullah, Naeem, Javed Ali Khan, Shaker El–Sappagh, Nora El-Rashidy, & Muhammad Sohail Khan. (2023). A Holistic Approach to Identify and Classify COVID-19 from Chest Radiographs, ECG, and CT-Scan Images Using ShuffleNet Convolutional Neural Network. Diagnostics. 13(1). 162–162. 44 indexed citations
12.
Mostafa, Reham R., et al.. (2023). Predicting CTS Diagnosis and Prognosis Based on Machine Learning Techniques. Diagnostics. 13(3). 492–492. 6 indexed citations
13.
Elshewey, Ahmed M., et al.. (2023). Bayesian Optimization with Support Vector Machine Model for Parkinson Disease Classification. Sensors. 23(4). 2085–2085. 65 indexed citations
14.
Alsekait, Deema Mohammed, Hager Saleh, Lubna A. Gabralla, et al.. (2023). Toward Comprehensive Chronic Kidney Disease Prediction Based on Ensemble Deep Learning Models. Applied Sciences. 13(6). 3937–3937. 29 indexed citations
15.
Gamel, Samah A., Esraa Hassan, Nora El-Rashidy, & Fatma M. Talaat. (2023). Exploring the effects of pandemics on transportation through correlations and deep learning techniques. Multimedia Tools and Applications. 83(3). 7295–7316. 27 indexed citations
16.
Ullah, Naeem, Javed Ali Khan, Sultan Almakdi, et al.. (2023). An effective approach for plant leaf diseases classification based on a novel DeepPlantNet deep learning model. Frontiers in Plant Science. 14. 1212747–1212747. 30 indexed citations
17.
18.
Hassan, Esraa, Fatma M. Talaat, Samah A. Gamel, et al.. (2023). Robust Deep Learning Model for Black Fungus Detection Based on Gabor Filter and Transfer Learning. Computer Systems Science and Engineering. 47(2). 1507–1525. 14 indexed citations
19.
Ali, Zainab H., John Zaki, & Nora El-Rashidy. (2022). Dynamic urban evaluation routing protocol for enhanced vehicle ad hoc networks. The Journal of Supercomputing. 79(6). 6017–6039. 5 indexed citations
20.
El-Rashidy, Nora, Tamer Abuhmed, Eslam Amer, et al.. (2021). Comprehensive Survey of Using Machine Learning in the COVID-19 Pandemic. Diagnostics. 11(7). 1155–1155. 45 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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