Ahmed Emad-Eldeen

548 total citations · 2 hit papers
16 papers, 217 citations indexed

About

Ahmed Emad-Eldeen is a scholar working on Electrical and Electronic Engineering, Renewable Energy, Sustainability and the Environment and Artificial Intelligence. According to data from OpenAlex, Ahmed Emad-Eldeen has authored 16 papers receiving a total of 217 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Electrical and Electronic Engineering, 8 papers in Renewable Energy, Sustainability and the Environment and 6 papers in Artificial Intelligence. Recurrent topics in Ahmed Emad-Eldeen's work include Photovoltaic System Optimization Techniques (8 papers), Solar Radiation and Photovoltaics (6 papers) and Energy Load and Power Forecasting (5 papers). Ahmed Emad-Eldeen is often cited by papers focused on Photovoltaic System Optimization Techniques (8 papers), Solar Radiation and Photovoltaics (6 papers) and Energy Load and Power Forecasting (5 papers). Ahmed Emad-Eldeen collaborates with scholars based in Egypt, Saudi Arabia and Japan. Ahmed Emad-Eldeen's co-authors include Montaser Abdelsattar, Mohamed A. Ismeil, Wael Z. Tawfik, Ahmed G. El‐Deen, Saad A. Mohamed Abdelwahab and Nabila Shehata and has published in prestigious journals such as PLoS ONE, Scientific Reports and IEEE Access.

In The Last Decade

Ahmed Emad-Eldeen

15 papers receiving 204 citations

Hit Papers

2 papers align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Evaluating Machine Learning and Deep Learning models for predicting Wind Turbine power output from environmental factors

2025 29 citations Montaser Abdelsattar, Mohamed A. Ismeil et al. PLoS ONE profile →
Automated Defect Detection in Solar Cell Images Using Deep Learning Algorithms

2025 26 citations Montaser Abdelsattar, Mohamed A. Ismeil et al. IEEE Access profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Ahmed Emad-Eldeen Egypt	8	89	60	57	43	29	16	217
Xingxing Wu China	9	186 2.1×	54 0.9×	21 0.4×	96 2.2×	18 0.6×	30	374
Chiranjit Dutta India	10	72 0.8×	12 0.2×	32 0.6×	59 1.4×	25 0.9×	30	306
Xiaoqing Liu China	11	344 3.9×	33 0.6×	15 0.3×	22 0.5×	18 0.6×	51	413
Abdullah Alwabli Saudi Arabia	10	105 1.2×	36 0.6×	38 0.7×	43 1.0×	3 0.1×	29	245
Harshit Gupta India	10	132 1.5×	9 0.1×	44 0.8×	38 0.9×	30 1.0×	45	306
Gaoyuan Zhang China	8	149 1.7×	20 0.3×	57 1.0×	7 0.2×	11 0.4×	14	260
Changqing Li China	12	178 2.0×	75 1.3×	26 0.5×	19 0.4×	19 0.7×	35	332
Zekun Zhang China	11	207 2.3×	33 0.6×	70 1.2×	13 0.3×	10 0.3×	25	361
Hong Ye China	8	174 2.0×	23 0.4×	48 0.8×	21 0.5×	16 0.6×	19	344

Countries citing papers authored by Ahmed Emad-Eldeen

Since Specialization

Citations

This map shows the geographic impact of Ahmed Emad-Eldeen'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 Ahmed Emad-Eldeen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ahmed Emad-Eldeen more than expected).

Fields of papers citing papers by Ahmed Emad-Eldeen

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ahmed Emad-Eldeen. 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 Ahmed Emad-Eldeen. The network helps show where Ahmed Emad-Eldeen may publish in the future.

Co-authorship network of co-authors of Ahmed Emad-Eldeen

This figure shows the co-authorship network connecting the top 25 collaborators of Ahmed Emad-Eldeen. A scholar is included among the top collaborators of Ahmed Emad-Eldeen 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 Ahmed Emad-Eldeen. Ahmed Emad-Eldeen is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

16 of 16 papers shown

Abdelsattar, Montaser, et al.. (2025). Comparative analysis of machine learning techniques for temperature and humidity prediction in photovoltaic environments. Scientific Reports. 15(1). 15650–15650. 6 indexed citations

Abdelsattar, Montaser, et al.. (2025). Advanced machine learning techniques for predicting power generation and fault detection in solar photovoltaic systems. Neural Computing and Applications. 37(15). 8825–8844. 16 indexed citations

Abdelsattar, Montaser, et al.. (2025). ResNet-based image processing approach for precise detection of cracks in photovoltaic panels. Scientific Reports. 15(1). 24356–24356. 5 indexed citations

Abdelsattar, Montaser, et al.. (2025). Applying Image Processing and Computer Vision for Damage Detection in Photovoltaic Panels. MEJ Mansoura Engineering Journal. 50(2). 2 indexed citations

Abdelsattar, Montaser, et al.. (2025). Evaluating Machine Learning and Deep Learning models for predicting Wind Turbine power output from environmental factors. PLoS ONE. 20(1). e0317619–e0317619. 29 indexed citations breakdown →

Abdelsattar, Montaser, et al.. (2025). Comparative analysis of deep learning architectures in solar power prediction. Scientific Reports. 15(1). 31729–31729. 1 indexed citations

Abdelsattar, Montaser, et al.. (2025). Automated Defect Detection in Solar Cell Images Using Deep Learning Algorithms. IEEE Access. 13. 4136–4157. 26 indexed citations breakdown →

Emad-Eldeen, Ahmed, et al.. (2024). AI-based approach for predicting the storage performance of zinc oxide-based supercapacitor electrodes. Journal of Energy Storage. 94. 112292–112292. 9 indexed citations

Abdelsattar, Montaser, et al.. (2024). Detecting Dusty and Clean Photovoltaic Surfaces Using MobileNet Variants for Image Classification. 6(1). 9–18. 2 indexed citations

10.

Emad-Eldeen, Ahmed, et al.. (2024). Utilizing machine learning and deep learning for enhanced supercapacitor performance prediction. Journal of Energy Storage. 100. 113556–113556. 23 indexed citations

11.

Abdelsattar, Montaser, et al.. (2024). Assessing Machine Learning Approaches for Photovoltaic Energy Prediction in Sustainable Energy Systems. IEEE Access. 12. 107599–107615. 26 indexed citations

12.

Abdelsattar, Montaser, et al.. (2024). Machine Learning-Based Prediction of Illuminance and Ultraviolet Irradiance in Photovoltaic Systems. 0(0). 1–14. 5 indexed citations

13.

Shehata, Nabila, et al.. (2023). Investigation of Perturb and Observe MPPT Technique for Photovoltaic Systems: Theoretical and Experimental Study. 1–6. 1 indexed citations

14.

Abdelsattar, Montaser, et al.. (2023). A Review on Detection of Solar PV Panels Failures Using Image Processing Techniques. 1–6. 9 indexed citations

15.

Emad-Eldeen, Ahmed, et al.. (2022). Data-driven machine learning approach for predicting the capacitance of graphene-based supercapacitor electrodes. Journal of Energy Storage. 55. 105411–105411. 57 indexed citations

16.

Emad-Eldeen, Ahmed, et al.. (2022). Artificial Intelligence-based Cyclic Voltammetry Behavior Model for Supercapacitance of Zinc oxide (ZnO) Nanocomposite. 1–5.

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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