Ahmed Emad-Eldeen

15 papers receiving 204 citations

Hit Papers

Evaluating Machine Learning and Deep Learning models for ...2025202620252025510152025

Peers

Ahmed Emad-Eldeen
Comparison fields: 5 of 52
  • Electrical and Electronic Engineering 89
  • Electronic, Optical and Magnetic Materials 60
  • Artificial Intelligence 57
  • Renewable Energy, Sustainability and the Environment 43
  • Polymers and Plastics 29
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Xingxing Wu China
Chiranjit Dutta India
Xiaoqing Liu China
Zekun Zhang China
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Harshit Gupta India
Abdullah Alwabli Saudi Arabia
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Hong Ye China
Ahmed Emad-Eldeen relative to Xingxing Wu China Xingxing Wu's profile →
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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

16 of 16 papers shown
#WorkIndexed citations
1 16
2
Evaluating Machine Learning and Deep Learning models for predicting Wind Turbine power output from environmental factorsbreakdown →
29
3 1
4 2
5 5
6 6
7
Automated Defect Detection in Solar Cell Images Using Deep Learning Algorithmsbreakdown →
26
8 9
9 2
10 23
11 26
12 5
13 1
14 9
15 57
16 0

About Ahmed Emad-Eldeen

Ahmed Emad-Eldeen is a scholar working on Renewable Energy, Sustainability and the Environment, Industrial and Manufacturing Engineering and Electronic, Optical and Magnetic Materials, having authored 16 papers that have together received 217 indexed citations. Recurring topics across this work include Photovoltaic System Optimization Techniques (8 papers), Solar Radiation and Photovoltaics (6 papers) and Energy Load and Power Forecasting (5 papers). The work is most often cited by research in Electronic, Optical and Magnetic Materials (60 citations), Renewable Energy, Sustainability and the Environment (43 citations) and Polymers and Plastics (29 citations). Ahmed Emad-Eldeen has collaborated with scholars based in Egypt, Saudi Arabia and Japan. Frequent co-authors include Montaser Abdelsattar, Mohamed A. Ismeil, Wael Z. Tawfik, Ahmed G. El‐Deen, Nabila Shehata and Saad A. Mohamed Abdelwahab. Their work appears in journals such as PLoS ONE, Scientific Reports and IEEE Access.

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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