Mohammed Qaraad
- Artificial Intelligence top 5%
- Renewable Energy, Sustainability and the Environment
- Computational Theory and Mathematics top 10%
- Computer Vision and Pattern Recognition
- Molecular Biology
- Co-authors
- Souad AmjadNazar K. HusseinMostafa A. ElhosseiniSeyedali MirjaliliMahmoud BadawyPassent ElkafrawyXumin ChenMohamed A. Farag
- Topics
- Metaheuristic Optimization Algorithms Research (7 papers)Advanced Multi-Objective Optimization Algorithms (6 papers)Gene expression and cancer classification (6 papers)
In The Last Decade
Mohammed Qaraad
15 papers receiving 369 citations
Peers
Comparison fields: 5 of 76
- Artificial Intelligence 266
- Renewable Energy, Sustainability and the Environment 85
- Computational Theory and Mathematics 73
- Computer Vision and Pattern Recognition 40
- Molecular Biology 38
Countries citing papers authored by Mohammed Qaraad
This map shows the geographic impact of Mohammed Qaraad'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 Mohammed Qaraad with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mohammed Qaraad more than expected).
Fields of papers citing papers by Mohammed Qaraad
This network shows the impact of papers produced by Mohammed Qaraad. 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 Mohammed Qaraad. The network helps show where Mohammed Qaraad may publish in the future.
Co-authorship network of co-authors of Mohammed Qaraad
This figure shows the co-authorship network connecting the top 25 collaborators of Mohammed Qaraad. A scholar is included among the top collaborators of Mohammed Qaraad 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 Mohammed Qaraad. Mohammed Qaraad is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 71 | |
| 2 | 18 | |
| 3 | 83 | |
| 4 | 15 | |
| 5 | 17 | |
| 6 | 43 | |
| 7 | 25 | |
| 8 | 12 | |
| 9 | 9 | |
| 10 | 30 | |
| 11 | 10 | |
| 12 | 30 | |
| 13 | 7 | |
| 14 | 5 | |
| 15 | 5 |
About Mohammed Qaraad
Mohammed Qaraad is a scholar working on Computational Theory and Mathematics, Artificial Intelligence and Health Information Management, having authored 15 papers that have together received 380 indexed citations. Recurring topics across this work include Metaheuristic Optimization Algorithms Research (7 papers), Advanced Multi-Objective Optimization Algorithms (6 papers) and Gene expression and cancer classification (6 papers). The work is most often cited by research in Artificial Intelligence (266 citations), Renewable Energy, Sustainability and the Environment (85 citations) and Computational Theory and Mathematics (73 citations). Mohammed Qaraad has collaborated with scholars based in Morocco, Egypt and Iraq. Frequent co-authors include Souad Amjad, Nazar K. Hussein, Mostafa A. Elhosseini, Seyedali Mirjalili, Mahmoud Badawy, Passent Elkafrawy, Xumin Chen, Mohamed A. Farag, Saima Hassan and Abdulqader M. Almars. Their work appears in journals such as Expert Systems with Applications, IEEE Access and Neural Computing and Applications.
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.