Mohammed Farsi
- Computer Networks and Communications top 5%
- Electrical and Electronic Engineering
- Artificial Intelligence
- Computer Vision and Pattern Recognition
- Information Systems
- Co-authors
- Mahmoud BadawyMostafa A. ElhosseiniHesham AliYousry AbdulAzeemEl‐Sayed AtlamB. R. ManjunathaOsama A. GhoneimIbrahim Gad
- Topics
- Anomaly Detection Techniques and Applications (3 papers)AI in cancer detection (2 papers)Mobile Ad Hoc Networks (2 papers)
- Cited by
- Computer Networks and CommunicationsElectrical and Electronic EngineeringArtificial Intelligence
- Partner nations
- Saudi ArabiaEgyptMalaysia
In The Last Decade
Mohammed Farsi
16 papers receiving 342 citations
Peers
Comparison fields: 5 of 79
- Computer Networks and Communications 187
- Electrical and Electronic Engineering 153
- Artificial Intelligence 59
- Computer Vision and Pattern Recognition 35
- Information Systems 28
Countries citing papers authored by Mohammed Farsi
This map shows the geographic impact of Mohammed Farsi'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 Farsi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mohammed Farsi more than expected).
Fields of papers citing papers by Mohammed Farsi
This network shows the impact of papers produced by Mohammed Farsi. 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 Farsi. The network helps show where Mohammed Farsi may publish in the future.
Co-authorship network of co-authors of Mohammed Farsi
This figure shows the co-authorship network connecting the top 25 collaborators of Mohammed Farsi. A scholar is included among the top collaborators of Mohammed Farsi 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 Farsi. Mohammed Farsi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 1 | |
| 3 | 1 | |
| 4 | 7 | |
| 5 | 1 | |
| 6 | 16 | |
| 7 | 21 | |
| 8 | 2 | |
| 9 | 6 | |
| 10 | 40 | |
| 11 | 53 | |
| 12 | 32 | |
| 13 | 4 | |
| 14 | 160 | |
| 15 | 4 | |
| 16 | 7 |
About Mohammed Farsi
Mohammed Farsi is a scholar working on Health Informatics, General Social Sciences and Artificial Intelligence, having authored 16 papers that have together received 356 indexed citations. Recurring topics across this work include Anomaly Detection Techniques and Applications (3 papers), AI in cancer detection (2 papers) and Mobile Ad Hoc Networks (2 papers). The work is most often cited by research in Computer Networks and Communications (187 citations), Electrical and Electronic Engineering (153 citations) and Artificial Intelligence (59 citations). Mohammed Farsi has collaborated with scholars based in Saudi Arabia, Egypt and Malaysia. Frequent co-authors include Mahmoud Badawy, Mostafa A. Elhosseini, Hesham Ali, Yousry AbdulAzeem, El‐Sayed Atlam, B. R. Manjunatha, Osama A. Ghoneim, Ibrahim Gad, Fatma M. Talaat and Hossam Magdy Balaha. Their work appears in journals such as IEEE Access, Neural Computing and Applications and Biomedical Signal Processing and Control.
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.