Hebah ElGibreen
- Computer Vision and Pattern Recognition top 10%
- Artificial Intelligence
- Signal Processing top 10%
- Plant Science
- Analytical Chemistry
- Co-authors
- Mohammed FaisalFahad R. AlbogamyMohammed AlgabriKamal Youcef‐ToumiYakoub BaziMehmet Sabih AksoyMansour ZuairMohamad Mahmoud Al Rahhal
- Topics
- Imbalanced Data Classification Techniques (4 papers)Machine Learning and Data Classification (3 papers)Speech and Audio Processing (2 papers)
- Journals
- PLoS ONEIEEE AccessSensors
- Partner nations
- Saudi ArabiaUnited StatesKuwait
In The Last Decade
Hebah ElGibreen
21 papers receiving 207 citations
Peers
Comparison fields: 5 of 60
- Computer Vision and Pattern Recognition 77
- Artificial Intelligence 76
- Signal Processing 44
- Plant Science 38
- Analytical Chemistry 19
Countries citing papers authored by Hebah ElGibreen
This map shows the geographic impact of Hebah ElGibreen'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 Hebah ElGibreen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hebah ElGibreen more than expected).
Fields of papers citing papers by Hebah ElGibreen
This network shows the impact of papers produced by Hebah ElGibreen. 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 Hebah ElGibreen. The network helps show where Hebah ElGibreen may publish in the future.
Co-authorship network of co-authors of Hebah ElGibreen
This figure shows the co-authorship network connecting the top 25 collaborators of Hebah ElGibreen. A scholar is included among the top collaborators of Hebah ElGibreen 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 Hebah ElGibreen. Hebah ElGibreen is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 8 | |
| 2 | 8 | |
| 3 | 12 | |
| 4 | 6 | |
| 5 | 6 | |
| 6 | 71 | |
| 7 | 3 | |
| 8 | 5 | |
| 9 | 2 | |
| 10 | 0 | |
| 11 | 3 | |
| 12 | 10 | |
| 13 | 41 | |
| 14 | 14 | |
| 15 | 2 | |
| 16 | 6 | |
| 17 | RULES - TL: A SIMPLE AND IMPROVED RULES ALGORITHM FOR INCOMPLETE AND LARGE DATA | 8 |
| 18 | 0 | |
| 19 | 7 | |
| 20 | 1 |
About Hebah ElGibreen
Hebah ElGibreen is a scholar working on Artificial Intelligence, Signal Processing and Computer Science Applications, having authored 23 papers that have together received 220 indexed citations. Recurring topics across this work include Imbalanced Data Classification Techniques (4 papers), Machine Learning and Data Classification (3 papers) and Speech and Audio Processing (2 papers). The work is most often cited by research in Signal Processing (44 citations), Computer Vision and Pattern Recognition (77 citations) and Artificial Intelligence (76 citations). Hebah ElGibreen has collaborated with scholars based in Saudi Arabia, United States and Kuwait. Frequent co-authors include Mohammed Faisal, Fahad R. Albogamy, Mohammed Algabri, Kamal Youcef‐Toumi, Yakoub Bazi, Mehmet Sabih Aksoy, Mansour Zuair, Mohamad Mahmoud Al Rahhal, Areej Al‐Wabil and Remya George. Their work appears in journals such as PLoS ONE, IEEE Access and Sensors.
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.