Nahla Barakat
- Artificial Intelligence top 5%
- Health Information Management top 0.5%
- Information Systems top 5%
- Computational Theory and Mathematics top 5%
- Computer Vision and Pattern Recognition
- Co-authors
- Andrew P. BradleyJoachim Diederich
- Topics
- Machine Learning and Data Classification (5 papers)Fuzzy Logic and Control Systems (5 papers)Neural Networks and Applications (5 papers)
- Journals
- NeurocomputingIEEE Transactions on Knowledge and Data EngineeringInternational Journal of Advanced Computer Science and Applications
In The Last Decade
Nahla Barakat
20 papers receiving 645 citations
Peers
Comparison fields: 5 of 110
- Artificial Intelligence 443
- Health Information Management 238
- Information Systems 134
- Computational Theory and Mathematics 81
- Computer Vision and Pattern Recognition 47
Countries citing papers authored by Nahla Barakat
This map shows the geographic impact of Nahla Barakat'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 Nahla Barakat with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nahla Barakat more than expected).
Fields of papers citing papers by Nahla Barakat
This network shows the impact of papers produced by Nahla Barakat. 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 Nahla Barakat. The network helps show where Nahla Barakat may publish in the future.
Co-authorship network of co-authors of Nahla Barakat
This figure shows the co-authorship network connecting the top 25 collaborators of Nahla Barakat. A scholar is included among the top collaborators of Nahla Barakat 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 Nahla Barakat. Nahla Barakat 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 | 7 | |
| 4 | 1 | |
| 5 | 23 | |
| 6 | 7 | |
| 7 | 5 | |
| 8 | 1 | |
| 9 | 3 | |
| 10 | 1 | |
| 11 | 250 | |
| 12 | 162 | |
| 13 | 6 | |
| 14 | 74 | |
| 15 | 75 | |
| 16 | 2 | |
| 17 | 28 | |
| 18 | 5 | |
| 19 | Learning-Based Rule-Extraction From Support Vector Machines: Performance On Benchmark Data Sets | 15 |
| 20 | Learning-based Rule-Extraction from Support Vector Machines | 34 |
About Nahla Barakat
Nahla Barakat is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Toxicology, having authored 21 papers that have together received 701 indexed citations. Recurring topics across this work include Machine Learning and Data Classification (5 papers), Fuzzy Logic and Control Systems (5 papers) and Neural Networks and Applications (5 papers). The work is most often cited by research in Health Information Management (238 citations), Health Informatics (30 citations) and Artificial Intelligence (443 citations). Nahla Barakat has collaborated with scholars based in Egypt, Australia and Oman. Frequent co-authors include Andrew P. Bradley and Joachim Diederich. Their work appears in journals such as Neurocomputing, IEEE Transactions on Knowledge and Data Engineering and International Journal of Advanced Computer Science 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.