Shady Shehata
- Artificial Intelligence top 5%
- Information Systems top 5%
- Computer Vision and Pattern Recognition
- Computer Networks and Communications
- Signal Processing
- Co-authors
- Fakhri KarrayMohamed S. KamelYuri QuintanaMd. Milon IslamKimberly E. ArnoldJohn WhitmerChristopher BrooksYifan Peng
- Topics
- Topic Modeling (10 papers)Natural Language Processing Techniques (7 papers)Web Data Mining and Analysis (5 papers)
- Journals
- IEEE AccessIEEE Transactions on Knowledge and Data EngineeringKnowledge and Information Systems
- Partner nations
- CanadaUnited Arab EmiratesUnited States
In The Last Decade
Shady Shehata
21 papers receiving 335 citations
Peers
Comparison fields: 5 of 78
- Artificial Intelligence 245
- Information Systems 139
- Computer Vision and Pattern Recognition 42
- Computer Networks and Communications 37
- Signal Processing 24
Countries citing papers authored by Shady Shehata
This map shows the geographic impact of Shady Shehata'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 Shady Shehata with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shady Shehata more than expected).
Fields of papers citing papers by Shady Shehata
This network shows the impact of papers produced by Shady Shehata. 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 Shady Shehata. The network helps show where Shady Shehata may publish in the future.
Co-authorship network of co-authors of Shady Shehata
This figure shows the co-authorship network connecting the top 25 collaborators of Shady Shehata. A scholar is included among the top collaborators of Shady Shehata 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 Shady Shehata. Shady Shehata is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 1 | |
| 3 | 9 | |
| 4 | 6 | |
| 5 | 0 | |
| 6 | 2 | |
| 7 | 4 | |
| 8 | 1 | |
| 9 | 4 | |
| 10 | 2 | |
| 11 | 105 | |
| 12 | The Predictive Learning Analytics Revolution: Leveraging Learning Data for Student Success | 17 |
| 13 | 15 | |
| 14 | 9 | |
| 15 | 20 | |
| 16 | 69 | |
| 17 | 36 | |
| 18 | 15 | |
| 19 | 41 | |
| 20 | 15 |
About Shady Shehata
Shady Shehata is a scholar working on Issues, ethics and legal aspects, Artificial Intelligence and Computer Science Applications, having authored 23 papers that have together received 383 indexed citations. Recurring topics across this work include Topic Modeling (10 papers), Natural Language Processing Techniques (7 papers) and Web Data Mining and Analysis (5 papers). The work is most often cited by research in Health Informatics (18 citations), Artificial Intelligence (245 citations) and Information Systems (139 citations). Shady Shehata has collaborated with scholars based in Canada, United Arab Emirates and United States. Frequent co-authors include Fakhri Karray, Mohamed S. Kamel, Fakhri Karray, Yuri Quintana, Md. Milon Islam, Kimberly E. Arnold, John Whitmer, Christopher Brooks, Yifan Peng and Hung-yi Lee. Their work appears in journals such as IEEE Access, IEEE Transactions on Knowledge and Data Engineering and Knowledge and Information Systems.
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.