Wesam M. Ashour
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 10%
- Information Systems top 10%
- Signal Processing top 10%
- Media Technology top 10%
- Topics
- Advanced Clustering Algorithms Research (18 papers)Data Mining Algorithms and Applications (12 papers)Data Management and Algorithms (9 papers)
- Journals
- Data Science JournalInternational Journal of Intelligent Systems and ApplicationsJournal of Artificial Intelligence and Soft Computing Research
- Partner nations
- Palestinian TerritoryGermanyIraq
In The Last Decade
Wesam M. Ashour
33 papers receiving 424 citations
Peers
Comparison fields: 5 of 83
- Artificial Intelligence 285
- Computer Vision and Pattern Recognition 133
- Information Systems 92
- Signal Processing 61
- Media Technology 32
Countries citing papers authored by Wesam M. Ashour
This map shows the geographic impact of Wesam M. Ashour'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 Wesam M. Ashour with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Wesam M. Ashour more than expected).
Fields of papers citing papers by Wesam M. Ashour
This network shows the impact of papers produced by Wesam M. Ashour. 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 Wesam M. Ashour. The network helps show where Wesam M. Ashour may publish in the future.
Co-authorship network of co-authors of Wesam M. Ashour
This figure shows the co-authorship network connecting the top 25 collaborators of Wesam M. Ashour. A scholar is included among the top collaborators of Wesam M. Ashour 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 Wesam M. Ashour. Wesam M. Ashour is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 1 | |
| 3 | 4 | |
| 4 | A New Model in Arabic Text Classification Using BPSO/REP-Tree | 7 |
| 5 | Combining IWC and PSO to Enhance Data Clustering | 0 |
| 6 | 1 | |
| 7 | 2 | |
| 8 | Improved Multi Threshold Birch Clustering Algorithm | 9 |
| 9 | 1 | |
| 10 | 35 | |
| 11 | Color Based Image Segmentation using Different Versions of K-Means in two Spaces. | 9 |
| 12 | 5 | |
| 13 | 12 | |
| 14 | 29 | |
| 15 | 7 | |
| 16 | 7 | |
| 17 | 22 | |
| 18 | Image Retrieval Based on Content Using Color Feature: Color Image Processing and Retrieving | 2 |
| 19 | 1 | |
| 20 | 90 |
About Wesam M. Ashour
Wesam M. Ashour is a scholar working on Artificial Intelligence, Signal Processing and Information Systems, having authored 36 papers that have together received 457 indexed citations. Recurring topics across this work include Advanced Clustering Algorithms Research (18 papers), Data Mining Algorithms and Applications (12 papers) and Data Management and Algorithms (9 papers). The work is most often cited by research in Artificial Intelligence (285 citations), Computer Vision and Pattern Recognition (133 citations) and Signal Processing (61 citations). Wesam M. Ashour has collaborated with scholars based in Palestinian Territory, Germany and Iraq. Frequent co-authors include Motaz Saad, Ahmed J. Afifi, Mohammed Abubaker, Olaf Hellwich, Didier Stricker and Colin Fyfe. Their work appears in journals such as Data Science Journal, International Journal of Intelligent Systems and Applications and Journal of Artificial Intelligence and Soft Computing Research.
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.