Ahmad Akl
- Computer Vision and Pattern Recognition top 5%
- Human-Computer Interaction top 2%
- Psychiatry and Mental health
- Biomedical Engineering
- Cognitive Neuroscience
- Co-authors
- Shahrokh ValaeeAlex MihailidisChen FengBabak TaatiJasper SnoekJeffrey KayeBelkacem ChikhaouiDaniel Austin
- Topics
- Context-Aware Activity Recognition Systems (5 papers)Dementia and Cognitive Impairment Research (3 papers)Nutritional Studies and Diet (2 papers)
- Cited by
- Human-Computer InteractionComputer Vision and Pattern RecognitionPsychiatry and Mental health
- Journals
- IEEE Transactions on Signal ProcessingIEEE Transactions on Biomedical EngineeringIEEE Journal of Biomedical and Health Informatics
- Partner nations
- CanadaUnited StatesSaudi Arabia
In The Last Decade
Ahmad Akl
9 papers receiving 375 citations
Peers
Comparison fields: 5 of 73
- Computer Vision and Pattern Recognition 171
- Human-Computer Interaction 140
- Psychiatry and Mental health 87
- Biomedical Engineering 83
- Cognitive Neuroscience 55
Countries citing papers authored by Ahmad Akl
This map shows the geographic impact of Ahmad Akl'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 Ahmad Akl with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ahmad Akl more than expected).
Fields of papers citing papers by Ahmad Akl
This network shows the impact of papers produced by Ahmad Akl. 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 Ahmad Akl. The network helps show where Ahmad Akl may publish in the future.
Co-authorship network of co-authors of Ahmad Akl
This figure shows the co-authorship network connecting the top 25 collaborators of Ahmad Akl. A scholar is included among the top collaborators of Ahmad Akl 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 Ahmad Akl. Ahmad Akl 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 | 4 | |
| 3 | 34 | |
| 4 | 35 | |
| 5 | 9 | |
| 6 | 95 | |
| 7 | 3 | |
| 8 | 133 | |
| 9 | 80 |
About Ahmad Akl
Ahmad Akl is a scholar working on Human-Computer Interaction, Transportation and Computer Vision and Pattern Recognition, having authored 9 papers that have together received 394 indexed citations. Recurring topics across this work include Context-Aware Activity Recognition Systems (5 papers), Dementia and Cognitive Impairment Research (3 papers) and Nutritional Studies and Diet (2 papers). The work is most often cited by research in Human-Computer Interaction (140 citations), Computer Vision and Pattern Recognition (171 citations) and Psychiatry and Mental health (87 citations). Ahmad Akl has collaborated with scholars based in Canada, United States and Saudi Arabia. Frequent co-authors include Shahrokh Valaee, Alex Mihailidis, Chen Feng, Babak Taati, Jasper Snoek, Jeffrey Kaye, Belkacem Chikhaoui, Daniel Austin, Nora Mattek and Ahmed Moustafa. Their work appears in journals such as IEEE Transactions on Signal Processing, IEEE Transactions on Biomedical Engineering and IEEE Journal of Biomedical and Health Informatics.
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.