Ali Fallah Tehrani
Impact in
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- Online Learning and Analytics
- E-Learning and Knowledge Management
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- Multi-Criteria Decision Making
Papers in
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- Bayesian Modeling and Causal Inference 4
- Imbalanced Data Classification Techniques 2
- Advanced Clustering Algorithms Research 2
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- Multi-Criteria Decision Making 11
- Co-authors
- Monica Ciolacu (2 shared papers)Eyke Hüllermeier (5 shared papers)Weiwei Cheng (3 shared papers)Paul Svasta (2 shared papers)Manish Aggarwal (1 shared paper)Diane Ahrens (5 shared papers)Krzysztof Dembczyński (1 shared paper)Marc Strickert (2 shared papers)
In The Last Decade
Ali Fallah Tehrani
20 papers receiving 469 citations
Peers
Comparison fields: 5 of 84
- Computer Science Applications 116
- Management Science and Operations Research 189
- Health Informatics 15
- Statistics and Probability 83
- Artificial Intelligence 205
Countries citing papers authored by Ali Fallah Tehrani
This map shows the geographic impact of Ali Fallah Tehrani'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 Ali Fallah Tehrani with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ali Fallah Tehrani more than expected).
Fields of papers citing papers by Ali Fallah Tehrani
This network shows the impact of papers produced by Ali Fallah Tehrani. 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 Ali Fallah Tehrani. The network helps show where Ali Fallah Tehrani may publish in the future.
Co-authors
The 8 scholars most cited alongside Ali Fallah Tehrani, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2018 | 113 | |
| 2 | 2017 | 88 | |
| 3 | 2012 | 74 | |
| 4 | 2012 | 62 | |
| 5 | 2019 | 56 | |
| 6 | 2016 | 24 | |
| 7 | 2011 | 14 | |
| 8 | 2013 | 13 | |
| 9 | 2020 | 9 | |
| 10 | 2017 | 8 | |
| 11 | 2014 | 7 | |
| 12 | Learning nonlinear monotone classifiers using the Choquet Integral | 2014 | 5 |
| 13 | 2023 | 4 | |
| 14 | 2016 | 4 | |
| 15 | 2017 | 4 | |
| 16 | 2020 | 3 | |
| 17 | 2020 | 2 | |
| 18 | The Choquet Kernel for Monotone Data | 2014 | 1 |
| 19 | 2021 | 1 | |
| 20 | 2021 | 1 |
About Ali Fallah Tehrani
Ali Fallah Tehrani is a scholar working on Artificial Intelligence, Management Science and Operations Research, Statistics and Probability, Information Systems and Computational Theory and Mathematics, having authored 20 papers that have together received 493 indexed citations. Recurring topics across this work include Multi-Criteria Decision Making (11 papers), Fuzzy Systems and Optimization (5 papers), Bayesian Modeling and Causal Inference (4 papers), Advanced Statistical Methods and Models (4 papers), Rough Sets and Fuzzy Logic (3 papers), Online Learning and Analytics (3 papers), Imbalanced Data Classification Techniques (2 papers) and Advanced Clustering Algorithms Research (2 papers). The work is most often cited by research in Computer Science Applications (116 citations), Management Science and Operations Research (189 citations), Health Informatics (15 citations), Statistics and Probability (83 citations) and Artificial Intelligence (205 citations). Ali Fallah Tehrani has collaborated with scholars based in Germany, Romania and India. Frequent co-authors include Monica Ciolacu, Eyke Hüllermeier, Weiwei Cheng, Paul Svasta, Manish Aggarwal, Diane Ahrens, Krzysztof Dembczyński and Marc Strickert. Their work appears in journals such as Pattern Recognition Letters, Journal of Retailing and Consumer Services, IEEE Transactions on Fuzzy Systems, Journal of Multi-Criteria Decision Analysis and Information Sciences.
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.