Nima Reyhani
Impact in
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- Stock Market Forecasting Methods
- Forecasting Techniques and Applications
- Grey System Theory Applications
- Signal Processing top 10%
- Time Series Analysis and Forecasting
Papers in
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- Neural Networks and Applications 7
- Advanced Software Engineering Methodologies 2
- Machine Learning and ELM 2
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- Face and Expression Recognition 4
- Co-authors
- Amaury Lendasse (4 shared papers)Hao Jin (2 shared papers)Yongnan Ji (2 shared papers)Antti Sorjamaa (1 shared paper)Kambiz Badie (5 shared papers)Ricardo Vigário (1 shared paper)Noboru Murata (3 shared papers)Erkki Oja (1 shared paper)
In The Last Decade
Nima Reyhani
17 papers receiving 378 citations
Peers
Comparison fields: 5 of 87
- Management Science and Operations Research 114
- Signal Processing 71
- Artificial Intelligence 159
- Environmental Engineering 39
- Building and Construction 29
Countries citing papers authored by Nima Reyhani
This map shows the geographic impact of Nima Reyhani'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 Nima Reyhani with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nima Reyhani more than expected).
Fields of papers citing papers by Nima Reyhani
This network shows the impact of papers produced by Nima Reyhani. 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 Nima Reyhani. The network helps show where Nima Reyhani may publish in the future.
Co-authors
The 25 scholars most cited alongside Nima Reyhani, 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 | 2007 | 325 | |
| 2 | Mutual Information and Gamma Test for Input Selection | 2005 | 12 |
| 3 | 2011 | 10 | |
| 4 | 2013 | 7 | |
| 5 | 2014 | 5 | |
| 6 | 2010 | 5 | |
| 7 | Determination of the Mahalanobis matrix using nonparametric noise estimations | 2006 | 4 |
| 8 | 2012 | 4 | |
| 9 | 2004 | 4 | |
| 10 | 2014 | 4 | |
| 11 | 2003 | 3 | |
| 12 | 2012 | 3 | |
| 13 | 2004 | 2 | |
| 14 | 2011 | 2 | |
| 15 | 2013 | 1 | |
| 16 | 2003 | 1 | |
| 17 | EM-algorithm for Training of State-space Models with Application to Time Series Prediction | 2006 | 1 |
| 18 | 2003 | 0 | |
| 19 | 2013 | 0 |
About Nima Reyhani
Nima Reyhani is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing, Information Systems and Computer Networks and Communications, having authored 19 papers that have together received 393 indexed citations. Recurring topics across this work include Neural Networks and Applications (7 papers), Face and Expression Recognition (4 papers), Blind Source Separation Techniques (4 papers), Control Systems and Identification (2 papers), Sparse and Compressive Sensing Techniques (2 papers), Advanced Software Engineering Methodologies (2 papers), Machine Learning and ELM (2 papers) and Educational Technology and Assessment (2 papers). The work is most often cited by research in Management Science and Operations Research (114 citations), Signal Processing (71 citations), Artificial Intelligence (159 citations), Environmental Engineering (39 citations) and Building and Construction (29 citations). Nima Reyhani has collaborated with scholars based in Finland, Iran and Japan. Frequent co-authors include Amaury Lendasse, Hao Jin, Yongnan Ji, Antti Sorjamaa, Kambiz Badie, Ricardo Vigário, Noboru Murata, Erkki Oja, Hideitsu Hino and Kyunghyun Cho. Their work appears in journals such as Neural Computation, Computer Networks, Neurocomputing, Signal Processing and PLoS ONE.
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.