Nima Reyhani
Impact in
- Signal Processing top 10%
- Time Series Analysis and Forecasting
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- Forecasting Techniques and Applications
- Stock Market Forecasting Methods
Papers in
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- Blind Source Separation Techniques 1
- Time Series Analysis and Forecasting 1
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- AI-based Problem Solving and Planning 2
- Speech Recognition and Synthesis 1
- Intelligent Tutoring Systems and Adaptive Learning 1
- Co-authors
- Amaury Lendasse (7 shared papers)Yongnan Ji (4 shared papers)Hao Jin (3 shared papers)Antti Sorjamaa B (2 shared papers)Soheil Behnezhad (3 shared papers)Michel Verleysen (2 shared papers)Kambiz Badie (5 shared papers)Erkki Oja A (1 shared paper)
In The Last Decade
Nima Reyhani
23 papers receiving 521 citations
Peers
Comparison fields: 5 of 51
- Signal Processing 47
- Management Science and Operations Research 59
- Environmental Engineering 42
- Artificial Intelligence 53
- Building and Construction 17
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
Showing the 20 most-cited of 25 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2007 | 353 | |
| 2 | 2005 | 63 | |
| 3 | 2005 | 22 | |
| 4 | Mutual Information and Gamma Test for Input Selection | 2005 | 15 |
| 5 | 2005 | 13 | |
| 6 | 2018 | 11 | |
| 7 | 2011 | 10 | |
| 8 | 2019 | 9 | |
| 9 | Determination of the Mahalanobis matrix using nonparametric noise estimations | 2006 | 7 |
| 10 | 2013 | 7 | |
| 11 | 2012 | 6 | |
| 12 | 2019 | 6 | |
| 13 | 2004 | 5 | |
| 14 | 2010 | 5 | |
| 15 | 2014 | 5 | |
| 16 | 2014 | 5 | |
| 17 | 2012 | 4 | |
| 18 | 2003 | 3 | |
| 19 | 2004 | 3 | |
| 20 | 2003 | 2 |
About Nima Reyhani
Nima Reyhani is a scholar working on Signal Processing, Artificial Intelligence, Computer Networks and Communications, Statistics and Probability and Pathology and Forensic Medicine, having authored 25 papers that have together received 558 indexed citations. Recurring topics across this work include AI-based Problem Solving and Planning (2 papers), Blind Source Separation Techniques (1 paper), Speech Recognition and Synthesis (1 paper), Statistical Methods and Inference (1 paper), Time Series Analysis and Forecasting (1 paper), Network Traffic and Congestion Control (1 paper), Energy Load and Power Forecasting (1 paper) and Intelligent Tutoring Systems and Adaptive Learning (1 paper). The work is most often cited by research in Signal Processing (47 citations), Management Science and Operations Research (59 citations), Environmental Engineering (42 citations), Artificial Intelligence (53 citations) and Building and Construction (17 citations). Nima Reyhani has collaborated with scholars based in Finland, Iran and Japan. Frequent co-authors include Amaury Lendasse, Yongnan Ji, Hao Jin, Antti Sorjamaa B, Soheil Behnezhad, Michel Verleysen, Kambiz Badie, Erkki Oja A, Ricardo Vigário and Mohammad Taghi Hajiaghayi. Their work appears in journals such as Neural Computation, PLoS ONE, Computer Networks, Signal Processing and Neurocomputing.
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.