Ali Amiri

627 total citations
30 papers, 429 citations indexed

About

Ali Amiri is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Signal Processing. According to data from OpenAlex, Ali Amiri has authored 30 papers receiving a total of 429 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Artificial Intelligence, 13 papers in Computer Vision and Pattern Recognition and 5 papers in Signal Processing. Recurrent topics in Ali Amiri's work include Face and Expression Recognition (7 papers), Geochemistry and Geologic Mapping (4 papers) and Machine Learning in Bioinformatics (3 papers). Ali Amiri is often cited by papers focused on Face and Expression Recognition (7 papers), Geochemistry and Geologic Mapping (4 papers) and Machine Learning in Bioinformatics (3 papers). Ali Amiri collaborates with scholars based in Iran, Malaysia and Canada. Ali Amiri's co-authors include Seyed Hadi Hosseini, Sai Hong Tang, Syamsiah Mashohor, Hong Zhang, Simon M. Lucas, Mahmood Fathy, Mansour Ziaii, Hossein Safari, Sadegh Karimpouli and Reza Noroozian and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Access and Neurocomputing.

In The Last Decade

Ali Amiri

30 papers receiving 409 citations

Peers

Ali Amiri
Comparison fields: 5 of 73
  • Computer Vision and Pattern Recognition 164
  • Artificial Intelligence 139
  • Electrical and Electronic Engineering 100
  • Control and Systems Engineering 75
  • Aerospace Engineering 62
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Citations per field, relative to Ali Amiri
Ali Amiri · 1×
Citations per year, relative to Ali Amiri
Ali Amiri · 1×

Countries citing papers authored by Ali Amiri

Since Specialization
Citations

This map shows the geographic impact of Ali Amiri'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 Amiri with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ali Amiri more than expected).

Fields of papers citing papers by Ali Amiri

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ali Amiri. 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 Amiri. The network helps show where Ali Amiri may publish in the future.

Co-authorship network of co-authors of Ali Amiri

This figure shows the co-authorship network connecting the top 25 collaborators of Ali Amiri. A scholar is included among the top collaborators of Ali Amiri 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 Ali Amiri. Ali Amiri is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
# Work Indexed citations
1 6
2 3
3 15
4 3
5 4
6 49
7 26
8 8
9 3
10 15
11 22
12 18
13 8
14
A Novel Video Retrieval System Using GED-based Similarity Measure
1
15
The background modeling for the video sequences using Radial Basis Function neural network
3
16
VIDEO SHOT BOUNDARY DETECTION USING GENERALIZED EIGENVALUE DECOMPOSITION AND GAUSSIAN TRANSITION DETECTION
17
17 8
18 2
19 6
20 26

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.

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