Alireza Jamshidi

860 citations
8 papers · 539 indexed · 1 hit paper · h-index 6
Topics
Artificial Intelligence in Healthcare and Education (2 papers)COVID-19 diagnosis using AI (2 papers)Anomaly Detection Techniques and Applications (2 papers)
Journals
SHILAP Revista de lepidopterologíaIEEE AccessFuture Internet
Partner nations
IranCzechiaAustralia

In The Last Decade

Alireza Jamshidi

7 papers receiving 521 citations

Hit Papers

Artificial Intelligence and COVID-19: Deep Learning Appro...20202026202220242020100200300

Peers

Alireza Jamshidi
Comparison fields: 5 of 107
  • Radiology, Nuclear Medicine and Imaging 248
  • Artificial Intelligence 181
  • Health Informatics 76
  • Biomedical Engineering 50
  • Health Information Management 49
Replace M.A. Chyad with:
M.A. Chyad Malaysia
Tianxiao Zhang United States
Subrato Bharati Bangladesh
Shiva Toumaj Iran
Farimah Hadjilooei Iran
Pedram Lalbakhsh Iran
Misbah Ahmad Pakistan
Abdullah M. Almuhaideb Saudi Arabia
A.M. Aleesa Malaysia
Gladston Moreira Brazil
Alireza Jamshidi relative to M.A. Chyad Malaysia M.A. Chyad's profile →
Citations per field
00.5×1.5×
M.A. Chyad · 1×
Citations per year

Countries citing papers authored by Alireza Jamshidi

Since Specialization
Citations

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

Fields of papers citing papers by Alireza Jamshidi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Alireza Jamshidi

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

All Works

8 of 8 papers shown
#WorkIndexed citations
1 26
2 37
3 14
4 0
5 69
6 20
7
Artificial Intelligence and COVID-19: Deep Learning Approaches for Diagnosis and Treatmentbreakdown →
369
8 4

About Alireza Jamshidi

Alireza Jamshidi is a scholar working on Health Informatics, Modeling and Simulation and Periodontics, having authored 8 papers that have together received 539 indexed citations. Recurring topics across this work include Artificial Intelligence in Healthcare and Education (2 papers), COVID-19 diagnosis using AI (2 papers) and Anomaly Detection Techniques and Applications (2 papers). The work is most often cited by research in Health Informatics (76 citations), Radiology, Nuclear Medicine and Imaging (248 citations) and Health Information Management (49 citations). Alireza Jamshidi has collaborated with scholars based in Iran, Czechia and Australia. Frequent co-authors include Mohammad Jamshidi, Jakub Talla, Ali Lalbakhsh, Pedram Lalbakhsh, Sobhan Roshani, Saeed Roshani, Zdeněk Peroutka, Farimah Hadjilooei, Bahare Mohamadzade and Nima Bayat-Makou. Their work appears in journals such as SHILAP Revista de lepidopterología, IEEE Access and Future Internet.

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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