Reza Paki

619 citations
7 papers · 396 indexed · 1 hit paper · h-index 6
Topics
Anomaly Detection Techniques and Applications (4 papers)COVID-19 epidemiological studies (3 papers)COVID-19 diagnosis using AI (3 papers)
Partner nations
IranUnited StatesItaly

In The Last Decade

Reza Paki

7 papers receiving 381 citations

Hit Papers

Improving time series forecasting using LSTM and attentio...2021202620222024202150100150

Peers

Reza Paki
Comparison fields: 5 of 92
  • Artificial Intelligence 146
  • Electrical and Electronic Engineering 96
  • Management Science and Operations Research 85
  • Radiology, Nuclear Medicine and Imaging 80
  • Modeling and Simulation 71
Replace Toshitaka Hayashi with:
Toshitaka Hayashi Japan
Parul Arora India
Hasnain Iftikhar Pakistan
Neeraj Gupta India
Gopal Chaudhary India
Misbah Ahmad Pakistan
Babita Majhi India
Seungwon Jung South Korea
Shaoze Cui China
H. Swapnarekha India
Reza Paki relative to Toshitaka Hayashi Japan Toshitaka Hayashi's profile →
Citations per field
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Toshitaka Hayashi · 1×
Citations per year

Countries citing papers authored by Reza Paki

Since Specialization
Citations

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

Fields of papers citing papers by Reza Paki

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Reza Paki

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

All Works

7 of 7 papers shown
#WorkIndexed citations
1 5
2 38
3 17
4 45
5
Improving time series forecasting using LSTM and attention modelsbreakdown →
164
6 124
7 3

About Reza Paki

Reza Paki is a scholar working on Modeling and Simulation, Management Science and Operations Research and Signal Processing, having authored 7 papers that have together received 396 indexed citations. Recurring topics across this work include Anomaly Detection Techniques and Applications (4 papers), COVID-19 epidemiological studies (3 papers) and COVID-19 diagnosis using AI (3 papers). The work is most often cited by research in Modeling and Simulation (71 citations), Management Science and Operations Research (85 citations) and Health Informatics (8 citations). Reza Paki has collaborated with scholars based in Iran, United States and Italy. Frequent co-authors include Hossein Abbasimehr, Aram Bahrini and Davoud Farajzadeh. Their work appears in journals such as Expert Systems with Applications, Chaos Solitons & Fractals and Neural Computing and Applications.

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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