Peyman Bayat

418 total citations
28 papers, 303 citations indexed

About

Peyman Bayat is a scholar working on Computer Networks and Communications, Information Systems and Artificial Intelligence. According to data from OpenAlex, Peyman Bayat has authored 28 papers receiving a total of 303 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Computer Networks and Communications, 6 papers in Information Systems and 6 papers in Artificial Intelligence. Recurrent topics in Peyman Bayat's work include Brain Tumor Detection and Classification (5 papers), IoT and Edge/Fog Computing (4 papers) and Cardiac pacing and defibrillation studies (3 papers). Peyman Bayat is often cited by papers focused on Brain Tumor Detection and Classification (5 papers), IoT and Edge/Fog Computing (4 papers) and Cardiac pacing and defibrillation studies (3 papers). Peyman Bayat collaborates with scholars based in Iran and Malaysia. Peyman Bayat's co-authors include Asadollah Shahbahrami, Gholamhossein Ekbatanifard, Shahram Jamali, Ali Almasirad, Mona Salimi, Sepideh Khaleghi, Kowsar Bagherzadeh, Homa Azizian, Zahra Mousavi and Abbas Shafiee and has published in prestigious journals such as IEEE Access, Applied Soft Computing and Computer Networks.

In The Last Decade

Peyman Bayat

27 papers receiving 291 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Peyman Bayat Iran 10 86 74 70 52 41 28 303
Mahesh Pawar India 8 69 0.8× 79 1.1× 63 0.9× 52 1.0× 20 0.5× 25 230
Danny Chen China 10 112 1.3× 50 0.7× 16 0.2× 70 1.3× 18 0.4× 34 378
Yubei Chen United States 9 85 1.0× 97 1.3× 10 0.1× 17 0.3× 37 0.9× 22 295
Tao Chi China 12 84 1.0× 68 0.9× 12 0.2× 21 0.4× 81 2.0× 36 341
Wael A. Awad Egypt 8 47 0.5× 135 1.8× 25 0.4× 26 0.5× 74 1.8× 37 320
Kapil Kumar Nagwanshi India 7 80 0.9× 113 1.5× 20 0.3× 70 1.3× 35 0.9× 36 253
Prasanalakshmi Balaji Saudi Arabia 8 32 0.4× 109 1.5× 57 0.8× 30 0.6× 35 0.9× 44 259
Bin Xia China 8 111 1.3× 92 1.2× 15 0.2× 113 2.2× 2 0.0× 19 355
Aziz Makandar India 8 89 1.0× 160 2.2× 19 0.3× 45 0.9× 124 3.0× 31 331
Asghar Ali Shah Pakistan 11 48 0.6× 93 1.3× 16 0.2× 93 1.8× 80 2.0× 47 403

Countries citing papers authored by Peyman Bayat

Since Specialization
Citations

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

Fields of papers citing papers by Peyman Bayat

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Peyman Bayat

This figure shows the co-authorship network connecting the top 25 collaborators of Peyman Bayat. A scholar is included among the top collaborators of Peyman Bayat 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 Peyman Bayat. Peyman Bayat 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
1.
Jamali, Shahram, et al.. (2022). EmcFIS: Evolutionary multi-criteria Fuzzy Inference System for virtual network function placement and routing. Applied Soft Computing. 117. 108427–108427. 12 indexed citations
2.
Motameni, Homayun, et al.. (2022). Solving the Task Starvation and Resources Problem Using Optimized SMPIA in Cloud. Computer Systems Science and Engineering. 42(2). 659–675. 2 indexed citations
3.
4.
Bayat, Peyman, et al.. (2021). Multiple sclerosis lesion segmentation from brain MRI using U-Net based on wavelet pooling. International Journal of Computer Assisted Radiology and Surgery. 16(9). 1459–1467. 19 indexed citations
5.
Bayat, Peyman, et al.. (2021). Evolutionary ensemble feature selection learning for image‐based assessment of lymphedema arm volume. Concurrency and Computation Practice and Experience. 34(1). 5 indexed citations
6.
Jamali, Shahram, et al.. (2021). A power-efficient and performance-aware online virtual network function placement in SDN/NFV-enabled networks. Computer Networks. 205. 108753–108753. 14 indexed citations
7.
Bayat, Peyman, et al.. (2021). Multi-Objective Task Scheduling Using Smart MPI-Based Cloud Resources. Computing and Informatics. 40(1). 104–144. 4 indexed citations
8.
Bayat, Peyman, et al.. (2021). Predicting the response to cardiac resynchronization therapy (CRT) using the deep learning approach. Journal of Applied Biomedicine. 41(2). 758–778. 4 indexed citations
9.
10.
Bayat, Peyman, et al.. (2020). Multiple sclerosis identification in brainMRIimages using wavelet convolutional neural networks. International Journal of Imaging Systems and Technology. 31(2). 778–785. 21 indexed citations
11.
Bayat, Peyman, et al.. (2020). Evaluation of Pattern Recognition Techniques in Response to Cardiac Resynchronization Therapy (CRT). 8(31). 197–206. 1 indexed citations
12.
Bayat, Peyman, et al.. (2020). Evaluation of Pattern Recognition Techniques in Response to Cardiac Resynchronization Therapy (CRT). 3(31). 197–206. 1 indexed citations
13.
Bayat, Peyman, et al.. (2019). Eye gesture blink password: a new authentication system with high memorable and maximum password length. Multimedia Tools and Applications. 78(12). 16861–16885. 7 indexed citations
14.
Shahbahrami, Asadollah, et al.. (2018). A Hybrid Optimization Algorithm for Learning Deep Models. 9(4). 59–71. 1 indexed citations
15.
Shahbahrami, Asadollah, et al.. (2018). An Efficient Implementation of Deep Convolutional Neural Networks for MRI Segmentation. Journal of Digital Imaging. 31(5). 738–747. 37 indexed citations
16.
Shahbahrami, Asadollah, et al.. (2018). AdaptAhead Optimization Algorithm for Learning Deep CNN Applied to MRI Segmentation. Journal of Digital Imaging. 32(1). 105–115. 45 indexed citations
17.
Shahbahrami, Asadollah, et al.. (2016). A Parallel Implementation of Modified Fuzzy Logic for Breast Cancer Detection. 7(224). 139–148. 1 indexed citations
18.
Azizian, Homa, Zahra Mousavi, Kowsar Bagherzadeh, et al.. (2016). Arylhydrazone derivatives of naproxen as new analgesic and anti-inflammatory agents: Design, synthesis and molecular docking studies. Journal of Molecular Graphics and Modelling. 67. 127–136. 20 indexed citations
19.
Avazpour, Iman, et al.. (2009). Segmenting CT images of bronchogenic carcinoma with bone metastases using PET intensity markers approach. Radiology and Oncology. 43(3). 12 indexed citations
20.
Challenger, Moharram, et al.. (2007). A new robust centralized DMX algorithm. 367–374. 2 indexed citations

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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