Mahdi Dibaei

510 total citations
10 papers, 320 citations indexed

About

Mahdi Dibaei is a scholar working on Information Systems, Artificial Intelligence and Computer Networks and Communications. According to data from OpenAlex, Mahdi Dibaei has authored 10 papers receiving a total of 320 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Information Systems, 5 papers in Artificial Intelligence and 4 papers in Computer Networks and Communications. Recurrent topics in Mahdi Dibaei's work include Software Engineering Research (3 papers), Advanced Software Engineering Methodologies (3 papers) and IoT and Edge/Fog Computing (2 papers). Mahdi Dibaei is often cited by papers focused on Software Engineering Research (3 papers), Advanced Software Engineering Methodologies (3 papers) and IoT and Edge/Fog Computing (2 papers). Mahdi Dibaei collaborates with scholars based in Australia, Iran and China. Mahdi Dibaei's co-authors include Xi Zheng, Yang Xiang, Ali Ghaffari, Shigang Liu, Hassan Abbas, Kun Jiang, Yuexin Zhang, Shui Yu, Usman Tariq and Ali Kashif Bashir and has published in prestigious journals such as SHILAP Revista de lepidopterología, Sensors and Australasian Journal of Paramedicine.

In The Last Decade

Mahdi Dibaei

8 papers receiving 307 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Mahdi Dibaei Australia	6	155	137	119	80	72	10	320
M. Omar Al-Kadri United Kingdom	10	176 1.1×	307 2.2×	137 1.2×	188 2.4×	121 1.7×	27	515
Branden Ghena United States	9	176 1.1×	175 1.3×	57 0.5×	65 0.8×	116 1.6×	19	363
Philipp Mundhenk Germany	11	215 1.4×	155 1.1×	62 0.5×	46 0.6×	74 1.0×	23	384
Mehmet Bozdal United Kingdom	6	190 1.2×	132 1.0×	36 0.3×	83 1.0×	82 1.1×	8	282
Benjamin Weyl Germany	6	219 1.4×	143 1.0×	60 0.5×	31 0.4×	36 0.5×	9	283
Rens W. van der Heijden Germany	11	167 1.1×	131 1.0×	49 0.4×	33 0.4×	84 1.2×	20	295
Samiha Ayed France	13	155 1.0×	177 1.3×	117 1.0×	47 0.6×	148 2.1×	37	382
Tuan Vuong United Kingdom	8	190 1.2×	290 2.1×	89 0.7×	182 2.3×	170 2.4×	10	451
Irshad Ahmed Abbasi Saudi Arabia	12	170 1.1×	249 1.8×	94 0.8×	24 0.3×	66 0.9×	34	391

Countries citing papers authored by Mahdi Dibaei

Since Specialization

Citations

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

Fields of papers citing papers by Mahdi Dibaei

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mahdi Dibaei

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

All Works

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10 of 10 papers shown

1.

Dibaei, Mahdi, et al.. (2024). Integration of Fault Tree Analysis into Model-Based Product Line Engineering in Railway Vehicles. 309–317.

2.

Dibaei, Mahdi, Kai-Steffen Hielscher, & Reinhard German. (2024). A Heuristic Variable Ordering Approach for Fault Tree Conversion to Binary Decision Diagram. 301–307.

3.

Dibaei, Mahdi, et al.. (2023). A Novel Method for Computing Minimal Cut Sets of Variant-Rich Systems in Fault Tree Analysis. 481–487. 2 indexed citations

4.

Dibaei, Mahdi, Xi Zheng, Xiwei Xu, et al.. (2021). Investigating the Prospect of Leveraging Blockchain and Machine Learning to Secure Vehicular Networks: A Survey. IEEE Transactions on Intelligent Transportation Systems. 23(2). 683–700. 89 indexed citations

5.

Kang, James Jin, Mahdi Dibaei, Gang Luo, et al.. (2021). An Energy-Efficient and Secure Data Inference Framework for Internet of Health Things: A Pilot Study. Sensors. 21(1). 312–312. 10 indexed citations

6.

Kang, James Jin, Mahdi Dibaei, Gang Luo, Wencheng Yang, & Xi Zheng. (2020). A Privacy-Preserving Data Inference Framework for Internet of Health Things Networks. Australasian Journal of Paramedicine. 1209–1214. 8 indexed citations

7.

Dibaei, Mahdi, Xi Zheng, Kun Jiang, et al.. (2020). Attacks and defences on intelligent connected vehicles: a survey. Digital Communications and Networks. 6(4). 399–421. 139 indexed citations

8.

Dibaei, Mahdi & Ali Ghaffari. (2020). Full-duplex medium access control protocols in wireless networks: a survey. Wireless Networks. 26(4). 2825–2843. 31 indexed citations

9.

Dibaei, Mahdi, et al.. (2019). Cyber Vulnerability Intelligence for Internet of Things Binary. IEEE Transactions on Industrial Informatics. 16(3). 2154–2163. 36 indexed citations

10.

Dibaei, Mahdi & Ali Ghaffari. (2017). TSIS: A Trust-Based Scheme for Increasing Security in Wireless Sensor Networks. SHILAP Revista de lepidopterología. 11(4). 45–52. 5 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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