Behrooz Masoumi

557 total citations
44 papers, 372 citations indexed

About

Behrooz Masoumi is a scholar working on Artificial Intelligence, Computer Networks and Communications and Computer Vision and Pattern Recognition. According to data from OpenAlex, Behrooz Masoumi has authored 44 papers receiving a total of 372 indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Artificial Intelligence, 20 papers in Computer Networks and Communications and 8 papers in Computer Vision and Pattern Recognition. Recurrent topics in Behrooz Masoumi's work include Optimization and Search Problems (9 papers), Metaheuristic Optimization Algorithms Research (8 papers) and Reinforcement Learning in Robotics (7 papers). Behrooz Masoumi is often cited by papers focused on Optimization and Search Problems (9 papers), Metaheuristic Optimization Algorithms Research (8 papers) and Reinforcement Learning in Robotics (7 papers). Behrooz Masoumi collaborates with scholars based in Iran. Behrooz Masoumi's co-authors include Mohammad Reza Keyvanpour, Babak Karasfi, Mohammad Reza Meybodi, Mohammad Bagher Menhaj, Eslam Nazemi, Saleh Yousefi, Karim Faez, Seyyed Mohsen Hashemi, Hamidreza Bakhshi and M. R. Meybodi and has published in prestigious journals such as SHILAP Revista de lepidopterología, Expert Systems with Applications and Reliability Engineering & System Safety.

In The Last Decade

Behrooz Masoumi

41 papers receiving 353 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Behrooz Masoumi Iran 11 159 85 80 51 35 44 372
Longfei Li China 9 323 2.0× 70 0.8× 73 0.9× 99 1.9× 17 0.5× 28 432
Michele Ianni Italy 9 279 1.8× 51 0.6× 100 1.3× 66 1.3× 23 0.7× 36 420
Najla Al-Nabhan Saudi Arabia 14 140 0.9× 133 1.6× 110 1.4× 92 1.8× 16 0.5× 47 450
Jiashuai Shi China 6 159 1.0× 63 0.7× 70 0.9× 44 0.9× 11 0.3× 7 339
Brendan Juba United States 10 233 1.5× 45 0.5× 92 1.1× 33 0.6× 17 0.5× 35 443
Yong Feng China 13 177 1.1× 199 2.3× 75 0.9× 102 2.0× 17 0.5× 70 548
Mohammed Elbes Jordan 12 172 1.1× 53 0.6× 73 0.9× 86 1.7× 21 0.6× 29 422
Hongrong Cheng China 8 137 0.9× 104 1.2× 37 0.5× 45 0.9× 11 0.3× 22 318
Faizan Javed United States 14 212 1.3× 69 0.8× 92 1.1× 145 2.8× 35 1.0× 49 542
Qiben Yan United States 10 183 1.2× 42 0.5× 109 1.4× 108 2.1× 15 0.4× 24 422

Countries citing papers authored by Behrooz Masoumi

Since Specialization
Citations

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

Fields of papers citing papers by Behrooz Masoumi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Behrooz Masoumi

This figure shows the co-authorship network connecting the top 25 collaborators of Behrooz Masoumi. A scholar is included among the top collaborators of Behrooz Masoumi 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 Behrooz Masoumi. Behrooz Masoumi 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.
Masoumi, Behrooz, et al.. (2024). Group deep neural network approach in semantic recommendation system for movie recommendation in online networks. Electronic Commerce Research. 26(1). 521–560. 1 indexed citations
2.
Masoumi, Behrooz, et al.. (2024). An Enhanced Ensemble Learning Method for Sentiment Analysis based on Q-learning. Iranian Journal of Science and Technology Transactions of Electrical Engineering. 48(3). 1261–1277. 1 indexed citations
3.
Masoumi, Behrooz, et al.. (2024). A novel robust memetic algorithm for dynamic community structures detection in complex networks. World Wide Web. 27(1). 3 indexed citations
4.
Menhaj, Mohammad Bagher, et al.. (2023). Wearable Sensors-Based Human Activity Recognition with Deep Convolutional Neural Network and Fuzzy Classification. Wireless Personal Communications. 133(2). 889–911. 2 indexed citations
5.
Faez, Karim, et al.. (2023). Convolutional neural network pruning based on misclassification cost. The Journal of Supercomputing. 79(18). 21185–21234. 3 indexed citations
6.
7.
Menhaj, Mohammad Bagher, et al.. (2022). Wearable Sensor-Based Human Activity Recognition in the Smart Healthcare System. Computational Intelligence and Neuroscience. 2022. 1–31. 73 indexed citations
8.
Nazemi, Eslam, et al.. (2021). Emergence-based self-advising in strong self-organizing systems: A case study in NASA ANTS mission. Expert Systems with Applications. 182. 115187–115187. 2 indexed citations
9.
Nazemi, Eslam, et al.. (2021). Entropy-based goal-oriented emergence management in self-organizing systems through feedback control loop: A case study in NASA ANTS mission. Reliability Engineering & System Safety. 210. 107506–107506. 2 indexed citations
10.
Masoumi, Behrooz, et al.. (2021). A Hybrid Framework for Personality Prediction based on Fuzzy Neural Networks and Deep Neural Networks. 9(3). 283–294. 2 indexed citations
11.
Masoumi, Behrooz, et al.. (2021). A New Random Forest Algorithm Based on Learning Automata. Computational Intelligence and Neuroscience. 2021(1). 5572781–5572781. 57 indexed citations
12.
Masoumi, Behrooz, et al.. (2020). MAMHOA: a multi-agent meta-heuristic optimization algorithm with an approach for document summarization issues. Journal of Ambient Intelligence and Humanized Computing. 11(11). 4967–4982. 7 indexed citations
13.
Meybodi, Mohammad Reza, et al.. (2020). Detecting community structure in signed and unsigned social networks by using weighted label propagation. Chaos An Interdisciplinary Journal of Nonlinear Science. 30(10). 103118–103118. 4 indexed citations
14.
Meybodi, Mohammad Reza, et al.. (2020). Chaotic memetic algorithm and its application for detecting community structure in complex networks. Chaos An Interdisciplinary Journal of Nonlinear Science. 30(1). 13125–13125. 9 indexed citations
15.
Masoumi, Behrooz, et al.. (2020). Solving Traveling Salesman Problem based on Biogeography-based Optimization and Edge Assembly Cross-over. SHILAP Revista de lepidopterología. 8(3). 313–329. 2 indexed citations
16.
Masoumi, Behrooz, et al.. (2019). Participative Biogeography-Based Optimization. SHILAP Revista de lepidopterología. 12(1). 79–91. 3 indexed citations
17.
Masoumi, Behrooz, et al.. (2017). Biogeography-Based Optimization Algorithm for Automatic Extractive Text Summarization. 28(1). 75–84. 3 indexed citations
18.
Bakhshi, Hamidreza, et al.. (2015). Combining Harmony search algorithm and Ant Colony Optimization algorithm to increase the lifetime of Wireless Sensor Networks. SHILAP Revista de lepidopterología. 1 indexed citations
19.
Masoumi, Behrooz, Mohammad Reza Meybodi, & Farnaz Abtahi. (2012). Learning Automata based Algorithms for Finding Optimal Policies in Fully Cooperative Markov Games. PRZEGLĄD ELEKTROTECHNICZNY. 280–289. 1 indexed citations
20.
Masoumi, Behrooz, et al.. (2011). A Honey Bee Algorithm To Solve Quadratic Assignment Problem. SHILAP Revista de lepidopterología. 4 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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