Atef Masmoudi

560 total citations · 1 hit paper
44 papers, 397 citations indexed

About

Atef Masmoudi is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Signal Processing. According to data from OpenAlex, Atef Masmoudi has authored 44 papers receiving a total of 397 indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Computer Vision and Pattern Recognition, 15 papers in Artificial Intelligence and 10 papers in Signal Processing. Recurrent topics in Atef Masmoudi's work include Advanced Data Compression Techniques (13 papers), Algorithms and Data Compression (11 papers) and Chaos-based Image/Signal Encryption (10 papers). Atef Masmoudi is often cited by papers focused on Advanced Data Compression Techniques (13 papers), Algorithms and Data Compression (11 papers) and Chaos-based Image/Signal Encryption (10 papers). Atef Masmoudi collaborates with scholars based in Tunisia, Saudi Arabia and Pakistan. Atef Masmoudi's co-authors include William Puech, Afif Masmoudi, Farman Ali, Raed Alsini, Med Salim Bouhlel, Nouri Masmoudi, Wajdi Alghamdi, Aamir Hamid, Abdullah Almuhaimeed and Haibo Chen and has published in prestigious journals such as Scientific Reports, IEEE Access and International Journal of Biological Macromolecules.

In The Last Decade

Atef Masmoudi

41 papers receiving 378 citations

Hit Papers

Predicting the thermal distribution in a convective wavy ... 2024 2026 2025 2024 10 20 30 40 50
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Predicting the thermal distribution in a convective wavy fin using a novel training physics-informed neural network method
    2024 54 citations Atef Masmoudi et al. profile →

Peers

Atef Masmoudi
Przemysław Spurek Poland
P. S. Hiremath India
Chang‐Kyu Song South Korea
Sergey Bereg United States
He Yin China
Przemysław Spurek Poland
Atef Masmoudi
Citations per year, relative to Atef Masmoudi Atef Masmoudi (= 1×) peers Przemysław Spurek

Countries citing papers authored by Atef Masmoudi

Since Specialization
Citations

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

Fields of papers citing papers by Atef Masmoudi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Atef Masmoudi

This figure shows the co-authorship network connecting the top 25 collaborators of Atef Masmoudi. A scholar is included among the top collaborators of Atef Masmoudi 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 Atef Masmoudi. Atef Masmoudi 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.
2.
Ali, Farman, et al.. (2025). Advancing neurological disease treatment: a computational approach for fibroblast growth factor detection. Biomedical Engineering Letters. 16(1). 167–176.
3.
Ali, Farman, et al.. (2025). IR-MBiTCN: Computational prediction of insulin receptor using deep learning: A multi-information fusion approach with multiscale bidirectional temporal convolutional network. International Journal of Biological Macromolecules. 311(Pt 2). 143844–143844. 4 indexed citations
4.
Khan, Atif, et al.. (2025). Global and Local Context Fusion in Heterogeneous Graph Neural Network for Summarizing Lengthy Scientific Documents. IEEE Access. 13. 53433–53447. 1 indexed citations
5.
Ali, Farman, et al.. (2025). Leveraging deep learning for epigenetic protein prediction: a novel approach for early lung cancer diagnosis and drug discovery. Health Information Science and Systems. 13(1). 28–28. 2 indexed citations
6.
Ali, Farman, et al.. (2024). IP-GCN: A deep learning model for prediction of insulin using graph convolutional network for diabetes drug design. Journal of Computational Science. 81. 102388–102388. 11 indexed citations
7.
Ali, Farman, et al.. (2024). VEGF-ERCNN: A deep learning-based model for prediction of vascular endothelial growth factor using ensemble residual CNN. Journal of Computational Science. 83. 102448–102448. 13 indexed citations
8.
Rehman, M. Israr Ur, Haibo Chen, Muhammad Imran Khan, Aamir Hamid, & Atef Masmoudi. (2024). Modeling and predicting heat transfer performance in bioconvection flow around a circular cylinder using an artificial neural network approach. Tribology International. 200. 110182–110182. 35 indexed citations
9.
10.
Alsini, Raed, et al.. (2024). Deep-VEGF: deep stacked ensemble model for prediction of vascular endothelial growth factor by concatenating gated recurrent unit with two-dimensional convolutional neural network. Journal of Biomolecular Structure and Dynamics. 43(16). 8893–8903. 15 indexed citations
11.
Ali, Zulfiqar, et al.. (2024). Assessing the generalization of forecasting ability of machine learning and probabilistic models for complex climate characteristics. Stochastic Environmental Research and Risk Assessment. 38(8). 2927–2947. 10 indexed citations
12.
Ali, Farman, et al.. (2024). An omics-driven computational model for angiogenic protein prediction: Advancing therapeutic strategies with Ens-deep-AGP. International Journal of Biological Macromolecules. 282(Pt 1). 136475–136475. 13 indexed citations
13.
Masmoudi, Atef, et al.. (2023). Dictionary-based histogram packing technique for lossless image compression. Journal of Visual Communication and Image Representation. 95. 103894–103894. 1 indexed citations
14.
Algarni, Abdulmohsen, et al.. (2023). Early prediction of Student academic performance based on Machine Learning algorithms: A case study of bachelor’s degree students in KSA. Education and Information Technologies. 29(11). 13247–13270. 8 indexed citations
15.
Elaoud, Anis, et al.. (2023). Machine learning approach for predicting soil penetration resistance under different moisture conditions. Journal of Terramechanics. 110. 39–45. 4 indexed citations
16.
Khan, Muhammad Naveed, et al.. (2023). Computational analysis of magnetized bio-convective partially ionized flow of second-order fluid on a bidirectional porous stretching sheet with Cattaneo–Christov theory. Journal of Computational Design and Engineering. 11(1). 247–260. 10 indexed citations
17.
Masmoudi, Atef, et al.. (2012). A New Method Which Combines Arithmetic Coding with RLE for Lossless Image Compression. Journal of Software Engineering and Applications. 5(1). 41–44. 12 indexed citations
18.
Masmoudi, Atef, William Puech, & Med Salim Bouhlel. (2010). An Efficient PRBG Based on Chaotic Map and Engel Continued Fractions. Journal of Software Engineering and Applications. 3(12). 1141–1147. 4 indexed citations
19.
Masmoudi, Atef, Med Salim Bouhlel, & William Puech. (2010). A new image cryptosystem based on chaotic map and continued fractions. HAL (Le Centre pour la Communication Scientifique Directe). 1504–1508. 7 indexed citations
20.
Masmoudi, Atef, et al.. (2003). Extension of the variance function of a steep exponential family. Journal of Multivariate Analysis. 92(2). 239–256. 3 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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