Sabeur Aridhi

2.0k total citations · 1 hit paper
31 papers, 790 citations indexed

About

Sabeur Aridhi is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Computer Networks and Communications. According to data from OpenAlex, Sabeur Aridhi has authored 31 papers receiving a total of 790 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Computer Vision and Pattern Recognition, 10 papers in Artificial Intelligence and 8 papers in Computer Networks and Communications. Recurrent topics in Sabeur Aridhi's work include Graph Theory and Algorithms (9 papers), Complex Network Analysis Techniques (5 papers) and Cloud Computing and Resource Management (5 papers). Sabeur Aridhi is often cited by papers focused on Graph Theory and Algorithms (9 papers), Complex Network Analysis Techniques (5 papers) and Cloud Computing and Resource Management (5 papers). Sabeur Aridhi collaborates with scholars based in France, Austria and Tunisia. Sabeur Aridhi's co-authors include Engelbert Mephu Nguifo, Malika Smaïl‐Tabbone, Marie‐Dominique Devignes, Silvio Danese, Laurent Peyrin‐Biroulet, Catherine Le Berre, Laure Fournier, William J. Sandborn, Wajdi Dhifli and Hassina Seridi and has published in prestigious journals such as Gastroenterology, Scientific Reports and Expert Systems with Applications.

In The Last Decade

Sabeur Aridhi

27 papers receiving 753 citations

Hit Papers

align trajectories

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.

Application of Artificial Intelligence to Gastroenterology and Hepatology

2019 333 citations Catherine Le Berre, William J. Sandborn et al. Gastroenterology profile →

Peers

Sabeur Aridhi

IS

AI

ONCOL

RNMI

CNC

Eva Iglesias Spain

Xin Qi Japan

Rafael Vargas Spain

Hassaan Malik Pakistan

Jean‐Michel Bruel France

Gordon United Kingdom

Sajjad Waheed Bangladesh

Tayyaba Anees Pakistan

Yi Zhou China

Eva Iglesias Spain

Sabeur Aridhi

120 ×0.6

IS

276 ×1.4

AI

31 ×0.2

ONCOL

41 ×0.3

RNMI

63 ×0.5

CNC

Citations per year, relative to Sabeur Aridhi Sabeur Aridhi (= 1×) peers Eva Iglesias

Countries citing papers authored by Sabeur Aridhi

Since Specialization

Citations

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

Fields of papers citing papers by Sabeur Aridhi

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sabeur Aridhi

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Islam, Kamrul, et al.. (2024). DKGR: A Distributed Framework for Geometric Knowledge Graph Embedding with Ray. Procedia Computer Science. 246. 900–909. 1 indexed citations

2.

Farah, Imed Riadh, et al.. (2024). Knowledge graph representation learning: A comprehensive and experimental overview. Computer Science Review. 56. 100716–100716.

3.

Karabadji, Nour El Islem, et al.. (2023). Accuracy and diversity-aware multi-objective approach for random forest construction. Expert Systems with Applications. 225. 120138–120138. 60 indexed citations

4.

Islam, Kamrul, et al.. (2023). Molecular-evaluated and explainable drug repurposing for COVID-19 using ensemble knowledge graph embedding. Scientific Reports. 13(1). 3643–3643. 10 indexed citations

5.

Aridhi, Sabeur, et al.. (2022). A distributed and incremental algorithm for large-scale graph clustering. Future Generation Computer Systems. 134. 334–347. 5 indexed citations

6.

Devignes, Marie‐Dominique, et al.. (2022). Improving automatic GO annotation with semantic similarity. BMC Bioinformatics. 23(S2). 433–433. 7 indexed citations

7.

Aridhi, Sabeur, João P. P. Gomes, José Antônio Fernandes de Macêdo, et al.. (2021). On the design of a similarity function for sparse binary data with application on protein function annotation. Knowledge-Based Systems. 238. 107863–107863. 1 indexed citations

8.

Ritchie, David W., et al.. (2020). GrAPFI: predicting enzymatic function of proteins from domain similarity graphs. BMC Bioinformatics. 21(1). 168–168. 7 indexed citations

9.

Aridhi, Sabeur, et al.. (2019). Multiple instance learning for sequence data with across bag dependencies. International Journal of Machine Learning and Cybernetics. 11(3). 629–642. 2 indexed citations

10.

Berre, Catherine Le, William J. Sandborn, Sabeur Aridhi, et al.. (2019). Application of Artificial Intelligence to Gastroenterology and Hepatology. Gastroenterology. 158(1). 76–94.e2. 333 indexed citations breakdown →

11.

Karabadji, Nour El Islem, et al.. (2019). A data sampling and attribute selection strategy for improving decision tree construction. Expert Systems with Applications. 129. 84–96. 16 indexed citations

12.

Hadjali, Allel, Haithem Mezni, Sabeur Aridhi, & Andrei Tchernykh. (2019). Special issue on “Uncertainty in Cloud Computing: Concepts, Challenges and Current Solutions”. International Journal of Approximate Reasoning. 111. 53–55. 1 indexed citations

13.

Aridhi, Sabeur, et al.. (2018). An experimental survey on big data frameworks. Future Generation Computer Systems. 86. 546–564. 86 indexed citations

14.

Mezni, Haithem, Sabeur Aridhi, & Allel Hadjali. (2018). The uncertain cloud: State of the art and research challenges. International Journal of Approximate Reasoning. 103. 139–151. 18 indexed citations

15.

Dhifli, Wajdi, Sabeur Aridhi, & Engelbert Mephu Nguifo. (2017). MR-SimLab: Scalable subgraph selection with label similarity for big data. Information Systems. 69. 155–163. 18 indexed citations

16.

Aridhi, Sabeur & Engelbert Mephu Nguifo. (2016). Big Graph Mining: Frameworks and Techniques. Big Data Research. 6. 1–10. 33 indexed citations

17.

Aridhi, Sabeur, et al.. (2015). A MapReduce-based approach for shortest path problem in large-scale networks. Engineering Applications of Artificial Intelligence. 41. 151–165. 28 indexed citations

18.

Aridhi, Sabeur, et al.. (2015). Prediction of Ionizing Radiation Resistance in Bacteria Using a Multiple Instance Learning Model. Journal of Computational Biology. 23(1). 10–20. 3 indexed citations

19.

Aridhi, Sabeur, Laurent d’Orazio, Mondher Maddouri, & Engelbert Mephu Nguifo. (2014). Un partitionnement basé sur la densité de graphe pour approcher la fouille distribuée de sous-graphes fréquents. HAL (Le Centre pour la Communication Scientifique Directe). 1 indexed citations

20.

Aridhi, Sabeur, et al.. (2014). Un partitionnement basé sur la densité de graphe pour approcher la fouille distribuée de sous-graphes fréquents. 33(9-10). 727–731. 1 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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