Khaled Bayoudh

674 total citations · 1 hit paper
6 papers, 408 citations indexed

About

Khaled Bayoudh is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Civil and Structural Engineering. According to data from OpenAlex, Khaled Bayoudh has authored 6 papers receiving a total of 408 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Computer Vision and Pattern Recognition, 2 papers in Artificial Intelligence and 1 paper in Civil and Structural Engineering. Recurrent topics in Khaled Bayoudh's work include Advanced Neural Network Applications (4 papers), Multimodal Machine Learning Applications (2 papers) and Domain Adaptation and Few-Shot Learning (2 papers). Khaled Bayoudh is often cited by papers focused on Advanced Neural Network Applications (4 papers), Multimodal Machine Learning Applications (2 papers) and Domain Adaptation and Few-Shot Learning (2 papers). Khaled Bayoudh collaborates with scholars based in Tunisia and France. Khaled Bayoudh's co-authors include Abdellatif Mtibaa and Fayçal Hamdaoui and has published in prestigious journals such as Information Fusion, Applied Intelligence and The Visual Computer.

In The Last Decade

Khaled Bayoudh

6 papers receiving 377 citations

Hit Papers

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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.

A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets

2021 238 citations Khaled Bayoudh, Fayçal Hamdaoui et al. The Visual Computer profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Khaled Bayoudh Tunisia	5	173	135	59	45	25	6	408
Fayçal Hamdaoui Tunisia	8	228 1.3×	146 1.1×	62 1.1×	57 1.3×	22 0.9×	25	448
Miguel A. Molina‐Cabello Spain	12	173 1.0×	136 1.0×	77 1.3×	43 1.0×	10 0.4×	45	370
R. R. Sedamkar India	6	167 1.0×	133 1.0×	76 1.3×	48 1.1×	34 1.4×	29	478
Saleh Albelwi Saudi Arabia	8	146 0.8×	152 1.1×	45 0.8×	27 0.6×	32 1.3×	15	413
Hussam Qassim United States	3	139 0.8×	100 0.7×	48 0.8×	41 0.9×	20 0.8×	4	348
Saurabh Maheshwari India	8	186 1.1×	49 0.4×	67 1.1×	77 1.7×	26 1.0×	26	392
Chang Wen China	11	218 1.3×	68 0.5×	35 0.6×	47 1.0×	65 2.6×	43	421
Iza Sazanita Isa Malaysia	13	205 1.2×	180 1.3×	96 1.6×	79 1.8×	20 0.8×	89	608

Countries citing papers authored by Khaled Bayoudh

Since Specialization

Citations

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

Fields of papers citing papers by Khaled Bayoudh

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Khaled Bayoudh

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

All Works

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6 of 6 papers shown

1.

Bayoudh, Khaled & Abdellatif Mtibaa. (2024). Hybrid-CT: a novel hybrid 2D/3D CNN-Transformer based on transfer learning and attention mechanisms for small object classification. Signal Image and Video Processing. 19(2). 2 indexed citations

2.

Bayoudh, Khaled. (2023). A survey of multimodal hybrid deep learning for computer vision: Architectures, applications, trends, and challenges. Information Fusion. 105. 102217–102217. 59 indexed citations

3.

Bayoudh, Khaled, Fayçal Hamdaoui, & Abdellatif Mtibaa. (2022). An Attention-based Hybrid 2D/3D CNN-LSTM for Human Action Recognition. 97–103. 7 indexed citations

4.

Bayoudh, Khaled, et al.. (2021). A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets. The Visual Computer. 38(8). 2939–2970. 238 indexed citations breakdown →

5.

Bayoudh, Khaled, Fayçal Hamdaoui, & Abdellatif Mtibaa. (2020). Hybrid-COVID: a novel hybrid 2D/3D CNN based on cross-domain adaptation approach for COVID-19 screening from chest X-ray images. Physical and Engineering Sciences in Medicine. 43(4). 1415–1431. 35 indexed citations

6.

Bayoudh, Khaled, Fayçal Hamdaoui, & Abdellatif Mtibaa. (2020). Transfer learning based hybrid 2D-3D CNN for traffic sign recognition and semantic road detection applied in advanced driver assistance systems. Applied Intelligence. 51(1). 124–142. 67 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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