Gaith Rjoub

1.4k total citations · 1 hit paper
23 papers, 752 citations indexed

About

Gaith Rjoub is a scholar working on Artificial Intelligence, Information Systems and Computer Networks and Communications. According to data from OpenAlex, Gaith Rjoub has authored 23 papers receiving a total of 752 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Artificial Intelligence, 10 papers in Information Systems and 8 papers in Computer Networks and Communications. Recurrent topics in Gaith Rjoub's work include Privacy-Preserving Technologies in Data (8 papers), IoT and Edge/Fog Computing (6 papers) and Blockchain Technology Applications and Security (6 papers). Gaith Rjoub is often cited by papers focused on Privacy-Preserving Technologies in Data (8 papers), IoT and Edge/Fog Computing (6 papers) and Blockchain Technology Applications and Security (6 papers). Gaith Rjoub collaborates with scholars based in Canada, United Arab Emirates and Jordan. Gaith Rjoub's co-authors include Jamal Bentahar, Omar Abdel Wahab, Ahmed Saleh Bataineh, Robin Cohen, Nagat Drawel, Saidul Islam, Witold Pedrycz, Hanae Elmekki, Hadi Otrok and Azzam Mourad and has published in prestigious journals such as Expert Systems with Applications, Information Sciences and Knowledge-Based Systems.

In The Last Decade

Gaith Rjoub

23 papers receiving 727 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.

A comprehensive survey on applications of transformers for deep learning tasks

2023 187 citations Saidul Islam, Hanae Elmekki et al. Expert Systems with Applications profile →

Peers

Gaith Rjoub

CNC

EEE

CVPR

K. Umamaheswari India

Yuxi Fan China

Munesh Chandra Trivedi India

Raneem Qaddoura Jordan

Subrota Kumar Mondal Macao

Samih M. Mostafa Egypt

Hui Lu China

John Panneerselvam United Kingdom

Yang Ji China

Caixia Yang China

K. Umamaheswari India

Gaith Rjoub

233 ×0.6

426 ×1.6

CNC

324 ×1.2

125 ×1.7

EEE

59 ×1.0

CVPR

Citations per year, relative to Gaith Rjoub Gaith Rjoub (= 1×) peers K. Umamaheswari

Countries citing papers authored by Gaith Rjoub

Since Specialization

Citations

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

Fields of papers citing papers by Gaith Rjoub

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Gaith Rjoub

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

All Works

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20 of 20 papers shown

Islam, Saidul, Jamal Bentahar, Robin Cohen, & Gaith Rjoub. (2025). A multi-modal unsupervised machine learning approach for biomedical signal processing during cardiopulmonary resuscitation. Information Sciences. 712. 122114–122114. 2 indexed citations

Rjoub, Gaith, et al.. (2025). A hybrid swarm intelligence approach for optimizing Multimodal Large Language Models deployment in edge-cloud-based Federated Learning environments. Computer Communications. 237. 108152–108152. 4 indexed citations

Rjoub, Gaith, Hanae Elmekki, Jamal Bentahar, et al.. (2025). Enhanced Dynamic Deep Q-Network for Federated Learning scheduling policies on IoT devices using explanation-driven trust. Knowledge-Based Systems. 318. 113574–113574. 2 indexed citations

Makhadmeh, Sharif Naser, Sofian Kassaymeh, Gaith Rjoub, et al.. (2025). Recent advances in multi-objective whale optimization algorithm, its versions and applications. Journal of King Saud University - Computer and Information Sciences. 37(7). 2 indexed citations

Rjoub, Gaith, et al.. (2025). Machine learning innovations in CPR: a comprehensive survey on enhanced resuscitation techniques. Artificial Intelligence Review. 58(8). 233–233. 4 indexed citations

Rjoub, Gaith, et al.. (2024). Enhancing IoT Intelligence: A Transformer-based Reinforcement Learning Methodology. 1418–1423. 2 indexed citations

Alkhdour, Tayseer, et al.. (2024). Detecting DDoS attacks using machine learning algorithms and feature selection methods. International Journal of Data and Network Science. 8(4). 2307–2318. 3 indexed citations

Islam, Saidul, Hanae Elmekki, Jamal Bentahar, et al.. (2023). A comprehensive survey on applications of transformers for deep learning tasks. Expert Systems with Applications. 241. 122666–122666. 187 indexed citations breakdown →

Rjoub, Gaith, Jamal Bentahar, & Omar Abdel Wahab. (2023). Explainable Trust-aware Selection of Autonomous Vehicles Using LIME for One-Shot Federated Learning. PolyPublie (École Polytechnique de Montréal). 524–529. 5 indexed citations

10.

Rjoub, Gaith, Jamal Bentahar, Omar Abdel Wahab, et al.. (2023). A Survey on Explainable Artificial Intelligence for Cybersecurity. IEEE Transactions on Network and Service Management. 20(4). 5115–5140. 50 indexed citations

11.

Rjoub, Gaith, Omar Abdel Wahab, Jamal Bentahar, Robin Cohen, & Ahmed Saleh Bataineh. (2022). Trust-Augmented Deep Reinforcement Learning for Federated Learning Client Selection. Information Systems Frontiers. 26(4). 1261–1278. 44 indexed citations

12.

Drawel, Nagat, et al.. (2022). Formal verification of group and propagated trust in multi-agent systems. Autonomous Agents and Multi-Agent Systems. 36(1). 19 indexed citations

13.

Rjoub, Gaith, Omar Abdel Wahab, Jamal Bentahar, & Ahmed Saleh Bataineh. (2022). Trust-driven reinforcement selection strategy for federated learning on IoT devices. Computing. 106(4). 1273–1295. 62 indexed citations

14.

Wahab, Omar Abdel, Gaith Rjoub, Jamal Bentahar, & Robin Cohen. (2022). Federated against the cold: A trust-based federated learning approach to counter the cold start problem in recommendation systems. Information Sciences. 601. 189–206. 74 indexed citations

15.

Rjoub, Gaith, Jamal Bentahar, & Omar Abdel Wahab. (2022). Explainable AI-based Federated Deep Reinforcement Learning for Trusted Autonomous Driving. 2022 International Wireless Communications and Mobile Computing (IWCMC). 318–323. 22 indexed citations

16.

Bataineh, Ahmed Saleh, Jamal Bentahar, Omar Abdel Wahab, Rabeb Mizouni, & Gaith Rjoub. (2021). Cloud as platform for monetizing complementary data for AI-driven services: A two-sided cooperative game. 443–449. 2 indexed citations

17.

Rjoub, Gaith, Jamal Bentahar, Omar Abdel Wahab, & Ahmed Saleh Bataineh. (2020). Deep and reinforcement learning for automated task scheduling in large‐scale cloud computing systems. Concurrency and Computation Practice and Experience. 33(23). 91 indexed citations

18.

Drawel, Nagat, et al.. (2020). Formalizing Group and Propagated Trust in Multi-Agent Systems. 60–66. 12 indexed citations

19.

Wahab, Omar Abdel, Robin Cohen, Jamal Bentahar, et al.. (2020). An endorsement-based trust bootstrapping approach for newcomer cloud services. Information Sciences. 527. 159–175. 36 indexed citations

20.

Rjoub, Gaith, Jamal Bentahar, & Omar Abdel Wahab. (2019). BigTrustScheduling: Trust-aware big data task scheduling approach in cloud computing environments. Future Generation Computer Systems. 110. 1079–1097. 69 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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