Ahmed Hussein

1.1k total citations · 1 hit paper
4 papers, 618 citations indexed

About

Ahmed Hussein is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Control and Systems Engineering. According to data from OpenAlex, Ahmed Hussein has authored 4 papers receiving a total of 618 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Artificial Intelligence, 2 papers in Computer Vision and Pattern Recognition and 1 paper in Control and Systems Engineering. Recurrent topics in Ahmed Hussein's work include Reinforcement Learning in Robotics (3 papers), Multimodal Machine Learning Applications (2 papers) and Robot Manipulation and Learning (1 paper). Ahmed Hussein is often cited by papers focused on Reinforcement Learning in Robotics (3 papers), Multimodal Machine Learning Applications (2 papers) and Robot Manipulation and Learning (1 paper). Ahmed Hussein collaborates with scholars based in United Kingdom. Ahmed Hussein's co-authors include Eyad Elyan, Mohamed Medhat Gaber, Chrisina Jayne and Eugene Santos and has published in prestigious journals such as ACM Computing Surveys, Neural Computing and Applications and BCU Open Access Repository (Birmingham City University).

In The Last Decade

Ahmed Hussein

4 papers receiving 591 citations

Hit Papers

Imitation Learning 2017 2026 2020 2023 2017 100 200 300 400 500

Peers

Ahmed Hussein
Xu Xie China
Ahmed Hussein
Citations per year, relative to Ahmed Hussein Ahmed Hussein (= 1×) peers Xu Xie

Countries citing papers authored by Ahmed Hussein

Since Specialization
Citations

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

Fields of papers citing papers by Ahmed Hussein

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ahmed Hussein

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

All Works

4 of 4 papers shown
1.
Hussein, Ahmed, Eyad Elyan, Mohamed Medhat Gaber, & Chrisina Jayne. (2017). Deep imitation learning for 3D navigation tasks. Neural Computing and Applications. 29(7). 389–404. 30 indexed citations
2.
Hussein, Ahmed, Mohamed Medhat Gaber, Eyad Elyan, & Chrisina Jayne. (2017). Imitation Learning. ACM Computing Surveys. 50(2). 1–35. 572 indexed citations breakdown →
3.
Hussein, Ahmed, Eyad Elyan, Mohamed Medhat Gaber, & Chrisina Jayne. (2017). Deep reward shaping from demonstrations. BCU Open Access Repository (Birmingham City University). 510–517. 14 indexed citations
4.
Santos, Eugene & Ahmed Hussein. (2004). Comparing Case-Based Bayesian Network and Recursive Bayesian Multi-net Classifiers. 2 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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