Ahoud Alhazmi

820 total citations · 1 hit paper
8 papers, 293 citations indexed

About

Ahoud Alhazmi is a scholar working on Artificial Intelligence, Computer Networks and Communications and Computer Vision and Pattern Recognition. According to data from OpenAlex, Ahoud Alhazmi has authored 8 papers receiving a total of 293 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Artificial Intelligence, 2 papers in Computer Networks and Communications and 2 papers in Computer Vision and Pattern Recognition. Recurrent topics in Ahoud Alhazmi's work include Adversarial Robustness in Machine Learning (5 papers), Anomaly Detection Techniques and Applications (2 papers) and Topic Modeling (2 papers). Ahoud Alhazmi is often cited by papers focused on Adversarial Robustness in Machine Learning (5 papers), Anomaly Detection Techniques and Applications (2 papers) and Topic Modeling (2 papers). Ahoud Alhazmi collaborates with scholars based in Australia, Saudi Arabia and China. Ahoud Alhazmi's co-authors include Quan Z. Sheng, Wei Emma Zhang, Chenliang Li, Congbo Ma, Adnan Mahmood and Subhash Sagar and has published in prestigious journals such as ACM Transactions on Intelligent Systems and Technology, Adelaide Research & Scholarship (AR&S) (University of Adelaide) and arXiv (Cornell University).

In The Last Decade

Ahoud Alhazmi

6 papers receiving 284 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.

Adversarial Attacks on Deep-learning Models in Natural Language Processing

2020 240 citations Wei Emma Zhang, Quan Z. Sheng et al. ACM Transactions on Intelligent Systems and Technology profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Ahoud Alhazmi Australia	4	248	68	48	45	42	8	293
Eli Lifland United States	4	326 1.3×	83 1.2×	48 1.0×	34 0.8×	38 0.9×	4	374
Jiazhu Dai China	7	267 1.1×	86 1.3×	62 1.3×	75 1.7×	20 0.5×	17	304
Andrea Paudice Italy	4	276 1.1×	89 1.3×	47 1.0×	95 2.1×	31 0.7×	5	336
Ali Shahin Shamsabadi United Kingdom	9	347 1.4×	35 0.5×	42 0.9×	31 0.7×	82 2.0×	17	401
Arjun Nitin Bhagoji United States	7	218 0.9×	54 0.8×	15 0.3×	52 1.2×	29 0.7×	18	249
Roei Schuster Israel	7	234 0.9×	93 1.4×	78 1.6×	86 1.9×	50 1.2×	10	301
Da Luo China	8	211 0.9×	25 0.4×	122 2.5×	31 0.7×	46 1.1×	12	307
Aleksandra Mileva North Macedonia	8	136 0.5×	67 1.0×	43 0.9×	107 2.4×	88 2.1×	32	256
Tavish Vaidya United States	7	240 1.0×	165 2.4×	114 2.4×	77 1.7×	43 1.0×	12	334
Chengfang Fang Singapore	8	163 0.7×	32 0.5×	22 0.5×	44 1.0×	23 0.5×	17	210

Countries citing papers authored by Ahoud Alhazmi

Since Specialization

Citations

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

Fields of papers citing papers by Ahoud Alhazmi

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ahoud Alhazmi

This figure shows the co-authorship network connecting the top 25 collaborators of Ahoud Alhazmi. A scholar is included among the top collaborators of Ahoud Alhazmi 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 Ahoud Alhazmi. Ahoud Alhazmi 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:

8 of 8 papers shown

1.

Alhazmi, Ahoud, et al.. (2025). Can Interpretability of Deep Learning Models Detect Textual Adversarial Distribution?. ACM Transactions on Intelligent Systems and Technology. 16(4). 1–24.

2.

Sheng, Quan Z., et al.. (2024). Distractor Generation in Multiple-Choice Tasks: A Survey of Methods, Datasets, and Evaluation. 14437–14458. 4 indexed citations

3.

Alhazmi, Ahoud, et al.. (2021). Memory Management via Ownership Concept Rust and Swift: Experimental Study. International Journal of Computer Applications. 183(22). 1–10.

4.

Zhang, Wei Emma, Quan Z. Sheng, Adnan Mahmood, et al.. (2020). The 10 Research Topics in the Internet of Things. Adelaide Research & Scholarship (AR&S) (University of Adelaide). 34–43. 32 indexed citations

5.

Alhazmi, Ahoud, et al.. (2020). Analyzing the Sensitivity of Deep Neural Networks for Sentiment Analysis: A Scoring Approach. Adelaide Research & Scholarship (AR&S) (University of Adelaide). 1–7. 1 indexed citations

6.

Zhang, Wei Emma, Quan Z. Sheng, Ahoud Alhazmi, & Chenliang Li. (2020). Adversarial Attacks on Deep-learning Models in Natural Language Processing. ACM Transactions on Intelligent Systems and Technology. 11(3). 1–41. 240 indexed citations breakdown →

7.

Alhazmi, Ahoud, et al.. (2020). Are Modern Deep Learning Models for Sentiment Analysis Brittleƒ An Examination on Part-of-Speech. Adelaide Research & Scholarship (AR&S) (University of Adelaide). 1–7. 1 indexed citations

8.

Zhang, Wei Emma, Quan Z. Sheng, & Ahoud Alhazmi. (2019). Generating Textual Adversarial Examples for Deep Learning Models: A Survey.. arXiv (Cornell University). 15 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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