Rula A. Hamid

2.1k total citations · 1 hit paper
33 papers, 1.4k citations indexed

About

Rula A. Hamid is a scholar working on Artificial Intelligence, Cognitive Neuroscience and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Rula A. Hamid has authored 33 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Artificial Intelligence, 10 papers in Cognitive Neuroscience and 8 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Rula A. Hamid's work include COVID-19 diagnosis using AI (7 papers), Autism Spectrum Disorder Research (7 papers) and Multi-Criteria Decision Making (4 papers). Rula A. Hamid is often cited by papers focused on COVID-19 diagnosis using AI (7 papers), Autism Spectrum Disorder Research (7 papers) and Multi-Criteria Decision Making (4 papers). Rula A. Hamid collaborates with scholars based in Iraq, Malaysia and Jordan. Rula A. Hamid's co-authors include A. S. Albahri, O. S. Albahri, A. A. Zaidan, A. H. Alamoodi, B. B. Zaidan, Jwan K. Alwan, M. A. Alsalem, Z.T. Al-Qaysi, K. I. Mohammed and Jameel R. Al‐Obaidi and has published in prestigious journals such as SHILAP Revista de lepidopterología, Information Fusion and Neural Computing and Applications.

In The Last Decade

Rula A. Hamid

31 papers receiving 1.4k 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.

Telehealth utilization during the Covid-19 pandemic: A systematic review

2021 242 citations A. H. Alamoodi, B. B. Zaidan et al. Computers in Biology and Medicine profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Rula A. Hamid Iraq	19	329	245	205	183	159	33	1.4k
Mahmood M. Salih Iraq	19	236 0.7×	107 0.4×	183 0.9×	376 2.1×	134 0.8×	42	1.6k
K. I. Mohammed Malaysia	22	519 1.6×	279 1.1×	116 0.6×	252 1.4×	171 1.1×	23	2.1k
Lalit Garg Malta	22	321 1.0×	98 0.4×	190 0.9×	115 0.6×	90 0.6×	124	1.8k
Leila Shahmoradi Iran	23	511 1.6×	171 0.7×	140 0.7×	68 0.4×	124 0.8×	122	2.1k
Z.T. Al-Qaysi Iraq	14	193 0.6×	163 0.7×	236 1.2×	50 0.3×	123 0.8×	34	1.0k
Tianshu Zhou China	20	280 0.9×	144 0.6×	173 0.8×	39 0.2×	69 0.4×	93	1.2k
Jwan K. Alwan Iraq	9	223 0.7×	162 0.7×	49 0.2×	34 0.2×	143 0.9×	12	912
Vicente Traver Spain	23	256 0.8×	58 0.2×	55 0.3×	133 0.7×	174 1.1×	90	1.6k
Mohammad Monirujjaman Khan Bangladesh	22	388 1.2×	231 0.9×	64 0.3×	56 0.3×	45 0.3×	241	2.1k
Mark Hoogendoorn Netherlands	19	828 2.5×	114 0.5×	48 0.2×	82 0.4×	71 0.4×	138	1.9k

Countries citing papers authored by Rula A. Hamid

Since Specialization

Citations

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

Fields of papers citing papers by Rula A. Hamid

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Rula A. Hamid. 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 Rula A. Hamid. The network helps show where Rula A. Hamid may publish in the future.

Co-authorship network of co-authors of Rula A. Hamid

This figure shows the co-authorship network connecting the top 25 collaborators of Rula A. Hamid. A scholar is included among the top collaborators of Rula A. Hamid 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 Rula A. Hamid. Rula A. Hamid 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

Hamid, Rula A., A. S. Albahri, A. H. Alamoodi, et al.. (2025). Fuzzy Decision‐Making Framework for Evaluating Hybrid Detection Models of Trauma Patients. Expert Systems. 42(3). 2 indexed citations

Albahri, A. S., Rula A. Hamid, Z.T. Al-Qaysi, et al.. (2025). Trust and explainability in robotic hand control via adversarial multiple machine learning models with EEG sensor data fusion: A fuzzy decision-making solution. Computers in Biology and Medicine. 196(Pt C). 110922–110922.

Albahri, A. S., Mohammed S. Al-Samarraay, O. S. Albahri, et al.. (2025). Evaluation and Benchmarking Trauma Patients in Intensive Care Units Using a Novel Decision-Making Framework. International Journal of Information Technology & Decision Making. 24(7). 2005–2041. 1 indexed citations

Hamid, Rula A., et al.. (2024). A Framework for Automated Big Data Analytics in Cybersecurity Threat Detection. 2024. 175–184. 1 indexed citations

Al-Qaysi, Z.T., A. S. Albahri, Rula A. Hamid, et al.. (2024). A comprehensive review of deep learning power in steady-state visual evoked potentials. Neural Computing and Applications. 36(27). 16683–16706. 7 indexed citations

Albahri, A. S., Laith Alzubaidi, Rula A. Hamid, et al.. (2024). A Trustworthy and Explainable Framework for Benchmarking Hybrid Deep Learning Models Based on Chest X-Ray Analysis in CAD Systems. International Journal of Information Technology & Decision Making. 24(8). 2533–2585. 20 indexed citations

Albahri, A. S., et al.. (2024). Fuzzy Evaluation and Benchmarking Framework for Robust Machine Learning Model in Real-Time Autism Triage Applications. International Journal of Computational Intelligence Systems. 17(1). 8 indexed citations

Albahri, A. S., et al.. (2024). Evaluation and benchmarking of hybrid machine learning models for autism spectrum disorder diagnosis using a 2-tuple linguistic neutrosophic fuzzy sets-based decision-making model. Neural Computing and Applications. 36(29). 18161–18200. 6 indexed citations

Albahri, A. S., et al.. (2023). Artificial intelligence-based approaches for improving the diagnosis, triage, and prioritization of autism spectrum disorder: a systematic review of current trends and open issues. Artificial Intelligence Review. 56(S1). 53–117. 26 indexed citations

10.

Albahri, A. S., Rula A. Hamid, O. S. Albahri, et al.. (2023). Explainable Artificial Intelligence Multimodal of Autism Triage Levels Using Fuzzy Approach-Based Multi-criteria Decision-Making and LIME. International Journal of Fuzzy Systems. 26(1). 274–303. 17 indexed citations

11.

Albahri, A. S., et al.. (2022). Intelligent triage method for early diagnosis autism spectrum disorder (ASD) based on integrated fuzzy multi-criteria decision-making methods. Informatics in Medicine Unlocked. 36. 101131–101131. 25 indexed citations

12.

Albahri, A. S., A. A. Zaidan, Hassan A. Alsattar, et al.. (2022). Towards physician's experience: Development of machine learning model for the diagnosis of autism spectrum disorders based on complex T‐spherical fuzzy‐weighted zero‐inconsistency method. Computational Intelligence. 39(2). 225–257. 35 indexed citations

13.

Albahri, A. S., et al.. (2022). Triage and priority-based healthcare diagnosis using artificial intelligence for autism spectrum disorder and gene contribution: A systematic review. Computers in Biology and Medicine. 146. 105553–105553. 38 indexed citations

14.

Albahri, A. S., et al.. (2022). Hybrid Diagnosis Models for Autism Patients Based on Medical and Sociodemographic Features Using Machine Learning and Multicriteria Decision-Making (MCDM) Techniques: An Evaluation and Benchmarking Framework. Computational and Mathematical Methods in Medicine. 2022. 1–19. 26 indexed citations

15.

Albahri, A. S., et al.. (2022). Diagnosis-Based Hybridization of Multimedical Tests and Sociodemographic Characteristics of Autism Spectrum Disorder Using Artificial Intelligence and Machine Learning Techniques: A Systematic Review. International Journal of Telemedicine and Applications. 2022. 1–26. 32 indexed citations

16.

Hamid, Rula A., A. S. Albahri, O. S. Albahri, & A. A. Zaidan. (2021). Dempster–Shafer theory for classification and hybridised models of multi-criteria decision analysis for prioritisation: a telemedicine framework for patients with heart diseases. Journal of Ambient Intelligence and Humanized Computing. 13(9). 4333–4367. 36 indexed citations

17.

Mohammed, R. T., Alhamzah Alnoor, O. S. Albahri, et al.. (2021). Interval type 2 trapezoidal‐fuzzy weighted with zero inconsistency combined with VIKOR for evaluating smart e‐tourism applications. International Journal of Intelligent Systems. 36(9). 4723–4774. 80 indexed citations

18.

Albahri, A. S., Jwan K. Alwan, Zahraa K. Taha, et al.. (2020). IoT-based telemedicine for disease prevention and health promotion: State-of-the-Art. Journal of Network and Computer Applications. 173. 102873–102873. 182 indexed citations

19.

Albahri, A. S., Rula A. Hamid, O. S. Albahri, & A. A. Zaidan. (2020). Detection-based prioritisation: Framework of multi-laboratory characteristics for asymptomatic COVID-19 carriers based on integrated Entropy–TOPSIS methods. Artificial Intelligence in Medicine. 111. 101983–101983. 86 indexed citations

20.

Albahri, A. S., Rula A. Hamid, Jwan K. Alwan, et al.. (2020). Role of biological Data Mining and Machine Learning Techniques in Detecting and Diagnosing the Novel Coronavirus (COVID-19): A Systematic Review. Journal of Medical Systems. 44(7). 122–122. 201 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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