Abdulaziz Alhumam

943 total citations
31 papers, 561 citations indexed

About

Abdulaziz Alhumam is a scholar working on Artificial Intelligence, Computer Networks and Communications and Information Systems. According to data from OpenAlex, Abdulaziz Alhumam has authored 31 papers receiving a total of 561 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Artificial Intelligence, 10 papers in Computer Networks and Communications and 9 papers in Information Systems. Recurrent topics in Abdulaziz Alhumam's work include COVID-19 diagnosis using AI (7 papers), IoT and Edge/Fog Computing (6 papers) and Software Engineering Research (5 papers). Abdulaziz Alhumam is often cited by papers focused on COVID-19 diagnosis using AI (7 papers), IoT and Edge/Fog Computing (6 papers) and Software Engineering Research (5 papers). Abdulaziz Alhumam collaborates with scholars based in Saudi Arabia, United States and Malaysia. Abdulaziz Alhumam's co-authors include N. Z. Jhanjhi, Zahrah A. Almusaylim, Shakeel Ahmed, Hafiz Farooq Ahmad, Parvathaneni Naga Srinivasu, Hamid Mukhtar, Mohamed E. Seliaman, Raihan Ur Rasool, Pratiyush Guleria and Zahid Anwar and has published in prestigious journals such as Scientific Reports, IEEE Access and Sensors.

In The Last Decade

Abdulaziz Alhumam

29 papers receiving 530 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Abdulaziz Alhumam Saudi Arabia	13	199	167	76	69	68	31	561
G. Narsimha India	12	232 1.2×	213 1.3×	34 0.4×	20 0.3×	121 1.8×	69	531
Rojalina Priyadarshini India	15	250 1.3×	264 1.6×	28 0.4×	28 0.4×	143 2.1×	79	627
Syed Atif Moqurrab Pakistan	13	164 0.8×	236 1.4×	20 0.3×	37 0.5×	149 2.2×	34	491
Vijayakumar Ponnusamy India	13	179 0.9×	163 1.0×	59 0.8×	60 0.9×	38 0.6×	88	631
Abhishek Raghuvanshi India	6	97 0.5×	118 0.7×	87 1.1×	41 0.6×	54 0.8×	19	383
Abdullah M. Baqasah Saudi Arabia	11	107 0.5×	106 0.6×	30 0.4×	32 0.5×	80 1.2×	36	360
Vani Rajasekar India	13	112 0.6×	134 0.8×	29 0.4×	70 1.0×	129 1.9×	47	508
Hamoud Alshammari Saudi Arabia	11	75 0.4×	98 0.6×	111 1.5×	69 1.0×	48 0.7×	27	392
Muhammad Ahsan Latif Pakistan	8	150 0.8×	136 0.8×	45 0.6×	18 0.3×	108 1.6×	22	416

Countries citing papers authored by Abdulaziz Alhumam

Since Specialization

Citations

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

Fields of papers citing papers by Abdulaziz Alhumam

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Abdulaziz Alhumam

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

Khan, Abdul Hannan, et al.. (2025). IoMT driven Alzheimer’s prediction model empowered with transfer learning and explainable AI approach in healthcare 5.0. Scientific Reports. 15(1). 35382–35382.

Srinivasu, Parvathaneni Naga, et al.. (2025). Exploring the impact of hyperparameter and data augmentation in YOLO V10 for accurate bone fracture detection from X-ray images. Scientific Reports. 15(1). 9828–9828. 4 indexed citations

Alhumam, Abdulaziz. (2025). Software cost estimation using TabNet and Harris Hawks Optimization. Scientific Reports. 15(1). 45434–45434.

Alhumam, Abdulaziz & Shakeel Ahmed. (2024). Software requirement engineering over the federated environment in distributed software development process. Journal of King Saud University - Computer and Information Sciences. 36(9). 102201–102201. 1 indexed citations

Sawalmeh, Ahmad, et al.. (2024). Deep learning adversarial attacks and defenses on license plate recognition system. Cluster Computing. 27(8). 11627–11644. 5 indexed citations

Alhumam, Abdulaziz. (2023). FEDRak: Federated Learning-Based Symmetric Code Statement Ranking Model for Software Fault Forecasting. Symmetry. 15(8). 1562–1562. 1 indexed citations

Ahmad, Hafiz Farooq, Wajid Rafique, Raihan Ur Rasool, et al.. (2023). Leveraging 6G, extended reality, and IoT big data analytics for healthcare: A review. Computer Science Review. 48. 100558–100558. 43 indexed citations

Guleria, Pratiyush, Shakeel Ahmed, Abdulaziz Alhumam, & Parvathaneni Naga Srinivasu. (2022). Empirical Study on Classifiers for Earlier Prediction of COVID-19 Infection Cure and Death Rate in the Indian States. Healthcare. 10(1). 85–85. 27 indexed citations

Alsmadi, Izzat, et al.. (2022). Adversarial Machine Learning in Text Processing: A Literature Survey. IEEE Access. 10. 17043–17077. 15 indexed citations

10.

Sawalmeh, Ahmad, et al.. (2022). Investigating the Factors Impacting Adversarial Attack and Defense Performances in Federated Learning. IEEE Transactions on Engineering Management. 71. 12542–12555. 5 indexed citations

11.

Alhumam, Abdulaziz. (2022). Effective Prediction of Software Defects using Random-tree Entropy based Feature Selection Framework. International Journal of Advanced Computer Science and Applications. 13(5). 3 indexed citations

12.

Ahmed, Shakeel, et al.. (2022). AAL and Internet of Medical Things for Monitoring Type-2 Diabetic Patients. Diagnostics. 12(11). 2739–2739. 21 indexed citations

13.

Alhumam, Abdulaziz. (2021). Software Fault Localization through Aggregation-Based Neural Ranking for Static and Dynamic Features Selection. Sensors. 21(21). 7401–7401. 5 indexed citations

14.

Hafsa, Noor E., et al.. (2021). An Ensemble of Global and Local-Attention Based Convolutional Neural Networks for COVID-19 Diagnosis on Chest X-ray Images. Symmetry. 13(1). 113–113. 27 indexed citations

15.

Ahmed, Shakeel & Abdulaziz Alhumam. (2021). Unified Computational Modelling for Healthcare Device Security Assessment. Computer Systems Science and Engineering. 37(1). 1–18. 8 indexed citations

16.

Ahmed, Shakeel & Abdulaziz Alhumam. (2021). Analyzing the Implications of COVID-19 Pandemic: Saudi Arabian Perspective. Intelligent Automation & Soft Computing. 27(3). 835–851. 4 indexed citations

17.

Ahmad, Hafiz Farooq, et al.. (2021). Correct Pronunciation Detection of the Arabic Alphabet Using Deep Learning. Applied Sciences. 11(6). 2508–2508. 13 indexed citations

18.

Ahmad, Hafiz Farooq, et al.. (2021). Investigating Health-Related Features and Their Impact on the Prediction of Diabetes Using Machine Learning. Applied Sciences. 11(3). 1173–1173. 55 indexed citations

19.

Liu, Guanxiong, et al.. (2020). ManiGen: A Manifold Aided Black-Box Generator of Adversarial Examples. IEEE Access. 8. 197086–197096. 5 indexed citations

20.

Alhumam, Abdulaziz, et al.. (2020). Convolutional Neural Network for Automatic Identification of Plant Diseases with Limited Data. Plants. 10(1). 28–28. 68 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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