Mohammed Alkhathami

740 total citations · 1 hit paper
52 papers, 392 citations indexed

About

Mohammed Alkhathami is a scholar working on Computer Networks and Communications, Artificial Intelligence and Electrical and Electronic Engineering. According to data from OpenAlex, Mohammed Alkhathami has authored 52 papers receiving a total of 392 indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Computer Networks and Communications, 12 papers in Artificial Intelligence and 12 papers in Electrical and Electronic Engineering. Recurrent topics in Mohammed Alkhathami's work include IoT and Edge/Fog Computing (14 papers), COVID-19 diagnosis using AI (8 papers) and Biometric Identification and Security (7 papers). Mohammed Alkhathami is often cited by papers focused on IoT and Edge/Fog Computing (14 papers), COVID-19 diagnosis using AI (8 papers) and Biometric Identification and Security (7 papers). Mohammed Alkhathami collaborates with scholars based in Saudi Arabia, Pakistan and United States. Mohammed Alkhathami's co-authors include Abdul Khader Jilani Saudagar, Maryam Mahsal Khan, Muhammad Badruddin Khan, Mozaherul Hoque Abul Hasanat, Deafallah Alsadie, Abdullah AlTameem, Fengling Han, Musleh Alsulami, Ron van Schyndel and Muhammad Amir Khan and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and Scientific Reports.

In The Last Decade

Mohammed Alkhathami

50 papers receiving 372 citations

Hit Papers

Anomaly detection in IoT-based healthcare: machine learni... 2024 2026 2025 2024 10 20 30 40 50

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Mohammed Alkhathami Saudi Arabia 13 114 100 65 63 60 52 392
Bilal Alhayani Türkiye 9 92 0.8× 177 1.8× 133 2.0× 68 1.1× 33 0.6× 11 521
Malek Alrashidi Saudi Arabia 11 86 0.8× 54 0.5× 52 0.8× 80 1.3× 30 0.5× 34 302
A.M. Aleesa Malaysia 7 178 1.6× 92 0.9× 50 0.8× 29 0.5× 51 0.8× 11 450
Byeong-Gwon Kang South Korea 11 81 0.7× 79 0.8× 54 0.8× 50 0.8× 30 0.5× 47 353
Allah Ditta Pakistan 12 163 1.4× 139 1.4× 96 1.5× 108 1.7× 21 0.3× 37 470
Sungchang Lee South Korea 12 196 1.7× 99 1.0× 92 1.4× 123 2.0× 17 0.3× 44 519
Gianfranco Lombardo Italy 11 135 1.2× 112 1.1× 42 0.6× 207 3.3× 25 0.4× 31 543
Firoz Khan United Arab Emirates 11 117 1.0× 198 2.0× 219 3.4× 37 0.6× 87 1.4× 28 450
Syed Muhammad Saqlain Pakistan 9 190 1.7× 48 0.5× 61 0.9× 57 0.9× 22 0.4× 24 377
Daniel Smilkov North Macedonia 6 253 2.2× 44 0.4× 44 0.7× 145 2.3× 22 0.4× 10 533

Countries citing papers authored by Mohammed Alkhathami

Since Specialization
Citations

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

Fields of papers citing papers by Mohammed Alkhathami

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mohammed Alkhathami

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

All Works

20 of 20 papers shown
1.
Mirza, Jawad, et al.. (2025). An energy-efficient task offloading in D2D-assisted IoT networks using matching algorithms. The Journal of Supercomputing. 81(4). 2 indexed citations
2.
Alkhathami, Mohammed, et al.. (2024). TMPAD: Time-Slot-Based Medium Access Control Protocol to Meet Adaptive Data Requirements for Trusted Nodes in Fog-Enabled Smart Cities. Applied Sciences. 14(3). 1319–1319. 2 indexed citations
3.
Ali, Bakhtiar, et al.. (2024). Secure Computing for Fog-Enabled Industrial IoT. Sensors. 24(7). 2098–2098. 5 indexed citations
4.
Javed, Muhammad Awais, et al.. (2024). A many-to-many matching with externalities solution for parallel task offloading in IoT networks. Journal of King Saud University - Computer and Information Sciences. 36(7). 102134–102134. 1 indexed citations
5.
Mirza, Jawad, et al.. (2024). User-IRS Association for Sum-Rate Maximization in Multi-IRS Aided Wireless Communication Networks. IEEE Access. 12. 167224–167235. 1 indexed citations
6.
Javed, Muhammad Awais, et al.. (2024). Secure Multi-Hop Assisted IoT Communications in Smart Cities. IEEE Access. 12. 108328–108341. 1 indexed citations
7.
Alkhathami, Mohammed. (2024). Collaborative Task Offloading in Edge Computing Enabled Web 3.0. Journal of Web Engineering. 681–698.
9.
Mirza, Jawad, et al.. (2024). IRS-User Association in Multi-IRS Assisted Cognitive Radio Networks. IEEE Access. 12. 70211–70219. 3 indexed citations
10.
Alsulami, Musleh, et al.. (2024). A Novel Machine Learning-Based Prediction Method for Early Detection and Diagnosis of Congenital Heart Disease Using ECG Signal Processing. SHILAP Revista de lepidopterología. 12(1). 4–4. 13 indexed citations
11.
Alsulami, Musleh, et al.. (2023). A Cardiac Deep Learning Model (CDLM) to Predict and Identify the Risk Factor of Congenital Heart Disease. Diagnostics. 13(13). 2195–2195. 2 indexed citations
12.
Khan, Muhammad Amir, Musleh Alsulami, Muhammad Mateen Yaqoob, et al.. (2023). Asynchronous Federated Learning for Improved Cardiovascular Disease Prediction Using Artificial Intelligence. Diagnostics. 13(14). 2340–2340. 17 indexed citations
13.
Javed, Muhammad Awais, et al.. (2023). Efficient Load Balancing for Blockchain-Based Healthcare System in Smart Cities. Applied Sciences. 13(4). 2411–2411. 11 indexed citations
14.
Yaqoob, Muhammad Mateen, Musleh Alsulami, Muhammad Amir Khan, et al.. (2023). Symmetry in Privacy-Based Healthcare: A Review of Skin Cancer Detection and Classification Using Federated Learning. Symmetry. 15(7). 1369–1369. 14 indexed citations
15.
Javed, Muhammad Awais, et al.. (2023). An FPGA-Based Performance Analysis of Hardware Caching Techniques for Blockchain Key-Value Database. Applied Sciences. 13(7). 4092–4092. 4 indexed citations
16.
Ali, Bakhtiar, et al.. (2023). Internet of Things-Assisted Vehicle Route Optimization for Municipal Solid Waste Collection. Applied Sciences. 14(1). 287–287. 2 indexed citations
17.
Yaqoob, Muhammad Mateen, Musleh Alsulami, Muhammad Amir Khan, et al.. (2023). Federated Machine Learning for Skin Lesion Diagnosis: An Asynchronous and Weighted Approach. Diagnostics. 13(11). 1964–1964. 20 indexed citations
18.
Javed, Muhammad Awais, et al.. (2022). AI-Enabled Energy-Efficient Fog Computing for Internet of Vehicles. Journal of Sensors. 2022. 1–14. 3 indexed citations
19.
Javed, Muhammad Awais, et al.. (2022). An Efficient MAC Protocol for Blockchain-Enabled Patient Monitoring in a Vehicular Network. Applied Sciences. 12(21). 10957–10957. 3 indexed citations
20.
Javed, Muhammad Awais, Mozaherul Hoque Abul Hasanat, Muhammad Badruddin Khan, et al.. (2022). Intelligent Task Offloading in Fog Computing Based Vehicular Networks. Applied Sciences. 12(9). 4521–4521. 18 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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