Fuad Ali Mohammed Al-Yarimi
Orcid: 0000-0002-2615-6340Affiliations:
- King Khalid University, Department of Computer Science, Applied College, Muhayel Aseer, Saudi Arabia
According to our database1,
Fuad Ali Mohammed Al-Yarimi authored at least 15 papers
between 2020 and 2026.
Collaborative distances:
Collaborative distances:
Timeline
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Bibliography
2026
Improving adversarial resilience for anomaly detection in the heterogeneous internet of things through ensemble models.
Future Gener. Comput. Syst., 2026
On the study of acoustic-driven bubble dynamics in Williamson Fluids with NARX neural networks.
Eng. Appl. Artif. Intell., 2026
2025
Sustainable Edge AI for Precision Agriculture: A Lightweight CNN Model for Aloe Vera Leaf Disease Diagnosis.
Expert Syst. J. Knowl. Eng., 2025
Machine learning-based investigation of activation energy for bio-convective boundary layer flow inspired by challenges in aerospace heat transfer systems.
Eng. Appl. Artif. Intell., 2025
Intelligent predictive networks of swimming of microorganism for tangent hyperbolic fluid flow with heat and mass transfer and Thompson and Troian slip conditions using Levenberg-Marquardt algorithm.
Eng. Appl. Artif. Intell., 2025
Enhancing User Experience in Free and Open-Source Software: An Integrated Maturity Model Framework.
IEEE Access, 2025
Integrating Pull Request Comment Analysis and Developer Profiles for Expertise-Based Recommendations in Global Software Development.
IEEE Access, 2025
2024
A Machine Learning Approach for Named Entity Recognition in Classical Arabic Natural Language Processing.
KSII Trans. Internet Inf. Syst., 2024
Enhancing road safety through advanced predictive analytics in V2X communication networks.
Comput. Electr. Eng., 2024
2023
Comput. Syst. Sci. Eng., 2023
2022
Intell. Autom. Soft Comput., 2022
Comput. Syst. Sci. Eng., 2022
2021
A Reliable NLP Scheme for English Text Watermarking Based on Contents Interrelationship.
Comput. Syst. Sci. Eng., 2021
Clust. Comput., 2021
2020
Electrocardiogram stream level correlated patterns as features to classify heartbeats for arrhythmia prediction.
Data Technol. Appl., 2020