The Babylonian Journal of Machine Learning (BJML) (EISSN: 3006-5429) is a specialized publication dedicated to the exploration and integration of modern machine learning methodologies. As a platform for researchers and scholars, the journal focuses on the intersection of cutting-edge advancements in machine learning. Through high-quality articles, it fosters interdisciplinary discussions aimed at propelling forward the field of machine learning research.

Vol. 2026 (2026)

 

 

Published: 2026-01-10

Cross-Layer Co-Design and Physics-Grounded Evaluation of Hybrid Reinforcement Learning and Federated ANN-PID Protocol Suites for Dense Urban UAV Delivery Networks

Randa Shaker Abd-Alhussain, Qasim Radam Mahmood, Hasan Mohammed Taqi Salih Albdair, Mohammad Khaleel Sallam Ma’aitah, Sanad Abdullah Alafghani, Ashraf Al-Shaikh Khalil, Ashraf Zaher

164-196

When GPUs Fail Quietly: Separating GPU Hardware Degradation from Monitoring Failure under Degraded Observability

Michael Bidollahkhani, Mohsen Seyedkazemi Ardebili, Andrea Bartolini, Rute C. Sofia, Julian Martin Kunkel

197–234

Explainable Prediction of Supplier Sanction Risk in Public Procurement: Comparing Random Forest and XGBoost under Forward Temporal Validation

Miluska Odely Rodríguez Saavedra, Robinson Bernardino Almanza Cabe, Rubén Washington Arguedas Catasi

258–273

An AI-Driven Intrusion Detection and Real-Time Autonomous Response Framework Using Network Traffic Logs: AMulti-Algorithm Approach with LightGBM Optimization

Ahmed Sileh Gifal, Hussein Mohammed Ali Makassees, Mayameen S. Kadhim, Ahmed Dheyaa Radhi, Rusul Mansoor Al-Amri, Liaw Geok Pheng

95-109

A Preemptive Ensemble Learning Framework for KnowledgeBased Network Intrusion Detection

Maad Kamal Al-anni, Ammar A Al-Hamadani, Rafah M. Almuttairi, Alharith A Abdullah

110-130

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