Intelligent Methods In Engineering Sciences

The Intelligent Methods in Engineering Sciences (Vol. 5, No. 2, 2026) presents a collection of research studies demonstrating the application of machine learning, deep learning, and data-driven analysis methods to agricultural, industrial, and supply chain problems. This issue features a comparative performance analysis of machine learning algorithms for the classification of dry bean varieties, an experimental evaluation of YOLO12 and YOLO13 models for detecting surface defects in steel materials, and a machine learning-based approach for predicting processing times and delays across different operational stages of supply chains. Together, these contributions highlight the potential of intelligent methods to improve classification accuracy, quality control processes, operational planning, and decision-making in real-world engineering and industrial applications.