Conveners
Optimization Control and Decision Making
- Bilal Usanmaz (Department of Computer Engineering, Faculty of Engineering, Ataturk University, Erzurum, Türkiye)
Optimization Control and Decision Making
- Elif KILIÇ DELİCE (Department of Industrial Engineering, Institute of Science, Atatürk University, Erzurum, Türkiye)
In this study, the parameters of the PI-type linear controller designed to regulate the liquid level in a coupled-tank system were optimized using three nature-inspired algorithms: Fata Morgana (FATA), Fox Optimization (FOX), and Moss Growth (MOSS). First, a nonlinear mathematical model of the coupled-tank system was derived to accurately represent the system dynamics. Subsequently, the...
This study examined public perception of artificial intelligence across Türkiye using open-access data from the 2024 Turkish General Social Survey (TGSS). To this end, the increasing role of artificial intelligence in every field was addressed, and studies on artificial intelligence perception across the country were reviewed. In this context, the analysis and presentation of the data used...
High-quality steel production is influenced by numerous factors, including chemical composition (particularly carbon content), process parameters (heat-treatment durations, charge amounts, preheating values), scrap quality, and the control mechanisms utilized during the production cycle. The relationships among these variables and the impact of process parameters on steel quality can be...
Physics-Informed Neural Networks (PINNs) are a deep learning approach that directly integrates the differential equations governing physical systems into the learning process of artificial neural networks. This method is increasingly preferred in engineering applications due to its low data requirements and its ability to produce more consistent results by embedding physical laws within the...
In this study, a deep learning model based on the Vision Transformer (ViT-B/16) architecture was employed to automatically classify cauliflower leaf diseases using image data. The VegNet dataset, which consists of four classes—Black Rot, Downy Mildew, Bacterial Spot Rot, and Healthy—was utilized to evaluate the effectiveness of the proposed method. All images were manually divided into...
Allocating public funds to the most suitable entrepreneurial projects is critical for ensuring resource efficiency. Since this allocation also influences economic growth, the project evaluation process requires a precise approach. This study presents a model for the KOSGEB Entrepreneur Support Program designed to remove subjectivity and establish a balanced evaluation framework. To achieve...
This article presents two complementary microcontroller-based platforms designed to enhance situational awareness and diagnostic capabilities in critical communication and energy infrastructures. The first system is an intelligent interruption detection unit that can distinguish between faults on the feed side and faults on the subscriber side in less than a second. The second system is a...
Manual annotation of large text datasets is both time- and cost-intensive, leading to a growing need for semi-supervised learning methods. Furthermore, inconsistencies among human labelers directly impact the quality of synthetic label generation due to the sensitivity of semi-supervised models to initial labels. This study examines the impact of multi-labeler consistency on BERT-based...
