Sürdürülebilir Kalkınma Amaçları

• 1

Yazarlar

• Abdel-Fattah, Abdul-Rahman(1)
• Bayrakdaroglu, Serdar(1)
• Belhaouari, Samir Brahim(1)
• Bilgilioglu, Burhan Baha(1)
• Ceylan, Halil Ibrahim(1)
• Dergaa, Ismail(1)
• Dervisoglu, Adalet(1)
• Dhahbi, Wissem(1)
• Kayikçioglu, Temel(1)
• Muntean, Raul Ioan(1)
• Omri, Abdelfatteh El(1)
• Turhan, Hilal(1)
• Yagmur, Nur(1)
• Yildirim, Omer(1)
• Özcan, Zubeyir(1)

• MakaleAçık Erişim2026/r/gumushane/10619
  YOLOv8 computer vision for automated offside detection in professional football validated through supervised learning

  Abdel-Fattah, Abdul-Rahman; Belhaouari, Samir Brahim; Ceylan, Halil Ibrahim; Bayrakdaroglu, Serdar; Muntean, Raul Ioan; Dhahbi, Wissem; Dergaa, Ismail; Omri, Abdelfatteh El

  Offside calls are integral to maintaining fairness and competitive integrity in football, yet current officiating processes are constrained by limitations in accuracy despite technological advancements. This investigation developed and validated an automated offside detection system using YOLOv8 object detection architecture to address documented limitations of Video Assistant Referee (VAR) systems in offside decision accuracy. Supervised learning validation employed 400 annotated instances from professional match footage. The YOLOv8 medium variant was configured for four-class detection, with a focus on extreme player positions, reducing computational complexity by approximately 70% relative to comprehensive player tracking while accepting accuracy trade-offs in marginal scenarios. The system was optimized for computational efficiency through highly accurate tracking of player positions. Data were partitioned into 40% (n = 160) for training and 60% (n = 240) for testing, with 5-fold cross-validation. The automated system achieved robust performance metrics across 240 test instances: overall accuracy of 83.0%, precision of 85.0%, recall of 87.0%, and F1-score of 86.0%. Confusion matrix analysis revealed true positives (n = 130), true negatives (n = 70), false positives (n = 22), and false negatives (n = 18). Individual case analysis demonstrated 80% agreement with ground truth classifications. System performance significantly exceeded the random classification baseline (p < 0.001). The strategic extreme player tracking approach achieved substantial computational efficiency while maintaining decision accuracy across diverse match scenarios. A YOLOv8-based offside detection system demonstrates proof-of-concept feasibility for decision-support applications in football officiating. Performance (83% accuracy, 95% CI: 79-87%) on 240 test instances remains below professional benchmarks (> 93%), with single-camera constraints and limited dataset size restricting generalizability to proof-of-concept validation rather than operational deployment.

  İncele
• MakaleAçık Erişim2021/r/gumushane/14621
  Satellite-derived shallow wetland bathymetry using different classification algorithms and datasets

  Dervisoglu, Adalet; Yagmur, Nur; Bilgilioglu, Burhan Baha

  Remote sensing (RS) effectively identifies, analyzes, and monitors wetlands. In addition to these two-dimensional studies, RS is used with several techniques in determining shallow water depths. The primary purpose of this study is to obtain shallow wetland bathymetry utilizing spectral reflections obtained at different water depths by field study and satellite images. Machine learning (ML) algorithms, which are widely used in remote sensing, are used in this study. Four algorithms were selected as random forest (RF), support vector machine (SVM), Neural Networks (NN), and Maximum Likelihood Classification (MLC). Since machine learning algorithms use training samples/datasets, the classification accuracy is directly related to selecting these data. The effect of pixel counts on classification was investigated by using two different training data set also. Duden (Kulu) Lake, which is a shallow wetland, was chosen as the study area. The Iterative Self-Organizing Data Analysis Technique (ISODATA) classification algorithm divided into as many clusters as possible was applied on Sentinel-2 multispectral images. All classes were rede-fined using measured spectral signatures and were created a bathymetric map. This map was used as reference data in creating training sets and the accuracy assessment of ML algorithms. When the water surface areas obtained from algorithms were compared with the bathymetric map and Normalized Difference Water Index, the best result was obtained with RF. According to the accuracy assessment results, it was seen that the number of training data affects the accuracy, and the best results were obtained with SVM and RF algorithms with training data containing more pixels (overall accuracy 93.87% and 92.64, kappa 0.89 and 0.87, respectively).

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  İncele
• BildiriYalnız künye2018/r/gumushane/16790
  Network Master: A Versatile and User-Friendly Educational Software for Simulation of Neural Networks

  Yildirim, Omer; Özcan, Zubeyir; Turhan, Hilal; Kayikçioglu, Temel

  In this study, an interface in which neural networks consisting of biological cells could be modeled and simulated using Neuron hoc programming. With this interface, the resulting action potentials and the behavior of the nerve network can be investigated in detail by making the desired stimuli to mathematical models of neural networks. The most important feature of the work is that the designed interface is completely user friendly. Another advantage of this software is that users can easily perform simulations of the neural network models without writing code.

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