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Öğe Automated evaluation of Cr-III coated parts using Mask RCNN and ML methodse(Elsevier Science Sa, 2021) Katirci, Ramazan; Yilmaz, Esra Kavalci; Kaynar, Oguz; Zontul, MetinIn this study, chrome coatings were carried out using a Cr-III electroplating bath. The coated parts were classified depending on their appearance. A new approach was developed to classify the coated parts automatically using artificial intelligence methods. Mask RCNN and machine learning (ML) methods such as Multilayer Perceptron (MLP), Support Vector Classifier (SVC), Gaussian Process (GP), K-nearest Neighbors (KNN), XGBoost, and Random Forest Classifier (RFC) were used together. Mask RCNN was used to clean the coated parts from the redundant data. The extracted data were flattened and converted to the row vectors for use as input in ML methods. ML algorithms were used to classify the coated parts as Pass and Fail. The classification accuracy was checked with the leave one out (loo) cross-validation method. RFC method gave the highest accuracy, 0.83, and F1 score, 0.88. The accuracy of Mask RCNN was checked using a dataset of separated validation images. It was observed that extracting the unnecessary data from the images increased the accuracy exceedingly. Moreover, the method exhibits a high potential to keep the parameters of the electroplating process under control.Öğe Comparison of the performance of classical and quantum machine-learning methods on the detection of sugar beet Cercospora leaf disease(Wiley, 2025) Katirci, Ramazan; Adem, Kemal; Tatar, Muhammed; Olmez, FatihImage processing and machine-learning (ML) techniques are essential for the detection of diseases and pests in plants. This study explored the application of quantum ML (QML) algorithms for the early detection of Cercospora beticola leaf disease in sugar beet, which causes significant impact on global sugar production. Using a dataset of 1065 images (739 diseased and 326 healthy), we extracted 70 ML statistical features, including 10 from the grey-level co-occurrence matrix (GLCM) and 60 colour-related features. Performance evaluations of classical ML algorithms, such as random forest (RF; 91.95% accuracy) and extreme gradient boosting (91.95% accuracy), demonstrated strong results compared to quantum approaches. Notably, the quantum support vector classifier (QSVC) achieved an accuracy of 85% with perfect recall of 1.00, while the variational quantum classifier (VQC) recorded an accuracy of 88.73%. Dimensionality reduction via principal component analysis reduced features from 70 to 5, enabling effective classification with competitive results: ML (RF) 91.41%, VQC with limited-memory Broyden-Fletcher-Goldfarb-Shanno with box constraints (L_BFGS_B) 88.73% and QSVC 85%. These findings highlight the potential of QML algorithms in improving agricultural disease identification and aiding in the advancement of more efficient, sustainable farming techniques.Öğe Effect of Process Parameters on the Electrodeposition of Zinc on 1010 Steel: Central Composite Design Optimization(Esg, 2020) Kul, Mehmet; Oskay, Kursad Oguz; Erden, Fuat; Akca, Erdem; Katirci, Ramazan; Koksal, Erkan; Akinci, EvindarIn the present work, we studied the effect of critical electrogalvanizing parameters on the quality of electrodeposited Zn films. The current density, electrodeposition time, and ZnCl2 concentration of electrolyte were optimized to maximize current efficiency and brightness, and also, to minimize the surface roughness. Importantly, regression models of the response variables were developed. These models could help industrial applications by providing definitive process conditions to obtain Zn coatings at a desired thickness, roughness and brightness with a high current efficiency. First, preliminary studies were conducted to determine the initial levels of the designated factors. Then, the optimization was conducted through the Central Composite Design by Design -Expert (trial version). Upon completion of the optimization, analysis of variance was also performed. The optimum values of current density, coating duration and ZnCl2 concentration were determined as 3.7 A/dm(2), 4.4 minutes, and 50 g/L, respectively, at a thickness of 6 mu m. Finally, a set of Zn films were deposited at this optimum conditions. The characterization of these films showed that the experimental results were in good accordance with model predictions, providing a bright (L*=83.69) and smooth (Ra=0.75 mu m) coating with excellent adhesion to steel substrate (pull-off strength > 29.41 MPa) at a current efficiency of 98.7%.Öğe Manganese(III) Acetate-Based Radical Cyclization Reactions for Pyranocoumarin and Pyranoquinoline Compounds: Synthesis, DFT and Molecular Docking Studies(Wiley-V C H Verlag Gmbh, 2022) Yakut, Mehtap; Yilmaz, Mehmet; Pekel, Tarik; Erkan, Sultan; Katirci, Ramazan; Bicer, EmrePyranofuroquinoline and pyranofurocoumarin derivatives are significant class of compounds due to being useful in many diverse applications. The synthesis of the compounds are succeeded in one step by radical cyclization reaction in the presence of manganese(III) acetate. Furoquinoline and furocoumarin reactants were used together with 1,1-disubstituted-, 1,2-disubstituted and cyclic alkenes giving pyranofuroquinoline and pyranofurocoumarin compounds in moderate to high yields. Also, we were able to isolate alkenyl and acetoxy- side-products in minor low yields. The electronic and optical features of furoquinoline and furocoumarin reactants and pyranofuroquinoline products were inquired using theoretical approaches (DFT and TD-DFT). Their excitation and emission spectrums were computed. The results revealed that the pyranofuroquinoline molecules did not show the energy transfer feature. It was observed that the behavior of nonlinear optic of furoquinoline is higher than the pyranofuroquinoline and furocoumarin molecules. But the average polarization and the anisotropy of the polarizability of pyranofuroquinoline increased and was found to be 262.7 and 141.9 respectively. Also, according to the calculated docking parameters, the investigated pyranofuroquinoline and pyranofurocoumarin compounds were found to have higher activity than the substances with anticancer and antibacterial standards.
