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- MakaleAçık Erişim2024A driver perspective-based investigation for lane line visibility of pavements on drivers’ lane utilization behaviors
Aydın, Metin Mutlu; Orhan, Eda
There have been significant advances in vehicle safety and road construction in all over the world and driver misbehaviours are still shown as one of the most important reasons of traffic accidents. When the current traffic accidents are examined, the negative effect of lane line quality and level of visibility related on the accidents comes to the forefront in underdeveloped and developing countries. In this study, the effect of lane line visibility level on drivers' lane utilization was investigated in detail. For this aim, a total of 17 different points in Türkiye were selected and the lateral positions of the vehicles on the road surface at these points were determined with the help of the screen scaling technique. In order to determine the level of lane line quality for an expert group and driver perspective, a face-to-face survey was conducted. From the analysis results, the lane line quality was determined low in urban and intercity roads, and the average maximum evaluation score was determined as 4.7 and the lowest score was determined as 1.42 where the best line quality was 5. This result showed that the level of lane line quality had significant differences at different points. Although, it was observed that the lateral position distributions in the examined points fit the normal distribution, the distribution of both the right/left wheels and their gravity center skewed to the right or left and deviated from the ideal conditions on road surfaces. It showed that the vehicles do not center the lanes exactly, but usually move close to the right or left edge of the lane. This situation also clearly revealed that the lane utilization disciplines of drivers are quite weak with the effect of the low lane line quality on road surfaces.
- MakaleAçık Erişim2021Automatic Classification of COVID-19 using CT-Scan Images
Reis, Hatice Catal
Medicine and engineering sciences have been working in close contact for common purposes. Machine learning algorithms are used in the medical field for early diagnosis prediction. The major aim of this study is to evaluate machine learning algorithms and deep learning algorithms using computed tomography scan (CT-scan) images for automated detection of the coronavirus disease 2019 (COVID-19) patients. We obtained seven hundred and fifty-seven (757) CT-scan images from a public platform. We applied four automated traditional classification methods to predict COVID-19 using deep learning and machine learning. These algorithms are SVM, AdaBoost, NASNetMobile, and InceptionV3. Comparative analyses are presented among the four models by considering metric performance factors to find the best model. The results show that the InceptionV3 model achieves better performance in terms of accuracy, precision, recall, Cohen’s kappa, F1-score, root mean squared error (RMSE), and receiver operating characteristic-area under the curve (ROC-AUC), in comparison with the other Covid-19 classifiers. Accordingly, the InceptionV3 approach is recommended for the automatic diagnosis of Covid-19 and assessments. This research can present a second point of view to medical experts and it can save time for researchers as the performance of standard machine learning methods in detecting COVID-19 is evaluated.
- MakaleAçık Erişim2020Determination of physicochemical and microbiological properties and fatty acid composition of butter produced in Trabzon, Turkey
Akgül, Halil İbrahim; Şengül, Mustafa; Ürkek, Bayram; Kotan, Tuba Erkaya
In this study, the some physicochemical properties, fatty acid compositions and microbiological properties of butter samples were studied. Butter samples (n = 30) were randomly collected from different local markets. Thus, butter samples were evaluated in terms of Turkish regulations, food safety and quality. The mean values of the butter samples for peroxide value (PV), iodine value (IV), saponification value (SV), Polenske value, Reichert-Meissl (RM) and Refractive index (RI) values were determined as 0.85 mEqO2 kg-1, 30.03, 220.09, 1.30, 25.60 and 1.4611, respectively. The moisture and fat values were not in harmony with Turkish Food Codex Butter, Other Milk Fat Based Spreadable Products and Anhydrous Milkfat Notification (the highest moisture content 16%, the lowest fat content 80%) in 23 and 13 butter samples, respectively. The salt values were coherent with Turkish Food Codex in all butter samples. Conjugated linoleic acid (CLA) rate of the butter samples was ranged from 0.15 to 1.32%. The average values of coliforms, total aerobic mesophilic bacteria (TAMB) and mould and yeasts counts were determined as 1.67, 6.33 and 5.22 log CFU g-1, respectively.
- DergiActa Scientiarum - Technology