Sürdürülebilir Kalkınma Amaçları

• 1

Yazarlar

• Badica, C.(1)
• Catal Reis, Hatice(1)
• Gedikli, Eyup(1)
• Ivanovic, M.(1)
• Kaya, Serhat(1)
• Koprinkova-Hristova, P.(1)
• Leon, F.(1)
• Manolopoulos, Y.(1)
• Turk, Veysel(1)
• Ucar, A.(1)
• Yaprak, Busranur(1)
• Yildirim, T.(1)

• BildiriAçık Erişim2024/r/gumushane/14568
  In Different Scenarios MobileNet-V1 for Cross-view Gait Recognition

  Yaprak, Busranur; Gedikli, Eyup; Badica, C.; Ivanovic, M.; Koprinkova-Hristova, P.; Leon, F.; Manolopoulos, Y.; Yildirim, T.; Ucar, A.

  Gait recognition aims to identify people from a distance by analyzing their walking style. Nevertheless, the efficacy of recognition drops significantly under cross-view and, appearance-based variations such as carrying and clothing. In this study, the performance of the MobileNet-V1 deep network is evaluated in various scenarios to address the cross-view gait recognition problem. In the first scenario, the fine-tuned MobileNet-V1 is evaluated on Gait Energy Images (GEI) as input data, while in the second scenario, the fine-tuned MobileNet-V1 is assessed with Optical Flows and masked RGB frames input data. In the last scenario, the first two scenarios are combined over a single fused deep network based on finetuned MobileNet-V1, and a single recognition process is performed using two different fused features data; GEI features with Optical Flow features, and GEI features with masked RGB frame features. In the evaluation process, a comprehensive data set for the cross-view gait recognition problem, CASIA-B is used for the experiments. The obtained results demonstrate that in the last scenario, the contribution of masked RGB frame features to the recognition rate of GEI is more significant.

  İncele
• MakaleAçık Erişim2022/r/gumushane/11422
  Automatic prediction of covid-19 from chest-computed tomography (CT) images using deep learning architectures; [Derin öğrenme mimarilerini kullanarak göğüs BT görüntülerinden otomatik Covid-19 tahmini]

  Turk, Veysel; Catal Reis, Hatice; Kaya, Serhat

  Machine learning has been actively used in disease detection and segmentation in recent years. For the last few years, the world has been coping with the Coronavirus disease 2019 (COVID-19) pandemic. Chest-computerized tomography (CT) is often a meaningful way to detect and detect patients with possible COVID-19. This study aims to classify COVID-19 and non-COVID-19 chest-CT images using deep learning (DL) algorithms and investigate whether we can achieve successful results in different parameters using four architectures. The study was performed on proved positive COVID-19 CT images, and the datasets were obtained from the GitHub public platform. The study evaluated four different deep learning architectures of VGG16, VGG19, LeNet-5, and MobileNet. The performance evaluations were used with ROC curve, recall, accuracy, F1-score, precision, and Root Mean Square Error (RMSE). MobileNet model showed the best result ; F1 score of 95%, the accuracy of 95%, the precision of 100%, recall of 90%, AUC of 95%, and RMSE of 0.23. On the other hand, VGG 19 model gave the lowest performance; F1 score of 90%, the accuracy of 89%, the precision of 90%, recall of 90%, AUC of 89%, and RMSE of 0.32. When the algorithms' performances were compared, the highest accuracy was obtained from MobileNet, LeNet-5, VGG16, and VGG19, respectively. This study has proven the usefulness of deep learning models to detect COVID-19 in chest-CT images based on the proposed model framework. Therefore, it can contribute to the literature in Medical and Engineering in COVID-19 detection research.

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  AtıfScopus2·OpenAlex2

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