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- 저자 : Urkmez, (검색결과 4 건)
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Intimacy of the Russian upper middle class with luxury fashion
Anna Peshkova,Taylan Urkmez,Ralf Wagner 한국마케팅과학회 2016 마케팅과학연구 Vol.26 No.2
Russia has developed into one of the most important markets for luxury goods in the world. The aim of this study is to determine the factors influencing Russian consumers’ intentions to purchase luxury fashion goods. We focus on the growing high-middle- and middleclass consumer behavior patterns. This study attempts to contest practitioners’ knowledge and folklore with research hypotheses and to evaluate these in a rigorous quantitative process. We investigate the factors influencing Russian consumers’ intentions to purchase goods of luxury fashion brands based on two different models. The “Attitude toward Luxury Brands” (social-adjustive function) quantifi es the extent to which luxury brands are facilitating self-expression of the owner and the projection of a particular image in socia settings. Additionally, we use the “Attitude toward Luxury Brands” (value-expressive function) in order to quantify the degree to which luxury brands are expressing the buyer’s self (beliefs, attitudes, values). The results of our analysis confi rm practitioners’ prior beliefs that Russian consumer behavior patterns in luxury markets predominantly correspond to characteristics of symbolic consumption.
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Predictive Value of the Platelet-To-Lymphocyte Ratio in Diagnosis of Prostate Cancer
Yuksel, Ozgur Haki,Urkmez, Ahmet,Akan, Serkan,Yldirim, Caglar,Verit, Ayhan Asian Pacific Journal of Cancer Prevention 2015 Asian Pacific journal of cancer prevention Vol.16 No.15
Purpose: To predict prostatic carcinoma using a logistic regression model on prebiopsy peripheral blood samples. Materials and Methods: Data of a total of 873 patients who consulted Urology Outpatient Clinics of Fatih Sultan Mehmet Training and Research Hospital between February 2008 and April 2014 scheduled for prostate biopsy were screened retrospectively. PSA levels, prostate volumes, prebiopsy whole blood cell counts, neutrophil and platelet counts, neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), biopsy results and Gleason scores in patients who had established diagnosis of prostate cancer (PCa) were evaluated. Results: This study was performed on a total of 873 cases, with an age range 48-76 years, divided into three groups as for biopsy results. with diagnoses of benign prostatic hyperplasia (BPH) (n=304, 34.8 %), PCa (n=265, 30.4 %) and histological prostatitis (n=304; 34.8 %). Intra- and intergroup comparative evaluations were performed. White blood cell and neutrophil counts in the histological prostatitis group were significantly higher than those of the BPH and PCa groups (p=0.001; p=0.004; p<0.01). A statistically significant intergroup difference was found for PLR (p=0.041; p<0.05) but not lymphocyte count (p>0.05). According to pairwise comparisons, PLR were significantly higher in the PCa group relative to BPH group (p=0.018, p<0.05, respectively). Though not statistically significant, higher PLR in cases with PCa in comparison with the prostatitis group was remarkable (p=0.067, and p>0.05, respectively). Conclusions: Meta-analyses showed that in patients with PSA levels over 4 ng/ml, positive predictive value of PSA is only 25 percent. Therefore, novel markers which can both detect clinically significant prostate cancer, and also prevent unnecessary biopsies are needed. Relevant to this issue in addition to PSA density, velocity, and PCA3, various markers have been analyzed. In the present study, PLR were found to be the additional predictor of prostatic carcinoma.
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A deep learning approach to permanent tooth germ detection on pediatric panoramic radiographs
Kaya Emine,Gunec Huseyin Gurkan,Aydin Kader Cesur,Urkmez Elif Seyda,Duranay Recep,Ates Hasan Fehmi 대한영상치의학회 2022 Imaging Science in Dentistry Vol.52 No.3
Purpose: The aim of this study was to assess the performance of a deep learning system for permanent tooth germ detection on pediatric panoramic radiographs. Materials and Methods: In total, 4518 anonymized panoramic radiographs of children between 5 and 13 years of age were collected. YOLOv4, a convolutional neural network (CNN)-based object detection model, was used to automatically detect permanent tooth germs. Panoramic images of children processed in LabelImg were trained and tested in the YOLOv4 algorithm. True-positive, false-positive, and false-negative rates were calculated. A confusion matrix was used to evaluate the performance of the model. Results: The YOLOv4 model, which detected permanent tooth germs on pediatric panoramic radiographs, provided an average precision value of 94.16% and an F1 value of 0.90, indicating a high level of significance. The average YOLOv4 inference time was 90 ms. Conclusion: The detection of permanent tooth germs on pediatric panoramic X-rays using a deep learning-based approach may facilitate the early diagnosis of tooth deficiency or supernumerary teeth and help dental practitioners find more accurate treatment options while saving time and effort.
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A deep learning approach to permanent tooth germ detection on pediatric panoramic radiographs
Kaya, Emine,Gunec, Huseyin Gurkan,Aydin, Kader Cesur,Urkmez, Elif Seyda,Duranay, Recep,Ates, Hasan Fehmi Korean Academy of Oral and Maxillofacial Radiology 2022 Imaging Science in Dentistry Vol.52 No.-
Purpose: The aim of this study was to assess the performance of a deep learning system for permanent tooth germ detection on pediatric panoramic radiographs. Materials and Methods: In total, 4518 anonymized panoramic radiographs of children between 5 and 13 years of age were collected. YOLOv4, a convolutional neural network (CNN)-based object detection model, was used to automatically detect permanent tooth germs. Panoramic images of children processed in LabelImg were trained and tested in the YOLOv4 algorithm. True-positive, false-positive, and false-negative rates were calculated. A confusion matrix was used to evaluate the performance of the model. Results: The YOLOv4 model, which detected permanent tooth germs on pediatric panoramic radiographs, provided an average precision value of 94.16% and an F1 value of 0.90, indicating a high level of significance. The average YOLOv4 inference time was 90 ms. Conclusion: The detection of permanent tooth germs on pediatric panoramic X-rays using a deep learning-based approach may facilitate the early diagnosis of tooth deficiency or supernumerary teeth and help dental practitioners find more accurate treatment options while saving time and effort
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