{"title":"Konvolüsyonel Sinir Ağları (CNN) ile Çin Sayı Örüntülerinin Sınıflandırması","authors":"Nihal Zuhal Kayali, Sevinç İLHAN OMURCA","doi":"10.53070/bbd.989668","DOIUrl":"https://doi.org/10.53070/bbd.989668","url":null,"abstract":"","PeriodicalId":41917,"journal":{"name":"Computer Science-AGH","volume":" ","pages":""},"PeriodicalIF":0.5,"publicationDate":"2021-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48299578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Classification of Knee Abnormality Using sEMG Signals with Boosting Ensemble Approaches","authors":"Ayşenur Altintaş, D. Yilmaz","doi":"10.53070/bbd.990889","DOIUrl":"https://doi.org/10.53070/bbd.990889","url":null,"abstract":"","PeriodicalId":41917,"journal":{"name":"Computer Science-AGH","volume":" ","pages":""},"PeriodicalIF":0.5,"publicationDate":"2021-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49089169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"MiniVGGNet Kullanılarak Hiperspektral Görüntü Sınıflandırma","authors":"Hüseyin Fırat, M. Uçan, D. Hanbay","doi":"10.53070/bbd.989102","DOIUrl":"https://doi.org/10.53070/bbd.989102","url":null,"abstract":"","PeriodicalId":41917,"journal":{"name":"Computer Science-AGH","volume":" ","pages":""},"PeriodicalIF":0.5,"publicationDate":"2021-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46604679","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Veri madenciliği yöntemleri kullanarak yoğun bakım ünitesindeki hastaların sınıflandırması","authors":"Emine Coşkun, Esra Gündoğan, M. Kaya, Reda Alhajj","doi":"10.53070/bbd.990718","DOIUrl":"https://doi.org/10.53070/bbd.990718","url":null,"abstract":"","PeriodicalId":41917,"journal":{"name":"Computer Science-AGH","volume":" ","pages":""},"PeriodicalIF":0.5,"publicationDate":"2021-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45181804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Gait based human identification: a comparative analysis","authors":"Kubilay Muhammed Sünnetci, M. Ordu, A. Alkan","doi":"10.53070/bbd.989226","DOIUrl":"https://doi.org/10.53070/bbd.989226","url":null,"abstract":"","PeriodicalId":41917,"journal":{"name":"Computer Science-AGH","volume":"1 1","pages":""},"PeriodicalIF":0.5,"publicationDate":"2021-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41382197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Kubilay Muhammed Sünnetci, Ahmet Alkan, Edanur Tar
— COVID-19 pandemic first broke out in December 2019 and has been affecting the world ever since. The number of COVID-19 patients is increasing rapidly in the world day by day, and it is known that the diagnosis of this disease is important for disease treatment. Chest X-ray images that are clinical adjuncts are widely used in the diagnosis of COVID-19 disease. In the study, machine learning-based models are developed using these images to reduce the workload of expert. In the data set used in the study, there are images obtained from a total of 137 COVID-19, 90 normal, and 90 pneumonia subjects. Here, 1000 image features are extracted for each image using AlexNet deep learning architecture. Afterward, the classifiers used in the study are trained using these image features. From the results, Accuracy (%), Sensitivity (%), Specificity (%), Precision (%), F1 score (%), and Matthews Correlation Coefficient (Matthews Correlation Coefficient, MCC) values of Cubic SVM that is the most successful classifier are equal to 95.27, 94.95, 97.76, 94.65, 94.79, and 0.9250, respectively.
{"title":"Göğüs X-Ray görüntülerinin AlexNet tabanlı sınıflandırılması","authors":"Kubilay Muhammed Sünnetci, Ahmet Alkan, Edanur Tar","doi":"10.53070/bbd.989192","DOIUrl":"https://doi.org/10.53070/bbd.989192","url":null,"abstract":"— COVID-19 pandemic first broke out in December 2019 and has been affecting the world ever since. The number of COVID-19 patients is increasing rapidly in the world day by day, and it is known that the diagnosis of this disease is important for disease treatment. Chest X-ray images that are clinical adjuncts are widely used in the diagnosis of COVID-19 disease. In the study, machine learning-based models are developed using these images to reduce the workload of expert. In the data set used in the study, there are images obtained from a total of 137 COVID-19, 90 normal, and 90 pneumonia subjects. Here, 1000 image features are extracted for each image using AlexNet deep learning architecture. Afterward, the classifiers used in the study are trained using these image features. From the results, Accuracy (%), Sensitivity (%), Specificity (%), Precision (%), F1 score (%), and Matthews Correlation Coefficient (Matthews Correlation Coefficient, MCC) values of Cubic SVM that is the most successful classifier are equal to 95.27, 94.95, 97.76, 94.65, 94.79, and 0.9250, respectively.","PeriodicalId":41917,"journal":{"name":"Computer Science-AGH","volume":" ","pages":""},"PeriodicalIF":0.5,"publicationDate":"2021-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48355703","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"SMARfacTory-Net: Mermerin Sınıflandırılması için Bilgisayarlı Görü, QR Kod ve Android Tabanlı Teknolojilerle Desteklenen Sistem Tasarımının Geliştirilmesi","authors":"Çağlar Gürkan, Merih Palandöken","doi":"10.53070/bbd.990867","DOIUrl":"https://doi.org/10.53070/bbd.990867","url":null,"abstract":"","PeriodicalId":41917,"journal":{"name":"Computer Science-AGH","volume":" ","pages":""},"PeriodicalIF":0.5,"publicationDate":"2021-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46622407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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