MF - Makale Koleksiyonu | Bilgisayar Mühendisliği Bölümü / Department of Computer EngineeringBilgisayar Mühendisliği Bölümüne ait makale koleksiyonunu içerir.https://hdl.handle.net/11729/612024-03-29T02:32:24Z2024-03-29T02:32:24ZMitosis detection using generic features and an ensemble of cascade adaboostsTek, Faik Borayhttps://hdl.handle.net/11729/53682023-02-13T12:28:03Z2013-05-30T00:00:00ZMitosis detection using generic features and an ensemble of cascade adaboosts
Tek, Faik Boray
Context: Mitosis count is one of the factors that pathologists use to assess the risk of metastasis and survival of the patients, which are affected by the breast cancer. Aims: We investigate an application of a set of generic features and an ensemble of cascade adaboosts to the automated mitosis detection. Calculation of the features rely minimally on object -level descriptions and thus require minimal segmentation. Materials and Methods: The proposed work was developed and tested on International Conference on Pattern Recognition (ICPR) 2012 mitosis detection contest data. Statistical Analysis Used: We plotted receiver operating characteristics curves of true positive versus false positive rates; calculated recall, precision, F -measure, and region overlap ratio measures. Results: We tested our features with two different classifier configurations: 1)An ensemble of single adaboosts, 2) an ensemble of cascade adaboosts. On the ICPR 2012 mitosis detection contest evaluation, the cascade ensemble scored 54, 62.7, and 58, whereas the non -cascade version scored 68, 28.1, and 39.7 for the recall, precision, and F -measure measures, respectively. Mostly used features in the adaboost classifier rules were a shape‑based feature, which counted granularity and a color-based feature, which relied on Red, Green, and Blue channel statistics. Conclusions: The features, which express the granular structure and color variations, are found useful for mitosis detection. The ensemble of adaboosts performs better than the individual adaboost classifiers. Moreover, the ensemble of cascaded adaboosts was better than the ensemble of single adaboosts for mitosis detection.
2013-05-30T00:00:00ZA novel hybrid edge detection technique: ABC-FAYelmenoğlu, Elif DenizÇelebi, NumanTaşçı, Tuğrulhttps://hdl.handle.net/11729/52702023-01-21T04:12:40Z2017-11-09T00:00:00ZA novel hybrid edge detection technique: ABC-FA
Yelmenoğlu, Elif Deniz; Çelebi, Numan; Taşçı, Tuğrul
Image processing is a vast research field with diversified set of practices utilized in so many application areas such as military, security, medical imaging, machine learning and computer vision based on extracted useful information from any kind of image data. Edges within images are undoubtedly accepted as one of the most significant features providing substantial practical information for various applications working on top of miscellaneous optimization algorithms to achieve better results. Artificial Bee Colony and Firefly algorithms are recently developed optimization algorithms and are used to obtain better results for various problems. In this study, a novel hybrid optimization technique is proposed by combining those algorithms aiming better quality in edge detection on grayscale images. The performance of the proposed algorithm is compared with individual performances of Artificial Bee Colony algorithm and the fundamental edge detection methods. The results are demonstrated that the proposed method is encouraging and also produces meaningful results for similar applications.
2017-11-09T00:00:00ZProgramlamaya giriş dersini alan öğrencilerin programlama öz yeterlilik algılarının ve programlamaya bakış açılarının incelenmesiBenli, Kristin SurpuhiTek, Faik Borayhttps://hdl.handle.net/11729/50422023-03-07T10:56:43Z2021-05-29T00:00:00ZProgramlamaya giriş dersini alan öğrencilerin programlama öz yeterlilik algılarının ve programlamaya bakış açılarının incelenmesi
Benli, Kristin Surpuhi; Tek, Faik Boray
Bu çalışmada üniversite öğrencilerinin Java programlama öz yeterlilik algıları, programlama öğrenme istekleri ve çalışma alışkanlıkları çeşitli değişkenlere göre (cinsiyet, bölüm, eğitim dili, harf notu, ders tekrarları vb.) istatistiksel yöntemler kullanılarak (T-testi, Mann Whitney U-testi, Kruskal Wallis H testi, tek yönlü varyans analizi, Ki-Kare testi) incelenmiştir. Çalışma grubu, farklı bölümlerde zorunlu olarak programlamaya giriş dersini alan 191 lisans öğrencisinden oluşmaktadır. Elde edilen sonuçlara göre öğrencilerin Java programlama öz yeterlilik algıları bölümlerine ve programlama öğrenme isteklerine göre farklılaşmaktadır. Çalışmada ayrıca Apriori algoritması kullanılarak birliktelik kuralları çıkartılmıştır. En yüksek güven değeri elde edilen kurala göre, programlama öğrenmeyi çok fazla isteyen, programlama öğrenmenin iş hayatında kendisine fayda sağlayacağını düşünen ve programlama dersinden başarı ile geçen öğrencilerin programlama öz yeterlilikleri yüksektir.; This study investigates university student’s (Java) programming self-efficacy, perspectives on programming, and study habits with respect to various variables (gender, department, course language, letter grade, course repeats) by using statistical methods (T-test, Mann Whitney U-test, Kruskal Wallis H test, one-way analysis of variance, Chi-Square test). The study group consisted of 191 undergraduate students who had to take the introductory programming course from different departments. The results indicate that students’ Java programming selfefficacy have relationships with their department and desire to learn programming. Furthermore, in this study, association rules were obtained by using Apriori algorithm. The highest confidence value rule suggests that higher programming self-efficacy can be associated with higher levels of desire to learn programming, higher belief in the utility of programming in professional life and having successfully passed the programming course.
2021-05-29T00:00:00ZFrom past to present: spam detection and identifying opinion leaders in social networksAltınel Girgin, Ayşe BernaGümüşçekiçci, Gizemhttps://hdl.handle.net/11729/48182024-03-27T14:59:29Z2022-06-22T00:00:00ZFrom past to present: spam detection and identifying opinion leaders in social networks
Altınel Girgin, Ayşe Berna; Gümüşçekiçci, Gizem
On microblogging sites, which are gaining more and more users every day, a wide range of ideas are quickly emerging, spreading, and creating interactive environments. In some cases, in Turkey as well as in the rest of the world, it was noticed that events were published on microblogging sites before appearing in visual, audio and printed news sources. Thanks to the rapid flow of information in social networks, it can reach millions of people in seconds. In this context, social media can be seen as one of the most important sources of information affecting public opinion. Since the information in social networks became accessible, research started to be conducted using the information on the social networks. While the studies about spam detection and identification of opinion leaders gained popularity, surveys about these topics began to be published. This study also shows the importance of spam detection and identification of opinion leaders in social networks. It is seen that the data collected from social platforms, especially in recent years, has sourced many state-of-art applications. There are independent surveys that focus on filtering the spam content and detecting influencers on social networks. This survey analyzes both spam detection studies and opinion leader identification and categorizes these studies by their methodologies. As far as we know there is no survey that contains approaches for both spam detection and opinion leader identification in social networks. This survey contains an overview of the past and recent advances in both spam detection and opinion leader identification studies in social networks. Furthermore, readers of this survey have the opportunity of understanding general aspects of different studies about spam detection and opinion leader identification while observing key points and comparisons of these studies.
This work is supported in part by the Scientific and Technological Research Council of Turkey (TUBITAK) through grant number 118E315 and grant number 120E187. Points of view in this document are those of the authors and do not necessarily represent the official position or policies of TUBITAK.
2022-06-22T00:00:00Z