Browsing by Author "Kaynar, Oǧuz"

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Citation - WoS: 3
Citation - Scopus: 12
NSEM: Duygu Analizi için Özgün Yıǧınlanmiş Topluluk Yöntemi
(Institute of Electrical and Electronics Engineers Inc., 2019) Işik, Yunus Emre; Görmez, Yasin; Kaynar, Oǧuz; Aydin, Zafer
Today, people often share their ideas, opinions and feelings through forums, social media sites, blogs and similar platforms. For this reason, access to these data has become very easy. Increase in the number of shares makes it possible to analyze and use these data in terms of marketing and politics. However, due to the large number of data, it is impossible that this analysis will be done by humans. Determination of what type of emotion is included automatically is done by sentiment analysis methods. In these methods, the text is defined as a mathematical vector and classified by machine learning methods. Ensemble methods are one of the most important methods used as classifiers in sentiment analysis. In these methods, a classifier error is tried to be solved by another classifier. In sentiment analysis, the feature vector that describes the text is as important as the classifier. Feature vectors obtained using different methods can make mistakes in different places. For this reason, in this study, NSEM is proposed for sentiment analysis, which is a new ensemble method that uses 2 different classifiers and 2 different feature extraction methods. As a result of the analysis, the proposed method is the most successful method with an accuracy rate of 79.1%. © 2019 Elsevier B.V., All rights reserved.
Citation - Scopus: 3
Protein İkincil Yapı Tahmini Için Makine Öǧrenmesi Yöntemlerinin Karşılaştırılması
(Institute of Electrical and Electronics Engineers Inc., 2018) Aydin, Zafer; Kaynar, Oǧuz; Görmez, Yasin; Işik, Yunus Emre
Three-dimensional structure prediction is one of the important problems in bioinformatics and theoretical chemistry. One of the most important steps in the three-dimensional structure prediction is the estimation of secondary structure. Due to rapidly growing databases and recent feature extraction methods datasets used for predicting secondary structure can potentially contain a large number of samples and dimensions. For this reason, it is important to use algorithms that are fast and accurate. In this study, various classification algorithms have been optimized for the second phase of a two-stage classifier on EVAset benchmark both in the original input space and in the space reduced using the information gain metric. The most accurate classifier is obtained as the support vector machine while the extreme learning machine is significantly faster in model training. © 2018 Elsevier B.V., All rights reserved.
Citation - Scopus: 1
Protein İkincil Yapı Tahmini için NR ve UniClust Veri Tabanlarının Karşılaştırılması
(Institute of Electrical and Electronics Engineers Inc., 2018) Aydin, Zafer; Kaynar, Oǧuz; Görmez, Yasin
Three-dimensional structure prediction is one of the important problems in bioinformatics and theoretical chemistry. One of the most important steps in the three-dimensional structure prediction is the estimation of secondary structure. Improving the accuracy rate in protein secondary structure prediction depends on computed attributes as well as the classification algorithms. In multiple alignment methods, which are often used to extract an attribute, the calculated values differ according to the database used for the alignment. For this reason, it is important to use a suitable database against which the target proteins are aligned to compute profile feature vectors. In this study, 5 different datasets are generated for the CB513 benchmark with the aid of two different alignment methods and three different databases. The profile features are fed as input to a two-stage hybrid classifier. According to the experimental results, the highest accuracy rate is obtained when UniClust database is used at the first stage of HHBlits alignment to calculate PSSM values and NR database is used at the first stage of HHBlits alignment to calculate structural profile matrices. © 2018 Elsevier B.V., All rights reserved.