Yazar "Karcioglu A.A." seçeneğine göre listele

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    Automatic Summary Extraction in Texts Using Genetic Algorithms [Genetik Algoritmalar Kullanilarak Metinlerde Otomatik Ozet Cikarma]
    (Institute of Electrical and Electronics Engineers Inc., 2020) Karcioglu A.A.; Yasa A.C.
    Automatic text summarization is one of the applications of natural language processing that has been studied for a long time. The increase in the amount of information in web resources has increased the need for automatic text summarization methods. It is difficult to design a system to produce abstracts created by human hands. For this reason, many researchers have focused on extracting sentences or paragraphs, which is a kind of summary. In this study, we introduce a method that was created using genetic algorithms to generate such summaries. After the texts are preprocessed, vocabulary is created and given as input to the proposed method. The sentence selection based on Genetic Algorithm is used to summarize and after that the summary is created, it is evaluated using the fitness function. In our first model, the fitness function is based on the frequency of each word and the word pair frequencies. The results of the applied model are discussed using the same dataset in another method based on tf-idf, with precision, recall, fscore and Rouge metrics. © 2020 IEEE.
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    Performance Evaluation of Classification Algorithms Using Hyperparameter Optimization
    (Institute of Electrical and Electronics Engineers Inc., 2021) Karcioglu A.A.; Bulut H.
    Classification problems have an important role in the field of machine learning and data mining. Classification problems are used in different areas such as disease diagnosis, estimation of bank customers, drug studies, sentiment analysis. Many classification algorithms have been developed in the literature and these algorithms have many different parameter inputs. In this study, it is aimed to increase the classification success by using hyperparameter optimization algorithms. K-nearest neighbor, support vector machines, decision tree and gradient boosting classification algorithms were applied to the frequently used 'heart and iris' datasets in the literature. Grid search and random search algorithms, which are hyperparameter optimization algorithms, are applied to these selected classification algorithms. As a result of the experimental studies, it has been observed that the accuracy of all classification algorithms increases when hyperparameter optimization algorithms are applied. The parameter values that give the best results are shown. © 2021 IEEE
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    Sentiment analysis of Turkish and english twitter feeds using Word2Vec model [Word2Vec modelini kullanarak türkçe ve ingilizce twitter mesajlarinin duygu analizi]
    (Institute of Electrical and Electronics Engineers Inc., 2019) Karcioglu A.A.; Aydin T.
    Social media has become an important part of daily life. With twitter, one of the most popular social media services, users express their feelings and thoughts to the whole world using twitter posts. For this reason, twitter feeds have become an important source of sentiment analysis. In this study, the apply of Word2Vec model in the classification of labeled data in English and Turkish Twitter feeds and the effect of getting root on feeds to Word2Vec model are investigated. Our study has two different data sets, English and Turkish. BOW and Word2Vec models were applied to each data set in the case where twitter feeds were not get roots and get roots were extracted. In this study, which is implemented in the Python programming language, the success percentages are compared by applying the scikit-learn classification algorithms, Linear SVM and Logistic Regression. © 2019 IEEE.