WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7150

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Institution Author: search.filters.institutionAuthor.Oğul, İskender Ülgen

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  • Conference Object
    Citation - WoS: 2
    Word2vec Kullanarak Eş Anlamlılık Temelinde Anahtar Kelime Çıkarımı
    (IEEE, 2019) Oğul, İskender Ülgen; Özcan, Caner; Hakdağlı, Özlem
    Nowadays, the data revealed by the online individuals are increasing exponentially. The raw information that increasing data holds, transformed into meaningful outputs using machine learning and deep learning methods. Generally, supervised learning methods are used for information extraction and classification. Supervised learning is based on the training set that classification algorithms are trained. In the proposed approach, keyword extraction solution is proposed to classify text data more convenient. The developed solution is based on the Word2Vec algorithm, which works by taking into consideration the semantic meaning of the words unlike general approaches that based on word frequency. A new approach, word embedding algorithm named Word2Vec, works by calculating the word weights, semantic relationship, and the final weights of vectors. The obtained keywords are trained with Name Bayes and Decision Trees methods and the performance of the proposed method is shown by classification example.
  • Article
    Citation - WoS: 5
    Citation - Scopus: 7
    Fast Texture Classification of Denoised Sar Image Patches Using Glcm on Spark
    (Türkiye Klinikleri Journal of Medical Sciences, 2020) Özcan, Caner; Ersoy, Okan; Oğul, İskender Ülgen
    Classification of a synthetic aperture radar (SAR) image is an essential process for SAR image analysis and interpretation. Recent advances in imaging technologies have allowed data sizes to grow, and a large number of applications in many areas have been generated. However, analysis of high-resolution SAR images, such as classification, is a time-consuming process and high-speed algorithms are needed. In this study, classification of high-speed denoised SAR image patches by using Apache Spark clustering framework is presented. Spark is preferred due to its powerful open-source cluster-computing framework with fast, easy-to-use, and in-memory analytics. Classification of SAR images is realized on patch level by using the supervised learning algorithms embedded in the Spark machine learning library. The feature vectors used as the classifier input are obtained using gray-level cooccurrence matrix which is chosen to quantitatively evaluate textural parameters and representations. SAR image patches used to construct the feature vectors are first applied to the noise reduction algorithm to obtain a more accurate classification accuracy. Experimental studies were carried out using naive Bayes, decision tree, and random forest algorithms to provide comparative results, and significant accuracies were achieved. The results were also compared with a state-of-the-art deep learning method. TerraSAR-X images of high-resolution real-world SAR images were used as data.
  • Conference Object
    Citation - WoS: 2
    Stream Text Data Analysis on Twitter Using Apache Spark Streaming
    (Institute of Electrical and Electronics Engineers Inc., 2018) Hakdağlı, Özlem; Özcan, Caner; Oğul, İskender Ülgen
    With today's developing technology, people's access to information and its production have reached a very fast level. These generated and obtained information are instantly created, entered into data systems and updated. Sources of streaming data can be transformed into valuable analysis results when they are handled with targeted methods. In this study, a text data field is determined to perform analysis on instantaneous generated data and Twitter, the richest platform for instant text data, is used. Twitter instantly generates a variety of data in large quantities and it presents it as open source using an API. A machine learning framework Apache Spark's stream analysis environment is used to analyze these resources. Situation analysis was performed using Support Vector Machine, Decision Trees and Logistic Regression algorithms presented under this environment. The results are presented in tables.