Oğul, İskender Ülgen

Profile URL
https://hdl.handle.net/11147/16029
Name Variants
Job Title
Email Address
Main Affiliation
01. Izmir Institute of Technology
Status
Website
ORCID ID
Scopus Author ID
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID

Sustainable Development Goals

NO POVERTY1
NO POVERTY
0
Research Products
ZERO HUNGER2
ZERO HUNGER
0
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GOOD HEALTH AND WELL-BEING3
GOOD HEALTH AND WELL-BEING
0
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QUALITY EDUCATION4
QUALITY EDUCATION
0
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GENDER EQUALITY5
GENDER EQUALITY
0
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CLEAN WATER AND SANITATION6
CLEAN WATER AND SANITATION
0
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AFFORDABLE AND CLEAN ENERGY7
AFFORDABLE AND CLEAN ENERGY
0
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DECENT WORK AND ECONOMIC GROWTH8
DECENT WORK AND ECONOMIC GROWTH
0
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INDUSTRY, INNOVATION AND INFRASTRUCTURE9
INDUSTRY, INNOVATION AND INFRASTRUCTURE
2
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REDUCED INEQUALITIES10
REDUCED INEQUALITIES
0
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SUSTAINABLE CITIES AND COMMUNITIES11
SUSTAINABLE CITIES AND COMMUNITIES
0
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RESPONSIBLE CONSUMPTION AND PRODUCTION12
RESPONSIBLE CONSUMPTION AND PRODUCTION
0
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CLIMATE ACTION13
CLIMATE ACTION
0
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LIFE BELOW WATER14
LIFE BELOW WATER
0
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LIFE ON LAND15
LIFE ON LAND
0
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PEACE, JUSTICE AND STRONG INSTITUTIONS16
PEACE, JUSTICE AND STRONG INSTITUTIONS
0
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PARTNERSHIPS FOR THE GOALS17
PARTNERSHIPS FOR THE GOALS
0
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Scholarly Output

5

Articles

1

Views / Downloads

7497/1551

Supervised MSc Theses

1

Supervised PhD Theses

0

WoS Citation Count

9

Scopus Citation Count

7

Patents

0

Projects

0

WoS Citations per Publication

1.80

Scopus Citations per Publication

1.40

Open Access Source

4

Supervised Theses

1

JournalCount
2018 26th Signal Processing and Communications Applications Conference, SIU 20181
2019 27th Signal Processing and Communications Applications Conference (SIU)1
SIU 2021 - 29th IEEE Conference on Signal Processing and Communications Applications, Proceedings1
Turkish Journal of Electrical Engineering and Computer Sciences1
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Author of Publication: search.filters.isAuthorOfPublication.be862efe-75e9-419e-ba38-31b53523038c

Scholarly Output Search Results

Now showing 1 - 5 of 5
  • Conference Object
    Doğal Dil Çıkarımı Modellerinde Bert Vektörlerinin Başarım Değerlendirmesi
    (Institute of Electrical and Electronics Engineers Inc., 2021) Tekir, Selma; Oğul, İskender Ülgen; 03.04. Department of Computer Engineering; 01. Izmir Institute of Technology; 03. Faculty of Engineering
    Doğal dil çıkarımı, düşünce ifade eden cümlelerin arasındaki ilişkiyi; karşıtlık, gerekseme veya tarafsızlık olarak sınıflandırmayı hedefler. Sınıflandırma görevini gerçekleştirmek için metinsel kaynaklar, vektör ya da gömme olarak adlandırılan matematiksel gösterimlere dönüştürülür. Bu çalışmada, hem statik (Glove, OntoNotes5) hem de bağlamsal (BERT) kelime gömme yöntemleri kullanılmıştır. Fikirsel cümleler arasındaki mantıksal ilişkilerin sınıflandırılması zordur zira cümleler karmaşık gramer yapılarına sahiptir ve cümlelerin işlenerek mantıksal gösterimlere dönüştürülmesi geleneksel doğal dil işleme çözümleri ile yetersiz kalmaktadır. Bu çalışma, sınıflandırma görevini gerçekleştirmek için ayrıştırılabilir ilgi ve doğal dil çıkarımı için gelişmiş LSTM (ESIM) derin öğrenme modellerini kullanmıştır. En iyi sonuç olan %88 doğruluk değeri SNLI veri kümesi üzerinde ESIM-BERT ile elde edilmiştir.
  • 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; Oğul, İskender Ülgen; Özcan, Caner; Oğul, İskender Ülgen; 01. Izmir Institute of Technology
    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.
  • 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) Oğul, İskender Ülgen; Ersoy, Okan; Oğul, İskender Ülgen; 01. Izmir Institute of Technology
    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
    Word2vec Kullanarak Eş Anlamlılık Temelinde Anahtar Kelime Çıkarımı
    (IEEE, 2019) Oğul, İskender Ülgen; Oğul, İskender Ülgen; Özcan, Caner; Hakdağlı, Özlem; 01. Izmir Institute of Technology
    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.
  • Master Thesis
    Classification of Contradictory Opinions in Text Using Deep Learning Methods
    (01. Izmir Institute of Technology, 2020) Oğul, İskender Ülgen; Tekir, Selma; Tekir, Selma; 03.04. Department of Computer Engineering; 01. Izmir Institute of Technology; 03. Faculty of Engineering
    Natural language inference (NLI) problem aims to ensure consistency as well as accuracy of propositions while making sense of natural language. Natural language inference aims to classify the relationship between two given sentences as contradiction, entailment or neutrality. To accomplish the classification task, sentences or words must be translated into mathematical representations called vectors or embedding. Vectorization of a sentence is as important as the complexity of the classification model. In this study, both pre-trained (Glove, Fasttext, Word2Vec) and contextual word embedding methods (BERT) were used for comparison and acquire the best result. One of the natural language processing tasks NLI, is highly complex and requires solutions. Conventional machine learning methods are insufficient to carry out natural language processing solutions. Therefore, more advanced solutions are required. This study used deep learning methods to perform the classification task. Unlike conventional machine learning approaches, deep learning approaches reduce errors while increasing accuracy by repeating the data many times. Opinion sentences have complex grammatical structures that are difficult to classify. This study used Decomposable Attention and Enhanced LSTM for natural language inference to perform NLI classification task. Using the advanced LSTM deep learning method and Bert contextual vectors for natural language extraction on the SNLI dataset, an accuracy result 88.0% very close state of the art result 92.1% was obtained. In order to show the usability of the developed solution in different NLI tasks, an accuracy of 80.02% was obtained in the studies performed on the MNLI data set.