Browsing by Author "Hark, Cengiz"

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Ağırlıklandırılmış Çizgelerde Tf-ıdf ve Eigen Ayrışımı Kullanarak Metin Sınıflandırma
(2019) Karcı, Ali; Uçkan, Taner; Seyyarer, Ebubekir; Hark, Cengiz
Günümüzde gerek metin gerekse cümle sınıflandırma problemleri üzerinde yoğunlukla çalışılmaktadır. Metinsınıflandırma işlemlerinde en önemli problemlerden biri sınıflandırılacak metinlerin yapısal olmamasıdır. Belli birformata sahip olmayan metinlerin öncelikle bir önişlemden geçirilmesi gerekmektedir. Bu çalışmada metinlerisınıflandırma işleminde öncelikle sınıflandırılacak metinlerin önişlemini yapmak amacıyla KUSH (Karci-UçkanSeyyarer-Hark) adında bir önişleme aracı geliştirildi. Sonrasında elde edilen işlenmiş metinlerinsınıflandırılmasında çizge tabanlı matematiksel bir yaklaşım sunulmaktadır. Yapılan çalışmada Türkiye’de iyibilinen 6 haber portalından ve 6 farklı alandan elde edilen metinleri içeren TTC-3600 veri seti kullanılmaktadır.Sınıflandırılacak metinler Tf (Terim frekansı) ve Idf (Ters doküman Frekansı) değerleri dikkate alınarak çeşitliönişlemlerden geçirildikten sonra kenar ve düğümlerden oluşan bir ağırlıklı çizge oluşturulmaktadır.Ağırlıklandırılmış çizgeler kullanılarak sınıflandırma işleminin etkililiği ve matematiksel verimliliği arttırılmıştır.Elde edilen çizgeyi ifade eden Komşuluk Matrisi ve Derece Matrisi kullanılarak Laplace Matrisi elde edilmektedir.Laplace Matrisinin özdeğer ayrışımı sonucunda elde edilen özdeğer ve özdeğer vektörleri ile metinlersınıflandırılmaktadır. Yapılan testler sonucunda sınıflandırma oranlarında dikkate değer bir doğruluk değerineulaşıldığı görülmektedir.
Applications and Comparisons of Optimization Algorithms Used in Convolutional Neural Networks
(Ieee, 2019) Seyyarer, Ebubekir; Uckan, Taner; Hark, Cengiz; Ayata, Faruk; Inan, Mevlut; Karci, Ali
Nowadays, it is clear that the old mathematical models are incomplete because of the large size of image data set. For this reason, the Deep Learning models introduced in the field of image processing meet this need in the software field In this study, Convolutional Neural Network (CNN) model from the Deep Learning Algorithms and the Optimization Algorithms used in Deep Learning have been applied to international image data sets. Optimization algorithms were applied to both datasets respectively, the results were analyzed and compared The success rate was approximately 96.21% in the Caltech 101 data set, while it was observed to be approximately 10% in the Cifar-100 data set.
Catsumm: Extractive Text Summarization Based on Spectral Graph Partitioning and Node Centrality
(2021) Karcı, Ali; Uçkan, Taner; Hark, Cengiz
In this paper, we introduce CatSumm (Cengiz, Ali, Taner Summarization), a novel method for multi-document document summarisation. The suggested method forms a summarization according to three main steps: Representation of input texts, the main stages of the CatSumm model, and sentence scoring. A Text Processing software, is introduced and used to protect the semantic loyalty between word groups at stage of representation of input texts. Spectral Sentence Clustering (SSC), one of the main stages of the CatSumm model, is the summarization process obtained from the proportional values of the sub graphs obtained after spectral graph segmentation. Obtaining super edges is another of the main stages of the method, with the assumption that sentences with weak values below a threshold value calculated by the standard deviation (SD) cannot be included in the summary. Using the different node centrality methods of the CatSumm approach, it forms the sentence rating phase of the recommended summarising approach, determining the significant nodes and hence significant nodes. Finally, the result of the CatSumm method for the purpose of text summarisation within the in the research was measured ROUGE metrics on the Document Understanding Conference (DUC-2004, DUC-2002) datasets. The presented model produced 44.073%, 53.657%, and 56.513% summary success scores for abstracts of 100, 200 and 400 words, respectively.
Extractive Text Summarization Via Graph Entropy
(Ieee, 2019) Hark, Cengiz; Uckan, Taner; Seyyarer, Ebubekir; Karci, Ali
There is growing interest in automatic summarizing systems. This study focuses on a subtractive, general and unsupervised summarization system. It is provided to represent the texts to be summarized with graphs and then graph entropy is used to interpret the structural stability and structural information content on the graphs representing the text files. The performance of the proposed text summarizing approach for the purpose of summarizing the text on the data set of Document Understanding Conference (DUC-2002) including open access texts and summaries of these texts was calculated using the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) evaluation metrics. Experimental processes were repeated for 200 and 400 word abstracts. Experimental results reveale that the proposed text summarizing system performs competitively with competitive methods for different ROUGE metrics.
Metin Özetlemesi için Düğüm Merkezliklerine Dayalı Denetimsiz Bir Yaklaşım
(2019) Hark, Cengiz; Karcı, Ali; Seyyarer, Ebubekir; Uçkan, Taner
Cümle seçerek özetleme çalışmaları kapsamında birçok farklı yaklaşım mevcuttur. Bu çalışmada tek dokümanlıçıkarıcı metin özetleme için yeni ve denetimsiz bir süreç önerilmektedir. Çalışma kapsamında metin dokümanlarıçizgelerle temsil edilmektedir. Sunulan yaklaşım temel olarak metinleri temsil eden çizgeleri kullanmakta vecümlelere yönelik bir ağırlıklandırma önermektedir. Önerilen sürecin farklı düğüm ağırlıklandırma yöntemlerinikullanarak önemli düğümleri belirlenmesi, önerilen özetleme sisteminin cümle puanlandırma aşamasınıoluşturmaktadır. Son olarak bu çalışma kapsamında metin özetleme amaçlı önerilen yaklaşımın, açık erişimlimetinler ve bu metinlere ait özetleri içeren Document Understanding Conference (DUC-2002) veri seti üzerindekiperformansı ROUGE değerlendirme metrikleri kullanılarak hesaplanmıştır. Yapılan deneysel çalışmalarsonucunda önerilen özetleme sisteminin geleneksel çizge tabanlı yaklaşımlar ile rekabet edebilir ölçüdeperformans değerleri ortaya koyduğunu göstermektedir. Önerilen özetleme yaklaşımı ile elde edilen ROUGE-2metriğinin Duyarlılık, Kesinlik ve F-Skor değerleri sırasıyla 0.17068, 0.15772, 0.16383 olarak hesaplandı. Ayrıcasunulan bu basit ve etkili yöntemin dilbilimsel bir süreç izlememesi oldukça önemlidir.
A New Multi-Document Summarisation Approach Using Saplings Growing-Up Optimisation Algorithms: Simultaneously Optimised Coverage and Diversity
(Sage Publications Ltd, 2024) Hark, Cengiz; Uckan, Taner; Karci, Ali
Automatic text summarisation is obtaining a subset that accurately represents the main text. A quality summary should contain the maximum amount of information while avoiding redundant information. Redundancy is a severe deficiency that causes unnecessary repetition of information within sentences and should not occur in summarisation studies. Although many optimisation-based text summarisation methods have been proposed in recent years, there exists a lack of research on the simultaneous optimisation of scope and redundancy. In this context, this study presents an approach in which maximum coverage and minimum redundancy, which form the two key features of a rich summary, are modelled as optimisation targets. In optimisation-based text summarisation studies, different conflicting objectives are generally weighted or formulated and transformed into single-objective problems. However, this transformation can directly affect the quality of the solution. In this study, the optimisation goals are met simultaneously without transformation or formulation. In addition, the multi-objective saplings growing-up algorithm (MO-SGuA) is implemented and modified for text summarisation. The presented approach, called Pareto optimal, achieves an optimal solution with simultaneous optimisation. Experimentation with the MO-SGuA method was tested using open-access (document understanding conference; DUC) data sets. Performance success of the MO-SGuA approach was calculated using the recall-oriented understudy for gisting evaluation (ROUGE) metrics and then compared with the competitive practices used in the literature. Testing achieved a 26.6% summarisation result for the ROUGE-2 metric and 65.96% for ROUGE-L, which represents an improvement of 11.17% and 20.54%, respectively. The experimental results showed that good-quality summaries were achieved using the proposed approach.
Overcurrent Relay Coordination of 154/34,5 Kv Hasancelebi Substation by League Championship Algorithm
(Ieee, 2017) Seyyarer, Abubekir; Akdag, Ozan; Hark, Cengiz; Karci, Ali; Yeroglu, Celaleddin
In this study, non-directional overcurrent relay coordination was done in 154/34.5 kV Malatya Teias Hasancelebi transformer centre using League Championship Algorithm (LCA). Standard inverse time characteristic based on IEC 255-3 is used for the relay is coordinated. The results obtained by the LCA have been used in virtual model, obtained by DigSilent software for overcurrent relays at the Hasancelebi transformer centre. Then, the overcurrent relay coordination was performed by examining the response of the overcurrent relays to the 3-phase fault currents generated in the model.
Ssc: Clustering of Turkish Texts by Spectral Graph Partitioning
(Gazi Univ, 2021) Uckan, Taner; Hark, Cengiz; Karci, Ali
There is growing interest in studies on text classification as a result of the exponential increase in the amount of data available. Many studies have been conducted in the field of text clustering, using different approaches. This study introduces Spectral Sentence Clustering (SSC) for text clustering problems, which is an unsupervised method based on graph-partitioning. The study explains how the proposed model proposed can be used in natural language applications to successfully cluster texts. A spectral graph theory method is used to partition the graph into non-intersecting sub-graphs, and an unsupervised and efficient solution is offered for the text clustering problem by providing a physical representation of the texts. Finally, tests have been conducted demonstrating that SSC can be successfully used for text categorization. A clustering success rate of 97.08% was achieved in tests conducted using the TTC-3600 dataset, which contains open-access unstructured Turkish texts, classified into categories. The SSC model proposed performed better compared to a popular k-means clustering algorithm.