Yazar "Hark, Cengiz" seçeneğine göre listele
Listeleniyor 1 - 14 / 14
Sayfa Başına Sonuç
Sıralama seçenekleri
Öğe Ağırlıklandırılmış Çizgelerde Tf-Idf ve Eigen Ayrışımı Kullanarak Metin Sınıflandırma(2019) Hark, Cengiz; Karcı, Ali; Uçkan, Taner; Seyyarer, EbubekirÖz: 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.Öğe Applications and Comparisons of Optimization Algorithms Used in Convolutional Neural Networks(Ieee, 2019) Seyyarer, Ebubekir; Uckan, Taner; Hark, Cengiz; Ayata, Faruk; Inan, Mevlut; Karci, AliNowadays, 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.Öğe CatSumm: Extractive Text Summarization based on Spectral Graph Partitioning and Node Centrality(2021) Uçkan, Taner; Hark, Cengiz; Karcı, AliIn 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.Öğe Comparison of Deep Learning Models and Optimization Algorithms in the Detection of Scoliosis and Spondylolisthesis from X-Ray Images(2024) Akkaya, Abdullah Erhan; Güneş, Harun; Hark, CengizThe spine is composed of pieces of bone called vertebrae that lie between the skull and the tailbone. Various medical conditions can affect the spine. In this study, two types of degenerative diseases, scoliosis, and spondylolisthesis, were studied. Deep AI architectures have recently enabled further disease diagnosis innovation using medical images. Various traditional and deep learning studies use medical images for disease diagnosis in the literature. This study aims to classify spine X-ray images according to three possible conditions (Normal, Scoliosis, and Spondylolisthesis) and to exploit the potential of these X-ray images to detect possible diseases occurring in the spine. The performance of deep learning models and optimization algorithms used in this process was evaluated. The study uses a data set created and/or analyzed during an existing study. This data set consists of images that belong to three different classes: scoliosis, spondylolisthesis, or x-ray images of normal (i.e. healthy) individuals. A total of 338 spine X-ray images, 188 scoliosis images, 79 spondylolisthesis images, and 71 normal images. Six different deep-learning architectures have been used in the study. These architectures are Alexnet, GoogLeNet, ResNet-18, ResNet-50, ResNet-101, and EfficientNet-bo. While working on these deep architectures, each model has been evaluated using different optimization algorithms. These optimization algorithms are RmsProp, SGDM, and Adam. According to the classification processes, the deep learning model with the highest accuracy value was Alexnet, and the optimization algorithm used with it, Sgdm (99.01%), and the training time lasted 38 seconds. According to the classification processes, the deep learning model with the fastest completion time (30 seconds) was Alexnet and the optimization algorithm used with it was RmsProp. An accuracy rate of 98.02% has been obtained in the training of this model.Öğe Extractive Text Summarization via Graph Entropy(Ieee, 2019) Hark, Cengiz; Uckan, Taner; Seyyarer, Ebubekir; Karci, AliThere 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.Öğe GloVe Kelime Gömmeleri ve Sinir Ağları ile Haber Metinlerinin Sınıflandırılması(2023) Hark, Hülya; Karakurt, Meral; Hark, Cengiz; Karcı, AliDijital haberlerin artan miktarları, istenilen türdeki haberlere doğru ve hızlı bir şekilde erişim için haber metinlerinin kategorilere ayrılmasını gerektirmektedir. Bu çalışmada, ön-eğitimli kelime gömülmelerinin, Uzun Ömürlü Kısa Dönem Bellek Ağı (Long-Short Term Memory, LSTM) ve Evrişimsel Sinir Ağları (Convolutional Neural Network, CNN) gibi derin öğrenme modelleri üzerindeki etkisi araştırılmaktadır. Global Vektör (GloVe) kelime gömülmelerinden alınan bağlamsal temsilleri girdi olarak alan LSTM ve CNN ağları kullanılarak haber metinleri sınıflandırılmıştır. Kapsamlı ve karşılaştırmalı araştırmaların eksikliği nedeniyle GloVe gömme katmanı tarafından sağlanan bağlamsal temsiller farklı sınıflandırıcılar ve veri setleri üzerinde test edilmektedir. Deneysel süreçler boyunca Türkçe Haber başlıklarından oluşan Turkish Headlines veri seti ve BBC News Classification veri setleri kullanılmıştır. Kelime gömülmelerinin ağlar üzerindeki etkisini ortaya koymak için deneysel süreçler aynı parametreler ile tekrarlanmıştır. LSTM modelinde Glove kelime gömülme yöntemi kullanıldığında modelin başarısının %81’den %91’e çıktığı gözlemlenmektedir. CNN modelinde ise Glove kelime gömülmelerinin modelin başarısının olumlu yansımadığı görülmektedir.Öğe Graph-Based Suggestion For Text Summarization(Ieee, 2018) Hark, Cengiz; Uckan, Taner; Seyyarer, Ebubekir; Karci, AliOne of the methods of text summarization within the context of Natural Language Processing (NLP) works is to summarize the text by selecting sentences from the original text. There are different approaches to summarize sentence selection. In this study, texts that do not have a certain structure have been preprocessed and transfer of the proposed diagram in a structured format in the form of an expression. Different feature extraction methods could be applied on the charts. Our method uses conceptually the diagrams obtained in the representation of the text. This study aims to suggest a method of summarization of texts with a linear weighting of the importance of sentences. Moreover, the method presented does not require the use of deep linguistic knowledge and this work can be adapted to different languages.Öğe Karc1 summarization: A simple and effective approach for automatic text summarization using Karc1 entropy(Elsevier Sci Ltd, 2020) Hark, Cengiz; Karci, AliIncreases in the amount of text resources available via the Internet has amplified the need for automated document summarizing tools. However, further efforts are needed in order to improve the quality of the existing summarization tools currently available. The current study proposes Karc1 Summarization, a novel methodology for extractive, generic summarization of text documents. Karc1 Entropy was used for the first time in a document summarization method within a unique approach. An important feature of the proposed system is that it does not require any kind of information source or training data. At the stage of presenting the input text, a tool for text processing was introduced; known as KUSH (named after its authors; Karc1, Uckan, Seyyarer, and Hark), and is used to protect semantic consistency between sentences. The Karc1 Entropy-based solution chooses the most effective, generic and most informational sentences within a paragraph or unit of text. Experimentation with the Karc1 Summarization approach was tested using openaccess document text (Document Understanding Conference; DUC-2002, DUC-2004) datasets. Performance achievement of the Karci Summarization approach was calculated using metrics known as Recall-Oriented Understudy for Gisting Evaluation (ROUGE). The experimental results showed that the proposed summarizer outperformed all current state-of-the-art methods in terms of 200-word summaries in the metrics of ROUGE-1, ROUGE-2, ROUGE-L, and ROUGE-W-1.2. In addition, the proposed summarizer outperformed the nearest competitive summarizers by a factor of 6.4% for ROUGE-1 Recall on the DUC-2002 dataset. These results demonstrate that Karci Summarization is a promising technique and it is therefore expected to attract interest from researchers in the field. Our approach was shown to have a high potential for adoptability. Moreover, the method was assessed as quite insensitive to disorderly and missing texts due to its KUSH text processing module.Öğe Metin çizgelerinde entropi ve optimizasyon tabanlı çıkarımsal metin özetleme(İnönü Üniversitesi, 2020) Hark, CengizMetin boyutlarında meydana gelen artış, otomatik belge özetleme araçlarına duyulan ihtiyacı arttırmaktadır. Tez çalışmasında, metin belgelerinin özetlenmesi için iki yeni metodoloji; Karcı Özetleme, Çok Amaçlı Modifiye Fidan Gelişim Algoritması (ÇA-FGA) sunulmaktadır. Benzersiz bir yaklaşımla, bir belge özetleme çalışmasında Karcı Entropi kullanılmış ve literatüre kazandırılmıştır. Önerilen sistem, eğitim verisi gerektirmemektedir. Optimizasyon teknikleri özetleme çalışmalarında sık kullanılan bir başka tekniktir. Ancak bu çalışmalarda, birbirleri ile çelişen farklı amaçlar genelde ağırlıklandırılarak veya birleştirilerek tek amaçlı probleme dönüştürülmektedir. Bu dönüşüm çözüm kalitesini doğrudan etkilemektedir. Tez çalışmasında Karcı Özetleme'ye ek olarak zengin bir özetin iki temel niteliği olan maksimum kapsam ve minimum fazlalığın optimizasyonun amaçları olarak modellendiği bir yaklaşım sunulmaktadır. Eklenen budama aşaması ile Fidan Gelişim Algoritması bir adım ileri taşınmış ve ÇA-FGA tanıtılmıştır. Sunulan yaklaşım kapsamında gerçek bir eşzamanlı optimizasyon uygulanmaktadır. Sunulan yaklaşımların sonuçları açık erişimli veri setleri (DUC 2002-2004) ile test edilmişlerdir. Yaklaşımların performansları, Recall-Oriented Understudy for Gisting Evaluation (ROUGE) ölçekleri kullanılarak incelenmiştir. Deneysel sonuçlar her iki yaklaşımında gelecek vaat eden teknikler olduğunu ortaya koymaktadır.Öğe Metin Özetlemesi için Düğüm Merkezliklerine Dayalı Denetimsiz Bir Yaklaşım(2019) Hark, Cengiz; Uçkan, Taner; Seyyarer, Ebubekir; Karcı, AliÖz: 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.Öğe 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, AliAutomatic 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.Öğe 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, CelaleddinIn 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.Öğe The power of graphs in medicine: Introducing BioGraphSum for effective text summarization(Cell Press, 2024) Hark, CengizIn biomedicine, the expansive scientific literature combined with the frequent use of abbreviations, acronyms, and symbols presents considerable challenges for text processing and summarization. The Unified Medical Language System (UMLS) has been a go-to for extracting concepts and determining correlations in these studies; hence, the BioGraphSum model introduced in this study aims to reduce this UMLS dependence. Through adoption of an innovative perspective, sentences within a piece of text are graphically conceptualized as nodes, enabling the concept of Malatya centrality to be leveraged. This approach focuses on pinpointing influential nodes on a graph and, by analogy, the most pertinent sentences within the text for summarization. In order to evaluate the performance of the BioGraphSum approach, a corpus was curated that consisted of 450 contemporary scientific research articles available on the PubMed database, aligned with proven research methodology. The BioGraphSum model was subjected to rigorous testing against this corpus in order to demonstrate its capabilities. Preliminary results, especially in the precision-based and f-score-based ROUGE-(1-2), ROUGE-L, and ROUGE-SU metrics reported significant improvements when compared to other existing models considered state-of-the-art in text summarization.Öğe SSC: Clustering of Turkish Texts By Spectral Graph Partitioning(Gazi Univ, 2021) Uckan, Taner; Hark, Cengiz; Karci, AliThere 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.
