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1. LLM Prompting Versus Fine-Tuning PLMs: A Comparative Study on Keyword Generation from Customer Feedback

Author

Er, Aleyna, Diri, Banu, Yöndem, Meltem Turhan, Rannenberg, Kai, Editor-in-Chief, Soares Barbosa, Luís, Editorial Board Member, Carette, Jacques, Editorial Board Member, Tatnall, Arthur, Editorial Board Member, Neuhold, Erich J., Editorial Board Member, Stiller, Burkhard, Editorial Board Member, Stettner, Lukasz, Editorial Board Member, Pries-Heje, Jan, Editorial Board Member, Kreps, David, Editorial Board Member, Rettberg, Achim, Editorial Board Member, Furnell, Steven, Editorial Board Member, Mercier-Laurent, Eunika, Editorial Board Member, Winckler, Marco, Editorial Board Member, Malaka, Rainer, Editorial Board Member, Maglogiannis, Ilias, editor, Iliadis, Lazaros, editor, Macintyre, John, editor, Avlonitis, Markos, editor, and Papaleonidas, Antonios, editor

Published
2024
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3. n-stage Latent Dirichlet Allocation: A Novel Approach for LDA

Author

Guven, Zekeriya Anil, Diri, Banu, and Cakaloglu, Tolgahan

Subjects
Computer Science - Computation and Language, Computer Science - Information Retrieval, H.3.3, I.2.7, I.7.0
Abstract

Nowadays, data analysis has become a problem as the amount of data is constantly increasing. In order to overcome this problem in textual data, many models and methods are used in natural language processing. The topic modeling field is one of these methods. Topic modeling allows determining the semantic structure of a text document. Latent Dirichlet Allocation (LDA) is the most common method among topic modeling methods. In this article, the proposed n-stage LDA method, which can enable the LDA method to be used more effectively, is explained in detail. The positive effect of the method has been demonstrated by the applied English and Turkish studies. Since the method focuses on reducing the word count in the dictionary, it can be used language-independently. You can access the open-source code of the method and the example: https://github.com/anil1055/n-stage_LDA, Comment: Published in: 2019 4th International Conference on Computer Science and Engineering (UBMK). This study is extension version of "Comparison of Topic Modeling Methods for Type Detection of Turkish News" http://dx.doi.org/10.1109/UBMK.2019.8907050 . Please citation this IEEE paper

Published
2021
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4. Evaluation of Non-Negative Matrix Factorization and n-stage Latent Dirichlet Allocation for Emotion Analysis in Turkish Tweets

Author

Guven, Zekeriya Anil, Diri, Banu, and Cakaloglu, Tolgahan

Subjects
Computer Science - Computation and Language, Computer Science - Information Retrieval, Computer Science - Machine Learning, H.3.3, I.2.7, I.7.0
Abstract

With the development of technology, the use of social media has become quite common. Analyzing comments on social media in areas such as media and advertising plays an important role today. For this reason, new and traditional natural language processing methods are used to detect the emotion of these shares. In this paper, the Latent Dirichlet Allocation, namely LDA, and Non-Negative Matrix Factorization methods in topic modeling were used to determine which emotion the Turkish tweets posted via Twitter. In addition, the accuracy of a proposed n-level method based on LDA was analyzed. Dataset consists of 5 emotions, namely angry, fear, happy, sad and confused. NMF was the most successful method among all topic modeling methods in this study. Then, the F1-measure of Random Forest, Naive Bayes and Support Vector Machine methods was analyzed by obtaining a file suitable for Weka by using the word weights and class labels of the topics. Among the Weka results, the most successful method was n-stage LDA, and the most successful algorithm was Random Forest., Comment: Published in: 2019 Innovations in Intelligent Systems and Applications Conference (ASYU). This paper is extension version of Comparison Method for Emotion Detection of Twitter Users (http://dx.doi.org/10.1109/ASYU48272.2019.8946435). Please citation this IEEE paper

Published
2021
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5. New developer metrics: Are comments as crucial as code contributions?

Author

Şeker, Abdulkadir, Diri, Banu, and Arslan, Halil

Subjects
Computer Science - Software Engineering
Abstract

Open-source code development has become widespread in recent years. As a result, open-source software platforms have also become popular, and millions of developers from diverse locations are able to contribute to the same projects. On these platforms, various knowledge about them is obtained from user activity. This information is used in the form of developer metrics to solve a variety of challenges. In this study, we proposed new developer metrics, including commenting and issue-related activity, that require less information. We concluded that commenting on any feature of a project can be equally as valuable as code contribution. In addition, besides the quantitative ones, metrics based on only the existence of the activity have been shown to offer also considerable results. We saw that issues were crucial in identifying user contributions. Even if a developer makes a contribution to only one issue on a project, the relation between the developer and the project is tight. The hit scores are relatively lower because of the sparsity problem of our dataset; even so, we believe that we have presented improvable and remarkable new developer metrics., Comment: 14 pages, 2 figures, This study submitted to Journal of Science China Information Sciences

Published
2020
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6. Summarising Big Data: Common GitHub Dataset for Software Engineering Challenges

Author

Şeker, Abdulkadir, Diri, Banu, and Arslan, Halil

Subjects
Computer Science - Software Engineering
Abstract

In open-source software development environments; textual, numerical and relationship-based data generated are of interest to researchers. Various data sets are available for this data, which is frequently used in areas such as software engineering and natural language processing. However, since these data sets contain all the data in the environment, the problem arises in the terabytes of data processing. For this reason, almost all of the studies using GitHub data use filtered data according to certain criteria. In this context, using a different data set in each study makes a comparison of the accuracy of the studies quite difficult. In order to solve this problem, a common dataset was created and shared with the researchers, which would allow us to work on many software engineering problems., Comment: 7 pages, The article was submitted to Cumhuriyet Science Journal

Published
2020
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7. Open Source Software Development Challenges: A Systematic Literature Review on GitHub

Author

Şeker, Abdulkadir, Diri, Banu, Arslan, Halil, and Amasyalı, Mehmet Fatih

Subjects
Computer Science - Software Engineering, Computer Science - Social and Information Networks
Abstract

Git is used as the distributed version control system for many open-source software projects. One Git-based service, GitHub, is the most common code hosting and repository service for open-source software projects. For researchers that study software engineering, the content that is hosted on these platforms provides much valuable data. There are some alternatives to get GitHub data such as GitHub Archive, GitHub API or GHTorrent. Among these options, GHTorrent is the most widely known and used GitHub dataset in the literature. Although there are some review studies about software engineering challenges across the GitHub platform, no review of GHTorrent dataset-specific research is available. In this study, the 172 studies that use GHTorrent as a data source were categorized within the scope of open source software development challenges and a systematic literature review was carried out. Moreover, the pros and cons of the dataset have been indicated and the focused issues of the literature on and the open challenges have been noted., Comment: This is old version, It include typo errors based on latex, I cant remove it

Published
2020
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8. Deep Neural Network Based Phishing Classification on a High-Risk URL Dataset

Author

Korkmaz, Mehmet, Kocyigit, Emre, Sahingoz, Ozgur Koray, Diri, Banu, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Abraham, Ajith, editor, Ohsawa, Yukio, editor, Gandhi, Niketa, editor, Jabbar, M.A., editor, Haqiq, Abdelkrim, editor, McLoone, Seán, editor, and Issac, Biju, editor

Published
2021
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9. Real-Time Content-Based Cyber Threat Detection with Machine Learning

Author

Kocyigit, Emre, Korkmaz, Mehmet, Sahingoz, Ozgur Koray, Diri, Banu, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Abraham, Ajith, editor, Piuri, Vincenzo, editor, Gandhi, Niketa, editor, Siarry, Patrick, editor, Kaklauskas, Arturas, editor, and Madureira, Ana, editor

Published
2021
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13. Enhanced Feature Selection Using Genetic Algorithm for Machine-Learning-Based Phishing URL Detection.

Author

Kocyigit, Emre, Korkmaz, Mehmet, Sahingoz, Ozgur Koray, and Diri, Banu

Subjects
COMPUTER security, MACHINE learning, FEATURE selection, GENETIC algorithms, PHISHING, UNIFORM Resource Locators
Abstract

In recent years, the importance of computer security has increased due to the rapid advancement of digital technology, widespread Internet use, and increased sophistication of cyberattacks. Machine learning has gained great interest in securing data systems because it offers the capability of automatically detecting and responding to security threats in real time, which is crucial for maintaining the security of computer systems and protecting data from malicious attacks. This study concentrates on phishing attack detection systems, a prevalent cyber-threat. These systems assess the features of the incoming requests to identify whether they are malicious or not. Although the number of features is increasing in these systems, feature selection has become an essential pre-processing phase that identifies the most important features of a set of available features to prevent overfitting problems, improve model performance, reduce computational cost, and decrease training and execution time. Leveraging genetic algorithms, known for simulating natural selection to identify optimal solutions, we propose a novel feature selection method, based on genetic algorithms and locally optimized, that is applied to a URL-based phishing detection system with machine learning models. Our research demonstrates that the proposed technique offers a promising strategy for improving the performance of machine learning models. [ABSTRACT FROM AUTHOR]

Published
2024
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15. NLP Based Phishing Attack Detection from URLs

Author

Buber, Ebubekir, Diri, Banu, Sahingoz, Ozgur Koray, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Abraham, Ajith, editor, Muhuri, Pranab Kr., editor, Muda, Azah Kamilah, editor, and Gandhi, Niketa, editor

Published
2018
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20. A Study on Turkish Meronym Extraction Using a Variety of Lexico-Syntactic Patterns

Author

Yıldız, Tuğba, Yıldırım, Savaş, Diri, Banu, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Vetulani, Zygmunt, editor, Uszkoreit, Hans, editor, and Kubis, Marek, editor

Published
2016
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22. A Hybrid Phishing Detection System Using Deep Learning-based URL and Content Analysis

Author

Korkmaz, Mehmet, Koçyiğit, Emre, Şahingöz, Özgür Koray, Diri, Banu, and Mühendislik ve Doğa Bilimleri Fakültesi

Subjects
Url-Based, Deep Learning, High-Risk Dataset, Phishing Detection, Electrical and Electronic Engineering, Two-Stage Hybrid System, Content-Based
Abstract

Phishing attacks are one of the most preferred types of attacks for cybercriminals, who can easily contact a large number of victims through the use of social networks, particularly through email messages. To protect end users, most of the security mechanisms control Uniform Resource Locator (URL) addresses because of their simplicity of implementation and execution speed. However, due to sophisticated attackers, this mechanism can miss some phishing attacks and has a relatively high false positive rate. In this research, a hybrid technique is proposed that uses not only URL features, but also content-based features as the second level of detection mechanism, thus improving the accuracy of the detection system while also minimizing the number of false positives. Additionally, most phishing detection algorithms use datasets that contain easily differentiated data pieces, either phishing or legitimate. However, in order to implement a more secure protection mechanism, we aimed to collect a larger and high-risk dataset. The proposed approaches were tested on this High-Risk URL and Content-Based Phishing Detection Dataset that only contains suspicious websites from PhishTank. According to experimental studies, an accuracy rate of 98.37 percent was achieved on a more realistic dataset for phishing detection. Abstract Phishing attacks are one of the most preferred types of attacks for cybercriminals, who can easily contact a large number of victims through the use of social networks, particularly through email messages. To protect end users, most of the security mechanisms control Uniform Resource Locator (URL) addresses because of their simplicity of implementation and execution speed. However, due to sophisticated attackers, this mechanism can miss some phishing attacks and has a relatively high false positive rate. In this research, a hybrid technique is proposed that uses not only URL features, but also content-based features as the second level of detection mechanism, thus improving the accuracy of the detection system while also minimizing the number of false positives. Additionally, most phishing detection algorithms use datasets that contain easily differentiated data pieces, either phishing or legitimate. However, in order to implement a more secure protection mechanism, we aimed to collect a larger and high-risk dataset. The proposed approaches were tested on this High-Risk URL and Content-Based Phishing Detection Dataset that only contains suspicious websites from PhishTank. According to experimental studies, an accuracy rate of 98.37 percent was achieved on a more realistic dataset for phishing detection.

Published
2022
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23. Predicting Defect Prone Modules in Web Applications

Author

Biçer, Mehmet Serdar, Diri, Banu, Barbosa, Simone Diniz Junqueira, Series Editor, Filipe, Joaquim, Series Editor, Kotenko, Igor, Series Editor, Sivalingam, Krishna M., Series Editor, Washio, Takashi, Series Editor, Yuan, Junsong, Series Editor, Zhou, Lizhu, Series Editor, Dregvaite, Giedre, editor, and Damasevicius, Robertas, editor

Published
2015
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27. Content Mining of Microblogs

Author

Cingiz, M. Özgür, Diri, Banu, Alhajj, Reda, Series editor, Glässer, Uwe, Series editor, Can, Fazli, editor, Özyer, Tansel, editor, and Polat, Faruk, editor

Published
2014
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28. An Integrated Approach to Automatic Synonym Detection in Turkish Corpus

Author

Yıldız, Tuğba, Yıldırım, Savaş, Diri, Banu, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Kobsa, Alfred, Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Goebel, Randy, Series editor, Tanaka, Yuzuru, Series editor, Wahlster, Wolfgang, Series editor, Siekmann, Jörg, Series editor, Przepiórkowski, Adam, editor, and Ogrodniczuk, Maciej, editor

Published
2014
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29. A Semantic Kernel for Text Classification Based on Iterative Higher–Order Relations between Words and Documents

Author

Altinel, Berna, Ganiz, Murat Can, Diri, Banu, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Kobsa, Alfred, editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Weikum, Gerhard, editor, Goebel, Randy, editor, Tanaka, Yuzuru, editor, Wahlster, Wolfgang, editor, Siekmann, Jörg, editor, Rutkowski, Leszek, editor, Korytkowski, Marcin, editor, Scherer, Rafał, editor, Tadeusiewicz, Ryszard, editor, Zadeh, Lotfi A., editor, and Zurada, Jacek M., editor

Published
2014
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33. Extraction of Part-Whole Relations from Turkish Corpora

Author

Yıldız, Tuğba, Yıldırım, Savaş, Diri, Banu, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, and Gelbukh, Alexander, editor

Published
2013
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37. A Fault Prediction Model with Limited Fault Data to Improve Test Process

Author

Catal, Cagatay, Diri, Banu, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Jedlitschka, Andreas, editor, and Salo, Outi, editor

Published
2008
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38. Author Attribution of Turkish Texts by Feature Mining

Author

Türkoğlu, Filiz, Diri, Banu, Amasyalı, M. Fatih, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Rangan, C. Pandu, editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Huang, De-Shuang, editor, Heutte, Laurent, editor, and Loog, Marco, editor

Published
2007
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39. Software Fault Prediction with Object-Oriented Metrics Based Artificial Immune Recognition System

Author

Catal, Cagatay, Diri, Banu, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Münch, Jürgen, editor, and Abrahamsson, Pekka, editor

Published
2007
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40. Automatic Turkish Text Categorization in Terms of Author, Genre and Gender

Author

Amasyalı, M. Fatih, Diri, Banu, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Dough, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Kop, Christian, editor, Fliedl, Günther, editor, Mayr, Heinrich C., editor, and Métais, Elisabeth, editor

Published
2006
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44. A Locally Searched Binary Artificial Bee Colony Algorithm Based on Hamming Distance for Binary Optimization

Author

ÖZGER, Zeynep Banu, BOLAT, Bülent, and DİRİ, Banu

Subjects
Computer Science::Neural and Evolutionary Computation, Mühendislik, artificial bee colony, lcsh:S, data mining, lcsh:S1-972, lcsh:Agriculture, Yapay arı kolonisi,Veri madenciliği,Sezgisel Algoritmalar,Makine Öğrenmesi, Engineering, machine learning, Artificial bee colony,Data mining,Heuristic algorithms,Machine learning, heuristic algorithms, lcsh:Technology (General), lcsh:T1-995, lcsh:Agriculture (General)
Abstract

Yapay Arı Kolonisi Algoritması sürekli uzay problemleri için geliştirilen, popülasyon tabanlı, doğadan esinlemeli bir optimizasyon algoritmasıdır. Bu çalışmanın amacı, büyük veride, öznitelik alt küme seçimi problemini efektif bir biçimde çözmek için Yapay Arı Koloni (YAK) Algoritmasının ikili bir versiyonunu geliştirmektir. YAK Algoritması başarılı bir global yakınsama sunmakla birlikte lokal bölgedeki olası çözümleri gözden kaçırabilmektedir. Algoritmanın komşu kaynak seçimi mekanizmasına, Hamming Mesafe ölçümü tabanlı bir yerel arama prosedürü eklenmiştir. Ayrıca, yeniden nüfus stratejisi ile popülasyonun çeşitliliği artırılmış ve erken yakınsama önlenmiştir. UCI Makine Öğrenmesi Havuzu’ndan, öznitelik sayısı 100’den fazla olan 14 veri kümesi seçilmiş ve önerilen yöntem ile öznitelik seçimi yapılmıştır. Algoritmanın performansı, yaygın kullanılan ve başarısı kanıtlanmış üç sezgisel algoritma ile sınıflandırma hatası, seçilen öznitelik sayısı ve hesapsal maliyet bakımından karşılaştırılmıştır. Elde edilen sonuçlar, YAK algoritmasına entegre edilen lokal arama prosedürünün, algoritmanın performansını tüm kriterler bakımından artırdığını göstermektedir., Artificial Bee Colony is a population based, bio-inspired optimization algorithm that developed for continues problems. The aim of this study is to develop a binary version of the Artificial Bee Colony (ABC) Algorithm to solve feature subset selection problem on bigger data. ABC Algorithm, has good global search capability but there is a lack of local search in the algorithm. To overcome this problem, the neighbor selection mechanism in the employed bee phase is improved by changing the new source generation formula that has hamming distance based local search capacity. With a re-population strategy, the diversity of the population is increased and premature convergence is prevented. To measure the effectiveness of the proposed algorithm, fourteen datasets which have more than 100 features were selected from UCI Machine Learning Repository and processed by the proposed algorithm. The performance of the proposed algorithm was compared to three well-known algorithms in terms of classification error, feature size and computation time. The results proved that the increased local search ability improves the performance of the algorithm for all criteria.

Published
2020

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