• From past to present: spam detection and identifying opinion leaders in social networks 

  Altınel Girgin, Ayşe Berna; Gümüşçekiçci, Gizem (Yildiz Teknik Univ., 2022-06-22)

  On microblogging sites, which are gaining more and more users every day, a wide range of ideas are quickly emerging, spreading, and creating interactive environments. In some cases, in Turkey as well as in the rest of the ...

• ComStreamClust: a communicative multi-agent approach to text clustering in streaming data 

  Najafi, Ali; Gholipour-Shilabin, Araz; Dehkharghani, Rahim; Mohammadpur-Fard, Ali; Asgari-Chenaghlu, Meysam (Springer Science and Business Media Deutschland GmbH, 2022-07-08)

  Topic detection is the task of determining and tracking hot topics in social media. Twitter is arguably the most popular platform for people to share their ideas with others about different issues. One such prevalent issue ...

• Categorization of the models based on structural information extraction and machine learning 

  Khalilipour, Alireza; Bozyiğit, Fatma; Utku, Can; Challenger, Moharram (Springer Science and Business Media Deutschland GmbH, 2022-07-21)

  As various engineering fields increasingly use modelling techniques, the number of provided models, their size, and their structural complexity increase. This makes model management, including finding these models, with ...

• Closeness and uncertainty aware adversarial examples detection in adversarial machine learning 

  Tuna, Ömer Faruk; Çatak, Ferhat Özgür; Eskil, Mustafa Taner (Elsevier Ltd, 2022-07)

  While deep learning models are thought to be resistant to random perturbations, it has been demonstrated that these architectures are vulnerable to deliberately crafted perturbations, albeit being quasi-imperceptible. These ...

• Battle Royale Optimizer for solving binary optimization problems 

  Akan, Taymaz; Agahian, Saeid; Dehkharghani, Rahim (Elsevier B.V., 2022-05)

  Battle Royale Optimizer (BRO) is a recently proposed metaheuristic optimization algorithm used only in continuous problem spaces. The BinBRO is a binary version of BRO. The BinBRO algorithm employs a differential expression, ...

• Uncertainty as a Swiss army knife: new adversarial attack and defense ideas based on epistemic uncertainty 

  Tuna, Ömer Faruk; Çatak, Ferhat Özgür; Eskil, Mustafa Taner (Springer, 2022-04-02)

  Although state-of-the-art deep neural network models are known to be robust to random perturbations, it was verified that these architectures are indeed quite vulnerable to deliberately crafted perturbations, albeit being ...

• Convolutional attention network for MRI-based Alzheimer's disease classification and its interpretability analysis 

  Türkan, Yasemin; Tek, Faik Boray (IEEE, 2021-09-17)

  Neuroimaging techniques, such as Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET), help to identify Alzheimer's disease (AD). These techniques generate large-scale, high-dimensional, multimodal ...

• k-Means clustering by using the calculated Z-scores from QEEG data of children with dyslexia 

  Eroğlu, Günet; Arman, Fehim (Taylor & Francis, 2022-05-15)

  Learning the subtype of dyslexia may help shorten the rehabilitation process and focus more on the relevant special education or diet for children with dyslexia. For this purpose, the resting-state eyes-open 2-min QEEG ...

• Tweet sentiment analysis for cryptocurrencies 

  Şaşmaz, Emre; Tek, Faik Boray (IEEE, 2021-10-13)

  Many traders believe in and use Twitter tweets to guide their daily cryptocurrency trading. In this project, we investigated the feasibility of automated sentiment analysis for cryptocurrencies. For the study, we targeted ...

• Exploiting epistemic uncertainty of the deep learning models to generate adversarial samples 

  Tuna, Ömer Faruk; Çatak, Ferhat Özgür; Eskil, Mustafa Taner (Springer, 2022-02-18)

  Deep neural network (DNN) architectures are considered to be robust to random perturbations. Nevertheless, it was shown that they could be severely vulnerable to slight but carefully crafted perturbations of the input, ...

• BinBRO: Binary Battle Royale Optimizer algorithm 

  (Rahkar Farshi), Taymaz Akan; Agahian, Saeid; Dehkharghani, Rahim (Elsevier Ltd, 2022-02-04)

  Stochastic methods attempt to solve problems that cannot be solved by deterministic methods with reasonable time complexity. Optimization algorithms benefit from stochastic methods; however, they do not guarantee to obtain ...

• ISIKUN at the FinCausal 2020: Linguistically informed machine-learning approach for causality identification in financial documents 

  Özenir, Hüseyin Gökberk; Karadeniz, İlknur (Association for Computational Linguistics (ACL), 2020)

  This paper presents our participation to the FinCausal-2020 Shared Task whose ultimate aim is to extract cause-effect relations from a given financial text. Our participation includes two systems for the two sub-tasks of ...

• On the online coalition structure generation problem 

  Flammini, Michele; Monaco, Gianpiero; Moscardelli, Luca; Shalom, Mordechai; Zaks, Shmuel (AI Access Foundationusc Information Sciences Inst, 2021)

  We consider the online version of the coalition structure generation problem, in which agents, corresponding to the vertices of a graph, appear in an online fashion and have to be partitioned into coalitions by an authority ...

• Machine learning 

  Yıldız, Olcay Taner (Institution of Engineering and Technology, 2020-01-01)

  [No abstract available]

• A FST description of noun and verb morphology of Azarbaijani Turkish 

  Ehsani, Razieh; Özenç, Berke; Solak, Ercan (Association for Computational Linguistics (ACL), 2021)

  We give a FST description of nominal and finite verb morphology of Azarbaijani Turkish. We use a hybrid approach where nominal inflection is expressed as a slot-based paradigm and major parts of verb inflection are expressed ...

• On the maximum cardinality cut problem in proper interval graphs and related graph classes 

  Boyacı, Arman; Ekim, Tınaz; Shalom, Mordechai (Elsevier B.V., 2022-01-04)

  Although it has been claimed in two different papers that the maximum cardinality cut problem is polynomial-time solvable for proper interval graphs, both of them turned out to be erroneous. In this work we consider the ...

• Unsupervised textile defect detection using convolutional neural networks 

  Koulali, Imane; Eskil, Mustafa Taner (Elsevier Ltd, 2021-12)

  In this study, we propose a novel motif-based approach for unsupervised textile anomaly detection that combines the benefits of traditional convolutional neural networks with those of an unsupervised learning paradigm. It ...

• Çizge evrişim ağı kullanarak patojen-konak ağlarında protein etkileşim tahmini 

  Koca, Mehmet Burak; Karadeniz, İlknur; Nourani, Esmaeil; Sevilgen, Fatih Erdoğan (IEEE, 2021-06-09)

  Proteinler yaşamsal faaliyetlerin gerçekleşmesinde kritik rol oynayan biyolojik moleküllerdir. Konak canlı proteinleri ile patojen proteinleri arasındaki etkileşimler patojenkonak etkileşim (PHI) ağlarını oluşturmaktadır. ...

• A mobile app that uses neurofeedback and multi-sensory learning methods improves reading abilities in dyslexia: A pilot study 

  Eroǧlu, Günet; Teber, Serap Tıraş; Ertürk, Kardelen; Kırmızı, Meltem; Ekici, Barış; Arman, Fehim; Balcısoy, Selim Saffet; Özcan, Yusuf Ziya; Çetin, Müjdat (Routledge, 2022-07-03)

  Reading comprehension is difficult to improve for children with dyslexia because of the continuing demands of orthographic decoding in combination with limited working memory capacity. Children with dyslexia get special ...

• Hierarchical b-Matching 

  Emek, Yuval; Kutten, Shay; Shalom, Mordechai; Zaks, Shmuel (Springer Science and Business Media Deutschland GmbH, 2021)

  A matching of a graph is a subset of edges no two of which share a common vertex, and a maximum matching is a matching of maximum cardinality. In a b-matching every vertex v has an associated bound bv, and a maximum ...

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