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Mental disorder and suicidal ideation detection from social media using deep neural networks
(Springer, 2024-12) Ezerceli, Özay; Dehkharghani, Rahim
Depression and suicidal ideation are global reasons for life-threatening injury and death. Mental disorders have increased especially among young people in recent years, and early detection of those cases can prevent suicide attempts. Social media platforms provide users with an anonymous space to interact with others, making them a secure environment to discuss their mental disorders. This paper proposes a solution to detect depression/suicidal ideation using natural language processing and deep learning techniques. We used Transformers and a unique model to train the proposed model and applied it to three diferent datasets: SuicideDetection, CEASEv2.0, and SWMH. The proposed model is evaluated using the accuracy, precision, recall, and ROC curve. The proposed model outperforms the state-of-theart in the SuicideDetection and CEASEv2.0 datasets, achieving F1 scores of 0.97 and 0.75, respectively. However, in the SWMH data set, the proposed model is 4% points behind the state-of-the-art precision providing the F1 score of 0.68. In the real world, this project could help psychologists in the early detection of depression and suicidal ideation for a more efcient treatment. The proposed model achieves state-of-the-art performance in two of the three datasets, so they could be used to develop a screening tool that could be used by mental health professionals or individuals to assess their own risk of suicide. This could lead to early intervention and treatment, which could save lives.
Text-to-SQL: a methodical review of challenges and models
(TÜBİTAK, 2024-05-20) Kanburoğlu, Ali Buğra; Tek, Faik Boray
This survey focuses on Text-to-SQL, automated translation of natural language queries into SQL queries. Initially, we describe the problem and its main challenges. Then, by following the PRISMA systematic review methodology, we survey the existing Text-to-SQL review papers in the literature. We apply the same method to extract proposed Text-to-SQL models and classify them with respect to used evaluation metrics and benchmarks. We highlight the accuracies achieved by various models on Text-to-SQL datasets and discuss execution-guided evaluation strategies. We present insights into model training times and implementations of different models. We also explore the availability of Text-to-SQL datasets in non-English languages. Additionally, we focus on large language model (LLM) based approaches for the Text-to-SQL task, where we examine LLM-based studies in the literature and subsequently evaluate the LLMs on the cross-domain Spider dataset. Finally, we conclude with a discussion of future directions for Text-to-SQL research, identifying potential areas of improvement and advancements in this field.
Analyst-aware incident assignment in security operations centers: a multi-factor prioritization and optimization framework
(Uğur Şen, 2025-07-15) Kılınçdemir, Eyüp Can; Çeliktaş, Barış
In this paper, we propose a comprehensive and scalable framework for incident assignment and prioritization in Security Operations Centers (SOCs). The proposed model aims to optimize SOC workflows by addressing key operational challenges such as analyst fatigue, alert overload, and inconsistent incident handling. Our framework evaluates each incident using a multi-factor scoring model that incorporates incident severity, service-level agreement (SLA) urgency, incident type, asset criticality, threat intelligence indicators, frequency of repetition, and a correlation score derived from historical incident data. We formalize this evaluation through a set of mathematical functions that compute a dynamic incident score and derive incident complexity. In parallel, analyst profiles are quantified using Analyst Load Factor (ALF) and Experience Match Factor (EMF), two novel metrics that account for both workload distribution and expertise alignment. The incident–analyst matching process is expressed as a constrained optimization problem, where the final assignment score is computed by balancing incident priority with analyst suitability. This formulation enables automated, real-time assignment of incidents to the most appropriate analysts, while ensuring both operational fairness and triage precision. The model is validated using algorithmic pseudocode, scoring tables, and a simplified case study, which illustrates the realworld applicability and decision logic of the framework in large-scale SOC environments. To validate the framework under real-world conditions, an empirical case study was conducted using 10 attack scenarios from the CICIDS2017 benchmark dataset. Overall, our contributions lie in the formalization of a dual-factor analyst scoring scheme and the integration of contextual incident features into an adaptive, rule-based assignment framework. To further strengthen operational value, future work will explore adaptive weighting mechanisms and integration with real-time SIEM pipelines. Additionally, feedback loops and supervised learning models will be incorporated to continuously refine analyst-incident matching and prioritization.
A hierarchical key assignment scheme: a unified approach for ccalability and efficiency
(IEEE, 2024-05-24) Çelikbilek, İbrahim; Çeliktaş, Barış; Özdemir, Enver
This study introduces a hierarchical key assignment scheme (HKAS) based on the closest vector problem in an inner product space (CVP-IPS). The proposed scheme offers a comprehensive solution with scalability, flexibility, cost-effectiveness, and high performance. The key features of the scheme include CVP-IPS based construction, the utilization of two public keys by the scheme, a distinct basis set designated for each class, a direct access scheme to enhance user convenience, and a rigorous mathematical and algorithmic presentation of all processes. This scheme eliminates the need for top-down structures and offers a significant benefit in that the lengths of the basis sets defined for classes are the same and the costs associated with key derivation are the same for all classes, unlike top-down approaches, where the higher class in the hierarchy generally incurs much higher costs. The scheme excels in both vertical and horizontal scalability due to its utilization of the access graph and is formally proven to achieve strong key indistinguishability security (S-KI-security). This research represents a significant advancement in HKAS systems, providing tangible benefits and improved security for a wide range of use cases.
Türkçe için biçimbirim temelli bir bileşen grameri yaklaşımı
(Beykoz Üniversitesi, 2024-12-26) Özenç, Berke; Solak, Ercan
Dilin modellenmesi, dil çalışmalarında önemli bir temel olarak yer alır. Farklı modelleme yöntemleri, farklı diller için uyarlanabilir olsa da bu uyarlamalar, hedef dil için her zaman yeterli olmayabilir. Bu durumdan en çok biçimbirimsel açıdan zengin diller etkilenir. Böyle bir dil için hazırlanacak model kurgulanırken dilin evrensel olarak ortak olan özelliklerinin yanı sıra, dilin kendine özgü özelliklerine odaklanılmalıdır. Bu makalede, bağımlı biçimbirim bakımından zengin bir görünüm sunan Türkçe ele alınarak uyarlanan gramer sunulmuştur. Çalışmada açıklanan gramer temelleri geleneksel üretici gramer yönteminden uyarlanmıştır. Bununla birlikte, sunulan gramer, biçimbirimleri söz dizimi elemanı olarak geleneksel söz dizimi elemanlarıyla birlikte, söz dizimine olan etkilerini ele almasıyla ve kullanılan özel bileşen kümesiyle geleneksel üretici gramer yöntemden ayrılır. Geleneksel yöntemden farklı olarak önerilen gramerde, tümce çözümlemesine sözcüklerden değil, biçimbirim elemanları olan sözcük gövdeleri, ekler, biçimbirimler ve bu gibi elemanların oluşturduğu gruplardan başlanır. Buna ek olarak Türkçenin söz dizimsel ve birimbirimsel özelliklerine göre kurgulanan bir bileşen kümesi de sunulmuştur. Sunulan bileşen kümesi, tümce, ad öbeği, eylem öbeği, belirteç öbeği gibi geleneksel sözdizimsel bileşenleri, öbek gövdesi olarak adlandırılan ara bir yapıyı ve çoğul eki, durum eki, zaman çekimi eki gibi, biçimbirimleri veya biçimbirim gruplarını temsil eden bileşenleri içerir.
A novel similarity based unsupervised technique for training convolutional filters
(IEEE, 2023-05-17) Erkoç, Tuğba; Eskil, Mustata Taner
Achieving satisfactory results with Convolutional Neural Networks (CNNs) depends on how effectively the filters are trained. Conventionally, an appropriate number of filters is carefully selected, the filters are initialized with a proper initialization method and trained with backpropagation over several epochs. This training scheme requires a large labeled dataset, which is costly and time-consuming to obtain. In this study, we propose an unsupervised approach that extracts convolutional filters from a given dataset in a self-organized manner by processing the training set only once without using backpropagation training. The proposed method allows for the extraction of filters from a given dataset in the absence of labels. In contrast to previous studies, we no longer need to select the best number of filters and a suitable filter weight initialization scheme. Applying this method to the MNIST, EMNIST-Digits, Kuzushiji-MNIST, and Fashion-MNIST datasets yields high test performances of 99.19%, 99.39%, 95.03%, and 90.11%, respectively, without applying backpropagation training or using any preprocessed and augmented data.
TENET: a new hybrid network architecture for adversarial defense
(Springer Science and Business Media Deutschland GmbH, 2023-08) Tuna, Ömer Faruk; Çatak, Ferhat Özgür; Eskil, Mustafa Taner
Deep neural network (DNN) models are widely renowned for their resistance to random perturbations. However, researchers have found out that these models are indeed extremely vulnerable to deliberately crafted and seemingly imperceptible perturbations of the input, referred to as adversarial examples. Adversarial attacks have the potential to substantially compromise the security of DNN-powered systems and posing high risks especially in the areas where security is a top priority. Numerous studies have been conducted in recent years to defend against these attacks and to develop more robust architectures resistant to adversarial threats. In this study, we propose a new architecture and enhance a recently proposed technique by which we can restore adversarial samples back to their original class manifold. We leverage the use of several uncertainty metrics obtained from Monte Carlo dropout (MC Dropout) estimates of the model together with the model’s own loss function and combine them with the use of defensive distillation technique to defend against these attacks. We have experimentally evaluated and verified the efficacy of our approach on MNIST (Digit), MNIST (Fashion) and CIFAR10 datasets. In our experiments, we showed that our proposed method reduces the attack’s success rate lower than 5% without compromising clean accuracy.
Mitosis detection using generic features and an ensemble of cascade adaboosts
(Elsevier, 2013-05-30) Tek, Faik Boray
Context: Mitosis count is one of the factors that pathologists use to assess the risk of metastasis and survival of the patients, which are affected by the breast cancer. Aims: We investigate an application of a set of generic features and an ensemble of cascade adaboosts to the automated mitosis detection. Calculation of the features rely minimally on object -level descriptions and thus require minimal segmentation. Materials and Methods: The proposed work was developed and tested on International Conference on Pattern Recognition (ICPR) 2012 mitosis detection contest data. Statistical Analysis Used: We plotted receiver operating characteristics curves of true positive versus false positive rates; calculated recall, precision, F -measure, and region overlap ratio measures. Results: We tested our features with two different classifier configurations: 1)An ensemble of single adaboosts, 2) an ensemble of cascade adaboosts. On the ICPR 2012 mitosis detection contest evaluation, the cascade ensemble scored 54, 62.7, and 58, whereas the non -cascade version scored 68, 28.1, and 39.7 for the recall, precision, and F -measure measures, respectively. Mostly used features in the adaboost classifier rules were a shape?based feature, which counted granularity and a color-based feature, which relied on Red, Green, and Blue channel statistics. Conclusions: The features, which express the granular structure and color variations, are found useful for mitosis detection. The ensemble of adaboosts performs better than the individual adaboost classifiers. Moreover, the ensemble of cascaded adaboosts was better than the ensemble of single adaboosts for mitosis detection.
A comparison of Auto Train Brain neurofeedback rewarding interfaces in terms of efficacy
(Acıbadem Mehmet Ali Aydınlar Üniversitesi, 2023-01-01) Eroğlu, Günet
Background/aim: Auto Train Brain is a mobile app that was specifically developed for dyslexic children to increase their reading speed and reading comprehension. In the original mobile app, only one unique neurofeedback user interface provided visually and audibly rewarding feedback to the subject with a red-green colored arrow on the screen. Later, new modules are added to the app with the end-users requests. These are the “youtube” video-based interface and “Spotify” auditory-based interface. In this research, we have compared the efficacy of the neurofeedback rewarding interfaces. Materials and methods: The experiment group consists of 20 dyslexic children aged 7-to 10 (15 males, 5 females) who were randomly assigned to one rewarding interface and used it at home for more than six months. Results: The result indicates that though the “youtube” interface is liked most by the participants, the arrow-based simple neurofeedback interface reduces theta brain waves more than other rewarding schemes. On the other hand, “youtube” and “Spotify” based interfaces increase Beta band powers more than the arrow interfaces in the cortex. The ”Spotify” user interface improves the fast brain waves more on the temporal lobes (T7 and T8) as the feedback given was only auditory. Conclusion: The results indicate that the relevant neurofeedback rewarding interface should be chosen based on the dyslexic child’s specific condition.
A novel hybrid edge detection technique: ABC-FA
(ISRES Organizasyon Turizm Eğitim Danışmanlık Ltd. Şti., 2017-11-09) Yelmenoğlu, Elif Deniz; Çelebi, Numan; Taşçı, Tuğrul
Image processing is a vast research field with diversified set of practices utilized in so many application areas such as military, security, medical imaging, machine learning and computer vision based on extracted useful information from any kind of image data. Edges within images are undoubtedly accepted as one of the most significant features providing substantial practical information for various applications working on top of miscellaneous optimization algorithms to achieve better results. Artificial Bee Colony and Firefly algorithms are recently developed optimization algorithms and are used to obtain better results for various problems. In this study, a novel hybrid optimization technique is proposed by combining those algorithms aiming better quality in edge detection on grayscale images. The performance of the proposed algorithm is compared with individual performances of Artificial Bee Colony algorithm and the fundamental edge detection methods. The results are demonstrated that the proposed method is encouraging and also produces meaningful results for similar applications.
Distribution games: a new class of games with application to user provided networks
(Institute of Electrical and Electronics Engineers Inc., 2022-11-29) Taşçı, Sinan Emre; Shalom, Mordechai; Korçak, Ömer
User Provided Network (UPN) is a promising solution for sharing the limited network resources by utilizing user capabilities as a part of the communication infrastructure. In UPNs, it is an important problem to decide how to share the resources among multiple clients in decentralized manner. Motivated by this problem, we introduce a new class of games termed distribution games that can be used to distribute efficiently and fairly the bandwidth capacity among users. We show that every distribution game has at least one pure strategy Nash equilibrium (NE) and any best response dynamics always converges to such an equilibrium. We consider social welfare functions that are weighted sums of bandwidths allocated to clients. We present tight upper bounds for the price of anarchy and price of stability of these games provided that they satisfy some reasonable assumptions. We define two specific practical instances of distribution games that fit these assumptions. We conduct experiments on one of these instances and demonstrate that in most of the settings the social welfare obtained by the best response dynamics is very close to the optimum. Simulations show that this game also leads to a fair distribution of the bandwidth.
Graph convolutional network based virus-human protein-protein interaction prediction for novel viruses
(Elsevier Ltd, 2022-08-13) Koca, Mehmet Burak; Nourani, Esmaeil; Abbasoğlu, Ferda; Karadeniz, İlknur; Sevilgen, Fatih Erdoğan
Computational identification of human-virus protein-protein interactions (PHIs) is a worthwhile step towards understanding infection mechanisms. Analysis of the PHI networks is important for the determination of path-ogenic diseases. Prediction of these interactions is a popular problem since experimental detection of PHIs is both time-consuming and expensive. The available methods use biological features like amino acid sequences, molecular structure, or biological activities for prediction. Recent studies show that the topological properties of proteins in protein-protein interaction (PPI) networks increase the performance of the predictions. The basic network projections, random-walk-based models, or graph neural networks are used for generating topologically enriched (hybrid) protein embeddings. In this study, we propose a three-stage machine learning pipeline that generates and uses hybrid embeddings for PHI prediction. In the first stage, numerical features are extracted from the amino acid sequences using the Doc2Vec and Byte Pair Encoding method. The amino acid embeddings are used as node features while training a modified GraphSAGE model, which is an improved version of the graph convolutional network. Lastly, the hybrid protein embeddings are used for training a binary interaction classifier model that predicts whether there is an interaction between the given two proteins or not. The proposed method is evaluated with comprehensive experiments to test its functionality and compare it with the state-of-art methods. The experimental results on the benchmark dataset prove the efficiency of the proposed model by having a 3–23% better area under curve (AUC) score than its competitors.
Programlamaya giriş dersini alan öğrencilerin programlama öz yeterlilik algılarının ve programlamaya bakış açılarının incelenmesi
(Düzce Üniversitesi, 2021-05-29) Benli, Kristin Surpuhi; Tek, Faik Boray
Bu çalışmada üniversite öğrencilerinin Java programlama öz yeterlilik algıları, programlama öğrenme istekleri ve çalışma alışkanlıkları çeşitli değişkenlere göre (cinsiyet, bölüm, eğitim dili, harf notu, ders tekrarları vb.) istatistiksel yöntemler kullanılarak (T-testi, Mann Whitney U-testi, Kruskal Wallis H testi, tek yönlü varyans analizi, Ki-Kare testi) incelenmiştir. Çalışma grubu, farklı bölümlerde zorunlu olarak programlamaya giriş dersini alan 191 lisans öğrencisinden oluşmaktadır. Elde edilen sonuçlara göre öğrencilerin Java programlama öz yeterlilik algıları bölümlerine ve programlama öğrenme isteklerine göre farklılaşmaktadır. Çalışmada ayrıca Apriori algoritması kullanılarak birliktelik kuralları çıkartılmıştır. En yüksek güven değeri elde edilen kurala göre, programlama öğrenmeyi çok fazla isteyen, programlama öğrenmenin iş hayatında kendisine fayda sağlayacağını düşünen ve programlama dersinden başarı ile geçen öğrencilerin programlama öz yeterlilikleri yüksektir.
A short proof of the size of edge-extremal chordal graphs
(Mahmut Akyiğit, 2022-08-30) Shalom, Mordechai
Blair et. al. [3] have recently determined the maximum number of edges of a chordal graph with a maximum degree less than d and the matching number at most ? by exhibiting a family of chordal graphs achieving this bound. We provide simple proof of their result.
From past to present: spam detection and identifying opinion leaders in social networks
(Yildiz Teknik Univ., 2022-06-22) Altınel Girgin, Ayşe Berna; Gümüşçekiçci, Gizem
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 world, it was noticed that events were published on microblogging sites before appearing in visual, audio and printed news sources. Thanks to the rapid flow of information in social networks, it can reach millions of people in seconds. In this context, social media can be seen as one of the most important sources of information affecting public opinion. Since the information in social networks became accessible, research started to be conducted using the information on the social networks. While the studies about spam detection and identification of opinion leaders gained popularity, surveys about these topics began to be published. This study also shows the importance of spam detection and identification of opinion leaders in social networks. It is seen that the data collected from social platforms, especially in recent years, has sourced many state-of-art applications. There are independent surveys that focus on filtering the spam content and detecting influencers on social networks. This survey analyzes both spam detection studies and opinion leader identification and categorizes these studies by their methodologies. As far as we know there is no survey that contains approaches for both spam detection and opinion leader identification in social networks. This survey contains an overview of the past and recent advances in both spam detection and opinion leader identification studies in social networks. Furthermore, readers of this survey have the opportunity of understanding general aspects of different studies about spam detection and opinion leader identification while observing key points and comparisons of these studies.
ComStreamClust: a communicative multi-agent approach to text clustering in streaming data
(Springer Science and Business Media Deutschland GmbH, 2023-12) Najafi, Ali; Gholipour-Shilabin, Araz; Dehkharghani, Rahim; Mohammadpur-Fard, Ali; Asgari-Chenaghlu, Meysam
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 is the COVID-19 pandemic. Detecting and tracking topics on these kinds of issues would help governments and healthcare companies deal with this phenomenon. In this paper, we propose a novel, multi-agent, communicative clustering approach, so-called ComStreamClust for clustering sub-topics inside a broader topic, e.g., the COVID-19 and the FA CUP. The proposed approach is parallelizable, and can simultaneously handle several data-point. The LaBSE sentence embedding is used to measure the semantic similarity between two tweets. ComStreamClust has been evaluated by several metrics such as keyword precision, keyword recall, and topic recall. Based on topic recall on different number of keywords, ComStreamClust obtains superior results when compared to the existing methods.
Closeness and uncertainty aware adversarial examples detection in adversarial machine learning
(Elsevier Ltd, 2022-07) Tuna, Ömer Faruk; Çatak, Ferhat Özgür; Eskil, Mustafa Taner
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 vulnerabilities make it challenging to deploy Deep Neural Network (DNN) models in security-critical areas. Recently, many research studies have been conducted to develop defense techniques enabling more robust models. In this paper, we target detecting adversarial samples by differentiating them from their clean equivalents. We investigate various metrics for detecting adversarial samples. We first leverage moment-based predictive uncertainty estimates of DNN classifiers derived through Monte-Carlo (MC) Dropout Sampling. We also introduce a new method that operates in the subspace of deep features obtained by the model. We verified the effectiveness of our approach on different datasets. Our experiments show that these approaches complement each other, and combined usage of all metrics yields 99 % ROC-AUC adversarial detection score for well-known attack algorithms.
Battle Royale Optimizer for solving binary optimization problems
(Elsevier B.V., 2022-05) Akan, Taymaz; Agahian, Saeid; Dehkharghani, Rahim
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, which utilizes a dissimilarity measure between binary vectors instead of a vector subtraction operator, used in the original BRO algorithm to find the nearest neighbor. To evaluate BinBRO, we applied it to two popular benchmark datasets: the uncapacitated facility location problem (UFLP) and the maximum-cut (Max-Cut) graph problems from OR-Library. An open-source MATLAB implementation of BinBRO is available on CodeOcean and GitHub websites.
Uncertainty as a Swiss army knife: new adversarial attack and defense ideas based on epistemic uncertainty
(Springer, 2022-04-02) Tuna, Ömer Faruk; Çatak, Ferhat Özgür; Eskil, Mustafa Taner
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 quasi-imperceptible. These vulnerabilities make it challenging to deploy deep neural network models in the areas where security is a critical concern. In recent years, many research studies have been conducted to develop new attack methods and come up with new defense techniques that enable more robust and reliable models. In this study, we use the quantified epistemic uncertainty obtained from the model's final probability outputs, along with the model's own loss function, to generate more effective adversarial samples. And we propose a novel defense approach against attacks like Deepfool which result in adversarial samples located near the model's decision boundary. We have verified the effectiveness of our attack method on MNIST (Digit), MNIST (Fashion) and CIFAR-10 datasets. In our experiments, we showed that our proposed uncertainty-based reversal method achieved a worst case success rate of around 95% without compromising clean accuracy.
k-Means clustering by using the calculated Z-scores from QEEG data of children with dyslexia
(Taylor & Francis, 2023) Eroğlu, Günet; Arman, Fehim
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 measurement data were collected from 112 children with dyslexia (84 male, 28 female) between 7 and 11 years old for 96 sessions per subject on average. The z-scores are calculated for each band power and each channel, and outliers are eliminated afterward. Using the k-Means clustering method, three different clusters are identified. Cluster 1 (19% of the cases) has positive z-scores for theta, alpha, beta-1, beta-2, and gamma-band powers in all channels. Cluster 2 (76% of the cases) has negative z-scores for theta, alpha, beta-1, beta-2, and gamma-band powers in all channels. Cluster 3 (5% of the cases) has positive z-scores for theta, alpha, beta-1, beta-2, and gamma-band powers at AF3, F3, FC5, and T7 channels and mostly negative z-scores for other channels. In Cluster 3, there is temporal disruption which is a typical description of dyslexia. In Cluster 1, there is a general brain inflammation as both slow and fast waves are detected in the same channels. In Cluster 2, there is a brain maturation delay and a mild inflammation. After Auto Train Brain training, most of the cases resemble more of Cluster 2, which may mean that inflammation is reduced and brain maturation delay comes up to the surface which might be the result of inflammation. Moreover, Cluster 2 center values at the posterior parts of the brain shift toward the mean values at these channels after 60 sessions. It means, Auto Train Brain training improves the posterior parts of the brain for children with dyslexia, which were the most relevant regions to be strengthened for dyslexia.

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