Işık University Institutional Repository
Digitally stores academic resources such as books, articles, dissertations, bulletins, reports, research data published directly or indirectly by İbn Haldun University at international standards, helps track the academic performance of the university, provides long term preservation for resources and makes publications available to Open Access in accordance with their copyright to increase the effect of publications.
Recent Submissions
Mental disorder and suicidal ideation detection from social media using deep neural networks
(Springer, 2024-12)
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.
Best proximity point theorems in non-Archimedean Menger probabilistic spaces
(University of Kragujevac, Faculty of Science, 2024)
In this work, we prove best proximity point theorems for γ-contractions with conditions the weak P-property in non-Archimedean Menger probabilistic metric spaces. We give the notion of γ- proximal contractions of first and second type in non-Archimedean Menger probabilistic metric spaces and also we establish best proximity point theorems for these proximal contractions. Lastly, we complete our study by giving examples that support our results.
Higher analogues of discrete topological complexity
(Springer-Verlag Italia S.R.L., 2024-06-13)
In this paper, we introduce the nth discrete topological complexity and study its properties such as its relation with simplicial Lusternik–Schnirelmann category and how the higher dimensions of discrete topological complexity relate with each other. Moreover, we find a lower bound of n-th discrete topological complexity which is given by the nth usual topological complexity of the geometric realisation of that complex. Furthermore, we give an example for the strict case of that lower bound.
Text-to-SQL: a methodical review of challenges and models
(TÜBİTAK, 2024-05-20)
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)
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.
