Sürdürülebilir Kalkınma Amaçları

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Araştırmacılar(31)

• Akçay, Özge
• Akyol, Sinem
• Alatas, Bilal
• Artuğer, Fırat
• Aslan, Alper
• Aslan, Serpil
• Baloglu, Ulas Baran
• Baskonus, Haci Mehmet
• Can, Ümit
• Çakıroğlu, Dilek
• Çelik, Yusuf
• Doğan, Gürkan
• Garipcan, Ali Murat
• Gencer, Burak
• Göktaş, Oktay
• Gül, Serpil
• Gündem, Merve Yılmazer
• Işık Göktaş, Nebile
• Kiziloluk, Soner
• Müezzinoğlu, Taha
• Oksuztepe, Eyyup
• Özkaynak, Fatih
• Serin, Faruk
• Talo, Muhammed
• Tuğrul, Feride
• Tümen, Vedat
• Unal, Inan
• Utku, Anıl
• Yakar, Şiyar
• Yildirim, Ozal
• Yilmazer, Merve

• MakaleAçık Erişim2026Scopus SJRQ2/r/munzur/4034
  A New Metacognitive Confidence Calculation Approach Based on Large Language Models for Natural Language Explanations

  Yilmazer Gundem, Merve; Sahin, Simay; Karakose, Mehmet

  Traditional methods used to measure metacognitive confidence rely on superficial features such as word count and ambiguity and therefore fail to adequately reflect the level of metacognitive confidence in statements. In this study, in addition to traditional text-based confidence measures, we used a Large Language Model (LLM) to directly analyze confidence and its ratio in statements. LLM-based confidence scores were calculated on statements from the e-SNLI and CoS-E open-source datasets, and the prediction performance obtained with traditional features was compared. The results indicate that traditional surface-level text features explain the variance in LLM-generated confidence scores to a limited extent, and that LLM-based scores are significantly correlated with independent researcher evaluations. This study contributes to the literature by offering a scalable and reproducible analytical framework for measuring metacognitive confidence. It is believed that the findings can be directly applied, particularly in applications where trust is critical, such as automated software test scenario generation, decision support systems, and risk management. The use of LLMs offers a framework for the LLM-based analysis of potential metacognitive confidence signals in natural language explanations by examining the sense of confidence and certainty with which statements are presented, rather than their factual accuracy.

  İncele
• MakaleAçık Erişim2026/r/munzur/4545
  Predicting middle school students' academic achievement in mathematics with aAnew hybrid approach

  Dogan, Gurkan; Demiralp, Demet

  The academic achievement of middle school students is one of the critical steps in their educational journey that affects their future academic and career prospects. During this transition period, students are faced with a variety of challenges related to their families, schools, and the individual themselves. Research shows that academic performance during these formative years is critical for future educational success and overall life success. Therefore, addressing students' academic achievement, which depends on various factors during this period, is of particular importance for both institutional and individual planning and orientation in the future. In this study, a new hybrid approach based on artificial neural networks that enables automatic analysis of data on family, school, and individual factors affecting middle school students' academic achievement in mathematics is proposed. A publicly available student performance dataset was used for training and testing the proposed hybrid approach and other models. This dataset consists of data such as mathematics grades, family information, residential information, and health status information for 395 students enrolled in two public schools in the Alentejo region of Portugal. The proposed approach achieved an R2 score of 88.6% in experimental studies with this data set, providing approximately 3% higher accuracy than its closest competitor among other methods in the literature.

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  AtıfOpenAlex1

  İncele
• MakaleAçık Erişim2026/r/munzur/6097
  Dikkat Tabanlı Görsel–İşitsel Akış Füzyonu ile Sahne Sınıflandırma

  Çelik, Yusuf

  Bu çalışmada, sahne sınıflandırma görevlerinde görsel ve işitsel verilerin birlikte kullanılmasına yönelik, Gated Cross-Modal Attention Fusion Network (GCM-AFN) adı verilen çok modlu bir derin öğrenme mimarisi önerilmektedir. Önerilen yapıda, görsel akıştan EfficientNetV2 omurgası kullanılarak derin özellikler çıkarılırken, işitsel akışta MFCC tabanlı zaman-frekans temsilleri kullanılmaktadır. Her iki akıştan elde edilen özellik temsilleri, kapılı dikkat mekanizması aracılığıyla birleştirilmektedir. Model, çok kipli bilgilerin etkin biçimde bir araya getirilmesini sağlamak amacıyla yardımcı akış çıktıları ve dikkat tabanlı bir füzyon stratejisi içermektedir. Çapraz doğrulamaya dayalı deneysel sonuçlar, önerilen yaklaşımın tek akışlı modeller ve geleneksel füzyon yöntemlerine kıyasla daha yüksek doğruluk, daha düşük varyans ve sınıf bazında daha tutarlı bir performans sergilediğini göstermektedir. Elde edilen bulgular, dikkat tabanlı füzyonun görsel–işitsel sahne sınıflandırma görevlerinde etkili bir bilgi birleştirme yaklaşımı sunduğunu ortaya koymaktadır.

  İncele
• MakaleAçık Erişim2026/r/munzur/6091
  Sentiment Analysis of Monkeypox Tweets Using a Hybrid CNN-BiGRU Based Deep Learning Model

  Can, Umit

  Recently, epidemics have disrupted people's lives. Following the COVID-19 pandemic, cases of monkeypox, a rare disease, began to appear worldwide. These incidents raised societal tension and anxiety. Understanding public attitudes, sentiments, and perceptions regarding the epidemic is essential for developing and implementing effective policies and countermeasures. Online social media platforms are essential venues for expressing feelings and views. In particular, Twitter provides valuable information for measuring societal sentiment. Machine learning and deep learning methods have recently been widely used in social network analysis. Deep learning models, which deliver strong performance, particularly on complex and nonlinear problems, are used in natural language processing and text mining. In this study, a Convolutional Neural Network Bidirectional Gated Recurrent Unit (CNN-BiGRU) hybrid deep learning model was developed by combining a CNN and a BiGRU and applied for the monkeypox sentiment analysis. The dataset was labeled as positive, negative, or neutral with Valence Aware Dictionary and sEntiment Reasoner (VADER). In addition, FastText and Term Frequency-Inverse Document Frequency (TF-IDF) word-embedding methods were used for feature extraction, and the performance of machine-learning and deep-learning methods was evaluated separately. As a result of the experimental studies, the FastText-CNN-BiGRU model obtained the highest results with 0.9382 accuracy, 0.9347 macro precision, 0.9367 macro recall, 0.9355 macro F-score, and 0.9875 macro ROC-AUC. The FastText-CNN model was the second most successful, achieving 0.9303 accuracy, 0.9268 macro precision, 0.9280 macro recall, 0.9271 macro F-score, and 0.9862 macro ROC-AUC. The FastText-BiGRU model was the third most successful, achieving 0.9262 accuracy, 0.9224 macro precision, 0.9255 macro recall, 9236 macro F-score, and 0.9838 macro ROC-AUC.

  İncele
• MakaleAçık Erişim2026/r/munzur/5943
  Design and analysis of an arduino-based low-cost true random number generator

  Erdem, Ebubekir; Kaner, Seyfullah; Garipcan, Ali Murat

  This study covers the design and statistical randomness-based performance analysis of an Arduino-based True Random Number Generator (TRNG) that uses environmental analog noise as a randomness source. The environmental analog noise used as the randomness source was sampled from the Arduino's A_0-A_4 pins, mixed at the bit level, and then subjected to the SHA-256 hash function in the post processing stage. The 256-bit random number blocks obtained using this method were transferred via the serial port and converted into a format suitable for NIST SP-800-22 and Dieharder tests. In terms of statistical randomness, the random numbers achieved successful results in all NIST tests and in 93.94% of the Dieharder tests. However, Shannon entropy and bias analyses showed that the generated numbers did not carry structural patterns. The study demonstrates that reliable randomness can be obtained even under limited hardware and low energy conditions, showing that the developed structure provides an accessible and reproducible platform for educational use, embedded system security, and lightweight cryptographic applications.

  İncele
• MakaleAçık Erişim2026/r/munzur/5941
  Deep learning-based short-term cryptocurrency price forecasting using LSTM models enhanced with technical indicators

  Çelik, Yusuf; İnce, İrem Sevda

  This study investigates short-term cryptocurrency price forecasting using deep learning methods. Minute-level price and volume data for BTC/USDT and ETH/USDT trading pairs were obtained through the Binance API and analyzed under different temporal resolutions, including hourly and 15-minute forecasting scenarios. Recurrent neural network architectures, including LSTM, GRU, and BiLSTM, were evaluated using different loss functions to identify effective configurations for short-term forecasting. Experimental results show that deep learning models can achieve high forecasting accuracy for high-frequency cryptocurrency data. In particular, the LSTM model trained with a weighted loss function demonstrated strong and stable prediction performance. In addition, incorporating technical indicators as auxiliary features enriched the feature space and improved forecasting capability. The results also suggest that aligning the temporal resolution of technical indicators with the prediction frequency can further enhance model performance. Overall, the proposed framework provides an effective deep learning–based approach for short-term cryptocurrency price forecasting in high-frequency financial markets.

  İncele
• MakaleAçık Erişim2026/r/munzur/5938
  Application of machine learning techniques for multi-class classification performance on mental disorders

  Can, Umit

  The contemporary social framework is predicated on a fiercely competitive system, which amplifies individual pressure and subsequently elevates the incidence of mental diseases, significantly affecting public health. Consequently, governmental and non-governmental groups allocate considerable resources to tackle these concerns. The precise identification of such illnesses via artificial intelligence techniques has become critically significant. These methodologies, widely used across several healthcare sectors, have been increasingly adopted in psychiatry. Evaluating psychiatric data to forecast disease accurately enables doctors to make better decisions and establishes a robust decision-support framework. This work employed prevalent machine learning techniques to identify mental illnesses utilizing a dataset with labels for seven distinct mental states—six disorders and one normative condition. The approaches were initially evaluated on an imbalanced dataset and subsequently on a balanced dataset, facilitating comparison of the outcomes. Among Categorical Boosting, Gradient Boosting, Extreme Gradient Boosting, Support Vector Machine, and Logistic Regression, the Support Vector Machine method demonstrated superior performance on both balanced and imbalanced datasets.

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  İncele
• MakaleYalnız künye2026Scopus SJRQ1/r/munzur/1519
  Innovative solutions for aquaculture: detecting fish diseases with hybrid deep learning model and explainable artificial intelligence

  Utku, Anıl; Barut, Abdulkadir; Alofaysan, Hind

  Early identification of fish diseases is critical to maintain aquaculture and minimize its financial burden. In the diagnosis of diseases, time is critical and scientists’ experience is imperative, which makes the artificial intelligence-based models necessary. It is intended by this study to integrate deep learning with explainable artificial intelligence methods to establish an artificial intelligence-powered framework for early detection of disease in fish. In this study, we developed a hybrid artificial intelligence system designed to detect diseases in freshwater fish by analyzing skin images. The primary objectives of the system are to minimize economic losses, prevent further disease spread, and ultimately increase aquaculture productivity. For this purpose, we used a dataset consisting of images from seven different disease categories, with 250 images in each class. A new hybrid model, Residual Network-50–Vision Transformer, was developed by integrating the strengths of Residual Network-50 and Vision Transformer architectures to improve classification accuracy. The proposed model was subjected to rigorous comparison with established architectures, including Visual Geometry Group 16, Mobile Convolutional Neural Network Version 2, Efficient Neural Network–Baseline Model B0, standalone Vision Transformer, and the original Residual Network-50. The results demonstrated that Residual Network-50–Vision Transformer outperformed the model, achieving an impressive 99.14% accuracy rate. Furthermore, the study employed local interpretable model-independent explanations to explain which image features most significantly influence the model’s classification decisions, thereby enhancing the interpretability and confidence in the model’s output. The application of such advanced image processing models in combination with explainable AI techniques demonstrates significant potential to support sustainable aquaculture by reducing disease outbreaks and associated financial losses.

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  AtıfScopus3·OpenAlex2

  İncele
• MakaleYalnız künye2026Scopus SJRQ1/r/munzur/1470
  MILA: An Innovative Approach to Identifying AI-Generated Content Using BERT, CNN, and BiLSTM

  Utku, Anil

  AI ethical principles are essential in balancing the social impacts of rapidly developing technology today. The widespread use of AI technologies has heightened the need for ethical and legal regulations in this area. In this process, fundamental principles such as data privacy, accountability, transparency, and human oversight take center stage to enhance the reliability of artificial intelligence and foster social trust. Generative AI refers to machines producing text, images, audio, and video content. However, generative AI brings ethical discussions since it can produce original content. Detecting AI-generated content is becoming increasingly complex due to the ability of AI models to produce increasingly more fluent and meaningful texts. This study created a hybrid MILA model using BERT, CNN, and BiLSTM models to detect AI-generated content. MILA was compared in detail with SVM, XGBoost, CNN, LSTM, and BiLSTM using sentence-level and article-level datasets. Experiments demonstrated that MILA outperformed the compared models, achieving 94.34% accuracy at the sentence-level and 93.62% accuracy at the article-level.

  AtıfOpenAlex1

  İncele
• MakaleYalnız künye2026Scopus SJRQ1/r/munzur/1463
  Exploring research themes in the Journal of Librarianship and Information Science: Insights from topic modelings

  Aslan, Alper; Özyurt, Özcan

  This study aims to explore research interests and trends in the journal by analyzing the entire publication history of the Journal of Library and Information Science (JoLIS) between 1969 and 2023 using Latent Dirichlet Allocation (LDA). A comprehensive topic modeling analysis was conducted on 1437 articles for which titles, abstracts and keywords were available. The research focuses on identifying key thematic shifts in the journal’s publications. The findings highlight that topics such as Information Literacy & Research and Librarian Roles & Skills are growing in importance, while traditional topics such as Cataloging & Classification are declining. The results reveal shifts in the focus of the LIS field, providing critical insights to guide future research. In particular, Information Literacy & Research emerges as a highly accelerating topic, reflecting the need for digital competencies in response to evolving information environments. Meanwhile, declining interest in Cataloging & Classification is in line with the automation of traditional library functions, shifting training priorities toward digital literacy and data management skills. By mapping the evolution of research priorities, this study serves as a valuable resource for LIS researchers and practitioners, helping them to align their work with future developments in the field.

  AtıfScopus9·WoS7·OpenAlex11

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