Sürdürülebilir Kalkınma Amaçları

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Araştırmacılar(3)

• Adlı, Fatih
• Ağdaş, Mehmet Tevfik
• Ünlü, Gökhan

• MakaleAçık Erişim2026/r/munzur/6073
  Ekonomik Politika Belirsizliği ve Borsa Dinamikleri: BIST 100'den Elde Edilen Kanıtlar

  Adli, Fatih

  Küresel ekonomide son dönemlerde belirsizliklerin artması finansal ve ekonomik göstergeleri önemli ölçüde etkilemiştir. Bu çalışmanın amacı küresel ekonomik politika belirsizliğinin BIST100 üzerindeki etkisini incelemektedir. Ayrıca dolar endeksi ve altın değişkenleri model kapsamına dâhil edilmiştir. Bu çalışmada, Ocak 1997 – Ekim 2025 dönemini kapsayan aylık frekansta toplanmış 346 gözlemden oluşan veri seti kullanılmıştır. Değişkenlerin durağanlıklarını tespit edebilmek için ADF ve PP birim kök testi uygulanmıştır. Analizde verilerin farklı durağanlık seviyelerine sahip olmasından dolayı en uygun yöntem olarak ARDL sınır testi tercih edilmiştir. Ayrıca modelde değişkenler arasındaki nedensellik ilişkisini ölçmek için Granger nedensellik testi uygulanmıştır. Bu kapsamda elde edilen bulgulara göre değişkenler arasında uzun dönemde eşbütünleşme ilişkisi tespit edilmiştir. Uzun dönem katsayı analizi bulgularına göre ekonomik politika belirsizliği ile BIST100 arasında negatif yönlü ilişki elde edilmiştir. Granger nedensellik bulguları, ekonomik politika belirsizliğinden BIST100 endeksine doğru tek yönlü nedensellik ilişkisinin bulunduğunu göstermektedir. Bulgular, ekonomik politika belirsizliğinin finansal piyasalar üzerindeki etkisini vurgulayan literatürü ampirik olarak desteklemektedir.

  AtıfOpenAlex1

  İncele
• MakaleAçık Erişim2025/r/munzur/4120
  Advanced Classification of Poxvirus-Based Skin Diseases Using Deep Learning Techniques

  Arik, Kaan; Agdas, Mehmet T.; Korkmaz, Adem; Kosunalp, Selahattin; Iliev, Teodor

  Viral infections, especially those of the poxvirus family, present significant diagnostic challenges due to their similar clinical symptoms. This study proposes an innovative deep learning-based approach to classify six categories of poxvirus-related skin diseases: chickenpox, cowpox, healthy, measles, monkeypox, and smallpox. A dataset of 9,120 augmented images was used to train, validate, and test three advanced deep-learning models-YOLOv8, YOLOv5, and ResNet32. Among the models, YOLOv8 demonstrated superior performance, achieving an accuracy of 99.80%, precision of 99.28%, and recall of 99.14%, significantly outperforming YOLOv5 and ResNet32. The results underscore the potential of YOLOv8 in medical image analysis, providing a robust and efficient tool for the early detection and accurate classification of viral skin diseases. Comparisons with related studies highlight the effectiveness of the proposed approach, making it a state-of-the-art solution for improving diagnostic accuracy in healthcare. Future work will focus on extending the dataset and evaluating the model's applicability in real-time clinical environments.

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  AtıfOpenAlex1

  İncele
• MakaleAçık Erişim2025/r/munzur/4872
  YOLOv8 for Road DamageRecognition: Deep Learning-Based Segmentation and Detection System

  Agdas, Mehmet Tevfik; Arik, Kaan

  Road surface detection is critical for improving traffic safety and reducing road maintenance costs. Because traditional methods are time-consuming and costly, deep learning-based image processing techniques offer an important alternative in this field. This study aims to develop a model that automates the detection and segmentation of road surface defects such as potholes, manhole covers, and culverts using deep-learning-based image processing techniques. In this study, a dataset previously used in the literature was preferred. It was observed that object detection was performed using a dataset from the literature. In this study, both object detection and object segmentation were performed using different parameters. To prove the success of object segmentation, both object detection and segmentation were performed using the YOLOv8 algorithm, which has previously obtained successful results. AdamW optimization and Auto Batch parameters were selected for this study. With these parameters, object detection was first performed with the YOLOv8s model, which is one of the variances of the YOLOv8 algorithm with the most successful results in the literature, and a successful 92.8% mAP@50 performance value was obtained according to the sources in the literature. In this study, the YOLOv8n, YOLOv8s, YOLOv8m, and YOLOv8l variance models of the YOLOv8 algorithm were used with the preferred parameters, and segmentation was performed. In object segmentation, a map@50 performance value of 90.9% in all classes and 99.1% in culverts was obtained using the YOLOv8l model. A map@50 performance value of 89.1% for pothole segmentation and 88% for manhole cover segmentation was obtained using the YOLOv8s model. The results of the analyses showed consistency in precision and recall values. These findings contribute significantly to improving road safety, reducing maintenance costs, and supporting sustainable urban infrastructure. Future research could explore integrating multiple data sources and adapt these models to more complex road conditions.

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  AtıfOpenAlex1

  İncele
• MakaleYayıncıda açık erişim2025Scopus SJRQ1/r/munzur/730
  Effectiveness of flipped classroom model in distance education: An experimental study in Turkish language course

  Aslan, Seda Aktı; Turgut, Yigit Emrah; Ünlü, Gökhan; Allison, Jordan

  This study investigates the impact of the Flipped Classroom Model (FCM) on university students’ learning achievement, self-directed learning skills (SLS), and course interaction within the context of live online classes (LOC). Employing a 14-week quasi-experimental design, the research involved an experimental group (n = 34) exposed to the FCM and a control group (n = 25) taught through traditional methods. Pre-tests indicated that the groups were not statistically different at baseline. Post-intervention analyses revealed no statistically significant differences between the groups in terms of immediate learning achievement or course interaction. However, results from a delayed post-test indicated that students in the experimental group demonstrated significantly higher retention of learning content compared to their counterparts. No significant improvements were observed in self-directed learning skills, suggesting that the FCM, as implemented, may not effectively foster such competencies across all learners. Furthermore, the model did not significantly enhance student interaction in the online classroom environment. These findings suggest that while the FCM may support long-term retention, its overall impact on achievement, interaction, and the development of SLS in LOC settings is limited and may be moderated by students’ existing self-regulatory capacities.

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• MakaleAçık Erişim2024Scopus SJRQ1/r/munzur/2283
  Ensemble Learning Framework for DDoS Detection in SDN-Based SCADA Systems

  Oyucu, Saadin; Polat, Onur; Türkoğlu, Muammer; Polat, Hüseyin; Aksöz, Ahmet; Ağdaş, Mehmet Tevfik

  Supervisory Control and Data Acquisition (SCADA) systems play a crucial role in overseeing and controlling renewable energy sources like solar, wind, hydro, and geothermal resources. Nevertheless, with the expansion of conventional SCADA network infrastructures, there arise significant challenges in managing and scaling due to increased size, complexity, and device diversity. Using Software Defined Networking (SDN) technology in traditional SCADA network infrastructure offers management, scaling and flexibility benefits. However, as the integration of SDN-based SCADA systems with modern technologies such as the Internet of Things, cloud computing, and big data analytics increases, cybersecurity becomes a major concern for these systems. Therefore, cyber-physical energy systems (CPES) should be considered together with all energy systems. One of the most dangerous types of cyber-attacks against SDN-based SCADA systems is Distributed Denial of Service (DDoS) attacks. DDoS attacks disrupt the management of energy resources, causing service interruptions and increasing operational costs. Therefore, the first step to protect against DDoS attacks in SDN-based SCADA systems is to develop an effective intrusion detection system. This paper proposes a Decision Tree-based Ensemble Learning technique to detect DDoS attacks in SDN-based SCADA systems by accurately distinguishing between normal and DDoS attack traffic. For training and testing the ensemble learning models, normal and DDoS attack traffic data are obtained over a specific simulated experimental network topology. Techniques based on feature selection and hyperparameter tuning are used to optimize the performance of the decision tree ensemble models. Experimental results show that feature selection, combination of different decision tree ensemble models, and hyperparameter tuning can lead to a more accurate machine learning model with better performance detecting DDoS attacks against SDN-based SCADA systems.

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  AtıfScopus36·OpenAlex27

  İncele
• MakaleAçık Erişim2024Scopus SJRQ2/r/munzur/1793
  Detection of Threats to Farm Animals Using Deep Learning Models: A Comparative Study

  Korkmaz, Adem; Agdas, Mehmet Tevfik; Kosunalp, Selahattin; Iliev, Teodor; Stoyanov, Ivaylo

  The increasing global population and environmental changes pose significant challenges to food security and sustainable agricultural practices. To overcome these challenges, protecting farm animals and effectively detecting potential environmental threats is critical for economic and ecological sustainability. In this context, the current study examined the animal detection capabilities and efficiency of advanced deep learning models, such as YOLOv8, Yolo-NAS, and Fast-RNN, across a dataset of 2462 images encompassing various animal species that could pose a risk to farm animals. After converting the images into a standardized format, they were divided into three sets for training, validation, and testing, and each model was evaluated on this dataset during the analysis process. The findings indicated that the YOLOv8 model demonstrated superior performance, with 93% precision, 85.2% recall, and 93.1% mAP50 values, while Yolo-NAS was particularly noteworthy for its high recall value, indicating a remarkable detection ability. The Fast-RNN model also offered significant efficiency with balanced performance. The results reveal the considerable potential of deep learning-based object detection technologies in protecting farm animals and enhancing farm security. Additionally, this study provides valuable insights for future model optimization and customization research.

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  AtıfScopus9·WoS7·OpenAlex7

  İncele
• MakaleAçık Erişim2023/r/munzur/5745
  Deep Learning-Based Automatic Helmet Detection System in Construction Site Cameras

  Korkmaz, Adem; Ağdaş, Mehmet Tevfik

  Ensuring worker safety in high-risk environments such as construction sites is of paramount importance. Personal protective equipment, particularly helmets, plays a critical role in preventing severe head injuries. This study aims to develop an automated helmet detection system using the state-of-the-art YOLOv8 deep learning model to enhance safety monitoring in real-time. The dataset used for the study consists of 16,867 images, with various data augmentation and preprocessing techniques applied to improve the model's robustness. The YOLOv8 model achieved a 96.9% mAP50 score, outperforming other deep learning models in similar studies. The results demonstrate the effectiveness of the YOLOv8 model for accurate and efficient helmet detection in construction sites, paving the way for improved safety monitoring and enforcement in the construction industry.

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  AtıfOpenAlex15·TR Dizin3

  İncele
• MakaleYalnız künye2021Scopus SJRQ1/r/munzur/3054
  Problem- based learning in live online classes: Learning achievement, problem-solving skill, communication skill, and interaction

  Aslan, Alper

  The purpose of the study is to investigate the effect of first aid training in live online classes using the problem-based learning approach on students' learning achievement, problem-solving skills, communication skill, and interaction. The pre-test and post-test quasi-experimental design with a control group was used in the quantitative dimension of the study. The study group was conducted with forty-five university students. While live online classes in the experimental group were carried out via the PBL approach, live online classes in the control group were traditionally carried out via teacher-based methods without any intervention. Zoom application was used in both groups during live online classes. Group work in the experimental group was conducted using breakout rooms. The process of data collection and the experimental procedure of the study was completed in six weeks. Within the scope of the study, courses took three 40 min periods every week. The Learning Achievement Test, the Perceived Problem-solving Skills Scale, the Communication Skills Scale, and the Online Course Interaction Level Determination Scale were used for data collection tools. The results of study show that students who attended live online classes using PBL had higher levels of learning achievement, problem-solving skills and live online class interaction than students who attended live online classes using teacher-based methods. No significant difference between the communication skills of the groups was observed.

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  AtıfScopus143·WoS76·OpenAlex229

  İncele
• MakaleAçık Erişim2021Scopus SJRQ1/r/munzur/3032
  Teachers' views related the middle school curriculum for distance education during the COVID-19 pandemic

  Aslan, Seda Akti; Turgut, Yigit Emrah; Aslan, Alper

  This qualitative study aims to examine teachers’ opinions concerning the middle school curricula that have been in use for years, in order to determine its suitability for distance education during the COVID-19 pandemic. The participants consisted of eighteen middle school teachers from different subject areas in Turkey. A maximum variation sampling method was employed for participant selection. The data was collected through semi-structured interviews. Due to the pandemic, the interviews were conducted using video conferencing applications. The data was analyzed through content analysis. According to the findings, the teachers report that while they are effective in achieving cognitive objectives, they have difficulties in achieving affective and psychomotor objectives. In addition, the teachers do not prefer to design their own materials, rather they mainly benefit from ready-to-use materials during emergency distance education. The teachers also reveal that direct instruction and questioning were the teaching methods and techniques they preferred in online courses and that they were unable to adapt other methods and techniques to distance education, which resulted in students experiencing teacher-centered activities. Moreover, the teachers complain about students not attending the online courses on time or even at all, their disinterest, a lack of instant feedback, limited communication with students, and insufficient course lengths. Another critical finding was that the teachers only use homework, end-of-unit quizzes, and participation to online courses as measurement and evaluation tools and agree that they are unable to administer reliable and valid evaluation tools. The limitations of the study and implications for future research are later discussed.

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  AtıfScopus18·WoS16·OpenAlex40

  İncele
• MakaleYalnız künye2021Scopus SJRQ1/r/munzur/2492
  Factors affecting ICT integration in TURKISH education: a systematic review

  Turgut, Yigit Emrah; Aslan, Alper

  This research aims to reveal factors affecting Information and Communication Technologies (ICT) integration in learning environments in Turkey. The meta-synthesis method is used to make in-depth analysis and to interpret qualitative research findings on this issue. Through inclusion and exclusion criteria, 60 studies among 907 candidate studies accessed from databases named Web of Science, Education Resource Information Center (ERIC) and the Turkish Academic Network and Information Center (ULAKBIM) are included in this research. As a result of analyses, it is revealed that ICT integration into learning environments in Turkey is affected by five factors; students, educational material, infrastructure, management, and teachers. Considering these factors, it has been found that certain factors become prominent namely: the ICT and pedagogy competence of teachers; students’ ICT competence; a lack of technical equipment and technical support; a lack of educational materials; the attitudes of school administrators; and the quality and lack of in-service training for ICT. It is concluded that the factors affecting the ICT integration in education are directly or indirectly related to each other, and that ICT integration should be addressed with all its stakeholders and phases as a whole.

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  AtıfScopus38·WoS28·OpenAlex51

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