Altınbaş Üniversitesi Kurumsal Akademik Arşivi
DSpace@Altınbaş, Altınbaş Üniversitesi tarafından doğrudan ve dolaylı olarak yayınlanan; kitap, makale, tez, bildiri, rapor, araştırma verisi gibi tüm akademik kaynakları uluslararası standartlarda dijital ortamda depolar, Üniversitenin akademik performansını izlemeye aracılık eder, kaynakları uzun süreli saklar ve telif haklarına uygun olarak Açık Erişime sunar.
Güncel Gönderiler
Wind farm sites selection using a machine learning approach and geographical information systems in Türkiye
(Springer Science and Business Media B.V., 2025) Khalaf, Oras Fadhil; Uçan, Osman Nuri; Alsamarai, Naseem Adnan
This research highlights the importance of integrating machine learning algorithms with Geographical Information Systems (GIS) applications in the field of renewable energy by finding a suitable site for wind farms due to their importance in preserving the environment to achieve efficiency and cost-effectiveness and reduce the environmental impact of fossil fuel energy sources. Using GIS various factors affecting wind energy localization were processed and analyzed including natural, socio-economic and environmental criteria. Ensemble learning of four supervised machine learning algorithms (Random Forest, K-Nearest Neighbor, Support Vector Machines, Naive Bayes) was used to classify suitable and unsuitable data representing geo-referenced points on the ground with three criteria for each site (wind speed, elevation and slope). The results of the algorithms varied in terms of accuracy and variance, then the results were collected, and the intersection between them was found so that the location classification would be agreed upon in the results of the algorithms used. The aim of using this technique is to reduce the error, increase the accuracy and avoid the bias or variance present in individual models. Accuracy of the algorithms result was respectively (K-Nearest Neighbor, Random Forest, Support Vector Machines, Naive Bayes) (93.022%, 93.018%, 95.095%, 89.553%). The final result is a map using GIS showing the suitable and unsuitable sites of wind farms in the study area (Türkiye) has been chosen as a study area in the research due to several factors that make it suitable for wind energy projects, including its geographical location, which gives it great climatic and terrain diversity, as it is surrounded by seas (Black Sea, Aegean Sea, and Mediterranean Sea), which leads to the activity of seasonal and continuous winds, which contributes to the activity of seasonal and permanent winds. Its drive to develop investment in renewable energy due to economic and population growth has increased the demand for energy and consequently the development of renewable and sustainable energy sources. This research contributes to supporting the global transition to sustainable energy by providing a new methodology for integrating multiple technologies to support a sustainable energy future.
A multi-objective supply chain optimization model for reliable remanufacturing problems with M/M/m/k queues
(Elsevier B.V., 2025) Hajipour, Vahid; Kaveh, Shermineh Hadad; Yiğit, Fatih; Gharaei, Ali
Product recovery is critical in reducing costs, enhancing profitability, and improving supply chain responsiveness to customer demands. Remanufacturing returned products, as part of the circular economy, is a central strategy in achieving these goals. This study presents a model that optimizes the remanufacturing process using in-house workstations and outsourcing to maximize supply chain profitability, reduce queue lengths, and ensure machine reliability. The remanufacturing system is modeled as an M/M/m/k queuing system, considering real-world supply chain constraints such as budget limitations, station capacity, and machine reliability. Supply chain optimization is achieved by maintaining efficiency while examining different remanufacturing policies and pricing strategies. The results show that expanding remanufacturing capacity enhances supply chain profitability, even with moderate increases in queue length. We provide valuable insights for supply chain managers aiming to optimize their remanufacturing processes and balance cost, efficiency, and reliability.
Advanced Deep Learning Models for Improved IoT Network Monitoring Using Hybrid Optimization and MCDM Techniques
(Multidisciplinary Digital Publishing Institute (MDPI), 2025) Qasim Jebur Al-Zaidawi, Mays; Çevik, Mesut
This study addresses the challenge of optimizing deep learning models for IoT network monitoring, focusing on achieving a symmetrical balance between scalability and computational efficiency, which is essential for real-time anomaly detection in dynamic networks. We propose two novel hybrid optimization methods—Hybrid Grey Wolf Optimization with Particle Swarm Optimization (HGWOPSO) and Hybrid World Cup Optimization with Harris Hawks Optimization (HWCOAHHO)—designed to symmetrically balance global exploration and local exploitation, thereby enhancing model training and adaptation in IoT environments. These methods leverage complementary search behaviors, where symmetry between global and local search processes enhances convergence speed and detection accuracy. The proposed approaches are validated using real-world IoT datasets, demonstrating significant improvements in anomaly detection accuracy, scalability, and adaptability compared to state-of-the-art techniques. Specifically, HGWOPSO combines the symmetrical hierarchy-driven leadership of Grey Wolves with the velocity updates of Particle Swarm Optimization, while HWCOAHHO synergizes the dynamic exploration strategies of Harris Hawks with the competition-driven optimization of the World Cup algorithm, ensuring balanced search and decision-making processes. Performance evaluation using benchmark functions and real-world IoT network data highlights superior accuracy, precision, recall, and F1 score compared to traditional methods. To further enhance decision-making, a Multi-Criteria Decision-Making (MCDM) framework incorporating the Analytic Hierarchy Process (AHP) and TOPSIS is employed to symmetrically evaluate and rank the proposed methods. Results indicate that HWCOAHHO achieves the most optimal balance between accuracy and precision, followed closely by HGWOPSO, while traditional methods like FFNNs and MLPs show lower effectiveness in real-time anomaly detection. The symmetry-driven approach of these hybrid algorithms ensures robust, adaptive, and scalable monitoring solutions for IoT networks characterized by dynamic traffic patterns and evolving anomalies, thus ensuring real-time network stability and data integrity. The findings have substantial implications for smart cities, industrial automation, and healthcare IoT applications, where symmetrical optimization between detection performance and computational efficiency is crucial for ensuring optimal and reliable network monitoring. This work lays the groundwork for further research on hybrid optimization techniques and deep learning, emphasizing the role of symmetry in enhancing the efficiency and resilience of IoT network monitoring systems.
A Hybrid Tree Convolutional Neural Network with Leader-Guided Spiral Optimization for Detecting Symmetric Patterns in Network Anomalies
(Multidisciplinary Digital Publishing Institute (MDPI), 2025) Al-Dulaimi, Reem Talal Abdulhameed; Türkben, Ayça Kurnaz
In the realm of cybersecurity, detecting Distributed Denial of Service (DDoS) attacks with high accuracy is a critical task. Traditional machine learning models often fall short in handling the complexity and high dimensionality of network traffic data. This study proposes a hybrid framework leveraging symmetry in feature distribution, network behavior, and model optimization for anomaly detection. A Tree Convolutional Neural Network (Tree-CNN) captures hierarchical symmetrical dependencies, while a deep autoencoder preserves latent symmetrical structures, reducing noise for better classification. A Leader-Guided Velocity-Based Spiral Optimization Algorithm is proposed to optimize the parameters of the system and achieve better performance. A Leader-Guided Velocity-Based Spiral Optimization Algorithm is introduced to maintain a symmetrical balance between exploration and exploitation, optimizing the autoencoder, Tree-CNN, and classification thresholds. Validation using three datasets—UNSW-NB15, CIC-IDS 2017, and CIC-IDS 2018—demonstrates the framework’s superiority. The model achieves 96.02% accuracy on UNSW-NB15, 99.99% on CIC-IDS 2017, and 99.96% on CIC-IDS 2018, with near-perfect precision and recall. Despite a slightly higher computational cost, the symmetrically optimized framework ensures high efficiency and superior detection, making it ideal for real-time complex networks. These findings emphasize the critical role of symmetrical network patterns and feature selection strategies for enhancing intrusion detection performance.
Enhancing Cold Cases Forensic Identification with DCGAN-based Personal Image Reconstruction
(University of Baghdad, 2025) AL-Muttairi, Hasan Sabah K.; Kurnaz, Sefer; Aljuboori, Abbas Fadhil
With the improvement of artificial intelligence and deep learning techniques, especially deep convolutional generative adversarial network (DCGAN), there has been a significant development in personal identity and generating images through facial reconstruction systems. This study focuses on proposing a model of personal image reconstruction from forensic sketches using DCGAN. The model comprises two networks: a generator to convert sketch images into real images and a feature network to determine the similarity of the generated images to real ones. Forensic sketches provided by relevant authorities are used as inputs to the proposed model. These sketches include details and information on the perpetrators or missing persons obtained from witnesses or the missing person parents. Prominent facial features extracted from the reconstructed images aid in the process of personal image reconstruction. The proposed model shows good results, achieving up to 99% accuracy in the generated images. The error ratio is reported to be as low as 0.92% based on the evaluation using the CUHKFaces dataset. This study presents a new approach to reconstructing human face images from forensic sketches using DCGAN.
