Browsing by Author "Aslan, M.F."

Now showing 1 - 3 of 3

Citation - WoS: 17
Citation - Scopus: 26
Consensus-Based Virtual Leader Tracking Swarm Algorithm With Gdrrt*-Pso for Path-Planning of Multiple-Uavs
(Elsevier B.V., 2024) Yildiz, B.; Aslan, M.F.; Durdu, A.; Kayabasi, A.
UAV technology is rapidly advancing and widely utilized, particularly in social and military domains, due to its extensive motion and maneuverability. Coordinating multiple UAVs enables more rapid and efficient task execution compared to a single UAV. The proliferation of UAVs across various sectors, including entertainment, transportation, delivery, and social domains, as well as military applications such as surveillance, tracking, and attack, has spurred research in swarm systems. In this study, a new swarm topology is presented by combining the Consensus-Based Virtual Leader Tracking Swarm Algorithm (CBVLTSA), which provides formation control in swarm systems, with the Goal Distance-based Rapidly-Exploring Random Tree with Particle Swarm Optimization (GDRRT*-PSO) route planning algorithm. Recently proposed, GDRRT* is notable for its efficient operation in expansive environments and rapid convergence to the goal. Within this framework, the path generated by GDRRT* is optimized using PSO to yield the shortest current route. CBVLTSA employs a potential push and pull function to facilitate cooperative, coordinated flight among swarm members. While applying pushing force to avoid collisions with each other and obstacles, members also exert pulling force to maintain flight formation while navigating to target points. This ensures controlled flight formation and collision-free traversal along the GDRRT*-PSO route. Consequently, unlike the others, the proposed algorithm achieves faster target reach with pre-planned routes, demonstrating a robust and flexible swarm topology with CBVLTSA. Moreover, we anticipate the significant utility of this algorithm across various swarm applications, including target detection, observation, tracking, trade and transportation logistics, and collective defense and attack strategies. © 2024 Elsevier B.V.
Citation - Scopus: 8
Performance Comparison of Extreme Learning Machines and Other Machine Learning Methods on Wbcd Data Set
(Institute of Electrical and Electronics Engineers Inc., 2021) Keskin, O.S.; Durdu, A.; Aslan, M.F.; Yusefi, A.
Breast cancer is one of the most common forms of cancer among women in our country and the world. Artificial intelligence studies are growing in order to reduce the mortality and early diagnosis needed for appropriate treatment. The Excessive Learning Machines (ELM) method, one of the machine learning approaches, is applied to the Wisconsin Breast Cancer Diagnostic (WBCD) dataset in this study, and the findings are compared to those of other machine learning methods. For this purpose, the same dataset is also classified using Multi-Layer Perceptron (MLP), Sequential Minimum Optimization (SMO), Decision Tree Learning (J48), Naive Bayes (NB), and K-Nearest Neighbor (KNN) methods. According to the results of the study, the ELM approach is more successful than other approaches on the WBCD dataset. It's also worth noting that as the number of neurons in the ELM grows, so does the learning ability of the network. However, after a certain number of neurons have passed, test performance begins to decline sharply. Finally, the ELM's performance is compared to the results of other studies in the literature. © 2021 IEEE.
ResNet-ViT-SVM a New Hybrid Architecture Proposal and Experimental Comparisons on Date Fruit
(Academic Press Inc., 2025) Sabanci, K.; Aslan, M.F.; Aslan, B.
Accurate classification of date fruit varieties is essential for quality control, intelligent sorting, and agricultural sustainability. This study proposes a novel hybrid deep learning framework, named ResNet-ViT-SVM, to classify nine different date fruit varieties with high precision. The dataset consists of 1658 images distributed across nine classes, captured under controlled and real-world conditions. The approach consists of three stages: (i) initial classification using a fine-tuned ResNet50 convolutional neural network, (ii) reclassification using a Vision Transformer (ViT), and (iii) fusion of deep features from both models, followed by final classification via Support Vector Machines (SVM). The novelty of our approach lies in this unique integration of CNN, Transformer, and SVM components in a three-stage pipeline. Experimental results show that the ResNet50 and ViT models individually achieved classification accuracies of 93.05 % and 95.47 %, respectively, while the proposed ResNet-ViT-SVM hybrid model significantly outperformed them, achieving up to 99.40 %. As part of the ablation study, the hybrid model achieved 100 % accuracy on laboratory images and 83.11 % accuracy on field images captured under natural orchard conditions, confirming its effectiveness across both controlled and real-world scenarios. These findings demonstrate that the hybrid architecture offers strong generalization capability across different data domains and represents a highly accurate, contactless, and automated solution for agricultural product classification tasks. © 2025 Elsevier B.V., All rights reserved.