Profile URL: https://hdl.handle.net/20.500.13091/13585
Job Title:Doç. Dr.
Email Address:mfaslan@ktun.edu.tr
Main Affiliation:10.03. Department of Artificial Intelligence and Machine Learning
Status: Current Staff
ORCID:
0000-0001-7549-0137
0000-0001-7549-0137Scopus ID:
57205362915
57205362915YÖK Akademik: DDFFB85A6FC7A71F
Google Scholar:
5RWfOG4AAAAJ
5RWfOG4AAAAJWeb of Science ID:
V-8019-2017
V-8019-2017Name Variants:
Aslan, Muhammet F. Aslan M. Fatih
26 results
Scholarly Output Search Results
Now showing 1 - 10 of 26
Article Citation - WoS: 237Citation - Scopus: 311Cnn-Based Transfer Learning-Bilstm Network: a Novel Approach for Covid-19 Infection Detection(ELSEVIER, 2021-01-01) Aslan, Muhammet Fatih; Ünlerşen, Muhammed Fahri; Sabancı, Kadir; Durdu, AkifCoronavirus disease 2019 (COVID-2019), which emerged in Wuhan, China in 2019 and has spread rapidly all over the world since the beginning of 2020, has infected millions of people and caused many deaths. For this pandemic, which is still in effect, mobilization has started all over the world, and various restrictions and precautions have been taken to prevent the spread of this disease. In addition, infected people must be identified in order to control the infection. However, due to the inadequate number of Reverse Transcription Polymerase Chain Reaction (RT-PCR) tests, Chest computed tomography (CT) becomes a popular tool to assist the diagnosis of COVID-19. In this study, two deep learning architectures have been proposed that automatically detect positive COVID-19 cases using Chest CT X-ray images. Lung segmentation (preprocessing) in CT images, which are given as input to these proposed architectures, is performed automatically with Artificial Neural Networks (ANN). Since both architectures contain AlexNet architecture, the recommended method is a transfer learning application. However, the second proposed architecture is a hybrid structure as it contains a Bidirectional Long Short-Term Memories (BiLSTM) layer, which also takes into account the temporal properties. While the COVID-19 classification accuracy of the first architecture is 98.14%, this value is 98.70% in the second hybrid architecture. The results prove that the proposed architecture shows outstanding success in infection detection and, therefore this study contributes to previous studies in terms of both deep architectural design and high classification success. (C) 2020 Elsevier B.V. All rights reserved.Article A Comparative Study of CNN and Vision Transformer Architectures for Fault Detection in Small Wind Turbine Blades(2026) Aslan, Muhammet Fatih; Balcı, Selami; Aslan, BusraAutomated fault detection in wind turbine blades is critical for ensuring the reliability and operational efficiency of wind energy systems. This study presents a systematic comparative analysis of five state-of-the-art deep learning architectures for binary fault classification (healthy versus faulty) on the CAI-SWTB dataset, comprising 6,000 RGB images of small wind turbine blades. The evaluated architectures span three distinct design paradigms: a classical Convolutional Neural Network (CNN) (ResNet-50), a modern CNN (EfficientNetV2-S), and three Vision Transformer (ViT)-based models (ViT-B/16, Data-efficient Image Transformer (DeiT)-S/16, and Swin-Tiny). All models were trained using a two-stage transfer learning protocol with ImageNet-pretrained weights, employing the AdamW optimizer and a cosine annealing learning rate schedule. EfficientNetV2-S achieved the highest classification performance with 99.75% accuracy, followed by ResNet-50 at 99.42%. Among the transformer-based models, Swin-Tiny outperformed both ViT-B/16 (65.33%) and DeiT-S/16 (82.75%), achieving 88.25% accuracy. Grad-CAM analysis confirmed that the best-performing models correctly localize structural defect regions in blade images, supporting their interpretability and suitability for real-world inspection applications.Article Advancing Remote Sensing with Few-Shot Learning: A Comprehensive Review of Methods, Challenges, and Future Directions(John Wiley and Sons Inc, 2025) Aslan, M.F.; Sabanci, K.; Durdu, A.; Kaousar, R.In this review, the details and developments of few-shot learning (FSL) techniques in different remote sensing (RS) studies including change monitoring, disaster management, urban monitoring, and agriculture are discussed in detail. Furthermore, a categorization is made by dividing FSL methods into three categories (metric-based, optimization-based, and transfer learning approaches) and considering hybrid approaches. Special attention is given to episodic training and meta-learning approaches that provide rapid adaptation to new classes with minimal examples. Furthermore, the integration of explainable artificial intelligence (XAI) and its real-time application capabilities are discussed. Important issues such as domain shift, class imbalance, and high dimensionality are discussed. Recent refinements such as task-level learning, data augmentation, and multimodal integration are examined. Finally, a coherent framework is suggested for further studies and practical FSL applications in the context of RS. As a result, it provides a more comprehensive perspective than previous reviews. This review aimed to guide future research in the integration of FSL with RS applications by analyzing the existing literature and pointing out important research gaps. © 2025 John Wiley & Sons Ltd.Article Fusion of Ct and Mr Liver Images by Surf-Based Registration(2019) Aslan, Muhammet Fatih; Durdu, Akif; Sabancı, KadirMedical imaging plays an important role in the diagnosis and treatment of different diseases. Images with more details are obtained by image fusion for more accurate analysis of medical images. In this study, Computed Tomography (CT) and Magnetic Resonance (MR) images of the liver from The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) are fused using different combinations of different wavelet types such as daubechies, coiflet and symlet. To accomplish this task, first the preprocessing steps are completed, and then registration is performed using Speed up Robust Features (SURF). As a result, to measure the quality of the obtained fusion image Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), Structural Similarity Index Measurement (SSIM), Mean Structural Similarity (MSSIM) and Feature Similarity Index (FSIM) metrics are used.Article Advances and Challenges in the Applications of Drone Systems in Precision Agriculture: A Review(Chinese Soc Agricultural Engineering, 2026) Lan, Yubin; Wang, Baoju; Wang, Guobin; Yan, Yu; Hussain, Mujahid; Aslan, Muhammet Fatih; Kaousar, RehanaClimate change, resource limitations, and increasing global food demand are accelerating the need for efficient and sustainable agricultural management practices. Unmanned aerial vehicles (UAVs) have emerged as a transformative technology in precision agriculture (PA) because of their capability to provide high-resolution, real-time, and site-specific crop monitoring. This review critically examines recent advancements (2016-2025) in UAV-assisted PA, focusing on UAV platforms, sensing technologies, data acquisition systems, information fusion methods, and artificial intelligence (AI)-driven analytical frameworks. Particular emphasis is placed on applications including crop monitoring, disease and pest detection, weed mapping, irrigation management, soil assessment, yield estimation, phenotyping, and precision spraying. The review highlights that integrating RGB, multispectral, hyperspectral, thermal, and LiDAR sensors with machine learning (ML) and deep learning (DL) algorithms substantially improves monitoring accuracy, operational efficiency, and agricultural decision-making compared with conventional practices. Algorithms such as Random Forest (RF), Support Vector Machine (SVM), convolutional neural networks (CNNs), and YOLO-based models have demonstrated strong effectiveness in yield prediction, disease recognition, and weed discrimination. Despite these advancements, several challenges continue to limit large-scale implementation, including restricted flight endurance, payload limitations, environmental sensitivity, data-processing complexity, interoperability issues, and limited AI model transferability across different agricultural environments. Furthermore, model performance remains highly dependent on sensor configuration, dataset quality, and field-specific environmental conditions. Recent developments indicate rapid commercialization of UAV technologies together with emerging trends in edge AI, explainable AI (XAI), UAV-IoT integration, cloud-based analytics, and autonomous multi-UAV systems. Overall, this review identifies major technological advancements, key operational limitations, and future research directions required to support scalable, reliable, and climate-resilient UAV-assisted agricultural systems.Article Citation - WoS: 36Citation - Scopus: 42Hvionet: a Deep Learning Based Hybrid Visual-Inertial Odometry Approach for Unmanned Aerial System Position Estimation(Pergamon-Elsevier Science Ltd, 2022-11-01) Aslan, Muhammet Fatih; Durdu, Akif; Yusefi, Abdullah; Yılmaz, AlperSensor fusion is used to solve the localization problem in autonomous mobile robotics applications by integrating complementary data acquired from various sensors. In this study, we adopt Visual- Inertial Odometry (VIO), a low-cost sensor fusion method that integrates inertial data with images using a Deep Learning (DL) framework to predict the position of an Unmanned Aerial System (UAS). The developed system has three steps. The first step extracts features from images acquired from a platform camera and uses a Convolutional Neural Network (CNN) to project them to a visual feature manifold. Next, temporal features are extracted from the Inertial Measurement Unit (IMU) data on the platform using a Bidirectional Long Short Term Memory (BiLSTM) network and are projected to an inertial feature manifold. The final step estimates the UAS position by fusing the visual and inertial feature manifolds via a BiLSTM-based architecture. The proposed approach is tested with the public EuRoC (European Robotics Challenge) dataset and simulation environment data generated within the Robot Operating System (ROS). The result of the EuRoC dataset shows that the proposed approach achieves successful position estimations comparable to previous popular VIO methods. In addition, as a result of the experiment with the simulation dataset, the UAS position is successfully estimated with 0.167 Mean Square Error (RMSE). The obtained results prove that the proposed deep architecture is useful for UAS position estimation. (c) 2022 Elsevier Ltd. All rights reserved.Article Medical Image Segmentation Methods: A Decision-Guided Survey Covering 2D/3D CNNs, Transformers, VLMs, SAM-Based Models and Diffusion Approaches(MDPI, 2026) Sabanci, Kadir; Aslan, Muhammet Fatih; Aslan, BusraRecent advances in medical image segmentation have introduced a wide spectrum of deep learning paradigms, including 2D/3D convolutional neural networks (CNNs), transformer-based architectures, vision-language models (VLMs), prompt-driven foundation models such as Segment Anything Model (SAM), and diffusion-based approaches. Although these methods have demonstrated remarkable performance across MRI, CT, PET, ultrasound, and endoscopic imaging, the rapid proliferation of architectures has created methodological uncertainty regarding optimal model selection under varying clinical and data constraints. Existing surveys primarily focus on architectural categorization, yet provide limited guidance for decision-oriented model selection. This study presents a comprehensive and decision-guided survey that systematically analyzes segmentation paradigms across imaging modalities, task types, dataset characteristics, and evaluation protocols. Beyond taxonomy, we propose a practical model selection framework that links clinical scenarios, such as small lesion detection, multi-organ 3D segmentation, limited-data regimes, and domain shift, to appropriate segmentation strategies. Furthermore, robustness, generalization, annotation variability, and benchmarking reproducibility are critically examined. By integrating architectural taxonomy, cross-modal comparative analysis, and a structured decision framework, this work provides a clinically oriented roadmap for selecting segmentation methods and highlights future research directions toward reliable and reproducible medical AI systems.Article Artificial Intelligence and Deep Learning Approaches for Fault Diagnosis in Power Systems: A Comprehensive Survey(Elsevier, 2026) Balci, Selami; Sinay, Merve; Kayabasi, Ahmet; Aslan, Muhammet Fatih; Aslan, BusraSolving faults in today's complex power systems consisting of distributed generators, microgrids, and renewable resources with traditional methods is a challenging task. This paper examines recent developments in artificial intelligence (AI) studies for power system fault diagnosis. The usefulness of AI approaches in transformers, transmission lines, and rotating machines, which are the most frequently utilized high-voltage power system elements, is highlighted. A total of 60 studies published in Web of Science indexed journals after 2020 are systematically reviewed, with 20 studies analyzed for each of the three power system components. The reviewed methods include convolutional neural networks, long short-term memory, ensemble machine learning (ML), transfer learning, and hybrid deep learning (DL) architectures applied to diverse datasets including dissolved gas analysis measurements, vibration signals, thermal images, and phasor measurement unit data. The reported classification accuracies across the reviewed studies range from 85% to 100%, with DL-based approaches consistently outperforming traditional ML methods. Data editing, parameter adjustment, and model modification suggestions are also offered to improve existing AI solutions. The results indicate that automatic, fast, lowcost, and high-accuracy fault analysis will be achievable in the near future through AI-based online solutions.Article Citation - WoS: 1Citation - Scopus: 3An Approach for Learning From Robots Using Formal Languages and Automata(EMERALD GROUP PUBLISHING LTD, 2019) Aslan, Muhammet Fatih; Durdu, Akif; Sabancı, Kadir; Erdogan, KemalPurpose In this study, human activity with finite and specific ranking is modeled with finite state machine, and an application for human-robot interaction was realized. A robot arm was designed that makes specific movements. The purpose of this paper is to create a language associated to a complex task, which was then used to teach individuals by the robot that knows the language. Design/methodology/approach Although the complex task is known by the robot, it is not known by the human. When the application is started, the robot continuously checks the specific task performed by the human. To carry out the control, the human hand is tracked. For this, the image processing techniques and the particle filter (PF) based on the Bayesian tracking method are used. To determine the complex task performed by the human, the task is divided into a series of sub-tasks. To identify the sequence of the sub-tasks, a push-down automata that uses a context-free grammar language structure is developed. Depending on the correctness of the sequence of the sub-tasks performed by humans, the robot produces different outputs. Findings This application was carried out for 15 individuals. In total, 11 out of the 15 individuals completed the complex task correctly by following the different outputs. Originality/value This type of study is suitable for applications to improve human intelligence and to enable people to learn quickly. Also, the risky tasks of a person working in a production or assembly line can be controlled with such applications by the robots.Conference Object Citation - Scopus: 8Performance Comparison of Extreme Learning Machines and Other Machine Learning Methods on Wbcd Data Set(Institute of Electrical and Electronics Engineers Inc., 2021-06-09) 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.
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Research Topics
Domains
Physical Sciences
Fields
Engineering
Subfields
Control and Systems EngineeringElectrical and Electronic Engineering
Specific Research Areas
Machine Fault Diagnosis Techniques
Power Systems Fault Detection
Electrical Fault Detection and Protection
Sustainable Development Goals
3GOOD HEALTH AND WELL-BEING
4
Research Products
9INDUSTRY, INNOVATION AND INFRASTRUCTURE
4
Research Products
2ZERO HUNGER
2
Research Products
12RESPONSIBLE CONSUMPTION AND PRODUCTION
2
Research Products
8DECENT WORK AND ECONOMIC GROWTH
2
Research Products
13CLIMATE ACTION
1
Research Products
11SUSTAINABLE CITIES AND COMMUNITIES
1
Research Products
7AFFORDABLE AND CLEAN ENERGY
1
Research Products
6CLEAN WATER AND SANITATION
1
Research Products
Documents
70
Citations
2352
h-index
24
Documents
63
Citations
1717
Publication Collaboration
| Affiliation Name | Count |
|---|---|
| Konya Technical University | 1 |
| Karamanoğlu Mehmetbey University | 1 |
| Adana Science and Technology University | 1 |
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Data obtained from OpenAlex
| Journal | Count |
|---|---|
| 2018 INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND DATA PROCESSING (IDAP) | 1 |
| Applied Sciences-Basel | 1 |
| Applied Sciences (Switzerland) | 1 |
| Applied Soft Computing | 1 |
| APPLIED SOFT COMPUTING | 1 |
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Scholarly Output
26
Articles
23
Views / Downloads
58/195
Supervised MSc Theses
0
Supervised PhD Theses
0
WoS Citation Count
780
Scopus Citation Count
1025
Patents
0
Projects
0
WoS Citations per Publication
30.00
Scopus Citations per Publication
39.42
Open Access Source
15
Supervised Theses
0
Scopus Quartile Distribution
Competency Cloud
