Yazar "Cakmak, Muhammet" seçeneğine göre listele

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    A Comprehensive Survey on Automatic Detection of Fake News Using Natural Language Processing: Challenges and Limitations
    (Institute of Electrical and Electronics Engineers Inc., 2024) Saleh, Alhadi Omran; Karaoglan, Kursat Mustafa; Cakmak, Muhammet
    The study examines how Natural Language Processing (NLP) can be used to automatically detect fake news and how it can be applied to fact-checking in the disciplines of linguistics, computer science, journalism, and information sciences. By evaluating the efficacy, dependability, and breadth of various NLP algorithms, this study demonstrates the possibilities and limitations of autonomous fake news identification. The importance of striking a balance between the technical form of information and its social-cognitive dimensions was revealed by the study. An overemphasis on the technical components can lead to fragmented comprehension, so it is essential to strike a balance between the technical form of information and its social-cognitive dimensions. In the age of digital self-publishing, the importance of authoritativeness in determining the credibility of information has also been raised as a significant concern. The report emphasizes the need for an integrative approach to prevent the spread of fake news, recommending interdisciplinary collaboration and the ongoing refinement of NLP research methods for future studies. © 2024 IEEE.
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    A New Lightweight Hybrid Model for Pistachio Classification Using Transformers and EfficientNet
    (Ieee-Inst Electrical Electronics Engineers Inc, 2025) Cakmak, Muhammet
    In recent years, Vision Transformers (ViTs) have gained prominence as a highly effective method for image classification, often outperforming traditional Convolutional Neural Networks (CNNs). However, their relatively slow processing speed limits their practical use, particularly in real-time applications. Conversely, CNN-based transfer learning models provide faster inference but may struggle with classification accuracy on complex datasets. To address these challenges, Temporal Coordinate Attention (TCA) modules have been introduced to optimize efficiency and performance. This study proposes a hybrid architecture combining EfficientNet, Vision Transformer, and Temporal Channel Attention modules that integrates the accuracy of ViTs, the computational efficiency of CNNs, and the enhancement capabilities of TCA modules. The model is designed to classify Siirt and Kirmizi pistachio varieties with high precision. It achieves outstanding results, including 99.07% accuracy, 99.12% recall, and a Cohen's Kappa score of 98.10%. These findings highlight the model's robustness, demonstrating its ability to perform reliable classifications with minimal bias, making it well-suited for real-world applications.
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    Classification of Intraoral Photographs with Deep Learning Algorithms Trained According to Cephalometric Measurements
    (Mdpi, 2025) Kartbak, Sultan Buesra Ay; Ozel, Mehmet Birol; Kocakaya, Duygu Nur Cesur; Cakmak, Muhammet; Sinanoglu, Enver Alper
    Background/Objectives: Clinical intraoral photographs are important for orthodontic diagnosis, treatment planning, and documentation. This study aimed to evaluate deep learning algorithms trained utilizing actual cephalometric measurements for the classification of intraoral clinical photographs. Methods: This study was executed on lateral cephalograms and intraoral right-side images of 990 patients. IMPA, interincisal angle, U1-palatal plane angle, and Wits appraisal values were measured utilizing WebCeph. Intraoral photographs were divided into three groups based on cephalometric measurements. A total of 14 deep learning models (DenseNet 121, DenseNet 169, DenseNet 201, EfficientNet B0, EfficientNet V2, Inception V3, MobileNet V2, NasNetMobile, ResNet101, ResNet152, ResNet50, VGG16, VGG19, and Xception) were employed to classify the intraoral photographs. Performance metrics (F1 scores, accuracy, precision, and recall) were calculated and confusion matrices were formed. Results: The highest accuracy rates were 98.33% for IMPA groups, 99.00% for interincisal angle groups, 96.67% for U1-palatal plane angle groups, and 98.33% for Wits measurement groups. Lowest accuracy rates were 59% for IMPA groups, 53% for interincisal angle groups, 33.33% for U1-palatal plane angle groups, and 83.67% for Wits measurement groups. Conclusions: Although accuracy rates varied among classifications and DL algorithms, successful classification could be achieved in the majority of cases. Our results may be promising for case classification and analysis without the need for lateral cephalometric radiographs.
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    Profile Photograph Classification Performance of Deep Learning Algorithms Trained Using Cephalometric Measurements: A Preliminary Study
    (Mdpi, 2024) Kocakaya, Duygu Nur Cesur; Ozel, Mehmet Birol; Kartbak, Sultan Busra Ay; Cakmak, Muhammet; Sinanoglu, Enver Alper
    Extraoral profile photographs are crucial for orthodontic diagnosis, documentation, and treatment planning. The purpose of this study was to evaluate classifications made on extraoral patient photographs by deep learning algorithms trained using grouped patient pictures based on cephalometric measurements. Cephalometric radiographs and profile photographs of 990 patients from the archives of Kocaeli University Faculty of Dentistry Department of Orthodontics were used for the study. FH-NA, FH-NPog, FMA and N-A-Pog measurements on patient cephalometric radiographs were carried out utilizing Webceph. 3 groups for every parameter were formed according to cephalometric values. Deep learning algorithms were trained using extraoral photographs of the patients which were grouped according to respective cephalometric measurements. 14 deep learning models were trained and tested for accuracy of prediction in classifying patient images. Accuracy rates of up to 96.67% for FH-NA groups, 97.33% for FH-NPog groups, 97.67% for FMA groups and 97.00% for N-A-Pog groups were obtained. This is a pioneering study where an attempt was made to classify clinical photographs using artificial intelligence architectures that were trained according to actual cephalometric values, thus eliminating or reducing the need for cephalometric X-rays in future applications for orthodontic diagnosis.
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    Sex Prediction of Hyoid Bone from Computed Tomography Images Using the DenseNet121 Deep Learning Model
    (Soc Chilena Anatomia, 2024) Bakici, Rukiye Sumeyye; Cakmak, Muhammet; Oner, Zulal; Oner, Serkan
    The study aims to demonstrate the success of deep learning methods in sex prediction using hyoid bone. The images of people aged 15-94 years who underwent neck Computed Tomography (CT) were retrospectively scanned in the study. The neck CT images of the individuals were cleaned using the RadiAnt DICOM Viewer (version 2023.1) program, leaving only the hyoid bone. A total of 7 images in the anterior, posterior, superior, inferior, right, left, and right-anterior-upward directions were obtained from a patient's cut hyoid bone image. 2170 images were obtained from 310 hyoid bones of males, and 1820 images from 260 hyoid bones of females. 3990 images were completed to 5000 images by data enrichment. The dataset was divided into 80 % for training, 10 % for testing, and another 10 % for validation. It was compared with deep learning models DenseNet121, ResNet152, and VGG19. An accuracy rate of 87 % was achieved in the ResNet152 model and 80.2 % in the VGG19 model. The highest rate among the classified models was 89 % in the DenseNet121 model. This model had a specificity of 0.87, a sensitivity of 0.90, an F1 score of 0.89 in women, a specificity of 0.90, a sensitivity of 0.87, and an F1 score of 0.88 in men. It was observed that sex could be predicted from the hyoid bone using deep learning methods DenseNet121, ResNet152, and VGG19. Thus, a method that had not been tried on this bone before was used. This study also brings us one step closer to strengthening and perfecting the use of technologies, which will reduce the subjectivity of the methods and support the expert in the decision-making process of sex prediction.
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    XAI-XGBoost: an innovative explainable intrusion detection approach for securing internet of medical things systems
    (Nature Portfolio, 2025) Hosain, Yousif; Cakmak, Muhammet
    The Internet of Medical Things (IoMT) has transformed healthcare delivery but faces critical challenges, including cybersecurity threats that endanger patient safety and data integrity. Intrusion Detection Systems (IDS) are essential for protecting IoMT networks, yet conventional models often struggle with class imbalance, lack interpretability, and are unsuitable for real-world deployment in sensitive healthcare settings. This study aims to develop an innovative, explainable IDS framework tailored for IoMT systems that ensures both high detection accuracy and model transparency. The proposed approach integrates a hybrid random sampling technique to mitigate class imbalance, Recursive Feature Elimination (RFE) for feature selection, and an optimized XGBoost classifier for robust attack detection. Explainable AI techniques, namely SHAP and LIME, are employed to provide global and local insights into model predictions, enhancing interpretability and trustworthiness. The system was evaluated using the WUSTL-EHMS-2020 dataset, which contains network flow and biometric data, achieving outstanding performance: 99.22% accuracy, 98.35% precision, 99.91% recall, 99.12% F1-score, and 100% ROC-AUC. The proposed framework outperforms several traditional Machine Learning (ML) models and state-of-the-art IDS approaches, demonstrating its robustness and suitability for practical healthcare environments. By integrating advanced ML with explainable AI, this work addresses the critical need for secure, interpretable, and high-performing IDS solutions in IoMT systems. The study concludes that explainability is not an optional feature but a fundamental requirement in healthcare cybersecurity, and the proposed framework represents a significant step towards safer and more accountable AI-driven security solutions for the IoMT ecosystem.