Sürdürülebilir Kalkınma Amaçları
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- Yüksek Lisans TeziAçık Erişim2026Ağ trafiği kullanarak saldırı tespit edebilen derin öğrenme modellerinin performans değerlendirmesi
Rachid Cheick Mohamed
Dijital altyapıların hızla büyümesi ve siber saldırıların giderek karmaşıklaşması, ağ tabanlı saldırı tespit sistemlerini modern siber güvenliğin temel bileşenlerinden biri hâline getirmiştir. İmza tabanlı yöntemler yeni tehditlerin tespitinde yetersiz kalabildiğinden, makine öğrenmesi ve derin öğrenme yöntemleri önemli alternatifler sunmaktadır. Bu çalışmada, tek boyutlu evrişimli sinir ağı (CNN) ile hibrit CNN-BiLSTM mimarisinin ikili ve çok sınıflı ağ saldırısı sınıflandırma performansları incelenmiştir. Modeller, CIC-IDS2017 ve UNSW-NB15 veri kümeleri üzerinde tabakalı beş katlı çapraz doğrulama, odak kaybı, QuantileTransformer ile ölçeklendirme ve SMOTE ile aşırı örnekleme içeren ortak bir deneysel düzen kullanılarak değerlendirilmiştir. Ayrıca rastgele orman ve XGBoost, UNSW-NB15 üzerinde klasik karşılaştırma modelleri olarak test edilmiştir. Bulgular, CNN modelinin CIC-IDS2017 ikili sınıflandırma görevinde %99,75 doğruluk ve 0,9999 ROC-AUC değerine ulaştığını göstermiştir. CNN-BiLSTM, UNSW-NB15 ikili sınıflandırma görevinde yanlış negatifleri yaklaşık %36 azaltmış; ancak çıkarım gecikmesi CNN’e göre 4-6 kat artmıştır. Çok sınıflı sınıflandırmada CNN, CIC-IDS2017 üzerinde 0,701 makro F1 skoruyla CNN-BiLSTM’den (0,671) daha yüksek performans göstermiştir. XGBoost ise derin öğrenme modelleriyle benzer F1 skorlarına daha kısa sürede ulaşmıştır. Azınlık saldırı sınıflarının tespiti tüm yapılandırmalarda temel bir güçlük olmayı sürdürmektedir. Sonuçlar, gerçek uygulama kısıtları altında derin öğrenme ve klasik makine öğrenmesi modelleri arasında seçim yapacak araştırmacı ve uygulayıcılar için karşılaştırmalı çıkarımlar sunmaktadır.
- MakaleAçık Erişim2025Scopus SJRQ1A multi-stage fusion deep learning framework merging local patterns with attention-driven contextual dependencies for cancer detection
Catal Reis, Hatice; Turk, Veysel
Cancer is a severe threat to public health. Early diagnosis of disease is critical, but the lack of experts in this field, the personal assessment process, the clinical workload, and the high level of similarity in disease classes make it difficult. In recent years, deep learning-based artificial intelligence models have shown promise, with the potential to increase diagnosis speed and accuracy. These models attract attention with their automatic learning and adaptation capabilities. In this study, the deep learning-based PADBSRNet model and the PADBSRNet-Vision Transformer (ViT) hybrid method are proposed for the detection of brain tumors and skin and lung cancers. PADBSRNet is a comprehensive deep neural network architecture that integrates separable and traditional convolution layers, multiple attention mechanisms, bidirectional recurrent neural networks, and cross-connections/multi-stage feature fusion strategies. This architecture offers significant advantages in terms of effectively extracting local-global, contextual features and accurately modeling long-term dependencies in image classification tasks. The second proposed approach developed a hybrid method that combines the advantages of the PADBSRNet model and the ViT model. Experimental analysis on medical datasets such as the Figshare Brain Tumor Dataset, IQ-OTH/NCCD Dataset, and Skin Cancer: Malignant vs. Benign Dataset has evaluated the proposed models' performances. According to the experimental results, the PADBSRNet model has shown successful performance with 95.24 %, 99.55 %, and 88.61 % accuracy rates, respectively. The experimental findings show that the proposed deep learning model can effectively learn the complex relationships and hidden patterns of cancer disease, thus producing applicable and effective results in cancer diagnosis.
AtıfScopus3OpenAlex2İncele - MakaleAçık Erişim2024Scopus SJRQ1Integration of a CNN-based model and ensemble learning for detecting post-earthquake road cracks with deep features
Reis, Hatice Catal; Turk, Veysel; Karacur, Soner; Kurt, Ahmet Melih
A safe and healthy infrastructure is essential for humanity. Maintenance and repair of roads, which are of great importance, especially in transportation, is essential. Roads can sometimes be damaged due to natural disasters or human factors. In this study, roads damaged as a result of the natural disaster and the 6 February 2023 Gaziantep-Kahramanmaras earthquakes, which had devastating/fatal consequences, were studied. In recent years, the use of machine learning methods in the automatic detection process of road cracks has become widespread. Here, deep learning architectures generally based on multilayer convolutional neural networks have become a popular solution, mainly thanks to their high discrimination power in visual data. In this study, it was aimed to automatically detect cracks on the roads in Gaziantep (Nurdagi) and Adiyaman provinces after the earthquake. The data set used in the experimental process consists of two categories: uncracked and cracked, and was collected by the researchers. ResNet152 deep learning architecture, traditional machine learning algorithms, and an ensemble learning (EL) model were used to classify cracks. Among the machine learning algorithms used in the experiments are support vector machine (SVM), K-nearest neighbors, Adaptive Boosting, and Naive Bayes. In the experimental process, ResNet152 architecture was used for feature extraction, and classification was carried out with machine learning algorithms. In addition, the EL method has been used to improve the success of machine learning algorithms. According to experimental results, the SVM algorithm and EL method gave the most successful results with 98.68% accuracy values. The proposed method can benefit experts in the automatic classification of road cracks. The study can contribute to activating rapid coordination and transportation networks in natural disasters that cause fatal and destructive causes, such as earthquakes.
AtıfScopus19WoS16OpenAlex13İncele - MakaleYayıncıda açık erişim2024Scopus SJRQ1Potato leaf disease detection with a novel deep learning model based on depthwise separable convolution and transformer networks
Catal Reis, Hatice; Turk, Veysel
Early diagnosis of plant diseases is essential in reducing economic losses for farmers and increasing production efficiency. Therefore, Computer-Aided Diagnosis (CAD) systems supported by artificial intelligence technologies can be developed to help diagnose diseases quickly and accurately by examining the symptoms and signs in plant leaves. In this study, Multi-head Attention Mechanism Depthwise Separable Convolution Inception Reduction Network (MDSCIRNet) architecture, an image-based deep convolutional neural network, is proposed for classifying potato leaf diseases. The main components of the MDSCIRNet architecture are depthwise separable convolution (DSC) and a multi-head attention mechanism. The proposed architecture has been compared with modern algorithms developed with DSC technology, such as Xception, MobileNet, and deep learning algorithms, such as ResNet101, InceptionV3, and EfficientNetB2, to evaluate its performance in the classification process. In addition, hybrid methods developed with the classical machine learning algorithms Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), Adaptive Boosting (AdaBoost), and MDSCIRNet model, integrated deep learning model, hard voting ensemble learning model. Suggested methods such as these were also used in the experimental process. Moreover, techniques such as Contrast Limited Adaptive Histogram Equalization (CLAHE), Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN), and Hypercolumn were used to improve the image quality of the data set. In the experimental process, the MDSCIRNet deep learning architecture achieved 99.24% accuracy in the study using the original dataset. While a 99.11% accuracy rate was achieved with the integrated deep learning model and hard voting ensemble learning model, the highest success rate of 99.33% was performed in the study conducted with the MDSCIRNet + SVM method. This study contributes to developing new and effective strategies in the agricultural industry for the early diagnosis and control of potato plant diseases. Machine learning-based approaches offer the potential to minimize economic losses and increase productivity in production by allowing farmers to intervene early.
AtıfScopus111WoS75OpenAlex83İncele - BildiriYalnız künye2023Pedestrian Inertial Navigation with Multi-Head CNN
Cetin, Gokhan; Kucuk, Mehmet Ali; Koroglu, Muhammed Taha
Traditional pedestrian dead-reckoning (DR) methods utilizing inertial measurement units (IMU) have been successfully employed in GPS-denied environments over the past two decades. However, despite continuous advancements in wearable IMU technology, the long-term solution still suffers from the issue of drift. In light of this, Deep Learning (DL) has emerged as a potential candidate for inertial navigation solutions, leveraging its powerful non-linear regression capabilities. Recent research has demonstrated that DL models trained on public datasets can effectively serve as end-to-end solutions. Most studies focus on smartphones, where the IMU is typically situated at the torso, and generally work with transformed data represented in the navigation coordinate frame to estimate pedestrian velocity and heading change at rates lower than the IMU sampling rate. In contrast, this study uses raw foot-mounted IMU data as input to a multi-headed structure consisting of independent convolutional neural networks (CNNs) and targets displacement directly and heading change. The proposed model is trained on a publicly available foot-mounted IMU dataset where pseudo ground-truth (GT) data is generated heuristically to provide supervision for the learning process. The results indicate that the Multi-Head CNN (MHCNN) model is capable of producing pedestrian trajectories at the IMU sampling rate, with positioning errors of less than 2m throughout the duration of the walks. The code for the trained network can be accessed in the associated repository, i.e., github.com/mtkoroglu/PIN-MHCNN.
AtıfScopus1İncele - MakaleAçık Erişim2022Scopus SJRQ3COVID-19 diagnosis with deep learning; [Diagnóstico de COVID-19 con Deep Learning]
Reis, Hatice Catal
The coronavirus disease 2019 (COVID-19) is fatal and spreading rapidly. Early detection and diagnosis of the COVID-19 infection will prevent rapid spread. This study aims to automatically detect COVID-19 through a chest computed tomography (CT) dataset. The standard models for automatic COVID-19 detection using raw chest CT images are presented. This study uses convolutional neural network (CNN), Zeiler and Fergus network (ZFNet), and dense convolutional network-121 (DenseNet121) architectures of deep convolutional neural network models. The proposed models are presented to provide accurate diagnosis for binary classification. The datasets were obtained from a public database. This retrospective study included 757 chest CT images (360 confirmed COVID-19 and 397 non-COVID-19 chest CT images). The algorithms were coded using the Python programming language. The performance metrics used were accuracy, precision, recall, F1-score, and ROC-AUC. Comparative analyses are presented between the three models by considering hyper-parameter factors to find the best model. We obtained the best performance, with an accuracy of 94,7%, a recall of 90%, a precision of 100%, and an F1-score of 94,7% from the CNN model. As a result, the CNN algorithm is more accurate and precise than the ZFNet and DenseNet121 models. This study can present a second point of view to medical staff.
AtıfScopus5WoS1OpenAlex6İncele - BildiriAçık Erişim2021Pneumonia Detection from Pediatric Lung X-Ray Images with Convolutional Neural Network Method
Demir, Yasin; Bingöl, Ozkan
Pneumonia can be cause diseases such as cough, fever and respiratory problems and to death in the progressive situations. Mostly lung x-ray images are used for diagnosis of pneumonia. In this study, an unique convolutional neural network model that can help experts in detection of pneumonia using lung x-ray images below 5 years of age. In the model, open access to 5840 images has been used, and various data increasing methods were used to increase the performance of the training and the success of the method. With the proposed convolutional neural network model, an accuracy rate of 94.55% has been achieved.
AtıfWoS2OpenAlex3İncele - BildiriYalnız künye2018R-peaks Detection with Convolutional Neural Network in Electrocardiogram Signal
Dogan, Ramazan Ozgur; Kayikçioglu, Temel
The performance of electrocardiogram applications is closely related to the succesful detection of R-peaks. Therefore, successful detection of R-peaks is of high importance. Electrocardiogram signals vary from person to person, so detection of R-peaks is getting difficult. In this study, Evolutionary Neural Network which is a deep learning model and imitates human vision, is used to detect R-peaks. The MIT-BIH Arrhythmia Database with 48 different records was used as the database and experimental results with very high accuracy were obtained.
