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Öğe A comparison of tree data structures in the streaming data clustering issue(Gazi Univ, Fac Engineering Architecture, 2024) Senol, Ali; Kaya, Mahmut; Canbay, YavuzProcessing streaming data is a challenging issue because of the limitation of time and resources. Clustering data streams is an efficient technique to analyze this kind of data. This study proposes two new streaming data clustering algorithms, BT-AR Stream and VP-AR Stream, inspired by the KD-AR Stream clustering algorithm [32]. Our algorithms used Ball-Tree and Vintage Tree data structures instead of KD-Tree. To reveal the efficiency of the proposed algorithms, we tested the algorithms on 18 benchmark datasets in terms of clustering qualities and runtime complexities. Then we compared obtained results with the results of the KD-AR Stream algorithm. According to the results, the BT-AR Stream algorithm was the most successful in terms of clustering quality and runtime complexity, as illustrated in Figure A.Purpose: This study aims to analyze and compare the efficiency of tree data structure in data stream clustering issues. We aim to reveal the efficiency of tree data structures in both clustering quality and runtime performance.Theory and Methods: To compare the efficiency of tree data structures in data stream clustering, we proposed two stream clustering algorithms inspired by KD-AR Stream. For this reason, we used Ball-Tree and Vintage-Tree data structures instead of KD-Tree and proposed two new stream clustering algorithms named BT-AR Stream and VP-AR Stream. To compare the success of algorithms, we tested them on 18 benchmark datasets and compared them in aspects of clustering quality and runtime complexity.Results: According to the results obtained in the experimental study, the BT-AR Stream algorithm, which uses Ball-Tree, was the most successful in both clustering quality and runtime complexity on the KDD, which is a high-dimensional dataset. On the other hand, the clustering quality of all algorithms was good on the other datasets. Conclusion: Although the clustering quality of all three algorithms was good, the BT-AR Stream algorithm was the most successful because KDD is high-dimensional. Furthermore, it is the fastest algorithm compared to the others.Öğe A new approach for physical human activity recognition from sensor signals based on motif patterns and long-short term memory(Elsevier Sci Ltd, 2022) Kuncan, Fatma; Kaya, Yilmaz; Yiner, Zueleyha; Kaya, MahmutNumerous studies have been carried out in recent years on the recognition, tracking, and discrimination of human activities. Automatic recognition of physical activities is often referred to as human activity recognition (HAR). There are generally vision-based and sensor-based approaches for activity recognition. The computer vision-based approach generally works well in laboratory conditions, but it can fail in real-world problems due to clutter, variable light intensity, and contrast. Sensor-based HAR systems are realized by continuously monitoring and analyzing physiological signals measured from heterogeneous sensors connected to the person's body. In this study, the Motif Patterns (MP) approach, which extracts features from sensor signals, is proposed for HAR. The success of the HAR systems depends on the effectiveness of the features extracted from the signals. The LSTM network is a special kind of recurrent neural network that has been used to make very successful predictions on time series data where long-term dependencies are. The LSTM network type offers a successful solution approach to solving long-term dependencies problems such as human activity recognition. The classification process was carried out with Long-Short Term Memory (LSTM) using MP features extracted from accelerometer, gyroscope, and magnetometer sensor signals. A large dataset of 9120 signals was used to test the proposed approach. A high success rate of 98.42 % was achieved with the proposed MP + LSTM method. As a result, it has been seen that the proposed approach has been obtained with a high success rate for HAR using sensor signals.Öğe A new approach to COVID-19 detection from x-ray images using angle transformation with GoogleNet and LSTM(Iop Publishing Ltd, 2022) Kaya, Yilmaz; Yiner, Zuleyha; Kaya, Mahmut; Kuncan, FatmaDeclared a pandemic disease, COVID-19 has affected the lives of millions of people and had significant effects on public health. Despite the development of effective vaccines against COVID-19, cases continue to increase worldwide. According to studies in the literature, artificial intelligence methods are used effectively for the detection of COVID-19. In particular, deep-learning-based approaches have achieved very good results in clinical diagnostic studies and other fields. In this study, a new approach using x-ray images is proposed to detect COVID-19. In the proposed method, the angle transform (AT) method is first applied to the x-ray images. The AT method proposed in this study is an important novelty in the literature, as there is no such approach in previous studies. This transformation uses the angle information created by each pixel on the image with the surrounding pixels. Using the AT approach, eight different images are obtained for each image in the dataset. These images are trained with a hybrid deep learning model, which combines GoogleNet and long short-term memory (LSTM) models, and COVID-19 disease detection is carried out. A dataset from the Mendeley database is used to test the proposed approach. A high classification accuracy of 98.97% is achieved with the AT + GoogleNet + LSTM approach. The results obtained were also compared with other studies in the literature. The presented results reveal that the proposed method is successful for COVID-19 detection using chest x-ray images. Direct transfer methods were also applied to the data set used in the study. However, worse results were observed according to the proposed approach. The proposed approach has the flexibility to be applied effectively to different medical images.Öğe A New Feature Selection Metric Based on Rough Sets and Information Gain in Text Classification(2023) Çekik, Rasim; Kaya, MahmutIn text classification, taking words in text documents as features creates a very high dimensional feature space. This is known as the high dimensionality problem in text classification. The most common and effective way to solve this problem is to select an ideal subset of features using a feature selection approach. In this paper, a new feature selection approach called Rough Information Gain (RIG) is presented as a solution to the high dimensionality problem. Rough Information Gain extracts hidden and meaningful patterns in text data with the help of Rough Sets and computes a score value based on these patterns. The proposed approach utilizes the selection strategy of the Information Gain Selection (IG) approach when pattern extraction is completely uncertain. To demonstrate the performance of the Rough Information Gain in the experimental studies, the Micro-F1 success metric is used to compare with Information Gain Selection (IG), Chi-Square (CHI2), Gini Coefficient (GI), Discriminative Feature Selector (DFS) approaches. The proposed Rough Information Gain approach outperforms the other methods in terms of performance, according to the results.Öğe Deep Metric Learning: A Survey(Mdpi, 2019) Kaya, Mahmut; Bilge, Hasan SakirMetric learning aims to measure the similarity among samples while using an optimal distance metric for learning tasks. Metric learning methods, which generally use a linear projection, are limited in solving real-world problems demonstrating non-linear characteristics. Kernel approaches are utilized in metric learning to address this problem. In recent years, deep metric learning, which provides a better solution for nonlinear data through activation functions, has attracted researchers' attention in many different areas. This article aims to reveal the importance of deep metric learning and the problems dealt with in this field in the light of recent studies. As far as the research conducted in this field are concerned, most existing studies that are inspired by Siamese and Triplet networks are commonly used to correlate among samples while using shared weights in deep metric learning. The success of these networks is based on their capacity to understand the similarity relationship among samples. Moreover, sampling strategy, appropriate distance metric, and the structure of the network are the challenging factors for researchers to improve the performance of the network model. This article is considered to be important, as it is the first comprehensive study in which these factors are systematically analyzed and evaluated as a whole and supported by comparing the quantitative results of the methods.Öğe DETECTION OF DUST ON SOLAR PANELS WITH DEEP LEARNING(2024) Sefer, Tuba; Kaya, MahmutSolar energy is an environmentally friendly, clean, and sustainable alternative. The widespread use of this energy source offers excellent environmental and economic benefits. However, some factors affect the efficiency of solar panels. One of these factors is dust. When dust accumulates on the surface of solar panels, it can significantly reduce the efficiency of energy production. Therefore, detecting and quickly removing dust from solar panels is crucial. Managing this process with unmanned artificial intelligence systems, especially in large areas, will provide significant advantages in terms of time and cost. In recent years, convolutional neural networks have achieved significant success in image classification. In particular, transfer learning methods have proven their success in this field. In this study, we aim to solve a new task with limited data using pre-trained deep learning models (EfficientNetB3, ResNet50, MobileNet, VGG19, Xception, InceptionResNetV2, VGG16, ResNet101, DenseNet201, EfficientNetB7) to classify dirty and clean solar panels. These models were chosen because they each have different strengths and have performed well on various tasks. The models with the best performance among these models are combined to improve classification prediction. The proposed ensemble learning approach achieved 99.31% classification accuracy by considering the prediction results of the models with a voting approach. As a result, this approach aims to optimize the maintenance processes of solar energy systems, improve energy efficiency, and support sustainable energy use in the long term.Öğe EGMA: Ensemble Learning-Based Hybrid Model Approach for Spam Detection(Mdpi, 2024) Bilgen, Yusuf; Kaya, MahmutSpam messages have emerged as a significant issue in digital communication, adversely affecting users' mental health, personal safety, and network resources. Traditional spam detection methods often suffer from low detection rates and high false positives, underscoring the need for more effective solutions. This paper proposes the EGMA model, an ensemble learning-based hybrid approach for spam detection in SMS messages, which integrates gated recurrent unit (GRU), multilayer perceptron (MLP), and hybrid autoencoder models utilizing a majority voting algorithm. The EGMA model enhances performance by incorporating additional statistical features extracted from message content and employing text vectorization techniques, such as Term Frequency-Inverse Document Frequency (TF-IDF) and CountVectorizer. The proposed model achieved impressive classification accuracies of 99.28% on the SMS Spam Collection dataset, 99.24% on the Email Spam dataset, 99.00% on the Enron-Spam dataset, 98.71% on the Super SMS dataset, and 95.09% on UtkMl's Twitter Spam dataset. These results demonstrate that the EGMA model outperforms individual models and existing methods in the literature, providing a robust solution for enhancing spam detection performance and effectively mitigating the threats that spam messages pose in digital communication.Öğe Makine Öğrenmesi Yaklaşımlarını Kullanarak Salgınları Erken Evrede Tespit Etme Alanındaki Eğilimler(2021) Şenol, Ali; Canbay, Yavuz; Kaya, MahmutTüm dünyayı etkisi altına alan COVID-19, salgınları erken dönemde tespit etmeye çalışan çalışmaların önemini ortaya koymaktadır. Herhangi bir salgın erken aşamada tespit edilebilirse, hastalığa yakalanan kişi sayısını azaltabilir ve gerekli tedavi daha erken sürede bulunabilir ve ek olarak tedavi masrafları da azaltılabilir. Salgınların erken aşamada tespit edilmesini sağlayan en önemli veri işleme yaklaşımlarından makine öğrenmesi, yeni gelen verileri, olayı veya durumu tahmin etmek için matematiksel modelleri ve istatistiksel yöntemleri kullanır. Makine öğrenmesi yaklaşımlarıyla, tıbbi veriler analiz edilerek ve işlenerek hastalıklar hakkında tahminlerde bulunulabilir. Çünkü daha önce toplanan hasta verileri, makine öğrenmesi yöntemleri kullanarak hastalıkların teşhis edilmesine imkân sağlayabilir. Hastalıkların yanı sıra, daha önce toplanan veriler kullanılarak salgınlar hakkında da tahminlerde bulunulabilir. Daha önce ortaya çıkan salgınların yeniden ortaya çıkışını tahmin etmek için denetimli öğrenme yaklaşımları olan Naive Bayes, Destek Vektör Makineleri (DVM), Karar Ağaçları (KA), Rastgele Orman (RO) ve Yapay Sinir Ağları (YSA) gibi birçok yaklaşım olsa da, temel bileşenler ve kümeleme analizi gibi denetimsiz öğrenme yaklaşımları da kullanılarak daha önce benzeri görülmemiş salgınlar tespit edilebilir. Bu çalışmada, bu alanda çalışmak isteyen araştırmacılara ışık tutmak amacıyla salgınları tespit etmeye yönelik geliştirilmiş olan makine öğrenmesi yaklaşımlarının ayrıntılı bir analizi sunulmaktadır.Öğe MCMSTStream: applying minimum spanning tree to KD-tree-based micro-clusters to define arbitrary-shaped clusters in streaming data(Springer London Ltd, 2024) Erdinc, Berfin; Kaya, Mahmut; Senol, AliStream clustering has emerged as a vital area for processing streaming data in real-time, facilitating the extraction of meaningful information. While efficient approaches for defining and updating clusters based on similarity criteria have been proposed, outliers and noisy data within stream clustering areas pose a significant threat to the overall performance of clustering algorithms. Moreover, the limitation of existing methods in generating non-spherical clusters underscores the need for improved clustering quality. As a new methodology, we propose a new stream clustering approach, MCMSTStream, to overcome the abovementioned challenges. The algorithm applies MST to micro-clusters defined by using the KD-Tree data structure to define macro-clusters. MCMSTStream is robust against outliers and noisy data and has the ability to define clusters with arbitrary shapes. Furthermore, the proposed algorithm exhibits notable speed and can handling high-dimensional data. ARI and Purity indices are used to prove the clustering success of the MCMSTStream. The evaluation results reveal the superior performance of MCMSTStream compared to state-of-the-art stream clustering algorithms such as DenStream, DBSTREAM, and KD-AR Stream. The proposed method obtained a Purity value of 0.9780 and an ARI value of 0.7509, the highest scores for the KDD dataset. In the other 11 datasets, it obtained much higher results than its competitors. As a result, the proposed method is an effective stream clustering algorithm on datasets with outliers, high-dimensional, and arbitrary-shaped clusters. In addition, its runtime performance is also quite reasonable.
