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Öğe Dominant color detection for online fashion retrievals(Batman Üniversitesi, 2024-07-07) Zeybek, Sultan; Çelik, MerveThis paper introduces a novel approach aimed at efficiently extracting dominant colors from online fashion images. The method addresses challenges related to detecting overlapping objects and computationally expensive methods by combining K-means clustering and graph-cut techniques into a framework. This framework incorporates an adaptive weighting strategy to enhance color extraction accuracy. Additionally, it introduces a two-phase fashion apparel detection method called YOLOv4, which utilizes U-Net architecture for clothing segmentation to precisely separate clothing items from the background or other elements. Experimental results show that K-means with YOLOv4 outperforms K-means with the U-Net model. These findings suggest that the U-Net architecture and YOLOv4 models can be effective methods for complex image segmentation tasks in online fashion retrieval and image processing, particularly in the rapidly evolving e-commerce environment.Öğe Decision tree-based direction detection using IMU data in autonomous robots(Batman Üniversitesi, 2024-07-07) Apaydın, Nafiye Nur; Kılıç, İrfan; Apaydın, Muhammet; Yaman, OrhanLocation detection plays a crucial role in various applications. In this study, a machine learning (ML) method using inertial measurement unit (IMU) data was developed to determine direction with the Global Positioning System (GPS). In this study, an electronic board was designed using an Arduino Mega, Altimu-10 IMU sensor, GPS module, and SD card module. This electronic board was placed on a car to create a new dataset. This dataset consists of 1952x11 data. The dataset was obtained using accelerometer (x, y, z), gyroscope (x, y, z), compass (x, y, z), and GPS sensor data. The Decision Tree Algorithm was proposed for direction determination in this study. The angles between each position and the previous position were calculated using the latitude and longitude values obtained from the collected data. Then, the data were divided into 4 classes: North, East, South, and West, based on specific angle ranges. Finally, a direction detection model was developed using IMU data in the proposed method, achieving an accuracy of approximately 82.11%.Öğe Systematic analysis of special education projects in eTwinning(Batman Üniversitesi, 2024-07-07) Güler, SipanIn this study, special education projects conducted in eTwinning (the European School Education Platform) were analyzed using the systematic analysis method. The projects determined to be implemented between 2017 and 2019 were examined. Twenty-five projects that met the inclusion criteria were analyzed as part of the research. As a result of the analysis, it was determined that the number of projects increased over time, the majority of the projects were conducted with Turkish partners, Turkish teachers established partnerships with Romanian teachers at the highest rate among the program countries, awareness activities comprised the majority of the projects, and the projects did not meet European standards adequately. In addition, it has been determined that the initiatives contribute to the development of teachers' and students' social and information technology skills.Öğe A comparative analysis of learning techniques in the context of Turkish spam detection(Batman Üniversitesi, 2024-07-07) Şengel, ÖznurShort Message Service (SMS) is a mobile messaging tool used by billions of people to communicate via a mobile phone. However, due to the lack of proper message filtering techniques, this form of communication is vulnerable to unwanted and junk messages. This paper compared SMS spam detection approaches based on machine learning methods such as Adaptive Boosting (AdaBoost), Extreme Gradient Boosting (XGBoost), K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), Multinominal Naïve Bayes (MNB), Logistic Regression (LR), and Support Vector Machines (SVM) and deep learning methods such as Convolutional Neural Networks (CNNs), Artificial Neural Networks (ANNs), and Long Short Term Memory (LSTM) in terms of f-score, accuracy, recall, precision, and a confusion matrix constructed for each strategy. The study tested two different preprocessing methods on two different Turkish SMS datasets to evaluate the approaches. The aim of this study is to contribute to the issue of spam filtering in Turkey. The results indicate that the highest accuracy values were achieved with Support Vector Machine (99.03%) using the first preprocessing method and Logistic Regression and Random Forest (98.07%) using the second preprocessing method on the BigTurkishSMS dataset, a combination of the two datasets used. As is the case with the majority of machine learning algorithms, the second preprocessing of the data set yielded superior results in deep learning models. The ANN model achieved the highest accuracy, with a score of 97.41%. The study employed a comparison of machine learning and deep learning techniques on Turkish SMS datasets, which will provide valuable insights for researchers working in this field.
