Riskli gebeliklerde, gebelik öncesinde anne adayının sahip olduğu kalp, akciğer, böbrek, yüksek tansiyon, diyabet ve karaciğer gibi çeşitli hastalıklar, gebelik sırasında anne adayının durumunu kötüleştirebilir. Anne yaşı, kalp atış hızı, kan oksijen seviyesi, kan basıncı, vücut sıcaklığı gibi tıbbi parametreleri analiz ederek ve bu parametrelere karşılık gelen değerleri inceleyerek, bazı hastalar için risk yoğunluğuna ilişkin bilgi tahmin edilebilir. Belirtilerde erken aşamada risk faktörleri sınıflandırılarak, gebelikle ilgili komplikasyonları azaltmak mümkündür. Anne risk sağlığını belirlemede makine öğrenimi yöntemlerinden yararlanmak mümkündür. Bu nedenle, bu çalışmada anne risk sağlığını belirlemek için 6 farklı makine öğrenimi yöntemi kullanılmıştır. Bu yöntemlerde elde edilen sonuçlar birbirleriyle karşılaştırılmış ve anne risk sağlığını tahmin etmede en başarılı yöntemin Karar Ağacı olduğu görülmüştür. Karar Ağacı yönteminde elde edilen doğruluk değeri %89,16’dır. Makalede kullanılan yöntemler arasında en düşük doğruluk oranı k-en yakın komşu (KNN) yöntemi ile elde edilmiştir ve bu oran %68,47'dir.

Urine sediment examination is one of the main tests used in the diagnosis of many diseases. Thanks to this test, many diseases can be detected in advance. Examining the results of this test is an intensive and time-consuming process. Therefore, it is very important to automatically interpret the urine sediment test results using computer-aided systems. In this study, a data set consisting of eight classes was used. The data set used in the study consists of 8509 particle images obtained by examining the particles in the urine sediment. A hybrid model based on textural and Convolutional Neural Networks (CNN) was developed to classify the images in the related data set. The features obtained using textural-based methods and the features obtained from CNN-based architectures were combined after optimizing using the Minimum Redundancy Maximum Relevance (mRMR) method. In this way, we aimed to extract different features of the same image. This increased the performance of the proposed model. The CNN-based ResNet50 architecture and textural-based Local Binary Pattern (LBP) method were used for feature extraction. Finally, the optimized and combined feature map was classified at different machine learning classifiers. In order to compare the performance of the model proposed in the study, results were also obtained from different CNN architectures. A high accuracy value of 96.0% was obtained in the proposed model.

In image classification applications, the most important thing is to obtain useful features. Convolutional neural networks automatically learn the extracted features during training. The classification process is carried out with the obtained features. Therefore, obtaining successful features is critical to achieving high classification success. This article focuses on providing effective features to enhance classification performance. For this purpose, the success of the process of concatenating features in classification is taken as basis. At first, the features acquired by feature transfer method are extracted from AlexNet, Xception, NASNETLarge, and EfficientNet‐B0 architectures, which are known to be successful in classification problems. Concatenating the features results in the creation of a new feature set. The method is completed by subjecting the features to various classification algorithms. The proposed pipeline is applied to the three datasets: “COVID‐19 Image Dataset,” “COVID‐19 Pneumonia Normal Chest X‐ray (PA) Dataset,” and “COVID‐19 Radiography Database” for COVID‐19 disease detection. The whole datasets contain three classes (normal, COVID, and pneumonia). The best classification accuracies for the three datasets are 98.8%, 95.9%, and 99.6%, respectively. Performance metrics are given such as: sensitivity, precision, specificity, and F1‐score values, as well. Contribution of paper is as follows: COVID‐19 disease is similar to other lung infections. This situation makes diagnosis difficult. Furthermore, the virus's rapid spread necessitates the need to detect cases as soon as possible. There has been an increased curiosity in computer‐aided deep learning models to provide the requirements. The use of the proposed method will be beneficial as it provides high accuracy.

Traumatic brain injuries and collisions from falls and electric shocks are among the leading causes of construction deaths. Helmets play an important role in protecting working people from accidents. However, wearing a hard hat in real life is often not strictly enforced among those who try. Therefore, it is important to check this and ensure that a helmet is worn. Today, the use of artificial intelligence-based object recognition systems has become widespread due to the advantages it provides. In this article, a one-step object detection approach based on deep learning is proposed to detect helmet use and control helmet wearing status. The model is based on the YOLOv5 architecture. In the feature extraction step of the method, ShuffleNetv2, which is a lightweight model for a fast detector, is used. The presented model has been examined on the Hard Hat Workers dataset. The architecture provided a recall value of 0.942 precision 0.91 in the corresponding dataset. The obtained results showed that the recommended model is suitable for use on construction sites to check whether a helmet is fitted.