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Due to the diversity of the scientific and intellectual orientations and social needs of the regions they lived in, muhaddiths generally followed different methods in receiving, narrating, and compiling hadiths. This situation has made it difficult to make generalizations about muhaddiths and their works, and has sometimes led to prejudice and incorrect evaluations about them. In this study, common rules that must be followed by scholars in the fields of hadith receiving, hadith narration, and hadith classification have been examined, and the explanation of points that will enable maximum benefit from scholars and hadith works has been aimed. A scholar of hadith is not obligated to only receive authentic hadiths. Therefore, their general tendency has been to receive hadiths from everyone without distinguishing between weak and reliable narrators. As the muhaddith narrates, receiving and avoiding actions that would harm the continuity of the hadith is the primary duty of a muhaddith. If he adheres to this rule, it cannot be said that even receiving a narration that he knows to be incorrect would harm the integrity or accuracy of the muhaddith technically. The responsibility of a hadith narrator is to declare the chain of transmission of the hadith and to narrate it with a suitable level of accuracy. For scholars of hadith, it is important not only to narrate authentic hadiths but also to transmit hadiths with high chains of transmission and rare types of narrations that are not found in everyone. [ABSTRACT FROM AUTHOR]

COVID-19, caused by the SARS-COV-2 virus, which has killed more than 6 million people, is one of the most contagious diseases in human history. It has seriously affected every area that people come into contact with, from business life to economy, from transportation to education, from social life to psychology. Although the developed vaccines provide a partial decrease in the number of deaths, the mutations that the virus constantly undergoes and the increase in the transmission rate accordingly reduce the effectiveness of the vaccines, and the number of deaths tends to increase as the number of infected people. It is undoubtedly important that the detection of this epidemic disease, which is the biggest crisis that humanity has experienced in the last century after World War II, is carried out accurately and quickly. In this study, a machine learning-based artificial intelligence method has been proposed for the detection of COVID-19 from computed tomography images. The features of images with two classes are extracted using the Local Binary Pattern. The images reserved for training in the dataset were used for training machine learning models. Trained models were tested with previously unused test images. While the Fine K-Nearest Neighbors model reached the highest accuracy with a value of 0.984 for the training images, the highest accuracy value was obtained by the Cubic Support Vector Machine with 0.93 for the test images. These results are higher than the deep learning-based study using the same data set. [ABSTRACT FROM AUTHOR]

Emotion recognition through physiological signals is of great importance for the examination of psychological states and the development of biofeedback-based applications. Thanks to the emergence of the concept of metaverse and the integration of physiological signal trackers into smart devices, this field of study has become a necessity and a subject of interest to researchers. In this study, an algorithm is proposed for emotion detection according to the two-category (valence–arousal) emotion model. ECG signal recordings from the MAHNOB-HCI database were used in the study. First, the noise on the ECG signals is eliminated in the preprocessing step. R peaks were detected by applying the Pan–Tompkins algorithm to the preprocessed ECG signals. Then, for each recording, the P-QRS-T fragment and the maximum and minimum values of the P, Q, R, S, and T waves were obtained as morphological features and combined with selected heart rate variability features to obtain a feature vector. By applying an automated feature engineering algorithm to this feature vector, new feature vectors with increased weight of distinctive features and increased number of samples are obtained as output. These features are classified with three different learning algorithms: support vector machines, feedforward neural network, and bidirectional long short-term memory. As a result of the study, good results were obtained with the bidirectional long short-term memory algorithm compared to the literature. According to these results, with bidirectional long short-term memory, the accuracy obtained was 78.28% for the valence category and 83.61% for the arousal category. [ABSTRACT FROM AUTHOR]

Lumbar spinal stenosis (LSS) is a condition that affects the quality of life of the 3 vertebrae, the disc and the canal in the lower back. In this region, the nerves in the canal may be subjected to pressure for various reasons, and disease occurs. Surgical intervention is required to treat canal stenosis, and the exact location and size of the spinal stenosis is critical to the surgery. The UNet model, which is an example of this network, can be further deepened with various deep learning networks. In this study, it will be the basis for creating a system that helps in the diagnosis of spinal stenosis by using a deeper network. The ResUNET model using ResNet as the backbone achieved an average IoU of 0.987. This study demonstrated that expert decision support systems using MR images can be used in the diagnosis of LSS. [ABSTRACT FROM AUTHOR]

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