An important feature of matching markets is that there typically exist many stable matchings. These matchings have a remarkable orderliness property in two-sided markets. They form a lattice according to the group preferences of one side that is opposite to the group preferences of the other side. The two extremal matchings, optimal for one side pessimal for the other, bear extreme inequity. Nonetheless, research and applications in the area mostly involved the extremal matchings and much less so the "middle" of the stable matchings where inequity may be resolved. This is partly because the optimal stable matching has proved very useful in applications on account of its algorithmic properties. It is also because the "middle" has proved challenging definitionally as well as computationally.

Summary Nowadays, artificial intelligence‐based medicine plays an important role in determining correlations not comprehensible to humans. In addition, the segmentation of organs at risk is a tedious and time‐consuming procedure. Segmentation of these organs or tissues is widely used in early diagnosis, treatment planning, and diagnosis. In this study, we trained semantic segmentation networks to segment healthy parotid glands using deep learning. The dataset we used in the study was obtained from Recep Tayyip Erdogan University Training and Research Hospital, and there were 72 T2‐weighted magnetic resonance (MR) images in this dataset. After these images were manually segmented by experts, masks of these images were obtained according to them and all images were cropped. Afterward, these cropped images and masks were rotated 45°, 120°, and 210°, quadrupling the number of images. We trained ResNet‐18/MobileNetV2‐based DeepLab v3+ without augmentation and ResNet‐18/MobileNetV2‐based DeepLab v3+ with augmentation using these datasets. Here, we set the training set and testing set sizes for all architectures to be 80% and 20%, respectively. We designed two different graphical user interface (GUI) applications so that users can easily segment their parotid glands by utilizing all of these deep learning‐based semantic segmentation networks. From the results, mean‐weighted dice values of MobileNetV2‐based DeepLab v3+ without augmentation and ResNet‐18‐based DeepLab v3+ with augmentation were equal to 0.90845–0.93931 and 0.93237–0.96960, respectively. We also noted that the sensitivity (%), specificity (%), F 1 score (%) values of these models were equal to 83.21, 96.65, 85.04 and 89.81, 97.84, 87.80, respectively. As a result, these designed models were found to be clinically successful, and the user‐friendly GUI applications of these proposed systems can be used by clinicians. This study is competitive as it uses MR images, can automatically segment both parotid glands, the results are meaningful according to the literature and have software application.

Abstract Attention deficit hyperactivity disorder (ADHD) is a common childhood neurodevelopmental disorder with symptoms of attention deficit, hyperactivity and impulsivity, with a prevalence of 8%–12% worldwide. Various studies have revealed that there may be a relationship between ADHD and temperament traits in the etiology of ADHD, which has a multifactorial etiology. According to our knowledge, there is no study in the literature that determines the use of machine learning methods in diagnosing ADHD uses a data set created with temperament characteristics. Different methods were used in this first study. The study included 60 ADHD patients and 60 control group children. The test scores of these children were collected from the Department of Child and Adolescent Psychiatry, Kahramanmaraş Sütçü İmam University Medical Faculty, after obtaining the necessary ethics committee permission. ADHD diagnosis was made according to DSM‐5 classification. According to temperament characteristics, the highest classification success in ADHD diagnosis was calculated as 92.5% in decision tree method. Long short term memory (LSTM), one of the deep learning methods, achieved 88% classification success. The success of both methods is quite high and they have been compared with some ADHD classification studies in the literature.

Abstract Diabetic retinopathy (DR) is a pathology occurring in the optic nerve due to an excessive blood sugar level in human body. It is one of the major reasons for visual impairment in the developed and developing countries. Patients with DR usually suffer from visual damages due to a high blood sugar level in retinal blood vessel walls. These damages may also leak into other retinal layers of the eye within time. As a result of these leakages and nutritional disorders, a number of lesions such as excudate, edema, microaneurysm, and hemorrhage may occur. In this respect, an accurate and effective detection of these lesions in earlier stages of DR plays an important role in the progression of the disease. In the proposed study, exudate and hemorrhages, which are important clinical findings for DR, were automatically detected from low contrast colored fundus images. Exudate and hemorrhages are lesions with different characteristics. However, in this study, high performance was achieved by making a three‐class semantic segmentation. In addition, a color space transformation was performed and the classical U‐Net algorithm was provided to achieve stable high performance in low contrast images. Finally, lesion images which were manually detected by a physician were matched with automatically segmented excudate and hemorrhage images using the proposed method. Thus, both segmentation and lesion detection performances of the proposed method were measured. The findings demonstrated that Dice and Jaccard similarity indexes were calculated nearly as 0.95 for the segmentation performance. A sensitivity of 98% and specificity value of 91% were measured for detection performance. It can be inferred from these figures that the proposed method can be effectively used as a supporting system by physicians for the detection and classification of lesions in the color fundus images for the diagnosis of DR.

Abstract Analysis based on the classification of electromyography (EMG) signals, the bioelectrical signs that appear during the contraction of the muscles, can be used in many prosthetic control applications. For this purpose, the classification of the EMG signal is considered a pattern‐recognition problem that can be used to improve the functionality and ease of control of reinforced upper‐limb prostheses. Four different EMG‐signal patterns taken from the biceps and triceps muscles were analyzed via hybrid deep‐learning methods after spectrogram‐based preprocessing. Four hundred EMG spectrograms obtained by preprocessing were classified with hybrid deep‐learning techniques based on AlexNet, GoogLeNet, and ResNet18. The classification was conducted using a support vector machine instead of the classification layers after the pooling layer of deep‐learning architectures used in the hybrid system. In general, acceptable classification results were achieved with all techniques used, and the highest performance was obtained with the hybrid system created with AlexNet architecture. The hybrid‐classification achievements with AlexNet, GoogLeNet, and Resnet18 were 99.17%, 95.83%, and 93.33%, respectively. These results show that the proposed architectures can be used in prosthetic controls created using EMG signals.

Since edge detection is a field of study used by various disciplines, it is of vital importance to calculate it accuretly. In addition, an edge detection algorithm may be involved in many image processing phases. A recent and contemporary approach, neutrosophy is based on neutrosophic logic, neutrosophic probability, neutrosophic set and neutrosophic statistics. This method yields better results compared to various other optimization methods. Neutrosophic Set (NS) is based on the origin, nature and scope of neutralities. In NS, problems are separated into true, false and indeterminacy subsets. It helps solve indeterminate situations effectively. It has recently been used in the field of image processing as indeterminate situations are also encountered in this field. Chan–Vese (CV) model is one of the successful region-based segmentation methods. The present study proposes a new NS-based edge detection method using CV algorithm. The proposed method combines the philosophical view of NS with successful segmentation characteristics of CV model. Obtained edge detection results are compared with different edge detection methods. The performances of each method are analyzed by using Figure of Merit (FOM) and Peak Signal-To-Noise Ratio (PSNR). The results suggest that the proposed method displays a better performance assessment compared to the used well-known methods.