Pes planus, colloquially known as flatfoot, is a deformity defined as the collapse, flattening or loss of the medial longitudinal arch of the foot. The first standard radiographic examination for diagnosing pes planus involves lateral and dorsoplantar weight-bearing radiographs. Recently, many artificial intelligence-based computer-aided diagnosis (CAD) systems and models have been developed for the detection of various diseases from radiological images. However, to the best of our knowledge, no model and system has been proposed in the literature for automated pes planus diagnosis using X-ray images. This study presents a novel deep learning-based model for automated pes planus diagnosis using X-ray images, a first in the literature. To perform this study, a new pes planus dataset consisting of weight-bearing X-ray images was collected and labeled by specialist radiologists. In the preprocessing stage, the number of X-ray images was augmented and then divided into 4 and 16 patches, respectively in a pyramidal fashion. Thus, a total of 21 images are obtained for each image, including 20 patches and one original image. These 21 images were then fed to the pre-trained MobileNetV2 and 21,000 features were extracted from the Logits layer. Among the extracted deep features, the most important 1312 features were selected using the proposed iterative ReliefF algorithm, and then classified with support vector machine (SVM). The proposed deep learning-based framework achieved 95.14% accuracy using 10-fold cross validation. The results demonstrate that our transfer learning-based model can be used as an auxiliary tool for diagnosing pes planus in clinical practice.

The main goal of this study is to implement real-time synchronization of a memristor-based chaotic circuit for secure communication on LabVIEW virtual instrument environments. Memristors can be used in chaotic circuits because of their nonlinear behavior. In this paper, an application for secure communication systems is proposed by using the chaotic property of the memristor. Phase portraits, Lyapunov exponential spectrum, and bifurcation diagrams of the memristor-based chaotic circuit were investigated. A Fuzzy-PID controller was used for master-slave synchronization of memristive chaotic circuit and in secure communication application, three images were also used for encryption and decryption. NI 6251 DAQ card was used to obtain real-time results. Information entropy values, correlation coefficients ( CC ), peak signal to noise ratio ( PSNR ), structural similarity index ( SSIM ), correlation coefficients for the horizontal, vertical and diagonal directions from the three encrypted images, number of pixels change rate ( NPCR ) and unified average changing intensity (UACI ) are calculated to test encryption/decryption performance in the designed system. The results obtained from real-time applications and simulations prove that memristor-based chaotic circuits can be used in secure communication systems where privacy is important.

The aim of this article is to develop a knowledge-based therapy for management of rats with respiratory distress. A mechanical ventilator was designed to achieve this aim. The designed ventilator is called an intelligent mechanical ventilator since fuzzy logic was used to control the pneumatic equipment according to the rat’s status. LabVIEW software was used to control all equipments in the ventilator prototype and to monitor respiratory variables in the experiment. The designed ventilator can be controlled both manually and by fuzzy logic. Eight female Wistar-Albino rats were used to test the designed ventilator and to show the effectiveness of fuzzy control over manual control on pressure control ventilation mode. The anesthetized rats were first ventilated for 20 min manually. After that time, they were ventilated for 20 min by fuzzy logic. Student’s t-test for p < 0.05 was applied to the measured minimum, maximum and mean peak inspiration pressures to analyze the obtained results. The results show that there is no statistical difference in the rat’s lung parameters before and after the experiments. It can be said that the designed ventilator and developed knowledge-based therapy support artificial respiration of living things successfully.