Nephropathy forecasting in diabetic patients using a GA-based type-2 fuzzy regression model

Automatic diagnosis of schizophrenia and attention deficit hyperactivity disorder in rs-fMRI modality using convolutional autoencoder model and interval type-2 fuzzy regression

Nowadays, many people worldwide suffer from brain disorders, and their health is in danger. So far, numerous methods have been proposed for the diagnosis of Schizophrenia (SZ) and attention deficit hyperactivity disorder (ADHD), among which functional magnetic resonance imaging (fMRI) modalities are known as a popular method among physicians. This paper presents an SZ and ADHD intelligent detection method of resting-state fMRI (rs-fMRI) modality using a new deep learning method. The University of California Los Angeles dataset, which contains the rs-fMRI modalities of SZ and ADHD patients, has been used for experiments. The FMRIB software library toolbox first performed preprocessing on rs-fMRI data. Then, a convolutional Autoencoder model with the proposed number of layers is used to extract features from rs-fMRI data. In the classification step, a new fuzzy method called interval type-2 fuzzy regression (IT2FR) is introduced and then optimized by genetic algorithm, particle swarm optimization, and gray wolf optimization (GWO) techniques. Also, the results of IT2FR methods are compared with multilayer perceptron, k-nearest neighbors, support vector machine, random forest, and decision tree, and adaptive neuro-fuzzy inference system methods. The experiment results show that the IT2FR method with the GWO optimization algorithm has achieved satisfactory results compared to other classifier methods. Finally, the proposed classification technique was able to provide 72.71% accuracy.

Generalized Type-2 Fuzzy Control for Type-I Diabetes: Analytical Robust System

The insulin injection rate in type-I diabetic patients is a complex control problem. The mathematical dynamics for the insulin/glucose metabolism can be different for various patients who undertake different activities, have different lifestyles, and have other illnesses. In this study, a robust regulation system on the basis of generalized type-2 (GT2) fuzzy-logic systems (FLSs) is designed for the regulation of the blood glucose level. Unlike previous studies, the dynamics of glucose–insulin are unknown under high levels of uncertainty. The insulin-glucose metabolism has been identified online by GT2-FLSs, considering the stability criteria. The learning scheme was designed based on the Lyapunov approach. In other words, the GT2-FLSs are learned using adaptation rules that are concluded from the stability theorem. The effect of the dynamic estimation error and other perturbations, such as patient activeness, were eliminated through the designed adaptive fuzzy compensator. The adaptation laws for control parameters, GT2-FLS rule parameters, and the designed compensator were obtained by using the Lyapunov stability theorem. The feasibility and accuracy of the designed control scheme was examined on a modified Bergman model of some patients under different conditions. The simulation results confirm that the suggested controller has excellent performance under various conditions.

Medical Images Encryption Based on Adaptive-Robust Multi-Mode Synchronization of Chen Hyper-Chaotic Systems

In this paper, a novel medical image encryption method based on multi-mode synchronization of hyper-chaotic systems is presented. The synchronization of hyper-chaotic systems is of great significance in secure communication tasks such as encryption of images. Multi-mode synchronization is a novel and highly complex issue, especially if there is uncertainty and disturbance. In this work, an adaptive-robust controller is designed for multimode synchronized chaotic systems with variable and unknown parameters, despite the bounded disturbance and uncertainty with a known function in two modes. In the first case, it is a main system with some response systems, and in the second case, it is a circular synchronization. Using theorems it is proved that the two synchronization methods are equivalent. Our results show that, we are able to obtain the convergence of synchronization error and parameter estimation error to zero using Lyapunov's method. The new laws to update time-varying parameters, estimating disturbance and uncertainty bounds are proposed such that stability of system is guaranteed. To assess the performance of the proposed synchronization method, various statistical analyzes were carried out on the encrypted medical images and standard benchmark images. The results show effective performance of the proposed synchronization technique in the medical images encryption for telemedicine application.