Stochastic disturbances often occur in real-world systems which can lead to undesirable system dynamics. Therefore, it is necessary to investigate stochastic disturbances in neural network modeling. As such, this paper examines the stability problem for Takagi-Sugeno fuzzy uncertain quaternion-valued stochastic neural networks. By applying Takagi-Sugeno fuzzy models and stochastic analysis, we first consider a general form of Takagi-Sugeno fuzzy uncertain quaternion-valued stochastic neural networks with time-varying delays. Then, by constructing suitable Lyapunov-Krasovskii functional, we present new delay-dependent robust and global asymptotic stability criteria for the considered networks. Furthermore, we present our results in terms of real-valued linear matrix inequalities that can be solved in MATLAB LMI toolbox. Finally, two numerical examples are presented with their simulations to demonstrate the validity of the theoretical analysis.

This paper investigates the global asymptotic stability problem for a class of quaternion-valued Takagi-Sugeno fuzzy BAM neural networks with time-varying delays. By applying Takagi-Sugeno fuzzy models, we first consider a general form of quaternion-valued Takagi-Sugeno fuzzy BAM neural networks with time-varying delays. Then, we apply the Cauchy-Schwarz algorithm and homeomorphism principle to obtain sufficient conditions for the existence and uniqueness of the equilibrium point. By utilizing suitable Lyapunov-Krasovskii functionals and newly developed quaternion-valued Wirtinger-based integral inequality, some sufficient criteria are obtained to guarantee the global asymptotic stability of the considered networks. Further, the results of this paper are presented in the form of quaternion-valued linear matrix inequalities, which can be solved using the MATLAB YALMIP toolbox. Two numerical examples are presented with their simulations to demonstrate the validity of the theoretical analysis.

Optical Coherence Tomography (OCT) provides cross-sectional and three-dimensional reconstructions of the target tissue, allowing precise imaging and quantitative analysis of individual retinal layers. These images, based on optical inhomogeneities, reveal intricate cellular structures and are vital for tasks like retinal segmentation. The proposed study uses OCT images to identify significant differences in peripapillary retinal nerve fiber layer thickness. Incorporating spectral-domain analysis of OCT images significantly enhances the evaluation of Purtcher Retinopathy. To streamline this process, the study introduces a Context Encoding Deep Neural Network (CEDNN), which eliminates the time-consuming manual segmentation process while improving the accuracy of retinal layer thickness measurements. Despite the excellent performance of deep learning-based Convolutional Neural Networks (CNNs) in multiclass ocular fluid segmentation and lesion identification, certain challenges remain. Specifically, segmentation accuracy declines in regions with very tiny patches of subretinal fluid, often due to limited training data. The proposed CEDNN addresses these limitations by reducing processing time and enhancing accuracy. The approach incorporates advanced diffusion techniques in the 2D segmentation process using a gradient convergence field that accounts for the anisotropic nature of image features. Experimental results on public datasets and clinical OCT images demonstrate that the CEDNN approach achieves remarkable performance, with an accuracy of 99.3%, sensitivity of 99.4%, and specificity of 99%. Furthermore, the use of 3D representations of surface data outperforms traditional 2D surface estimates, enhancing segmentation quality. The system also incorporates temporal dimension estimation, making it feasible to forecast rapid disease progression. This advanced approach holds significant potential for improving retinal disease detection and analysis, setting a new benchmark in automated OCT-based diagnostics.