Search

Neutrosophic sets (NS) have referred to as interval fuzzy sets applied in minimizing the uncertainty and fuzziness in computer-vision and machine-learning communities and hence employed for several applications. As far as medical image processing applications are concerned NSs are obtained as an important technique for de-noising. Also, fuzzy segmentation with machine and deep learning is determined as a familiar procedure that splits input image into distinct regions for precise learning. Several research works conducted in different image-processing domains. However, less works was focused on denoising and segmentation of medical image processing with minimal time complexity and accuracy. In this work we plan to develop a Kalman--Bucy Filtered Neutrosophic Neuro Fuzzy Image Denoising (KBF-NNFID) method with the objective of reducing the noisy artifacts with higher peak signal-to-noise ratio in a computationally efficient manner. First, medical images obtained from Brain MRI LGG segmentation dataset are subjected to filtering employing Kalman Bucy Filtering algorithm with series of measurements examined. Second with the filtered medical images provided as input, uncertainty is handled by utilizing Neutrosophic Neuro Fuzzy set (NNFS) with help of the membership grade. With the aid of three membership grades, i.e., truth, indeterminacy and falsity, uncertainty involved in noisy image are said to be handled in a time efficient manner. By this way, an efficient image denoising process is performed with better PSNR. Experimental evaluation is carried out using medical images with different performance metrics such as enhanced PSNR and true positive rate up to 13%, 14% as well minimum execution time by 38% using medical images. [ABSTRACT FROM AUTHOR]

In this manuscript, we will use the new modified version of the Runge-Kutta method suitable for solving fuzzy two coupled systems of Nonlinear Ordinary Differential Equations (ODE). With the aid of a numerical example, we will demonstrate the accuracy of the RK-4 coupled method for solving these two coupled differential equations. To find the analytical solutions we use Laplace Adomian Decomposition Method since it is a semianalytical method used well in many existing studies on dynamical systems. In order to tell the accuracy, we use the error analysis technique. With the help of numerical simulations, we are able to show at what point of t, both x(t) and y(t) will interact in order to support the theory. [ABSTRACT FROM AUTHOR]

First some fundamental tasks of fuzzy neutrosophic soft matrix is characterized and presented a new fuzzy neutrosophic soft complement matrix dependent on reference work and the trace of fuzzy neutrosophic soft matrix. A few properties and models are examined. In conclusion, a fuzzy neutrosophic soft matrix for a choice strategy is characterized. [ABSTRACT FROM AUTHOR]