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The main purpose of identifying and locating the retina vessels is to specify from the fundus image the various tissues of the vascular structure, which can be wide or tight. The classification of vessels in the retinal image often confronts several challenges, such as the low contrast accompanying the fundus image, the inhomogeneity of the background lighting, and the noise. Moreover, fuzzy c-means (FCM) is one of the most frequently used algorithms for medical image segmentation due to its effectiveness. Hence, many FCM method derivatives have been developed to improve their noise robustness and time-consuming. This paper aims to analyze the performance of some improved FCM algorithms to recommend the best ones for the segmentation of retinal blood vessels. Eight derivatives of FCM algorithm are detained in this study: FCM, EnFCM, SFCM, FGFCM, FRFCM, DSFCM_N, FCM_SICM and SSFCA. The performance analysis is conducted from three viewpoints: noise robustness, blood vessels segmentation performance, and time-consuming. At first, the noise robustness of improved FCM clustering algorithms is evaluated using a synthetic image degraded by various types and levels of noise. Then, the ability of the selected algorithms to segment retinal blood vessels is assessed based on images from the DRIVE and STARE databases after a pre-processing phase. Finally, the time consumption of each algorithm is measured. The experiments demonstrate that the FRFCM and DSFCM_N algorithms achieve better results in terms of noise robustness and blood vessels segmentation. Regarding the running time, the FRFCM algorithm requires less time than other algorithms in the segmentation of retinal images. The results of this study are extensively discussed, and some suggestions are proposed at the end of this paper. [ABSTRACT FROM AUTHOR]

To apply laser forming process in reality, it is required to know the relationships between the deformed shape and scanning paths along with heating conditions. The deformation due to laser scanning depends on various factors, namely laser power, scan speed, spot diameter, scan position, number of scans, and many others. This article presents soft computing-based methods to predict deformations for a set of heating conditions, and also to determine the heating lines and heat conditions, in order to get a desired shape (i.e., inverse analysis). A novel attempt has been made in this paper to carry out analysis and synthesis (inverse analysis) of laser forming process using both genetic-neural network (GA-NN) and genetic adaptive neuro-fuzzy inference system (GA-ANFIS). During the analysis, laser power, scan speed, spot diameter, scan position and number of scans are taken as inputs and bending angle is considered as the output. A batch mode of training has been used for both the approaches with the help of some experimental data. The performances of the developed approaches have been tested on some real experimental data. Both the approaches are found to be effective to predict the bending angles and carry out the process synthesis successfully. GA-NN approach is found to perform better than the GA-ANFIS approach in predicting the bending angles, and both the approaches are able to provide comparable predictions in inverse analysis. [ABSTRACT FROM AUTHOR]

We present two types of fuzzy solutions to linear systems of first order differential equations having fuzzy initial conditions. The first solution, called the extension principle solution, fuzzifies the crisp solution and then checks to see if its α-cuts satisfy the differential equations. The second solution, called the classical solution, solves the fuzzified differential equations and then checks to see if the solution always defines a fuzzy number. Three applications are presented: (1) predator–prey models; (2) the spread of infectious diseases; and (3) modeling an arms race. [ABSTRACT FROM AUTHOR]