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Fuzzy Logic Method for Predicting the Effect of Main Fabric Parameters Influencing Drape Phenomenon

Thouraya Hamdi, Adel Ghith, Faten Fayala

Autex Research Journal

2020

Vol. 20, no. 3

220--227

The main aspect of this research was to predict the drape parameters and describe clearly the drape phenomenon using fuzzy logic method. Forecasting features allow manufacturers to save time and improve their productivity. The bending rigidity, (in warp, weft, and skew direction), shear rigidity, and weight of fabric samples were used as the key input variables for the model, whereas drape coefficient, drape distance ratio, folds depth index, and node number were used as output/response variables. The results show that changing the values of fabric parameters significantly affected the fabric drape and a representative correlation values were found between the experimental values and those calculated by the fuzzy system.

Enhanced CAE system for detection of exudates and diagnosis of diabetic retinopathy stages in fundus retinal images using soft computing techniques

Janaki Sathya D., Geetha K.

Polish Journal of Medical Physics and Engineering

2019

Vol. 25, Iss. 2

131--139

Diabetic Retinopathy (DR) is one of the leading causes of visual impairment. Diabetic Retinopathy is the most recent technique of identifying the intensity of acid secretion in the eye for diabetic patients. The identification of DR is performed by visual analysis of retinal images for exudates (fat deposits) and the main patterns are traced by ophthalmologists. This paper proposes a fully automated Computer Assisted Evaluation (CAE) system which comprises of a set of algorithms for exudates detection and to classify the different stages of Diabetics Retinopathy, which are identified as either normal or mild or moderate or severe. Experimental validation is performed on a real fundus retinal image database. The segmentation of exudates is achieved using fuzzy C-means clustering and entropy filtering. An optimal set obtained from the statistical textural features (GLCM and GLHM) is extracted from the segmented exudates for classifying the different stages of Diabetics Retinopathy. The different stages of Diabetic Retinopathy are classified using three classifiers such as Back Propagation Neural Network (BPN), Probabilistic Neural Network (PNN) and Support Vector Machine (SVM). The experimental results show that the SVM classifiers outperformed other classifiers for the examined fundus retinal image dataset. The results obtained confirm that with new a set of texture features, the proposed methodology provides better performance when compared to the other methods available in the literature. These results suggest that our proposed method in this paper can be useful as a diagnostic aid system for Diabetic Retinopathy.