No abstract

This paper introduces a new approach for the segmentation of skin lesions in dermoscopic images based on wavelet network (WN). The WN presented here is a member of fixed-grid WNs that is formed with no need of training. In this WN, after formation of wavelet lattice, determining shift and scale parameters of wavelets with two screening stage and selecting effective wavelets, orthogonal least squares algorithm is used to calculate the network weights and to optimize the network structure. The existence of two stages of screening increases globality of the wavelet lattice and provides a better estimation of the function especially for larger scales. R, G, and B values of a dermoscopy image are considered as the network inputs and the network structure formation. Then, the image is segmented and the skin lesions exact boundary is determined accordingly. The segmentation algorithm were applied to 30 dermoscopic images and evaluated with 11 different metrics, using the segmentation result obtained by a skilled pathologist as the ground truth. Experimental results show that our method acts more effectively in comparison with some modern techniques that have been successfully used in many medical imaging problems.

Image classification is an issue that utilizes image processing, pattern recognition and classification methods. Automatic medical image classification is a progressive area in image classification, and it is expected to be more developed in the future. Because of this fact, automatic diagnosis can assist pathologists by providing second opinions and reducing their workload. This paper reviews the application of the adaptive neuro-fuzzy inference system (ANFIS) as a classifier in medical image classification during the past 16 years. ANFIS is a fuzzy inference system (FIS) implemented in the framework of an adaptive fuzzy neural network. It combines the explicit knowledge representation of an FIS with the learning power of artificial neural networks. The objective of ANFIS is to integrate the best features of fuzzy systems and neural networks. A brief comparison with other classifiers, main advantages and drawbacks of this classifier are investigated.

No abstract

No abstract

No abstract

Diabetes is considered as a global affecting disease with an increasing contribution to both mortality rate and cost damage in the society. Therefore, tight control of blood glucose levels has gained significant attention over the decades. This paper proposes a method for blood glucose level regulation in type 1 diabetics. The control strategy is based on combining the fuzzy logic theory and single order sliding mode control (SOSMC) to improve the properties of sliding mode control method and to alleviate its drawbacks. The aim of the proposed controller that is called SOSMC combined with fuzzy on-line tunable gain is to tune the gain of the controller adaptively. This merit causes a less amount of control effort, which is the rate of insulin delivered to the patient body. As a result, this method can decline the risk of hypoglycemia, a lethal phenomenon in regulating blood glucose level in diabetics caused by a low blood glucose level. Moreover, it attenuates the chattering observed in SOSMC significantly. It is worth noting that in this approach, a mathematical model called minimal model is applied instead of the intravenously infused insulin–blood glucose dynamics. The simulation results demonstrate a good performance of the proposed controller in meal disturbance rejection and robustness against parameter changes. In addition, this method is compared to fuzzy high-order sliding mode control (FHOSMC) and the superiority of the new method compared to FHOSMC is shown in the results.

Wheat‐flour dough is a viscoelastic material with nonlinear rheological behavior. Extensograph is a useful system for dough rheological measurement. Our purpose in this research was to apply soft computation tools for predicting the extensograph properties of dough from several physicochemical properties of flour. This study used the resulting model to suggest modifications of processing conditions for reducing economic loss and minimizing product quality deterioration. A generalized feed‐forward artificial neural network (ANN) with a back‐propagation learning algorithm was employed to estimate the extensograph properties of dough. Trial and error and genetic algorithm (GA) were applied in the training phase for developing an ANN with an optimized structure. Developed ANN using GA has excellent potential for predicting the extensograph properties of dough. Sensitivity analyses were conducted to explore the ability of inputs in predicting the extensograph properties of dough. Results showed gluten index was the most sensitive input in dough extensograph characterizations. PRACTICAL APPLICATIONS Extensograph is a suitable instrument for measuring the stretching properties of dough to make reliable statements about the baking behavior of the wheat‐flour dough in practical industrial applications and in research. Rheological measurements of each batch in the production line are very useful and make online and in‐time process adjustments possible, but this is usually impractical in an industrial setting. Therefore, accurate prediction of dough rheology could provide many benefits to the baking industry for satisfying consumer demands. In the current study, genetic algorithm‐neural network approach was applied to predict extensograph properties of dough as affected by physicochemical properties of flour. In comparison with trial and error, genetic algorithm can determine an artificial neural network's topology and inputs in less time with excellent performance in prediction. According to the results of sensitivity analyses, of the seven investigated inputs, changes in gluten index have the most effect on estimating extensograph properties of dough.