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This paper investigates the sensitivity of statistical fingerprints to different phase resolution (PR) and amplitude bins (AB) sizes of partial discharge (PD) φ - q - n (phase-amplitude-number) patterns. In particular, this paper compares the capability of the ensemble neural network (ENN) and the single neural network (SNN) in recognizing and distinguishing different resolution sizes of φ - q - n discharge patterns. The training fingerprints for both the SNN and ENN comprise statistical fingerprints from different φ - q - n measurements. The result shows that there exists statistical distinction for different PR and AB sizes on some of the statistical fingerprints. Additionally, the ENN and SNN outputs change depending on training and testing with different PR and AB sizes. Furthermore, the ENN appears to be more sensitive in recognizing and discriminating the resolution changes when compared with the SNN. Finally, the results are assessed for practical implementation in the power industry and benefits to practitioners in the field are highlighted. [ABSTRACT FROM AUTHOR]

Both fuzzy logic, as the basis of many inference systems, and Neural Networks, as a powerful computational model for classification and estimation, have been used in many application fields since their birth. These two techniques are somewhat supplementary to each other in a way that what one is lacking of the other can provide. This led to the creation of Neuro-Fuzzy systems which utilize fuzzy logic to construct a complex model by extending the capabilities of Artificial Neural Networks. Generally speaking all type of systems that integrate these two techniques can be called Neuro-Fuzzy systems. Key feature of these systems is that they use input–output patterns to adjust the fuzzy sets and rules inside the model. The paper reviews the principles of a Neuro-Fuzzy system and the key methods presented in this field, furthermore provides survey on their applications for technical diagnostics and measurement. [ABSTRACT FROM AUTHOR]

This article deals with classification problems involving unequal probabilities in each class and discusses metrics to systems that use multilayer perceptrons neural networks (MLP) for the task of classifying new patterns. In addition we propose three new pruning methods that were compared to other seven existing methods in the literature for MLP networks. All pruning algorithms presented in this paper have been modified by the authors to do pruning of neurons, in order to produce fully connected MLP networks but being small in its intermediary layer. Experiments were carried out involving the E. coli unbalanced classification problem and ten pruning methods. The proposed methods had obtained good results, actually, better results than another pruning methods previously defined at the MLP neural network area. [ABSTRACT FROM AUTHOR]

Wheat product quality is closely related to wheat seed purity. Purity is an important factor that has a considerable impact on wheat product prices in grain storage silos. The aim of this paper was to introduce a machine vision based approach as a primarily step for fabricating an automatic wheat purity determination and grading device. Experimental data consists of 52 color, morphology, and texture characteristic parameters, extracted from images of samples, including four local wheat grades and eight common weed seeds growing in wheat fields of Iran, were used to build the classification models. A new algorithm that combines Imperialist Competitive Algorithm (ICA) and Artificial Neural Networks (ANNs) has been used for two purposes: to find the best characteristic parameters set and to create robust classification models. Based upon the results obtained from this study, the total classification rate of ICA-ANN approach for wheat grains vs. non-wheat seeds, wheat grain classes, and non-wheat seed classes was 96.25%, 87.50%, and 77.22%, respectively. [ABSTRACT FROM AUTHOR]

• Add a differential sequence to reduce the impact of noise in ECG signal. • Add a variable nonlinear layer to enhance the non-linear capability of the model. • Adaptive Learning rate is used to converge the training process. • The performance of proposed CNN is better than the other widely used algorithms. • The proposed algorithm can serve as an adjunct tool to assist clinicians in confirming their diagnosis. In this paper, an improved convolutional neural network (CNN) named Feature Selection CNN (FS-CNN) and Synthetic Minority Over-sampling Technique (SMOTE) named Multiply Stochastic SMOTE (MS-SMOTE) are proposed for imbalanced ECG multi-factor classification. FS-CNN includes convolutional expression layer, multi-residual block, and decision layer. The primary purpose of FS-CNN is to find the most influential features during automatic model optimization dynamically. MS-SMOTE combines the advantage of SMOTE and borderline SMOTE to balance the number of different categories. By dynamically selecting SMOTE and borderline SMOTE based on the proportion of data, the synthetic data can have a better category property. Comparing to other wide-used algorithms, our method is better than most other algorithms in many indicators. And in simulation 2, our method can deal with a large number of ECG multi-factor data classifications. Also, two self-controlled experiments are designed to examine how different parameters affect the result of the above problem. [ABSTRACT FROM AUTHOR]

• Classification of roughness to unmeasured surfaces is firstly proposed. • No need to artificially design any indicators. • The method used in this article is robust to the light source environment. • This method is combined with machine vision system. At present, machine vision roughness detection mostly needs to design roughness related indexes based on images, and the index design has human intervention and is heavily dependent on the light source environment. To solve this problem, the paper classifies the surface roughness based on the deep convolutional neural network method, which can realize the roughness detection without index design. The most important thing is that the detection method has good light source robustness under different light source environments. The study adopts an end-to-end image analysis method, by means of image enhancement pre-processing of a small number of source images, after multi-layer convolution and pooling operations, as well as comprehensive processing of fully connected and classification layers, the convolutional kernel can automatically extract the features of the image, and finally the surface roughness obtained by vertical disc cutter milling can be classified and predicted. In addition, based on the experimental light source environment with two different luminance of night and day, by comparing with the technically mature ResNet50 and DenseNet121 convolutional neural network models, the deep convolutional neural network Xception model not only has high roughness classification accuracy, but also has more light source environment robustness. This method makes the online measurement of visual roughness possible. [ABSTRACT FROM AUTHOR]

• To design and develop effective kidney disease detection approach. • An artificial neural network and multi-kernel k-means clustering algorithms were used. • Stone and tumor were effectively identified by medical ultra sound images. • Performance of linear + Quadratic kernel approach is analyzed with sensitivity, specificity and accuracy. • The proposed linear + Quadratic kernel approach achieves the maximum accuracy compare to other methods. The main aim of this paper is to design and develop an approach for kidney disease detection and segmentation using a combination of clustering and classification approach. Nowadays, kidney stone detection and segmentation is one of the crucial procedures in surgical and treatment planning for ultrasound images. However, at present, kidney stone segmentation in ultrasound images is mostly performed manually in clinical practice. Apart from being time-consuming, manual stone delineation is difficult and depends on the individual operator. Therefore, in this work, we proposed a kidney stone detection using artificial neural network and segmentation using multi-kernel k-means clustering algorithm. Normally, the system comprises of four modules like (i) preprocessing, (ii) feature extraction, (iii) classification and (iv) segmentation. Primarily, we eliminate the noise present in the input image using median filter. Then, we extract the important GLCM features from the image. After that, we classify the image as normal or abnormal using neural network classifier. Finally, the abnormal images are given to the segmentation stage to segment the stone and tumor part separately using multi. Kernel K-means clustering algorithm. The experimentation results show that the proposed system as linear + quadratic based segmentation achieves the maximum accuracy of 99.61%, compare with all other methods. [ABSTRACT FROM AUTHOR]