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a b s t r a c tThis paper proposes a novel synthetic aperture radar (SAR) image segmentation algorithm based on the neutrosophic set (NS) and improved artificial bee colony (I-ABC) algorithm. In this algorithm, threshold value estimation is considered as a search procedure that searches for a proper value in a grayscale interval. Therefore, I-ABC optimization algorithm is presented to search for the optimal threshold value. In order to get an efficient and powerful fitness function for I-ABC algorithm, the input SAR image is transformed into the NS domain. Then, a neutrosophic T and I subset images are obtained. A co-occurrence matrix based on the neutrosophic T and I subset images is constructed, and two-dimensional gray entropy function is described to serve as the fitness function of I-ABC algorithm. Finally, the optimal threshold value is quickly explored by the employed, onlookers and scouts bees in I-ABC algorithm. This paper contributes to SAR image segmentation in two aspects: (1) a hybrid model, having two different feature extraction methods, is proposed. (2) An optimal threshold value is automatically selected by maximizing the separability of the classes in gray level image by incorporating a simple and fast search strategy. The effectiveness of the proposed algorithm is demonstrated by application to real SAR images.

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A novel active learning-based electrocardiogram (ECG) signal classification method using eigenvalues and deep learning is proposed. Six statistical features relating to ECG beat intervals are calculated separately for each heartbeat. Both statistical features and eigenvalues of ECG beats are combined into a single feature vector. The eigenvalues of ECG beats are used as an input to denoising autoencoder (DAE). Weighted ECG beat intervals are calculated by using tenfold cross-validation approach. To learn an efficient feature representation from the hybrid feature vector, DAE is used in an unsupervised way. After completing the feature learning procedure, a softmax regression layer is added on the top of the resulting hidden layer of DAE, and thus a suitable deep neural network (DNN) architecture is built. The learned features obtained from the autoencoder layers are fed to the softmax regression layer for classification. To update weights of the proposed eigenvalues-based DNN model, ECG beats are labelled by the medical expert are used. In order to determine the most informative beats, entropy and Breaking-Ties are also used as selection criteria. The proposed method is evaluated in terms of ECG beats classes. The classification performance of the authors' proposed model is also compared with the several conventional machine learning classifiers.