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Hyperspectral sensors, due to the acquisition of a large number of spectral bands, always have particular importance in monitoring the phenomena of the earth's surface. The classification of hyperspectral images is the most crucial method of processing hyperspectral data, and many efforts have been made to increase its accuracy. In recent years, convolutional neural networks (CNNs) and spatial features have been a big part of how well hyperspectral images can be classified. CNNs, due to the automatic generation of features and reduction of parameters, compared to multilayer perceptron neural networks by sharing parameters in each layer, have received much attention from researchers in the field of pattern recognition. In previous research, much attention has not been paid to the simultaneous use of spatial feature extraction methods in CNNs. For this reason, in this paper, a new CNNs architecture has been introduced to classify hyperspectral images, which uses the spectral-spatial vector obtained from the morphological profiles method as the network's input. Morphological profiles, which include a set of opening and closing filters, are generally applied to the components of the image that contain the most information. In this research, non-linear principal component analysis (non-linear PCA) implemented by neural networks was used to summarize hyperspectral image information. Therefore, the main innovation of this paper is to provide a three-stage framework for applying deep learning. The first step is to reduce the dimensions of the hyperspectral image using non-linear PCA, the second stage is to extract spatial features using the morphological profiles method, and the third step is to prepare the inputs and design the CNNs architecture. The proposed method was evaluated on three benchmark hyperspectral images of Pavia, Berlin and Telops. The results of the experiments show the superiority of the proposed method by 13, 11 and 17% in the overall accuracy parameter compared to the support vector machines (SVM) classification algorithm in the Pavia, Berlin and Telops images, respectively. [ABSTRACT FROM AUTHOR]

Figurative language (FL) seems ubiquitous in all social media discussion forums and chats, posing extra challenges to sentiment analysis endeavors. Identification of FL schemas in short texts remains largely an unresolved issue in the broader field of natural language processing, mainly due to their contradictory and metaphorical meaning content. The main FL expression forms are sarcasm, irony and metaphor. In the present paper, we employ advanced deep learning methodologies to tackle the problem of identifying the aforementioned FL forms. Significantly extending our previous work (Potamias et al., in: International conference on engineering applications of neural networks, Springer, Berlin, pp 164–175, 2019), we propose a neural network methodology that builds on a recently proposed pre-trained transformer-based network architecture which is further enhanced with the employment and devise of a recurrent convolutional neural network. With this setup, data preprocessing is kept in minimum. The performance of the devised hybrid neural architecture is tested on four benchmark datasets, and contrasted with other relevant state-of-the-art methodologies and systems. Results demonstrate that the proposed methodology achieves state-of-the-art performance under all benchmark datasets, outperforming, even by a large margin, all other methodologies and published studies. [ABSTRACT FROM AUTHOR]