Nowadays, hyperspectral images (HIs) are widely used for land cover land use (LCLU) mapping. Hyperspectral sensors collect spectral data in numerous adjacent spectral bands, which are usually redundant. Hyperspectral data processing comes with important challenges such as huge processing time, difficulties in transfer, and storage. In this study, two supervised and Unsupervised dimensionality reduction methods are proposed for hyperspectral feature extraction based on the band clustering technique. In the first method, the Unsupervised method, after the Unsupervised band clustering stage with some statistical attributes, the principal component transform is used in each cluster, and the first PC component is considered an extracted feature. In the second method, the supervised method, bands are clustered based on training samples mean vectors of each class, and the weighted mean operator is used for feature extraction in each cluster. The experiment is conducted on the classification of real famous HI named Indian Pines. Comparing the obtained results and some other state of art methods proved the proposed method's efficiency.

Mohaghegh, S. Noferesti*, and M. Rajaei Abstract: In the era of big data, automatic data analysis techniques such as data mining have been widely used for decision-making and have become very effective. Among data mining techniques, classification is a common method for decision making and prediction. Classification algorithms usually work well on balanced datasets. However, one of the challenges of the classification algorithms is how to correctly predicting the label of new samples based on learning on imbalanced datasets. In this type of dataset, the heterogeneous distribution of the data in different classes causes examples of the minority class to be ignored in the learning process, while this class is more important in some prediction problems. To deal with this issue, in this paper, an efficient method for balancing the imbalanced dataset is presented, which improves the accuracy of the machine learning algorithms to correct prediction of the class label of new samples. According to the evaluations, the proposed method has a better performance compared to other methods based on two common criteria in evaluating the classification of imbalanced datasets, namely "Balanced Accuracy" and "Specificity".

The purpose of stance detection is to identify the author's stance toward a particular topic or claim. Stance detection has become a key component in applications such as fake news detection, claim validation, argument searching, and author profiling. Although significant progress has been made in stance detection in languages such as English, little attention has been paid in some other languages, including Persian.  One of the main problems of research in Persian stance detection is the shortage of appropriate datasets. In this article, to address this problem, we consider data augmentation, the artificial creation of training data, which is used to conquer the shortage of datasets. In this research, we studied several methods of data augmentation such as EDA, back-translation, and merging source dataset with similar one in English language. The experimental results indicate that combining the primary data set with the translation of another dataset with similar content in another language (for example English) result in a significant improvement in the performance of the model.

In this paper an algorithm for classification of land cover from remote sensing data based on the combination of supervised and Unsupervised neural networks is presented. The proposed algorithm composed of Self- Organizing Map (SOM) and Multi-Layer Perceptron (MLP) algorithms. Since the SOM is an Unsupervised algorithm, it can’t determine the accurate label of image pixels by itself; therefore, in this paper, the MPL was used to determine the final label. The proposed algorithm includes two steps. First, the image segmented is performed using the SOM algorithm; second, the labels of all SOM’s neurons are determined using MLP and training data to produce the land cover thematic map. In this paper the different numbers of neurons were considered in SOM structure for reducing the number of mathematics operations. In this case, the PCA algorithm was applied for initialization SOM and the result of this part shows that the number of operations was reduced significantly. The algorithm performed on Landsat (ETM+) and Ikonos images to demonstrate its capability. Different combination algorithms were preformed in this work. All these algorithms were used to produce land cover map and then they obtained result were compared with SOM-MLP results. The obtained results showed the ability of SOM-MLP algorithm for land cover classification. In this regard, the result of maximum likelihood classifier, minimum distance algorithm and MLP were compared to proposed algorithms result. Finally, it is concluded that the SOM-MLP improves the accuracy of classification, and it is suitable to remote sensing data when there is not enough ground truth data for training.