Accurate land cover information is required for science, monitoring, and reporting. Land
cover changes naturally over time, as well as a result of anthropogenic activities. Monitoring …

To meet the recent demands for automated security systems, this study proposes a novel
single-template strategy that uses mean templates and local stability-weighted dynamic time …

Multivariate time series (MTS) data are widely used in a very broad range of fields, including
medicine, finance, multimedia and engineering. In this paper a new approach for MTS …

Imbalanced time-series classification (ITSC) is ubiquitous in many real-world applications. In
this study, a novel cost-sensitive deep learning framework, namely ACS-ATCN, is proposed …

Echo state networks (ESNs) are a new approach to recurrent neural networks (RNNs) that
have been successfully applied in many domains. Nevertheless, an ESN is a predictive …

Cottonseed provides abundant and high-quality raw materials for biodiesel, fine chemical,
and other industries due to its rich oil, but phosphorus-deficient farmland and finite …

In this paper, we propose support vector-based supervised learning algorithms, called
multiclass support vector data description with weighted dynamic time warping kernel …

An echo state network (z) can provide an efficient dynamic solution for predicting time series
problems. However, in most cases, ESN models are applied for predictions rather than …

Measuring the similarity or distance between two time series sequences is critical for the
classification of a set of time series sequences. Given two time series sequences, X and Y …

Time-series data is an appealing study topic in data mining and has a broad range of
applications. Many approaches have been employed to handle time series classification …