This paper focuses on a classification problem for volatile time series. One of the most
popular approaches for time series classification is dynamic time warping and feature-based …

This paper presents a novel method for learning the cyclic contents of stochastic time series:
the deep time-growing neural network (DTGNN). The DTGNN combines supervised and …

A support vector machine (SVM) is a popular algorithm for classification learning. The
classical SVM effectively manages classification tasks defined by means of numerical …

This paper proposes a framework for binary classification of the time series with cyclic
characteristics. The framework presents an iterative algorithm for learning the cyclic …

Plants monitor their surrounding environment and control their physiological functions by
producing an electrical response. We recorded electrical signals from different plants by …

We revise the current model of learning pattern recognition in the Mushroom Bodies of the
insects using current experimental knowledge about the location of learning, olfactory …

In this paper a kernel for time-series data is introduced so that it can be used for any data
mining task that relies on a similarity or distance metric. The main idea of our kernel is that it …

Deep learning is an important element of artificial intelligence, especially in applications
such as image classification in which various architectures of neural network, eg …

While motif discovery methods have come a long way over the years, they generally match
occurrences based on the similar shape of the whole subsequence. As patterns in …

Text mining is the subfield of data mining. Text analysis or mining is enormously growing
field for research simultaneously to artificial intelligence and data mining. Unstructured data …