Multivariate time series classification (MTSC) based on deep learning (DL) has attracted
increasingly more research attention. The performance of a DL-based MTSC algorithm is …

Over the years, many deep learning algorithms have been developed for time series
classification (TSC). A learning model's performance usually depends on the quality of the …

Federated learning is an emerging machine learning approach that allows the construction
of a model between several participants who hold their own private data. This method is …

Time series prediction is one of the most important applications of Fuzzy Cognitive Maps
(FCMs). In general, the state of FCMs in forecasting depends only on the state of the …

This paper proposes a dual-network-based feature extractor, perceptive capsule network
(PCapN), for multivariate time series classification (MTSC), including a local feature network …

Time series classification is an increasingly attractive field with the appearance of new
problems in an expanding digitalized world. Most of the proposals in the state-of-the-art …

Time series classification (TSC) is a supervised task in which time series data are
associated with predefined classes. Time ordering and correlations of the samples should …

Abstract Fuzzy Cognitive Mapping (FCM) and the extensive family of models derived from it
have firmly established their strong position in the landscape of machine learning …

The CO2 reduction promise must be widely adopted if governments are to decrease future
emissions and alter the trajectory of urban mobility. However, from a long-term perspective …

Fuzzy cognitive maps (FCMs) learning is a hot topic in recent years. However, as the
number of concepts increases in FCMs, it is difficult to learn the sparse and robust FCMs …