Machine learning in materials science: Recent progress and emerging applications
T Mueller, AG Kusne… - Reviews in computational …, 2016 - Wiley Online Library
This chapter addresses the role that data‐driven approaches, especially machine learning
methods, are expected to play in materials research in the immediate future. Machine …
methods, are expected to play in materials research in the immediate future. Machine …
Time-series classification methods: Review and applications to power systems data
Chapter Overview The diffusion in power systems of distributed renewable energy
resources, electric vehicles, and controllable loads has made advanced monitoring systems …
resources, electric vehicles, and controllable loads has made advanced monitoring systems …
Static and dynamic hand gesture recognition in depth data using dynamic time war**
G Plouffe, AM Cretu - IEEE transactions on instrumentation and …, 2015 - ieeexplore.ieee.org
This paper discusses the development of a natural gesture user interface that tracks and
recognizes in real time hand gestures based on depth data collected by a Kinect sensor …
recognizes in real time hand gestures based on depth data collected by a Kinect sensor …
TFGAN: Traffic forecasting using generative adversarial network with multi-graph convolutional network
Traffic forecasting constitutes a task of great importance in intelligent transport systems.
Owing to the non-Euclidean structure of traffic data, the complicated spatial correlations, and …
Owing to the non-Euclidean structure of traffic data, the complicated spatial correlations, and …
Time works well: Dynamic time war** based on time weighting for time series data mining
H Li - Information Sciences, 2021 - Elsevier
Dynamic time war** is one of the most important similarity measurement methods for time
series data mining. Owing to the different influence of various time points, an extension of …
series data mining. Owing to the different influence of various time points, an extension of …
Learning a mahalanobis distance-based dynamic time war** measure for multivariate time series classification
Multivariate time series (MTS) datasets broadly exist in numerous fields, including health
care, multimedia, finance, and biometrics. How to classify MTS accurately has become a hot …
care, multimedia, finance, and biometrics. How to classify MTS accurately has become a hot …
Epidemiology of coronavirus COVID-19: Forecasting the future incidence in different countries
J Stübinger, L Schneider - Healthcare, 2020 - mdpi.com
This paper forecasts the future spread of COVID-19 by exploiting the identified lead-lag
effects between different countries. Specifically, we first determine the past relation among …
effects between different countries. Specifically, we first determine the past relation among …
kmlShape: an efficient method to cluster longitudinal data (time-series) according to their shapes
Background Longitudinal data are data in which each variable is measured repeatedly over
time. One possibility for the analysis of such data is to cluster them. The majority of clustering …
time. One possibility for the analysis of such data is to cluster them. The majority of clustering …
An analytical framework based on the recency, frequency, and monetary model and time series clustering techniques for dynamic segmentation
H Abbasimehr, A Bahrini - Expert Systems with Applications, 2022 - Elsevier
Nowadays, banks use data mining and business intelligence tools and techniques to
analyze their customers' behavior. Customer segmentation is a widely adopted analytical …
analyze their customers' behavior. Customer segmentation is a widely adopted analytical …
Efficient learning of timeseries shapelets
In timeseries classification, shapelets are subsequences of timeseries with high
discriminative power. Existing methods perform a combinatorial search for shapelet …
discriminative power. Existing methods perform a combinatorial search for shapelet …