Deep learning for time series classification: a review
Abstract Time Series Classification (TSC) is an important and challenging problem in data
mining. With the increase of time series data availability, hundreds of TSC algorithms have …
mining. With the increase of time series data availability, hundreds of TSC algorithms have …
Multilevel wavelet decomposition network for interpretable time series analysis
Recent years have witnessed the unprecedented rising of time series from almost all kindes
of academic and industrial fields. Various types of deep neural network models have been …
of academic and industrial fields. Various types of deep neural network models have been …
[HTML][HTML] Machine learning models for classification and identification of significant attributes to detect type 2 diabetes
Abstract Type 2 Diabetes (T2D) is a chronic disease characterized by abnormally high blood
glucose levels due to insulin resistance and reduced pancreatic insulin production. The …
glucose levels due to insulin resistance and reduced pancreatic insulin production. The …
[图书][B] An introduction to computational physics
T Pang - 2006 - books.google.com
Thoroughly revised for its second edition, this advanced textbook provides an introduction to
the basic methods of computational physics, and an overview of progress in several areas of …
the basic methods of computational physics, and an overview of progress in several areas of …
Development of wavelet-based kalman online sequential extreme learning machine optimized with boruta-random forest for drought index forecasting
Drought is a stochastic and recurring hydrological natural hazard that occurs due to a
shortage of precipitation over a period of time. Drought forecasting in water resources …
shortage of precipitation over a period of time. Drought forecasting in water resources …
Efficient multi-scale network with learnable discrete wavelet transform for blind motion deblurring
X Gao, T Qiu, X Zhang, H Bai, K Liu… - Proceedings of the …, 2024 - openaccess.thecvf.com
Coarse-to-fine schemes are widely used in traditional single-image motion deblur; however
in the context of deep learning existing multi-scale algorithms not only require the use of …
in the context of deep learning existing multi-scale algorithms not only require the use of …
HARDC: A novel ECG-based heartbeat classification method to detect arrhythmia using hierarchical attention based dual structured RNN with dilated CNN
Deep learning-based models have achieved significant success in detecting cardiac
arrhythmia by analyzing ECG signals to categorize patient heartbeats. To improve the …
arrhythmia by analyzing ECG signals to categorize patient heartbeats. To improve the …
[HTML][HTML] Eeg and deep learning based brain cognitive function classification
S Sridhar, V Manian - Computers, 2020 - mdpi.com
Electroencephalogram signals are used to assess neurodegenerative diseases and
develop sophisticated brain machine interfaces for rehabilitation and gaming. Most of the …
develop sophisticated brain machine interfaces for rehabilitation and gaming. Most of the …
[HTML][HTML] A wavelet-based steganographic method for text hiding in an audio signal
O Veselska, O Lavrynenko, R Odarchenko, M Zaliskyi… - Sensors, 2022 - mdpi.com
The developed method of steganographic hiding of text information in an audio signal based
on the wavelet transform acquires a deep meaning in the conditions of the use by an …
on the wavelet transform acquires a deep meaning in the conditions of the use by an …
A new framework to estimate breathing rate from electrocardiogram, photoplethysmogram, and blood pressure signals
Breathing Rate (BR) is a key physiological parameter measured in a wide range of clinical
settings. However, it is still widely measured manually. In this paper, a novel framework is …
settings. However, it is still widely measured manually. In this paper, a novel framework is …