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Real-Time Motor Fault Detection by 1-D Convolutional Neural Networks
(
Institute of Electrical and Electronics Engineers Inc.
, 2016 , Article)
Early detection of the motor faults is essential and artificial neural networks are widely used for this purpose. The typical systems usually encapsulate two distinct blocks: feature extraction and classification. Such ...
Performance evaluation for compression-accuracy trade-off using compressive sensing for EEG-based epileptic seizure detection in wireless tele-monitoring
(
IEEE
, 2013 , Conference Paper)
Brain is the most important part in the human body controlling muscles and nerves; Electroencephalogram (EEG) signals record brain electric activities. EEG signals capture important information pertinent to different ...
Performance Comparison of classification algorithms for EEG-based remote epileptic seizure detection in Wireless Sensor Networks
(
IEEE Computer Society
, 2014 , Conference Paper)
Identification of epileptic seizure remotely by analyzing the electroencephalography (EEG) signal is very important for scalable sensor-based health systems. Classification is the most important technique for wide-ranging ...
Efficiency validation of one dimensional convolutional neural networks for structural damage detection using a SHM benchmark data
(
International Institute of Acoustics and Vibration, IIAV
, 2018 , Conference Paper)
In this paper, a novel one dimensional convolution neural network (1D-CNN) based structural damage assessment technique is validated with a benchmark study published by IASC-ASCE Structural Health Monitoring Task Group in ...
Long-term epileptic EEG classification via 2D mapping and textural features
(
Elsevier Ltd
, 2015 , Article)
Interpretation of long-term Electroencephalography (EEG) records is a tiresome task for clinicians. This paper presents an efficient, low cost and novel approach for patient-specific classification of long-term epileptic ...
Structural damage detection in real time: Implementation of 1D convolutional neural networks for SHM applications
(
Springer
, 2017 , Conference Paper)
Most of the classical structural damage detection systems involve two processes, feature extraction and feature classification. Usually, the feature extraction process requires large computational effort which prevent the ...
Sepsis Prediction in Intensive Care Unit Using Ensemble of XGboost Models
(
IEEE Computer Society
, 2019 , Conference Paper)
Sepsis is caused by the dysregulated host response to infection and potentially is the main cause of 6 million death annually. It is a highly dynamic syndrome and therefore the early prediction of sepsis plays a key role ...
Convolutional neural networks for real-time and wireless damage detection
(
Springer New York LLC
, 2020 , Conference Paper)
Structural damage detection methods available for structural health monitoring applications are based on data preprocessing, feature extraction, and feature classification. The feature classification task requires considerable ...
Automatic handedness detection from off-line handwriting
(
IEEE
, 2013 , Conference Paper)
In forensics, the handedness detection or the classification of writers into left or right-handed helps investigators focusing more on a certain category of suspects. However, only a few studies have been carried out in ...
On the use of time-frequency features for detecting and classifying epileptic seizure activities in non-stationary EEG signals
(
Institute of Electrical and Electronics Engineers Inc.
, 2014 , Conference Paper)
This paper proposes new time-frequency features for detecting and classifying epileptic seizure activities in non-stationary EEG signals. These features are obtained by translating and combining the most relevant time-domain ...