A review of vibration-based damage detection in civil structures: From traditional methods to Machine Learning and Deep Learning applications
Monitoring structural damage is extremely important for sustaining and preserving the
service life of civil structures. While successful monitoring provides resolute and staunch …
service life of civil structures. While successful monitoring provides resolute and staunch …
[HTML][HTML] A review on deep learning methods for ECG arrhythmia classification
Deep Learning (DL) has recently become a topic of study in different applications including
healthcare, in which timely detection of anomalies on Electrocardiogram (ECG) can play a …
healthcare, in which timely detection of anomalies on Electrocardiogram (ECG) can play a …
Learning dynamics and heterogeneity of spatial-temporal graph data for traffic forecasting
Accurate traffic forecasting is critical in improving safety, stability, and efficiency of intelligent
transportation systems. Despite years of studies, accurate traffic prediction still faces the …
transportation systems. Despite years of studies, accurate traffic prediction still faces the …
ECG arrhythmia classification using STFT-based spectrogram and convolutional neural network
J Huang, B Chen, B Yao, W He - IEEE access, 2019 - ieeexplore.ieee.org
The classification of electrocardiogram (ECG) signals is very important for the automatic
diagnosis of heart disease. Traditionally, it is divided into two steps, including the step of …
diagnosis of heart disease. Traditionally, it is divided into two steps, including the step of …
Automated atrial fibrillation detection using a hybrid CNN-LSTM network on imbalanced ECG datasets
Atrial fibrillation is a heart arrhythmia strongly associated with other heart-related
complications that can increase the risk of strokes and heart failure. Manual …
complications that can increase the risk of strokes and heart failure. Manual …
Predicting urban region heat via learning arrive-stay-leave behaviors of private cars
Urban region heat refers to the extent of which people congregate in various regions when
they travel to and stay in a specified place. Predicting urban region heat facilitates broad …
they travel to and stay in a specified place. Predicting urban region heat facilitates broad …
CNN-LSTM architecture for predictive indoor temperature modeling
Indoor temperature modeling is a crucial part towards efficient Heating, Ventilation and Air
Conditioning (HVAC) systems. Data-driven black-box approaches have been an attractive …
Conditioning (HVAC) systems. Data-driven black-box approaches have been an attractive …
Dynamically weighted balanced loss: class imbalanced learning and confidence calibration of deep neural networks
KRM Fernando, CP Tsokos - IEEE Transactions on Neural …, 2021 - ieeexplore.ieee.org
Imbalanced class distribution is an inherent problem in many real-world classification tasks
where the minority class is the class of interest. Many conventional statistical and machine …
where the minority class is the class of interest. Many conventional statistical and machine …
An enhanced ResNet-50 deep learning model for arrhythmia detection using electrocardiogram biomedical indicators
R Anand, SV Lakshmi, D Pandey, BK Pandey - Evolving Systems, 2024 - Springer
Electrocardiogram (ECG) is one among the most common detecting techniques in the
analysis and detection of cardiac arrhythmia adopted due to its cost efficiency and simplicity …
analysis and detection of cardiac arrhythmia adopted due to its cost efficiency and simplicity …
Using deep convolutional neural network for emotion detection on a physiological signals dataset (AMIGOS)
Recommender systems have been based on context and content, and now the
technological challenge of making personalized recommendations based on the user …
technological challenge of making personalized recommendations based on the user …