Deep learning for time series classification and extrinsic regression: A current survey
Time Series Classification and Extrinsic Regression are important and challenging machine
learning tasks. Deep learning has revolutionized natural language processing and computer …
learning tasks. Deep learning has revolutionized natural language processing and computer …
WilDect-YOLO: An efficient and robust computer vision-based accurate object localization model for automated endangered wildlife detection
Objective. With climatic instability, various ecological disturbances, and human actions
threaten the existence of various endangered wildlife species. Therefore, an up-to-date …
threaten the existence of various endangered wildlife species. Therefore, an up-to-date …
An efficient federated distillation learning system for multitask time series classification
This article proposes an efficient federated distillation learning system (EFDLS) for multitask
time series classification (TSC). EFDLS consists of a central server and multiple mobile …
time series classification (TSC). EFDLS consists of a central server and multiple mobile …
A federated learning system with enhanced feature extraction for human activity recognition
With the rapid growth of mobile devices, wearable sensor-based human activity recognition
(HAR) has become one of the hottest topics in the Internet of Things. However, it is …
(HAR) has become one of the hottest topics in the Internet of Things. However, it is …
Deep contrastive representation learning with self-distillation
Recently, contrastive learning (CL) is a promising way of learning discriminative
representations from time series data. In the representation hierarchy, semantic information …
representations from time series data. In the representation hierarchy, semantic information …
SelfMatch: Robust semisupervised time‐series classification with self‐distillation
Over the years, a number of semisupervised deep‐learning algorithms have been proposed
for time‐series classification (TSC). In semisupervised deep learning, from the point of view …
for time‐series classification (TSC). In semisupervised deep learning, from the point of view …
Densely knowledge-aware network for multivariate time series classification
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 …
increasingly more research attention. The performance of a DL-based MTSC algorithm is …
Assessing project portfolio risk via an enhanced GA-BPNN combined with PCA
L Bai, C Song, X Zhou, Y Tian, L Wei - Engineering Applications of Artificial …, 2023 - Elsevier
Assessing project portfolio risk (PPR) is essential for organizations to grasp the overall risk
levels of project portfolios (PPs) and realize PPR mitigation. However, current research is …
levels of project portfolios (PPs) and realize PPR mitigation. However, current research is …
EEG-based cross-subject emotion recognition using Fourier-Bessel series expansion based empirical wavelet transform and NCA feature selection method
Automated emotion recognition using brain electroencephalogram (EEG) signals is
predominantly used for the accurate assessment of human actions as compared to facial …
predominantly used for the accurate assessment of human actions as compared to facial …
DA-Net: Dual-attention network for multivariate time series classification
Multivariate time series classification is one of the increasingly important issues in machine
learning. Existing methods focus on establishing the global long-range dependencies or …
learning. Existing methods focus on establishing the global long-range dependencies or …