Distance-based one-class time-series classification approach using local cluster balance
Deciding the signal length is an important challenge for one-class time-series classification
(OCTSC). This paper aims to develop an OCTSC algorithm that does not require model …
(OCTSC). This paper aims to develop an OCTSC algorithm that does not require model …
Interpretable synthetic signals for explainable one-class time-series classification
This research paper introduces an innovative approach for explainable one-class time-
series classification (XOCTSC). The proposed method involves generating pseudounseen …
series classification (XOCTSC). The proposed method involves generating pseudounseen …
Patient deterioration detection using one-class classification via cluster period estimation subtask
Deterioration is the significant degradation of the physical state prior to death. Detecting the
deterioration of patients could provide an early warning to their families in instances of …
deterioration of patients could provide an early warning to their families in instances of …
Ensemble deep random vector functional link for self-supervised direction-of-arrival estimation
J He, X Li, P Liu, L Wang, H Zhou, J Wang… - … Applications of Artificial …, 2023 - Elsevier
Abstract Direction-of-arrival (DOA) estimation is a key step in the passive target location. The
primary issues with traditional DOA estimation methods are the huge computation and weak …
primary issues with traditional DOA estimation methods are the huge computation and weak …
Differential Convolutional Fuzzy Time Series Forecasting
Fuzzy time series forecasting (FTSF) is a typical forecasting method with wide application.
Traditional FTSF is regarded as an expert system which leads to loss of the ability to …
Traditional FTSF is regarded as an expert system which leads to loss of the ability to …
[HTML][HTML] Two-Stream Network One-Class Classification Model for Defect Inspections
S Lee, C Luo, S Lee, H Jung - Sensors, 2023 - mdpi.com
Defect inspection is important to ensure consistent quality and efficiency in industrial
manufacturing. Recently, machine vision systems integrating artificial intelligence (AI)-based …
manufacturing. Recently, machine vision systems integrating artificial intelligence (AI)-based …
ITFD: an instance-level triplet few-shot detection network under weighted pair-resampling
X Chen, C Peng, C Qiu, L Luo, D Huang, Z Liu - Applied Intelligence, 2023 - Springer
Few-shot object detection has been widely applied in industrial applications, endangered
detection, tumor lesion detection, etc. Although many excellent few-shot detection models …
detection, tumor lesion detection, etc. Although many excellent few-shot detection models …
Recurrent auto-encoder with multi-resolution ensemble and predictive coding for multivariate time-series anomaly detection
As large-scale time-series data can easily be found in real-world applications, multivariate
time-series anomaly detection has played an essential role in diverse industries. It enables …
time-series anomaly detection has played an essential role in diverse industries. It enables …
Canonical mean filter for almost zero-shot multi-task classification
Y Li, H Wang, X Ye - Applied Intelligence, 2023 - Springer
The support set plays a key role in providing conditional prior for fast adapting the feature
extractors in few-shot tasks. The representative few-shot method CNAPs used a simple …
extractors in few-shot tasks. The representative few-shot method CNAPs used a simple …
Machine Learning Could be Easier if All Data Were MNIST
H Fujita, G Guizzi - … , Tools and Techniques: Proceedings of the …, 2023 - books.google.com
MNIST is a famous image dataset; several researchers evaluated their algorithms using
MNIST and provided high accuracy. However, the accuracies were degraded on other …
MNIST and provided high accuracy. However, the accuracies were degraded on other …