Tsmae: a novel anomaly detection approach for internet of things time series data using memory-augmented autoencoder
With the development of communication, the Internet of Things (IoT) has been widely
deployed and used in industrial manufacturing, intelligent transportation, and healthcare …
deployed and used in industrial manufacturing, intelligent transportation, and healthcare …
Embedded machine learning using microcontrollers in wearable and ambulatory systems for health and care applications: A review
MS Diab, E Rodriguez-Villegas - IEEE Access, 2022 - ieeexplore.ieee.org
The use of machine learning in medical and assistive applications is receiving significant
attention thanks to the unique potential it offers to solve complex healthcare problems for …
attention thanks to the unique potential it offers to solve complex healthcare problems for …
AnoFed: Adaptive anomaly detection for digital health using transformer-based federated learning and support vector data description
In digital healthcare applications, anomaly detection is an important task to be taken into
account. For instance, in ECG (Electrocardiogram) analysis, the aim is often to detect …
account. For instance, in ECG (Electrocardiogram) analysis, the aim is often to detect …
Lightweight neural architecture search for temporal convolutional networks at the edge
Neural Architecture Search (NAS) is quickly becoming the go-to approach to optimize the
structure of Deep Learning (DL) models for complex tasks such as Image Classification or …
structure of Deep Learning (DL) models for complex tasks such as Image Classification or …
Unsupervised transformer-based anomaly detection in ECG signals
Anomaly detection is one of the basic issues in data processing that addresses different
problems in healthcare sensory data. Technology has made it easier to collect large and …
problems in healthcare sensory data. Technology has made it easier to collect large and …
ECG-TCN: Wearable cardiac arrhythmia detection with a temporal convolutional network
Personalized ubiquitous healthcare solutions require energy-efficient wearable platforms
that provide an accurate classification of bio-signals while consuming low average power for …
that provide an accurate classification of bio-signals while consuming low average power for …
Pooling random forest and functional data analysis for biomedical signals supervised classification: Theory and application to electrocardiogram data
F Maturo, R Verde - Statistics in Medicine, 2022 - Wiley Online Library
Scientific progress has contributed to creating many devices to gather vast amounts of
biomedical data over time. The goal of these devices is generally to monitor people's health …
biomedical data over time. The goal of these devices is generally to monitor people's health …
Attention autoencoder for generative latent representational learning in anomaly detection
Today, accurate and automated abnormality diagnosis and identification have become of
paramount importance as they are involved in many critical and life-saving scenarios. To …
paramount importance as they are involved in many critical and life-saving scenarios. To …
Real-time rockburst assessment based on a novel hybrid convolutional long short-term memory network based on microseismic monitoring data
L Zheng, S Lin, H Guo, X Cao, H Zheng - Engineering Failure Analysis, 2025 - Elsevier
Rockburst is a common geological disaster in underground engineering, posing significant
challenges. Data-driven methods provide powerful tools for the assessment and early …
challenges. Data-driven methods provide powerful tools for the assessment and early …
A nearest neighbor-based active learning method and its application to time series classification
H Gweon, H Yu - Pattern Recognition Letters, 2021 - Elsevier
Although the one nearest neighbor approach is widely used in time series classification, its
successful performance requires enough labeled data, which is often difficult to obtain due …
successful performance requires enough labeled data, which is often difficult to obtain due …