Time series change point detection with self-supervised contrastive predictive coding
Change Point Detection (CPD) methods identify the times associated with changes in the
trends and properties of time series data in order to describe the underlying behaviour of the …
trends and properties of time series data in order to describe the underlying behaviour of the …
Federated self-supervised learning of multisensor representations for embedded intelligence
Smartphones, wearables, and Internet-of-Things (IoT) devices produce a wealth of data that
cannot be accumulated in a centralized repository for learning supervised models due to …
cannot be accumulated in a centralized repository for learning supervised models due to …
n-gage: Predicting in-class emotional, behavioural and cognitive engagement in the wild
The study of student engagement has attracted growing interests to address problems such
as low academic performance, disaffection, and high dropout rates. Existing approaches to …
as low academic performance, disaffection, and high dropout rates. Existing approaches to …
Espresso: Entropy and shape aware time-series segmentation for processing heterogeneous sensor data
Extracting informative and meaningful temporal segments from high-dimensional wearable
sensor data, smart devices, or IoT data is a vital preprocessing step in applications such as …
sensor data, smart devices, or IoT data is a vital preprocessing step in applications such as …
Cross-position activity recognition with stratified transfer learning
Human activity recognition (HAR) aims to recognize the activities of daily living by utilizing
the sensors attached to different body parts. HAR relies on the machine learning models …
the sensors attached to different body parts. HAR relies on the machine learning models …
A hybrid gene selection method for microarray recognition
DNA microarray data is expected to be a great help in the development of efficient diagnosis
and tumor classification. However, due to the small number of instances compared to a large …
and tumor classification. However, due to the small number of instances compared to a large …
What will you do for the rest of the day? an approach to continuous trajectory prediction
Understanding and predicting human mobility is vital to a large number of applications,
ranging from recommendations to safety and urban service planning. In some travel …
ranging from recommendations to safety and urban service planning. In some travel …
Hidden Markov model-based smart annotation for benchmark cyclic activity recognition database using wearables
Activity monitoring using wearables is becoming ubiquitous, although accurate cycle level
analysis, such as step-counting and gait analysis, are limited by a lack of realistic and …
analysis, such as step-counting and gait analysis, are limited by a lack of realistic and …
A multivariate time series segmentation algorithm for analyzing the operating statuses of tunnel boring machines
The segmentation of tunnel boring machine (TBM) time series plays a crucial role in
analyzing TBM operating statuses and mining potential information from the collected …
analyzing TBM operating statuses and mining potential information from the collected …
[HTML][HTML] A stroke risk detection: improving hybrid feature selection method
Y Zhang, Y Zhou, D Zhang, W Song - Journal of medical Internet research, 2019 - jmir.org
Background Stroke is one of the most common diseases that cause mortality. Detecting the
risk of stroke for individuals is critical yet challenging because of a large number of risk …
risk of stroke for individuals is critical yet challenging because of a large number of risk …