Objective The proper handling of missing values is critical to delivering reliable estimates
and decisions, especially in high-stakes fields such as clinical research. In response to the …

Digital agriculture increasingly relies on the availability and accuracy of measurement data
collected from various sensors. Of this data, water quality attracts great attention due to its …

Abstract The Internet of Things (IoT) platform allows physical devices to connect directly to
the internet and upload data continuously. Insecure access makes IoT platforms vulnerable …

Predictive analysis of electronic health records (EHR) is a fundamental task that could
provide actionable insights to help clinicians improve the efficiency and quality of care. EHR …

Data sets on gameplay, called digital biomarkers, contain many characteristics of game
players and are associated with mental health problems. In fact, an avatar's behavior during …

We investigate the problem of discovering and modeling regime shifts in an ecosystem
comprising multiple time series known as co-evolving time series. Regime shifts refer to the …

Time series data sensed by underwater wireless sensor networks (UWSNs) play a crucial
role in prediction and decision-making in marine applications. Unfortunately, equipment and …

We introduce a novel classification framework for time-series imputation using deep
learning, with a particular focus on clinical data. By identifying conceptual gaps in the …

This paper uses Neural ODE (NODE) based models to generate continuous medical time
series. We also introduce a new technique to design the generative adversarial network …

Uncertainty quantification is a critical yet unsolved challenge for deep learning, especially
for the time series imputation with irregularly sampled measurements. To tackle this …