A review of irregular time series data handling with gated recurrent neural networks
Irregular time series data is becoming increasingly prevalent with the growth of multi-sensor
systems as well as the continued use of unstructured manual data recording mechanisms …
systems as well as the continued use of unstructured manual data recording mechanisms …
Application of machine learning techniques to the analysis and prediction of drug pharmacokinetics
R Ota, F Yamashita - Journal of Controlled Release, 2022 - Elsevier
In this review, we describe the current status and challenges in applying machine-learning
techniques to the analysis and prediction of pharmacokinetic data. The theory of …
techniques to the analysis and prediction of pharmacokinetic data. The theory of …
[HTML][HTML] Machine learning approach using MLP and SVM algorithms for the fault prediction of a centrifugal pump in the oil and gas industry
PF Orrù, A Zoccheddu, L Sassu, C Mattia, R Cozza… - Sustainability, 2020 - mdpi.com
The demand for cost-effective, reliable and safe machinery operation requires accurate fault
detection and classification to achieve an efficient maintenance strategy and increase …
detection and classification to achieve an efficient maintenance strategy and increase …
Real-time probabilistic forecasting of river water quality under data missing situation: Deep learning plus post-processing techniques
Y Zhou - Journal of Hydrology, 2020 - Elsevier
Quantifying the uncertainty of probabilistic water quality forecasting induced by missing input
data is fundamentally challenging. This study introduced a novel methodology for …
data is fundamentally challenging. This study introduced a novel methodology for …
[HTML][HTML] Five-week warning of COVID-19 peaks prior to the Omicron surge in Detroit, Michigan using wastewater surveillance
Wastewater-based epidemiology (WBE) is useful in predicting temporal fluctuations of
COVID-19 incidence in communities and providing early warnings of pending outbreaks. To …
COVID-19 incidence in communities and providing early warnings of pending outbreaks. To …
[HTML][HTML] A county-level soybean yield prediction framework coupled with XGBoost and multidimensional feature engineering
Yield prediction is essential in food security, food trade, and field management. However,
due to the associated complex formation mechanisms of yield, accurate and timely yield …
due to the associated complex formation mechanisms of yield, accurate and timely yield …
Recurrent-based regression of Sentinel time series for continuous vegetation monitoring
Dense time series of optical satellite imagery describing vegetation activity provide essential
information for the efficient and regular monitoring of vegetation. Nevertheless, the temporal …
information for the efficient and regular monitoring of vegetation. Nevertheless, the temporal …
[HTML][HTML] A data-driven approach to improve customer churn prediction based on telecom customer segmentation
Numerous valuable clients can be lost to competitors in the telecommunication industry,
leading to profit loss. Thus, understanding the reasons for client churn is vital for …
leading to profit loss. Thus, understanding the reasons for client churn is vital for …
[HTML][HTML] Deep anomaly detection in horizontal axis wind turbines using graph convolutional autoencoders for multivariate time series
Wind power is one of the fastest-growing renewable energy sectors instrumental in the
ongoing decarbonization process. However, wind turbines are subjected to a wide range of …
ongoing decarbonization process. However, wind turbines are subjected to a wide range of …
Imdiffusion: Imputed diffusion models for multivariate time series anomaly detection
Anomaly detection in multivariate time series data is of paramount importance for ensuring
the efficient operation of large-scale systems across diverse domains. However, accurately …
the efficient operation of large-scale systems across diverse domains. However, accurately …