LSTM learning with Bayesian and Gaussian processing for anomaly detection in industrial IoT
The data generated by millions of sensors in the industrial Internet of Things (IIoT) are
extremely dynamic, heterogeneous, and large scale and pose great challenges on the real …
extremely dynamic, heterogeneous, and large scale and pose great challenges on the real …
EdgeLSTM: Towards deep and sequential edge computing for IoT applications
The time series data generated by massive sensors in Internet of Things (IoT) is extremely
dynamic, heterogeneous, large scale and time-dependent. It poses great challenges (eg …
dynamic, heterogeneous, large scale and time-dependent. It poses great challenges (eg …
A simple and efficient template matching algorithm
We propose a general framework for object tracking in video images. It consists of low-order
parametric models for the image motion of a target region. These models are used to predict …
parametric models for the image motion of a target region. These models are used to predict …
Machine learning approach for predicting wall shear distribution for abdominal aortic aneurysm and carotid bifurcation models
M Jordanski, M Radovic, Z Milosevic… - IEEE journal of …, 2016 - ieeexplore.ieee.org
Computer simulations based on the finite element method represent powerful tools for
modeling blood flow through arteries. However, due to its computational complexity, this …
modeling blood flow through arteries. However, due to its computational complexity, this …
Predicting spatiotemporal impacts of weather on power systems using big data science
Due to the increase in extreme weather conditions and aging infrastructure deterioration, the
number and frequency of electricity network outages is dramatically escalating, mainly due …
number and frequency of electricity network outages is dramatically escalating, mainly due …
Learning customer behaviors for effective load forecasting
X Wang, M Zhang, F Ren - IEEE Transactions on Knowledge …, 2018 - ieeexplore.ieee.org
Load forecasting has been deeply studied because of its critical role in Smart Grid. In current
Smart Grid, there are various types of customers with different energy consumption patterns …
Smart Grid, there are various types of customers with different energy consumption patterns …
Structured prediction of sparse dependent variables for traffic state estimation in large-scale networks
Currently, one of the biggest challenges in modern traffic engineering is related to traffic
state estimation (TSE). Although many machine learning and domain models can be used …
state estimation (TSE). Although many machine learning and domain models can be used …
Multi-domain and multi-view networks model for clustering hospital admissions from the emergency department
As the healthcare industry continues to generate a massive amount of medical data,
healthcare organizations integrate data-driven insights into their clinical and operational …
healthcare organizations integrate data-driven insights into their clinical and operational …
Gaussian conditional random fields extended for directed graphs
For many real-world applications, structured regression is commonly used for predicting
output variables that have some internal structure. Gaussian conditional random fields …
output variables that have some internal structure. Gaussian conditional random fields …
Adaptive skip-train structured regression for temporal networks
A broad range of high impact applications involve learning a predictive model in a temporal
network environment. In weather forecasting, predicting effectiveness of treatments …
network environment. In weather forecasting, predicting effectiveness of treatments …