[HTML][HTML] Process intensification 4.0: A new approach for attaining new, sustainable and circular processes enabled by machine learning
EA López-Guajardo, F Delgado-Licona… - … and Processing-Process …, 2022 - Elsevier
This paper reviews system-level transformations converging into the next generation of
Process Intensification strategies defined as PI4. 0. Process Intensification 4.0 uses data …
Process Intensification strategies defined as PI4. 0. Process Intensification 4.0 uses data …
Data mining and analytics in the process industry: The role of machine learning
Data mining and analytics have played an important role in knowledge discovery and
decision making/supports in the process industry over the past several decades. As a …
decision making/supports in the process industry over the past several decades. As a …
Extraction and evaluation of melt pool, plume and spatter information for powder-bed fusion AM process monitoring
With the continuous development of additive manufacturing technique, the issue on built
quality has caught increasing attentions. To improve the quality of built parts, the process …
quality has caught increasing attentions. To improve the quality of built parts, the process …
Big data analytics for preventive medicine
Medical data is one of the most rewarding and yet most complicated data to analyze. How
can healthcare providers use modern data analytics tools and technologies to analyze and …
can healthcare providers use modern data analytics tools and technologies to analyze and …
Big data analytics for intrusion detection system: Statistical decision-making using finite dirichlet mixture models
N Moustafa, G Creech, J Slay - Data Analytics and Decision Support for …, 2017 - Springer
An intrusion detection system has become a vital mechanism to detect a wide variety of
malicious activities in the cyber domain. However, this system still faces an important …
malicious activities in the cyber domain. However, this system still faces an important …
adVAE: A self-adversarial variational autoencoder with Gaussian anomaly prior knowledge for anomaly detection
Recently, deep generative models have become increasingly popular in unsupervised
anomaly detection. However, deep generative models aim at recovering the data distribution …
anomaly detection. However, deep generative models aim at recovering the data distribution …
Detecting anomalies in time series data via a deep learning algorithm combining wavelets, neural networks and Hilbert transform
The quest for more efficient real-time detection of anomalies in time series data is critically
important in numerous applications and systems ranging from intelligent transportation …
important in numerous applications and systems ranging from intelligent transportation …
Forecasting emergency department overcrowding: A deep learning framework
As the demand for medical cares has considerably expanded, the issue of managing patient
flow in hospitals and especially in emergency departments (EDs) is certainly a key issue to …
flow in hospitals and especially in emergency departments (EDs) is certainly a key issue to …
Wind power prediction using bootstrap aggregating trees approach to enabling sustainable wind power integration in a smart grid
Precise prediction of wind power is important in sustainably integrating the wind power in a
smart grid. The need for short-term predictions is increased with the increasing installed …
smart grid. The need for short-term predictions is increased with the increasing installed …
Seasonal ARMA-based SPC charts for anomaly detection: Application to emergency department systems
Monitoring complex production systems is primordial to ensure management, reliability and
safety as well as maintaining the desired product quality. Early detection of emergent …
safety as well as maintaining the desired product quality. Early detection of emergent …