[HTML][HTML] A literature review of fault diagnosis based on ensemble learning
Z Mian, X Deng, X Dong, Y Tian, T Cao, K Chen… - … Applications of Artificial …, 2024 - Elsevier
The accuracy of fault diagnosis is an important indicator to ensure the reliability of key
equipment systems. Ensemble learning integrates different weak learning methods to obtain …
equipment systems. Ensemble learning integrates different weak learning methods to obtain …
A survey on the application of process discovery techniques to smart spaces data
During the last years, a number of studies have experimented with applying process
discovery techniques with the goal of automatically modelling human routines as if they …
discovery techniques with the goal of automatically modelling human routines as if they …
Explainable concept drift in process mining
The execution of processes leaves trails of event data in information systems. These event
data are analyzed to generate insights and improvements for the underlying process …
data are analyzed to generate insights and improvements for the underlying process …
Concept drift handling: A domain adaptation perspective
Data stream prediction is challenging when concepts drift, processing time, and memory
constraints come into account. Concept drift refers to changes in data distribution over time …
constraints come into account. Concept drift refers to changes in data distribution over time …
A review of AI and machine learning contribution in business process management (process enhancement and process improvement approaches)
Purpose The significance of business processes has fostered a close collaboration between
academia and industry. Moreover, the business landscape has witnessed continuous …
academia and industry. Moreover, the business landscape has witnessed continuous …
Cyber security and beyond: Detecting malware and concept drift in AI-based sensor data streams using statistical techniques
Abstract In the Industrial Internet of Things (IIoT), mobile devices can be used to remotely
monitor and control industrial processes, equipment, and machinery. They can also be used …
monitor and control industrial processes, equipment, and machinery. They can also be used …
[HTML][HTML] Dynamic feature selection model for adaptive cross site scripting attack detection using developed multi-agent deep Q learning model
Web applications' popularity has raised attention in various service domains, which
increased the concern about cyber-attacks. One of these most serious and frequent web …
increased the concern about cyber-attacks. One of these most serious and frequent web …
Leveraging machine learning for automatic topic discovery and forecasting of process mining research: A literature review
Process mining is a relatively new discipline that focuses on gaining process-centric
knowledge from event logs collected by enterprise systems. From an academic standpoint …
knowledge from event logs collected by enterprise systems. From an academic standpoint …
Improving Process Mining Maturity–From Intentions to Actions
J Brock, K Brennig, B Löhr, C Bartelheimer… - Business & Information …, 2024 - Springer
Process mining is advancing as a powerful tool for revealing valuable insights about
process dynamics. Nevertheless, the imperative to employ process mining to enhance …
process dynamics. Nevertheless, the imperative to employ process mining to enhance …
TS-DM: A Time Segmentation-Based Data Stream Learning Method for Concept Drift Adaptation
Concept drift arises from the uncertainty of data distribution over time and is common in data
stream. While numerous methods have been developed to assist machine learning models …
stream. While numerous methods have been developed to assist machine learning models …