Predictive maintenance and intelligent sensors in smart factory
With the arrival of new technologies in modern smart factories, automated predictive
maintenance is also related to production robotisation. Intelligent sensors make it possible to …
maintenance is also related to production robotisation. Intelligent sensors make it possible to …
Tackling faults in the industry 4.0 era—a survey of machine-learning solutions and key aspects
The recent advancements in the fields of artificial intelligence (AI) and machine learning
(ML) have affected several research fields, leading to improvements that could not have …
(ML) have affected several research fields, leading to improvements that could not have …
Distribution bias aware collaborative generative adversarial network for imbalanced deep learning in industrial IoT
The impact of Internet of Things (IoT) has become increasingly significant in smart
manufacturing, while deep generative model (DGM) is viewed as a promising learning …
manufacturing, while deep generative model (DGM) is viewed as a promising learning …
Explainable AI over the Internet of Things (IoT): Overview, state-of-the-art and future directions
SK Jagatheesaperumal, QV Pham… - IEEE Open Journal …, 2022 - ieeexplore.ieee.org
Explainable Artificial Intelligence (XAI) is transforming the field of Artificial Intelligence (AI) by
enhancing the trust of end-users in machines. As the number of connected devices keeps on …
enhancing the trust of end-users in machines. As the number of connected devices keeps on …
[HTML][HTML] A comparative study on online machine learning techniques for network traffic streams analysis
Modern networks generate a massive amount of traffic data streams. Analyzing this data is
essential for various purposes, such as network resources management and cyber-security …
essential for various purposes, such as network resources management and cyber-security …
A review of real-time fault diagnosis methods for industrial smart manufacturing
W Yan, J Wang, S Lu, M Zhou, X Peng - Processes, 2023 - mdpi.com
In the era of Industry 4.0, highly complex production equipment is becoming increasingly
integrated and intelligent, posing new challenges for data-driven process monitoring and …
integrated and intelligent, posing new challenges for data-driven process monitoring and …
The balancing trick: Optimized sampling of imbalanced datasets—A brief survey of the recent State of the Art
This survey paper focuses on one of the current primary issues challenging data mining
researchers experimenting on real‐world datasets. The problem is that of imbalanced class …
researchers experimenting on real‐world datasets. The problem is that of imbalanced class …
Solving misclassification of the credit card imbalance problem using near miss
In ordinary credit card datasets, there are far fewer fraudulent transactions than ordinary
transactions. In dealing with the credit card imbalance problem, the ideal solution must have …
transactions. In dealing with the credit card imbalance problem, the ideal solution must have …
An analysis on financial statement fraud detection for Chinese listed companies using deep learning
W Xiuguo, D Shengyong - IEEE Access, 2022 - ieeexplore.ieee.org
Financial fraud has extremely damaged the sustainable growth of financial markets as a
serious problem worldwide. Nevertheless, it is fairly challenging to identify frauds with highly …
serious problem worldwide. Nevertheless, it is fairly challenging to identify frauds with highly …
Systematic review of class imbalance problems in manufacturing
Class imbalance (CI) is a well-known problem in data science. Nowadays, it is affecting the
data modeling of many of the real-world processes that are being digitized. The …
data modeling of many of the real-world processes that are being digitized. The …