Machine learning-enabled internet of things (iot): Data, applications, and industry perspective
J Bzai, F Alam, A Dhafer, M Bojović, SM Altowaijri… - Electronics, 2022 - mdpi.com
Machine learning (ML) allows the Internet of Things (IoT) to gain hidden insights from the
treasure trove of sensed data and be truly ubiquitous without explicitly looking for knowledge …
treasure trove of sensed data and be truly ubiquitous without explicitly looking for knowledge …
Fault diagnosis based on extremely randomized trees in wireless sensor networks
Abstract Wireless Sensor Network (WSN) being highly diversified cyber–physical system
makes it vulnerable to numerous failures, which can cause devastation towards safety …
makes it vulnerable to numerous failures, which can cause devastation towards safety …
A distributed sensor-fault detection and diagnosis framework using machine learning
SU Jan, YD Lee, IS Koo - Information Sciences, 2021 - Elsevier
The objective of this work is to design a sensor-fault detection and diagnosis system for the
Internet of Things and Cyber-Physical Systems. The challenge is, however, achieving this …
Internet of Things and Cyber-Physical Systems. The challenge is, however, achieving this …
Fault identification of photovoltaic array based on machine learning classifiers
MM Badr, MS Hamad, AS Abdel-Khalik… - IEEE …, 2021 - ieeexplore.ieee.org
Fault identification in Photovoltaic (PV) array is a contemporary research topic motivated by
the higher penetration levels of PV systems in recent electrical grids. Therefore, this work …
the higher penetration levels of PV systems in recent electrical grids. Therefore, this work …
χ2-BidLSTM: A Feature Driven Intrusion Detection System Based on χ2 Statistical Model and Bidirectional LSTM
In a network architecture, an intrusion detection system (IDS) is one of the most commonly
used approaches to secure the integrity and availability of critical assets in protected …
used approaches to secure the integrity and availability of critical assets in protected …
Intelligent fault identification strategy of photovoltaic array based on ensemble self-training learning
Identifying Photovoltaic (PV) array faults is crucial for improving the service life and
consolidating system performance overall. The strategies based on the supervised Machine …
consolidating system performance overall. The strategies based on the supervised Machine …
Machinery condition monitoring in the era of industry 4.0: A relative degree of contribution feature selection and deep residual network combined approach
S Mei, M Yuan, J Cui, S Dong, J Zhao - Computers & Industrial Engineering, 2022 - Elsevier
Condition monitoring (CM) is gaining importance under the profound and comprehensive
impact of Industry 4.0 on the modern manufacturing industry, which can monitor the health …
impact of Industry 4.0 on the modern manufacturing industry, which can monitor the health …
A review and comparison of the state-of-the-art techniques for atrial fibrillation detection and skin hydration
Atrial fibrillation (AF) is one of the most common types of cardiac arrhythmia, with a
prevalence of 1–2% in the community, increasing the risk of stroke and myocardial …
prevalence of 1–2% in the community, increasing the risk of stroke and myocardial …
Sensor fault detection of vehicle suspension systems based on transmissibility operators and Neyman–Pearson test
As sensors become increasingly important and popular in vehicle suspension systems for
control and monitoring purposes, the sensor faults endanger the reliability and safety of …
control and monitoring purposes, the sensor faults endanger the reliability and safety of …
Fault Detection in Wastewater Treatment Plants: Application of Autoencoders Models with Streaming Data
Water is a fundamental human resource and its scarcity is reflected in social, economic and
environmental problems. Water used in human activities must be treated before reusing or …
environmental problems. Water used in human activities must be treated before reusing or …