[HTML][HTML] A review of ensemble learning and data augmentation models for class imbalanced problems: Combination, implementation and evaluation
Class imbalance (CI) in classification problems arises when the number of observations
belonging to one class is lower than the other. Ensemble learning combines multiple models …
belonging to one class is lower than the other. Ensemble learning combines multiple models …
A deep learning-based intrusion detection system for MQTT enabled IoT
A large number of smart devices in Internet of Things (IoT) environments communicate via
different messaging protocols. Message Queuing Telemetry Transport (MQTT) is a widely …
different messaging protocols. Message Queuing Telemetry Transport (MQTT) is a widely …
[PDF][PDF] A self monitoring and analyzing system for solar power station using IoT and data mining algorithms
S Shakya - Journal of Soft Computing Paradigm, 2021 - scholar.archive.org
Renewable energy sources are gaining a significant research attention due to their
economical and sustainable characteristics. In particular, solar power stations are …
economical and sustainable characteristics. In particular, solar power stations are …
[HTML][HTML] Data aggregation protocols for WSN and IoT applications–A comprehensive survey
BA Begum, SV Nandury - Journal of King Saud University-Computer and …, 2023 - Elsevier
Data aggregation involves the integration of correlated data generated by various wireless
sensors and devices in WSN and IoT networks, in order to arrive at meaningful interpretation …
sensors and devices in WSN and IoT networks, in order to arrive at meaningful interpretation …
[HTML][HTML] Machine learning empowered COVID-19 patient monitoring using non-contact sensing: An extensive review
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which caused the
coronavirus disease 2019 (COVID-19) pandemic, has affected more than 400 million people …
coronavirus disease 2019 (COVID-19) pandemic, has affected more than 400 million people …
Multi-sensor gearbox fault diagnosis by using feature-fusion covariance matrix and multi-Riemannian kernel ridge regression
Intelligent fault diagnosis of gearbox holds important implications for the safety assessment
and risk analysis of rotating machinery. Due to many monitoring variables in engineering …
and risk analysis of rotating machinery. Due to many monitoring variables in engineering …
Composite neuro-fuzzy system-guided cross-modal zero-sample diagnostic framework using multi-source heterogeneous non-contact sensing data
Zero-sample diagnostic methods have gained recognition in addressing the scarcity of
gearbox fault samples, thereby being regarded as a promising technique to guarantee …
gearbox fault samples, thereby being regarded as a promising technique to guarantee …
A transfer learning strategy based on numerical simulation driving 1D Cycle-GAN for bearing fault diagnosis
X Liu, S Liu, J Xiang, R Sun - Information Sciences, 2023 - Elsevier
Most transfer learning (TL) models generally need the fault data from similar scenarios to
achieve cross-domain bearing fault diagnosis. However, due to the bearings are mostly in …
achieve cross-domain bearing fault diagnosis. However, due to the bearings are mostly in …
A fuzzy preference-based Dempster-Shafer evidence theory for decision fusion
Dempster-Shafer evidence theory (DS) is an effective instrument for merging the collected
pieces of basic probability assignment (BPA), and it exhibits superiority in achieving …
pieces of basic probability assignment (BPA), and it exhibits superiority in achieving …
A deep reinforcement learning based hybrid algorithm for efficient resource scheduling in edge computing environment
F Xue, Q Hai, T Dong, Z Cui, Y Gong - Information Sciences, 2022 - Elsevier
Edge computing can greatly decrease the delay between users and cloud servers, which
can significantly improve system service performance. However, it remains challenging for …
can significantly improve system service performance. However, it remains challenging for …