[HTML][HTML] Energy metaverse: the conceptual framework with a review of the state-of-the-art methods and technologies
Z Ma - Energy Informatics, 2023 - Springer
The transition to green energy systems is vital for addressing climate change, with a focus
on renewable sources like wind and solar. This change requires substantial investment …
on renewable sources like wind and solar. This change requires substantial investment …
[HTML][HTML] A novel edge architecture and solution for detecting concept drift in smart environments
The proliferation of the Internet of Things (IoT), artificial intelligence (AI), the adoption of 5G,
and progress towards 6G technology have led to the accumulation of massive amounts of …
and progress towards 6G technology have led to the accumulation of massive amounts of …
An adaptive imbalance modified online broad learning system-based fault diagnosis for imbalanced chemical process data stream
J Men, C Zhao - Expert Systems with Applications, 2023 - Elsevier
Modern chemical process industry is becoming larger and more complicated to achieve a
higher level of technical functionality. There is less tolerance for functional degeneration …
higher level of technical functionality. There is less tolerance for functional degeneration …
Elastic online deep learning for dynamic streaming data
R Su, H Guo, W Wang - Information Sciences, 2024 - Elsevier
Dynamic streaming data is widespread in various real-world scenarios, and the distribution
may change under unforeseen disturbances. The decrease in predicted performance …
may change under unforeseen disturbances. The decrease in predicted performance …
ABT-SVDD: A method for uncertainty handling in domain adaptation using belief function theory
Abstract Domain adaptation involves adapting a model trained on one domain to work
effectively on another, which can have different statistical properties, such as distributions …
effectively on another, which can have different statistical properties, such as distributions …
Transfer learning for concept drifting data streams in heterogeneous environments
Learning in non-stationary environments remains challenging due to dynamic and unknown
probability distribution. This issue is even more problematic when there is a lack of …
probability distribution. This issue is even more problematic when there is a lack of …
非平衡概念漂移数据流主动学习方法
李艳红， 王甜甜， 王素格， 李德玉 - 自动化学报, 2024 - aas.net.cn
数据流分类研究在开放, 动态环境中如何提供更可靠的数据驱动预测模型,
关键在于从实时到达且不断变化的数据流中检测并适应概念漂移. 目前, 为了检测概念漂移和 …
关键在于从实时到达且不断变化的数据流中检测并适应概念漂移. 目前, 为了检测概念漂移和 …
Explainable Anomaly Detection in Industrial Streams
J Jakubowski, P Stanisz, S Bobek… - European Conference on …, 2023 - Springer
Anomaly detection in industrial environment is a complex task, which requires to consider
multiple characteristics of the data from industrial sensors and anomalies itself. Such data is …
multiple characteristics of the data from industrial sensors and anomalies itself. Such data is …
The Best ML Classifier (s): An empirical study on the learning of imbalanced and resampled credit card data
SS Rawat, AK Mishra - 2023 Second International Conference …, 2023 - ieeexplore.ieee.org
Machine Learning-based fraud detection systems are more effective at detecting financial
fraud. Credit card fraud detection is one of them. The datasets used in learning by the …
fraud. Credit card fraud detection is one of them. The datasets used in learning by the …
Concept drift in smart city scenarios
H Mehmood - 2023 - oulurepo.oulu.fi
Exponential population growth and urbanisation pose potential challenges to mobility,
governance, well-being, the environment, and the safety of modern cities. This demands …
governance, well-being, the environment, and the safety of modern cities. This demands …