Adaptive windowing based recurrent neural network for drift adaption in non-stationary environment

In today's digital era, many applications generate massive data streams that must be sequenced and processed immediately. Therefore, storing large amounts of data for analysis is impractical. Now, this infinite amount of evolving data confronts concept drifts in data stream classification. Conc...

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Veröffentlicht in:	Journal of ambient intelligence and humanized computing Jg. 14; H. 10; S. 14125 - 14139
Hauptverfasser:	Suryawanshi, Shubhangi, Goswami, Anurag, Patil, Pramod, Mishra, Vipul
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Germany Springer Nature B.V 01.10.2023 Springer Berlin Heidelberg
Schlagworte:	Adaptation Airlines Algorithms Artificial intelligence Classification COVID-19 Data transmission Datasets Decision making Deep learning Drift Fisher information Machine learning Neural networks Nonstationary environments Original Research Pandemics Recurrent neural networks Gated recurrent unit Catastrophic forgetting Non-stationary data streams Concept drift Adaptive window Fisher information matrix
ISSN:	1868-5137, 1868-5145
Online-Zugang:	Volltext
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