HIGHLIGHTS
- who: Xiaojuan Wang et al. from the Beijing University of Posts and Telecommunications, No10, Xitucheng Road, Haidian District, Beijing, China have published the research: Human Activity Recognition Based on an Efficient Neural Architecture Search Framework Using Evolutionary Multi-Objective Surrogate-Assisted Algorithms, in the Journal: Electronics 2023, 12, 50. of /2023/
- what: The authors propose HARNAS an efficient approach for automatic for HAR. Besides, the authors propose a CNN-LSTM-based model for search space, which uses CNN to extract the features of the data automatically and uses an LSTM neural_network to classify the action . . .
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