SETransformer combines a Transformer encoder, channel attention, and attention pooling for WISDM activity recognition, but the architecture is permutation-invariant and the reported comparison omits the model itself.
A comparative analysis of k-nearest neighbor, genetic, support vector machine, decision tree, and long short term memory algorithms in machine learning
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SETransformer: A Hybrid Attention-Based Architecture for Robust Human Activity Recognition
SETransformer combines a Transformer encoder, channel attention, and attention pooling for WISDM activity recognition, but the architecture is permutation-invariant and the reported comparison omits the model itself.