Table 3.

list of the selected approaches and corresponding parameters

Method	Parameters/range	Optimal parameters
K nearest neighbor (kNN)	K = 5,…,1000	K = 80
RNN [30]	RNN cell type: LSTM, GRU; RNN depth: 1, 2; number of hidden layers: 32, 64, and 128; learning rate: 1e−3. Batch size of 128; 200 epochs	Cell type: GRU, hidden layers = 128, depth = 1, dropout = 0.7
Bidirectional RNN conditional random field (bidirectional-RNN-CRF) [31]	RNN cell type: LSTM, GRU; RNN depth: 1, 2; number of hidden layers: 32, 64, and 128; learning rate: 1e−3. Batch size of 128; 200 epochs	Cell type: GRU, hidden layers = 128, depth = 1, dropout = 0.9
Support Vector Machine (SVM)	Kernel: RBF/LINEAR; penalty parameter C: 1e−5, 1e−4,…, 1e5	C = 1, kernel = Linear