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Proposed solutions | Performance metrics | Limitations |
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EBT [3] | Sensitivity, specificity, false-positive rate, and -score | Increased computational time |
CNN and LSTM [20] | -score, area under curve (AUC), precision, and recall | No normalization and parameter tuning |
PCA and RE [21] | ROC, specificity, and sensitivity | PCA only works for linear data |
Fuzzy logic [22] | Generalized bell curve membership function | Increased computational time |
Fuzzy logic [23] | Accuracy, -score, AUC | Issues related to renewable sources are not handled |
Semisupervised deep neural network (DNN) [25] | Precision, true and false-positive rates, recall, and -score | High false-positive rate |
LSTM and GMM [26] | AUC, MCC, recall, and accuracy | Data imbalance is not handled |
MODWPT and RUSBoost [27] | -score, AUC, and precision | Oversampling issue is not tackled |
Blackhole algorithm [28] | Average execution time and convergence | High false-positive rate |
MIC and CFSFDP [29] | -score, precision, and recall | Low precision and recall |
LSTM [31] | Accuracy, sensitivity, and specificity | Overfitting is not handled well |
LSTM and regression [32] | -score, recall, and precision | Low -score |
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