Table 6 Performance comparison of proposed model with existing work on a similar dataset25.
From: Computer intelligence based model for mental health detection among Indian farming communities
Source | Dataset | Feature extraction | Approach used | Accuracy (%) |
|---|---|---|---|---|
Chitre et al.26 | SAVEE | Short-term Fourier Transform (STFT) | RestNet50 | 67.36 |
Ottoni et al.27 | SAVEE | MFCC, ZCR, RMS, Chroma, Mel Spectrogram | CNN | 90.62 |
Panda et al.28 | SAVEE | MFCC, ZCR, RMS, STFT, Mel Spectrogram | KNN | 91% |
Rathod et al.29 | SAVEE | Zero Crossing Rate, Chroma Shift, MFCC, RMS, Mel Spectrogram, Spectral Rolloff, Spectral Centroid | MLP, CNN, CNN with Optimizers | 95.44 |
Gummula et al30. | SAVEE | bidirectional encoder representations from transformers (BERT) model | KNN,RF, DE, CNN | 98.76 |
Kothuri et al31. | SAVEE | SFLA-IWSS Shuffled Frog Leaping Algorithm (SFLA)-Incremental Wrapper- Based Subset Feature Selection (IWSS) | Multi-Class Support Vector Machine (MC-SVM) | 97.05 |
Singh et al32. | SAVEE | – | Deep Neural Network | 81.7%, |
Proposed model with SAVEE | 99.7% | |||
