Hyperparameters used in the CNN-based model
| Hyper parameter | Value |
|---|---|
| Learning Rate | 1 × 10−3 |
| Batch Size | 64 |
| Epochs | 50 |
| Dropout (CNN1) | 0.5 |
| Dropout (Inception Block) | 0.3 |
| Filters (Initial Conv-1D) | 32 |
| Kernel Size (Initial Conv-1D) | 3 |
| Filters (Inception Block) | 32 |
| Kernel Sizes (Inception Block) | 1, 3, 5 |
| Optimizer | Adam |
| Loss Function | Binary Cross entropy |
| Callback: ReduceLROnPlateau | Yes |
| Callback: ModelCheckpoint | Yes |
| Validation Split | 20% |
| Hyper parameter | Value |
|---|---|
| Learning Rate | 1 × 10−3 |
| Batch Size | 64 |
| Epochs | 50 |
| Dropout (CNN1) | 0.5 |
| Dropout (Inception Block) | 0.3 |
| Filters (Initial Conv-1D) | 32 |
| Kernel Size (Initial Conv-1D) | 3 |
| Filters (Inception Block) | 32 |
| Kernel Sizes (Inception Block) | 1, 3, 5 |
| Optimizer | Adam |
| Loss Function | Binary Cross entropy |
| Callback: ReduceLROnPlateau | Yes |
| Callback: ModelCheckpoint | Yes |
| Validation Split | 20% |
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