Algorithm 1: ShuffleNet-Light Architecture for Features Extraction and Classification of PSLs

Input:Input Tensor ($X$), 2-D of (256 × 256 × 3) PSLs training dataset. Output:Obtained and Classified feature map$x=(x_1,x_2,\dots ,x_n)$augmented 2-D image Main Process: Step 1. Define number of stages = 4 Step 2. Iterate for Each Stage (a)“Depthwise-CNN is applied to tensor x by kernel size of (3 × 3), which includes a number of filters; branch normalization, the ReLU activation function, Pointwise-CNN by kernel size of (1 × 1), branch normalization, and the GELU” activation function are applied. (b)“Pointwise-CNN is applied to tensor x by kernel size of (1 × 1), which includes a number of filters, branch normalization, ReLU activation function, Pointwise-CNN by kernel size of (1 × 1), branch normalization, GELU” activation function are applied. Step 3. Fscale = Squeeze and Excitation (SE) block contains expansion (1 × 1 × 3) layers. Step 4. Fcat(i) = concatenation (# features-maps) Step 5. channel = shuffle (x) [End Step 2] Step 6. Model Construction (a)Define Global-average-pooling layer (b)Define Fully-Connected (FC) Layer and applied GELU function. Step 7. Afterward, the feature map$x=(x_1,x_2,\dots ,x_n)$generated, which is recognized by Softmax function. Step 8. Test samples$y_i^t$are predicted to the class label using the decision function of the below equation.$y_i^t=\sum _{t=0}^{M-1}{f}_t\left(x_i\right)$