## 3 Model Specification  
We compared the performance of two widely used deep learning techniques. In this case, we contrasted a one-dimensional convolutional neural network with long short-term memory (LSTM) (1D-CNN). Deep learning or deep structured learning refers to neural network types with many layers, such as LSTM or 1D-CNN. When it comes to recalling information from prior occurrences, these networks perform better than normal neural networks. The network's closed-loop design keeps the information safe. Every network in the loop gets input and data from the preceding network, performs the specified action, and then outputs data while sending them to the next network. While some applications just  
Figure 1: Shipment Mode Class Imbalance. require the most recent data, others may call for more historical data. Such examples can be learned by Recurrent Neural Networks (RNNs), specifically Long Short-Term Memory (LSTM) Networks [86]. These networks are specially designed to circumvent the recurrent networks' issue with long-term dependency. Long-term memory systems (LSTMs) are effective in recalling information. The validity of the model may be impacted by extra previous information; hence LSTMs are an obvious option for usage. The LSTM block contains four gates: cell state gate retains \(g\) information over time, forget gate \(g_{f}\) regulates the extent of the value maintained in the cell, input gate \(g_{i}\) controls the extent of the value flow in the cell, and the output gate \(g_{o}\) controls the extent of the value in the cell to be utilized for computing the output. A completely linked layer and an activation function are included in each gate. The LSTM block also has three inputs: cell state \(s_{t-1}\); previously hidden state \(h_{t-1}\); and current input \(x_{t}\), as well as three outputs: cell state \(s_{t}\); hidden state \(h_{t}\); and current output \(y_{t}\). The current output is created depending on the hidden state. The mathematical formulation of the LSTM units is as follows:  
\[g_{f}=\gamma\left(W_{f}[x_{t},h_{t-1}]\right) \tag{9}\]  
\[g_{i}=\gamma(W_{i}[x_{t},h_{t-1}]) \tag{10}\]  
\[g=\tan\text{h}(W[x_{t},h_{t-1}]) \tag{11}\]  
\[g_{o}=\gamma(W_{o}[x_{t},h_{t-1}]) \tag{12}\]  
\[s_{t}=g_{f}s_{t-1}g_{i}g \tag{13}\]  
\[h_{t}=g_{o}\tan\text{h}(s_{t}) \tag{14}\]  
\[y_{t}=\gamma\left(W^{\prime}h_{t}\right) \tag{15}\]  
A unified neural network of 1D-CNN, on the other hand, is made up of 12 layers: five 1D convolutional neural network (1D-CNN) layers, two dropout layers, one max-pooling layer, one flattened layer, and three fully connected layers. The signals are first routed via the first convolutional layer, which has a filter size of 32. The kernel of the first layer of our proposed 1D-Convolutional network is set to 3, indicating that all weights will be shared by every stride of the signal's input and output layers. The filter size is raised from 32 to 64 in the second 1D-convolutional layer, and the kernel size is adjusted to 3 in all 1D convolutional layers. Furthermore, the filter size is set to 128 in the third 1D-convolutional layer, but the padding is set to the same value in all three 1D-convolutional layers. The fourth layer is a max pooling layer that is used to down-sample the input representation from layer three. The pooling size is set to 3, the strides are set to 2, and the padding is set to the same. The fifth and seventh convolutional layer filter sizes are increased to 256 and 512, respectively, with the same padding and kernel size set to 3. The sixth and eighth layers are dropout layers with a value of 0.2 that are positioned between the fifth and sixth convolutional layers to reduce overfitting. The ninth layer is a flattening layer that reduces the input data to a single dimension. The flatten layer's single dimension was fed into the fully connected layer 10 with 512 nodes, and the leaky ReLU activation function was employed in all activation functions. Layer 11 has 256 nodes and is completely connected.