## 3 Methods
### Classification Methods
#### 3.5.2 Neural Methods  
Several types of neural models and neuron architectures were tested. Like non-neural methods, the models sought to classify accidents based on other factors.  
The first neural method tested was the multilayer perceptron (MLP), a structure with an arbitrary number of neurons in input, output, and hidden layers [27]. The MLP is unique in that it is aligned with the logic of brain functioning (neurophysiology), which involves continuously adjusting the weights of its synapses to classify a target variable [28]. This alignment with brain functioning gives MLP a unique potential in classifying work accidents. Although it is one of the first neural networks developed, the application of MLP to classify work accidents in the footwear industry has yet to be explored by previous studies. Table 1 presents the configurations tested for the MLP.  
Another neural network tested was long short-term memory (LSTM). This type of network works with memory cells, which come from complex gate units. Gates work collaboratively and are self-designed, overcoming the naive approach of traditional MLPs [38]. MLPs are accurate when working with recent information, but the LSTM gate structure also has good properties for processing data with long-term dependence [60]. LSTM has rare applications in classification problems, although it has been successfully used to classify accidents in nuclear plants [64]. No studies have been conducted in the footwear industry to classify accidents using LSTM.  
The bidirectional long short-term memory (Bi-LSTM) network was also tested. This network operates using two LSTMs in parallel, seeking to process both previous and subsequent information in the database [65]. Authors such as [PERSON] et al. [66] successfully employed Bi-LSTM to monitor the amount of gas emission in underground mining activity and classify the presence or absence of explosion risk. [PERSON] et al. [67] classified the severity of traffic accidents using a Bi-LSTM. The use of Bi-LSTM for classifying work accidents is scarce.  
Another neural model tested was the GRU neural network. LSTM networks work with the logic of three gates responsible for data input, output, and forgetting. In contrast, GRU makes use of two gates, one for updating (selection of valuable information to be added) and another for redefinition (selection of information to be eliminated) [38]. This simplification in the number of gates makes this network more efficient than LSTM in many situations [68]. A recent review on the use of ML and DL suggests a low use of GRU in security [69]. Therefore, more information is needed about the performance of this network in processing work accident data. Table 2 presents the hyperparameters used in LSTM, Bi-LSTM, and GRU.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Iteration** & **Hyperparameter** & **Values** \\ \hline
1 & Hidden layers & 1, 2, 3, and 4 \\  & Number of input neurons & 9, 19, 29, 39, 49, and 49 \\  & Number of hidden layer neurons & 3, 13, 23, 33, 43, and 53 \\  & Activation function & ReLU, tanh, and Sigmoid \\
2 & Dropout & 0.10, 0.20, 0.30, and 0.40 \\
3 & Epoch & 1000 and 2000 \\  & Optimizer & RMSprop, AdaGrad, Adam, and SGD \\
4 & Learning rate & 0.01, 0.001, and 0.005 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of hyperparameters and values tested in the MLP.  
Finally, the last algorithm tested was the 1D convolutional neural network (1D-CNN). This network works according to a hierarchical structure and learns the relationship between input and output data with convolution operations on a one-dimensional matrix [27]. Studies, such as that by [PERSON] et al. [70], have observed that 1D-CNN presented the best accuracy for classifying traffic accidents compared to the other models tested. However, reviews did not present studies that sought to classify work accidents using 1D-CNN [52]. Table 3 presents the parameters tested in 1D-CNN.