## 3 Results and Discussion
### Computational Results of ANN  
In research on the thermal degradation process using the ANN approach, the data are typically partitioned randomly into three subsets: training, validation, and testing. However, the primary goal of this study was to reduce the number of experimental trials by employing the ANN model. To achieve this, various combinations of heating rates--selecting three rates from four distinct values--were utilized during training to predict the remaining one. To demonstrate the applicability of ANN models to real-life scenarios, the datasetwas divided into two parts for the training and testing phases. These two parts consist of data with different heating rates so that predicting a heating rate not used during the training phase would highlight the realistic capabilities of the ANN models. This procedure helps to minimize the risk of overfitting in the studied data. For example, if the 5, 10, and 20 \({}^{\circ}\)C/min heating rates were selected for training, the model would be tested to predict the remaining heating rate, which is 40 \({}^{\circ}\)C/min. In this study, 20% of the datasets were split away indiscriminately for the validation process. The ANN model was implemented in Python using the Keras library with TensorFlow as the backend. The Adam optimizer was employed for backpropagation. Two different transfer functions, Tansig and Sigmoid, were tested. The network models were trained for 1000 iterations with a target error of 10\({}^{-6}\). Multiple runs were performed with different parameters to compare the neural models. Various architectures with different numbers of layers and neurons were tested across these runs. The parameters used and the corresponding results for two distinct datasets--chitin and chitosan experiment sets--are presented in Table 2, which summarizes the parameters used and the results obtained for both chitin and chitosan data. Table 2 also provides a detailed summary of the unique neural network structures (e.g., 5-10-5 indicates three hidden layers with 5, 10, and 5 neurons, respectively), along with the details of the corresponding neural architecture and the combination of heating rate data used during the training, validation, and testing processes. Additionally, the transfer functions utilized in the model, as well as the test and validation regression coefficient (\(R^{2}\)) scores for chitin and chitosan, are presented in the above table. To evaluate performance, the R\({}^{2}\) score was calculated. Generally, R\({}^{2}\) values are assessed in three ranges: R\({}^{2}\) > 0.9 shows desirable performance and R\({}^{2}\) values between 0.8 and 0.9 indicates fair performance, while R\({}^{2}\) < 0.8 implies unsatisfying performance of the model. According to the results, the best-performing neural network model structure for the chitin data is NN 4, which consists of three layers (10-20-10) with a Sigmoid transfer function, achieving an R\({}^{2}\) value of 0.9995. For the chitosan data, the best-performing model is NN 1, which also has three layers (5-10-5) but uses a Tansig transfer function, achieving an R\({}^{2}\) value of 0.9997. As the underlying mechanisms of the thermal degradation of chitin and chitosan differ, this results in certain mathematical complexities during model development. Consequently, the same neural structure may yield different performance results across different datasets. Given that the regression model of the TGA is a complex polynomial function with a relatively small number of parameters, the ANN model requires increased complexity to achieve accurate predictions. To evaluate model performance, mean squared error (MSE), root mean square error (RMSE), and mean absolute error (MAE) were used as key indicators. The TG data were normalized to a 0-1 scale to clearly emphasize the performance outcomes. Among the tested models, only those demonstrating stability and high performance were selected for further analysis.