## 3 Results and Discussion
### Computational Results of ANN
The detailed statistical results are presented in Table 3.  
The table above presents the performance metrics of ANN models with varying architectures for the chitosan and chitin datasets. It lists 12 different ANN architectures with increasing complexity, ranging from level 1 to level 12, representing growth in the number of layers and total neurons. The simplest structure is 1-3-1, comprising just 5 neurons across 3 layers, while the most complex is 15-20-20-20-15, with 90 neurons distributed across 5 layers. The minimum MSE value for chitosan was calculated for the model structure 5-5-5 \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multirow{2}{*}{\begin{tabular}{c} **Network** \\ **Structure** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Heating Rate** \\ **Rate for Tearing** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Heating Rate** \\ **Rate for Test** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Transfer** \\ **Function** \\ \end{tabular} } & \multicolumn{2}{c}{\begin{tabular}{c} **Chitin Data** \\ **Test R\({}^{2}\)** \\ **Score** \\ \end{tabular} } & \multicolumn{2}{c}{\begin{tabular}{c} **Chitosan Data** \\ **Validation** \\ **Score** \\ \end{tabular} } \\ \hline NN11 & 1-5-1 & \begin{tabular}{c} 5 \({}^{\circ}\)C/min \\ 10 \({}^{\circ}\)C/min \\ 20 \({}^{\circ}\)C/min \\ \end{tabular} & 40 \({}^{\circ}\)C/min & Sigmoid & 0.8896 & 0.8936 & 0.7895 & 0.8026 \\ \hline NN12 & 5-20-30-20-5 & \begin{tabular}{c} 5 \({}^{\circ}\)C/min \\ 10 \({}^{\circ}\)C/min \\ 40 \({}^{\circ}\)C/min \\ \end{tabular} & 20 \({}^{\circ}\)C/min & Sigmoid & 0.9883 & 0.9901 & 0.9366 & 0.93999 \\ \hline NN13 & 3-10-2 & \begin{tabular}{c} 10 \({}^{\circ}\)C/min \\ 20 \({}^{\circ}\)C/min \\ 40 \({}^{\circ}\)C/min \\ \end{tabular} & 5 \({}^{\circ}\)C/min & Sigmoid & 0.899 & 0.9199 & 0.9124 & 0.9205 \\ \hline NN14 & 1-3-1 & \
\begin{tabular}{c} 10 \({}^{\circ}\)C/min \\ 20 \({}^{\circ}\)C/min \\ 40 \({}^{\circ}\)C/min \\ \end{tabular} & 5 \({}^{\circ}\)C/min & Tansig & 0.7455 & 0.7041 & 0.7887 & 0.7999 \\ \hline NN15 & 1-3-1 &
\begin{tabular}{c} 5 \({}^{\circ}\)C/min \\ 10 \({}^{\circ}\)C/min \\ 20 \({}^{\circ}\)C/min \\ \end{tabular} & 40 \({}^{\circ}\)C/min & Tansig & 0.7856 & 0.7745 & 0.6698 & 0.6784 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._at complexity level 3 with 15 neurons. For chitin, the minimum MSE value was found in the model structure 5-10-5 at complexity level 3 with 20 neurons. Figures 4a,b, below, compare the experimental data and model predictions for various heating rates in the chitosan and chitin datasets, respectively. In these figures, the x-axis represents the actual heating rate values, while the y-axis represents the predicted heating rate values. The results reveal a nearly perfect linear correlation between the actual values (blue points) and the predicted values (red points), indicating minimal prediction error [31]. The predicted values almost perfectly overlap with the actual data, demonstrating a well-trained model with strong generalization capabilities--especially if these results are obtained from a test set. The analysis results underscore the power of ANNs in predicting outcomes in thermal degradation experiments. It is evident that further weight loss data with different parameters can be realistically predicted using the experimental results obtained through ANN modeling.