## 6 Supervised and Unsupervised Machine Learning Model
### Unsupervised Machine Learning  
#### 6.2.1 Artificial Neural Network Model (Black Box Model)  
The ANN is an ML model based on the black box model (BBM) that works similarly to human neurons in neural network architecture to recognize and decode the dataset's underlying relationship. Furthermore, ANN can learn nonlinear functions that are appropriate  
Figure 17: A Markov chain with 5 states (labelled \(S_{1}\) to \(S_{5}\) ) with selected state transitions. Figure 18: A segment of score dataset, ground truth (FR) vs. prediction (PR). for high fluctuation datasets such as those used in onboard ship measurements to predict the desired outcome. As a result, it is commonly used to improve ship powering performance by forecasting FOC based on a variety of operational data factors. [PERSON] et al. [25] proposed using the ANN model to analyze and forecast ship fuel consumption based on the table's dataset in [66]. Pre-processing raw operational data is more important than tuning hyperparameters in the neural network to improve prediction accuracy to model the neural network accurately. Furthermore, tuning neural network hyperparameters does not outperform the data pre-processing in terms of enhancing the robustness of the model learning capability. Several works in [25] focused on pre-processing the raw input data. Such pre-processing involves smoothing spline filtering algorithms applied to the entire signal in denoising outliers as shown in Figure 19, and Gaussian mixture model (GMM) clustering techniques shown in Figure 17, with the data parameters listed in Table 3.  
The clustering technique classifies the operation with a high-frequency signal as a single cluster to indicate that the data cluster had somewhat similar operation states, which might aid the neural network's learning by reducing computation time and focusing the cluster's effect on FOC. The analysis also emphasized the significance of clustering data parameters that significantly influenced the FOC reported in [63] study. A clustering methodology applied to the engine power of a vessel is shown in Figure 20.  
The ANN model was trained on post-processed datasets by varying hyperparameters such as the predefined range of hidden layers and neurons within the selected activation feature. The post-processed data is divided into three sets: a training set for learning, a validating set to prevent overfitting, and a testing set to validate model functionality. Through this method, the model will arrive at a converging solution in which raising the hyperparameter hidden layers and neurons represented as configuration have no significant impact on model accuracy, as shown in Table 4, and \(R\) values do not vary much with node increment. Figure 19: Denoised data for SOG using spline filtering [25]. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Data** & **Parameter** & **Remarks** \\ \hline \multirow{5}{*}{Input} & Average draft (m) & Ship State \\  & Trim (m) & ME Power (kW) \\  & Shaft Speed (RPM) & Engine operation \\  & STW (knots) & Navigation speed \\  & SOG (knots) & Weather condition \\  & Relative Wind Speed (m/s) & Weather condition \\ \hline \multirow{2}{*}{Output} & ME Fuel Consumption & \\  & (tonnes/day) & Fuel consumption \\ \end{tabular}
\end{table} 
Table 3: Data information used for ship prediction [25]. Table 5 shows the comparison of the \(R\) values and computation time for three different ML techniques, i.e., ANN, regression method (RM) and support vector (SV) summarized in [25]. Table 5 shows that in general, the ANN outperformed the RM and SV with the exponential sigmoid function having the highest prediction accuracy (\(R\) value). In comparison, the SV is able to predict relatively higher accuracy than the RM method with the quadratic SV method having the higher accuracy. However, the \(R\) values for RM and SV are very small, i.e., lesser than 0.5, and the computational time for the quadratic SV method is significantly high compared with other methods compared in the table.