# The Impact of the Human Factor on Communication During a Collision Situation in Maritime Navigation
## 3 Results
### Accuracy Results  
The accuracy results of the model for the translation of a message written in an ontology into natural language are given below. The accuracy measurements for the second level, which include complete marine collision scenarios with all sentences in all the scenarios, are given below. The accuracy obtained using the learning curve (Figure 5) is given below. The prototype was developed using the Python version 3.12.2 programming language [34], object-oriented programming techniques [35], and the Natural Language Toolkit programming library [36; 37] for natural language processing. The scikit-learn library [38] was also used to enable the use of machine learning methods for the proposed translation model. The TKinter library [39] served for programming a user-friendly graphical user interface (GUI) in the form of a desktop application. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Rate** & **PPV** & **NPV** & **SE** & **SP** & **ACC** & **ERR** & **F** \\ \hline
**Accuracy** & 0.8571 & 0.9384 & 0.9473 & 0.8356 & 0.8926 & 0.1073 & 0.9499 \\ \hline \hline \end{tabular}
\end{table} 
Table 16: Accuracy rates for the model of translation from natural language to ontology for all the scenarios. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Predicted Data/Actual Data** & **1** & **2** \\ \hline
1 & 4 & 0 \\
2 & 1 & 3 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Accuracy measurements for the first level, which includes single sentences for the second scenario. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Predicted Data/Actual Data** & **1** & **2** \\ \hline
1 & 72 & 12 \\
2 & 4 & 61 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Accuracy measurements for the second level, which include complete marine collision scenarios with all sentences in all the scenarios. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Factor** & **PPV** & **NPV** & **SE** & **SP** & **ACC** & **ERR** & **F** \\ \hline
**Accuracy** & 1 & 0.75 & 0.8 & 1 & 0.875 & 0.125 & 0.825 \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Accuracy rates for the model of translation from natural language to ontology for the second scenario. The software implements the prototype for the conversion of sentences from natural language into instruction sequences expressed as an ontology [40]. The working prototype includes a number of steps where the navigator's messages are located in the input, and they are created as sequence of natural-language sentences. The results are formed as an ontology sequence of messages. At the beginning, the prototype tokenizes [12] the data into sentences consisting of individual words. The next step erases non-relevant words in the spoken messages, which are very often typical in human spoken interactions (for example, but, please, however, etc.). Afterwards, the stemming process is carried out, which includes, for example, reducing the number of similar words that come from a single root parent. Lemmatizations, in turn, have the goal of reducing the number of words by using a root word. This should, for example, minimize words with different endings. The information reduction is followed by labelling the speech parts, such as verbs, nouns, or adjectives, as well as identifying the message's types. This must be carried out before running the classifier, which recognizes message types issued by officers, such as requests, demands, or others. The next step, on the basis of the gained knowledge formed as rules, involves the creation of the message sequence formed as classes from a set of ontologies. This kind of result will be unambiguously readable by the autonomous system.