# A Pilot Study of Stacked Autoencoders for Ship Mode Classification
## 6 Conclusions  
Artificial intelligence research using ship data is being actively conducted as the shipping market evolves. However, studies have not been performed on classifying ship operating modes, despite previous studies on using ship data to predict power loads. An SAE has the advantage of being able to perform effective classification by analyzing the features of the input data. Therefore, we conducted a pilot study on deep learning models that can classify ship operating modes using an SAE. Furthermore, experiments were performed to compare the performance according to changes in the structure of the SAE and changes in the size of its latent layer. The key points to be verified through research are as follows:  
1. TPR, FPR, accuracy, and MCC were selected as evaluation metrics to perform experiments that compared the performance according to changes in the structure of the SAE and the size of its latent layer. Even though previous studies have not been conducted in this area, performance comparison among the models was possible according to changes in the structure of the SAE and the size of its latent layer.
2. In the results of the SAE model comparison experiments, the (5-128) structure with a latent layer size of 9 showed the best operating mode classification performance.
3. The classification performance for the In Port and At Sea modes used in the experiments was generally excellent; however, the classification performance for the Stand By mode was low. Therefore, more data are needed to improve the prediction performance of the Stand By mode.  
Through this pilot study, we found that our VAE-based deep learning model can be used to analyze ship operating modes. Furthermore, a model that can be used as a comparison target group for the classification of the actual operating state of the ship in the future was found. Based on this model structure, it will be possible to develop enhanced models. Although the classification performance for the Stand By mode was limited because of the imbalanced data, it was possible to propose a VAE model structure that can maximize the data classification performance of the In Port and At Sea modes. However, further research is required to address some limitations of this study. First, the issue of handling imbalanced data needs to be studied using real ship data; data-level techniques, algorithm-level methods, and hybrid methodologies can be utilized to find the most appropriate method to improve the classification model. Second, research to compare denoising, sparse, and stacking autoencoder models could be carried out to improve classification performance and establish which autoencoder-based model best interprets the features of real ship data.  
**Author Contributions:** Conceptualization, methodology, and software, J.-Y.K.; project administration, funding acquisition, J.-S.O. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by the Korea Institute of Marine Science and Technology Promotion (KIMST), funded by the Korea Coast Guard (20190460).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The author declares no conflict of interest.