## 5 Comparative Analysis  
In this section, we conduct a comparative analysis of different Machine Learning (ML) techniques employed in maritime systems. This includes a review of their performance in various applications, their advantages and disadvantages, and the contexts in which they are most suitable.  
* Supervised Learning Algorithms [19]: These algorithms learn from labeled datasets to predict outcomes for new data. They have been widely used in applications such as vessel traffic prediction and anomaly detection. Methods like decision trees, support vector machines (SVMs) [34], and Logistic Regression are quite popular.
* Advantages: They are generally easy to interpret, making them suitable for applications where interpretability is important.
* Disadvantages: They require a large amount of labeled data for training and may not perform well when the data are unstructured or have complex relationships.
* Unsupervised Learning Algorithms [31]: These algorithms find patterns and structures in unlabeled data. Clustering techniques and dimensionality reduction methods fall under this category. They have found applications in anomaly detection and data exploration.
* Advantages: They can handle unstructured and unlabeled data, making them suitable for exploratory data analysis and situations where labeled data are not readily available.
* Disadvantages: Their results are often harder to interpret compared to supervised learning algorithms.
* Reinforcement Learning Algorithms: These algorithms learn optimal actions through trial and error to maximize some notion of cumulative reward [9]. They are ideal for sequential decision-making problems and have potential applications in optimal route planning and resource allocation.
* Advantages: They can handle dynamic environments and adapt to new situations, making them suitable for applications with changing conditions.
* Disadvantages: They require a lot of data and computational resources, and designing a suitable reward function can be challenging.
* Deep Learning Algorithms: These are complex algorithms inspired by the structure of the human brain (neural networks) [10]. They can model complex relationships and have been used in image recognition tasks, such as detecting objects or features in satellite and radar imagery.
* Advantages: They can handle large volumes of unstructured data and can model complex relationships, making them suitable for applications involving image, text, or sound data.
* Disadvantages: They require large amounts of data and computational resources, and their results are often not easily interpretable.  
Each ML technique has its strengths and weaknesses and is best suited for different types of problems and datasets. When choosing a technique, it is crucial to consider the nature of the problem, the availability and type of data, and the computational resources at hand. As the field of ML continues to evolve, we can expect the development of new techniques that can address the current limitations and offer improved performance in various maritime applications.  
The Underutilization of Machine Learning in the Maritime Industry: Challenges and Potential Solutions