## 4 AI-Driven Concepts in MCNs
### Conventional AI/ML Methods  
Supervised learning (SL) is best for well-defined problems with historical data (e.g., predicting equipment failures and classifying anomalies) and involves training a model on a dataset where each input is paired with a known output or label. The model learns to predict this output for unseen data. It is most effective when large volumes of labeled data are available. The general SL workflow is depicted in Figure 3. In the maritime domain, Supervised learning can be used for multiple reasons, such as:  
* **Predictive maintenance:** By labeling historical sensor readings (e.g., vibrations, temperature) with failure events, supervised models (like Random Forests or Neural Networks) can predict when components like engines or communication modules are likely to fail [40].
* **Vessel/Traffic classification:** Based on features like movement patterns, communication signatures, or radar echoes, ships can be automatically categorized (e.g., cargo, tanker, fishing) to assist with traffic control or border security [58].
* **Weather-aware routing:** SL models can learn to forecast wave height, storms, or sea temperature patterns based on labeled environmental data. This may help to learn optimal paths and predict environmental hazards based on historical weather and performance outcomes [59].  
These predictive tasks may be implemented by algorithms such as Linear Regression, Decision Trees, Support Vector Machines (SVMs), Random Forests, Neural Networks (e.g., Multilayer Perceptrons), Support Vector Machines (SVMs), Gradient Boosting Machines (e.g., XGBoost) [56].  
Unsupervised Learning (UL) and Semi-Supervised Learning (SemiSL) methods are valuable when labeled data is scarce, which is a common scenario in MCNs due to the cost of data labeling in remote or high-risk environments. UL operates on unlabeled data, attempting to uncover hidden patterns or structures, as illustrated in Figure 4. As is often the case for deep-sea operations or rare fault conditions, anomalies need to be detected,including unusual ship movement, communication silence, or off-route behavior. On the other hand, SemiSL uses a small amount of labeled data and a large pool of unlabeled data to improve learning efficiency. Both methods enable a variety of ML functions in the MCN sector, including:  
* **Anomaly detection:** With UL, it is feasible to identify unusual communication or behavior patterns (e.g., sudden route deviation) [60].
* **Clustering for traffic modeling:** Clustering algorithms (like K-Means DBSCAN) can group ships by trajectory behavior to identify outliers [61].
* **Sensor data compression:** UL can also cluster sensor streams with similar characteristics to compress data before satellite uplink, saving bandwidth [62]. This can be completed by Principal Component Analysis (PCA) or autoencoders, which can learn compact representations of sensor data and flag unusual deviations from typical patterns as potential faults or security threats.
* **Ship activity classification with low-size data:** Given that acquiring labeled data from resource-constrained devices (e.g., from AUVs or remote sensors), SemiSL may help to train a behavior classification model with limited expert-labeled data and automatically generalize to new traffic data [63].
* **Early fault detection:** SemiSL can use a few known fault events to improve recognition across large unlabeled sensor datasets [64].