## 3 AI Implementations in Maritime Transport Systems  
AI, with its processing capabilities and ability to integrate multiple systems, has the potential to enhance and improve many tasks on the vessels. The maritime industry is increasingly interested in applying advanced AI and ML technologies to address issues related to sustainability, efficiency, and regulatory compliance [15]. This section describes the significance of using AI algorithms in maritime systems (Figure 2):  
* Navigation Systems--AI algorithms assist in navigational decision-making to avoid collisions and enhance the accuracy of a ship's navigational position, thereby increasing navigational safety;
* Hazardous Material Handling--AI-based tools that provide information on the rules for segregation and separation of hazardous cargo eliminate the possibility of human error, thereby preventing the risk of explosions, fires, and marine pollution;
* Risk Analysis and Management--AI algorithms enable risk management on ships during navigational tasks, mooring operations, technical inspections, regular operations, and shipyard repairs;
* Crew Resource Management--Tools that support the smooth operation of sea and port watches, as well as maintenance work on the ship, while ensuring compliance with rest time requirements for the crew in relation to working hours;
* Energy Efficiency--AI that supports route planning between ports and controls the use of the main engine and RPM (revolutions per minute) significantly reduces fuel costs and protects the marine environment by lowering exhaust emissions. Sustainable energy consumption should not be limited to the ship while it is at sea; it must also be considered during its time in port. The application of machine learning (ML), as a key subdomain of artificial intelligence (AI), can be viewed as a component of the digital transformation process aimed at advancing green practices in maritime port logistics. In the realm of environmental sustainability, emissions and energy consumption are the most commonly studied issues. Future research is distinguished by two broad directions: shifting focus to a greater diversity of machine learning approaches for promoting sustainability in ports and leveraging new perspectives to implement more environmentally friendly practices in port operations [1];
* Predictive Maintenance--Managing, controlling, and executing repair and maintenance work requires proper time management and planning according to the operating conditions of the ship. AI algorithms that analyze historical inspection and maintenance data improve the planning and management of upcoming inspections, eliminating the possibility of missing necessary repairs and periodic maintenance.  
The use of artificial intelligence is not only aimed at managing maintenance on ships but also at creating tools to support shore-based inspectors carrying out Port State Control (PSC) inspections. This inspection is seen as a safeguard to protect maritime safety, protect the marine environment, and ensure decent working and living conditions for seafarers. A substandard vessel may be detained during an inspection if serious deficiencies are found on board. However, developing accurate models to predict ship detention based on general ship factors (e.g., age of ship, type, flag), dynamic factors (e.g., flag changes in the ship), and historical inspection factors (e.g., the total number of previous detentions in PSC inspections, time since last PSC inspection, number of deficiencies in last PSC inspection) prior to an inspection is not a trivial task, as low detention rates lead to a highly unbalanced dataset of inspection records. To address this problem, experts propose a classification model called balanced random forest (BRF) for predicting vessel detentions. Numerical experiments show that this model can achieve an average improvement of 73.72% in identifying detained vessels, using the Hong Kong port as an example [16].  
Figure 2: Potential AI implementations in maritime transport systems.