Dmlbc: Dependable Machine Learning for Seaports Using Blockchain Technology - Fernandez-Campusano, C (King Saud bin Abdulaziz University, 2024)
The technological shifts driven by Industry 4.0 and the impact of the COVID-19 pandemic has ushered new challenges for seaports, necessitating a transformation in their information and communications systems to enhance global competitiveness. As the United Nations (UN) International Maritime Organization mandates the adoption of a Maritime Single Window in short time, seaports are compelled to adapt their operations. Addressing these challenges, this research introduces the Dependable Machine Learning for seaports using Blockchain (DMLBC) method, offering a secure technological system that integrates transactional data among various logistics stakeholders. DMLBC enhances efficiency in document control, traceability, loading processes, and collaborative workflows. The method employs a architecture grounded in Blockchain (BC) and harnesses Machine Learning Techniques. In a practical application, DMLBC is implemented in a real case study, demonstrating its efficacy for decision making of port operations. The research discusses the contributions that the DMLBC method has compared to what exists in the bibliographic review and in other port industries worldwide. Describes future exploration directions and It emphasizes the potential for generating additional predictions by incorporating others Key Performance Indicators (KPI)s, integrating DMLBC with Decision Support System (DSS), and delving into the realm of Post-Quantum Cryptography (PQC) to fortify the security of seaport operations in the evolving technological landscape. © 2024 The Author(s)