Developing an AI-Interpretable recommender system for maritime logistics chain

The research project aims to develop a special computer program so called “interpretable recommender system” to suggest the most efficient AI-Interpretable recommender system for maritime logistics chain to transport goods by ship. The program will use advanced math and machine learning to analyze large amounts of data about different ships and their movements. The goal is to help shipping companies save time and money by making more informed decisions. By creating an “interpretable” program, the researchers aim to make it easy for decision-makers to understand how the program makes its recommendations, which will help build trust in the system. The partner organization is expected to benefit from the improved efficiency and cost savings achieved by using the program.

Faculty Supervisor:

Fayez Gebali

Student:

Partner:

Soshianest Enterprise Miner Inc

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

University of Victoria

Program: