APRICOIN: An Adaptive Approach for Prioritizing High-Risk Containers Inspections

Risk evaluation of the containers remains a difficult task, often due to incomplete or ambiguous information on containers. In addition, the evaluation process needs to be adapted on an ongoing basis to cope with emerging risk factors. Furthermore, high-risk container inspection is commonly hindered by a low inspection capacity, which leads to a major issue: how can we prioritize the container inspection if the number of suspect containers exceeds the inspection capacity? Container inspection prioritizing may be the answer. In this paper, we propose a novel approach for adaptively prioritizing container inspection (APRICOIN). First, we enhance the container information flow to alleviate the problem of incomplete information by proposing an enhanced container descriptive. Second, we introduce the APRICOIN algorithm, which combines frequent pattern mining and a fuzzy logic system, to assess the container's risk score. The frequent pattern growth algorithm is proposed to retrieve the key criteria for evaluating container risk. This is done through mining frequent criteria sets within the historic data set of container inspections by customs. The mined frequent criteria sets are used to assess fuzzy inference rules which are periodically readjusted to integrate new key criteria. Thereafter, the fuzzy logic system uses the obtained fuzzy inference rules to calculate a container's risk score. Our major contribution consists of providing a new adaptive approach for assessing a container's risk through combining frequent criteria mining and fuzzy logic. An illustrative study and a comparison with alternative approaches are performed to validate the proposed algorithm.