Publication:
A Location-Based Fraud Detection in Shipping Industry Using Machine Learning Comparison Techniques

Simple item page
dc.citedby0
dc.contributor.authorSubramaniam G.A.L.en_US
dc.contributor.authorMahmoud M.A.en_US
dc.contributor.authorAbdulwahid S.N.en_US
dc.contributor.authorGunasekaran S.S.en_US
dc.contributor.authorid57223391179en_US
dc.contributor.authorid55247787300en_US
dc.contributor.authorid57361650900en_US
dc.contributor.authorid55652730500en_US
dc.date.accessioned2025-03-03T07:46:06Z
dc.date.available2025-03-03T07:46:06Z
dc.date.issued2024
dc.description.abstractThis chapters discusses fraud detection specifically within the shipping industryShipping industry using data analyticsData analytics techniques. The shipping industry is experiencing significant growth due to globalization and the rise of e-commerce, particularly during the recent pandemic. This expansion attracts fraudsters who exploit the system by transporting illegal or banned items using fake declaration documents. The immense volume of shipments makes manual inspection and verification unsustainable, increasing operational costs and causing delays that affect the supply chain and raise consumer prices. An automated solutionAutomated solution is needed to address this issue and prevent further impacts on the industry and society. A study reviewed existing fraud detectionFraud detection models and identified the most effective algorithm for the shipping industry. Using RapidMiner, various algorithms were tested. The study found that k-NNK-NN is the most effective in terms of performance and accuracy for detecting fraud within the shipping industry. ? The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/978-3-031-67317-7_2
dc.identifier.epage26
dc.identifier.scopus2-s2.0-85205000211
dc.identifier.spage15
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85205000211&doi=10.1007%2f978-3-031-67317-7_2&partnerID=40&md5=6a7eaf0a49c03181a22ef8641b068b39
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36957
dc.identifier.volume553
dc.pagecount11
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.sourceScopus
dc.sourcetitleStudies in Systems, Decision and Control
dc.titleA Location-Based Fraud Detection in Shipping Industry Using Machine Learning Comparison Techniquesen_US
dc.typeBook chapteren_US
dspace.entity.typePublication
Files
Collections
SCOPUS