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Predicting Public Transport User Honesty: A Machine Learning Approach to Lost Item Returns

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Bibliographic Details
Published in:International Journal of Intelligent Systems and Applications
Format: Online Article RSS Article
Published: 2026
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container_title International Journal of Intelligent Systems and Applications
description
discipline_display Engineering & Technology
discipline_facet Engineering & Technology
format Online Article
RSS Article
genre Journal Article
id rss_article:42998
institution FRELIP
journal_source_facet International Journal of Intelligent Systems and Applications
publishDate 2026
publishDateSort 2026
record_format rss_article
spellingShingle Predicting Public Transport User Honesty: A Machine Learning Approach to Lost Item Returns
— — — — — — Artificial Intelligence
Computer Science & IT
Engineering & Technology
sub_discipline_display Computer Science & IT
sub_discipline_facet Computer Science & IT
subject_display — — — — — — Artificial Intelligence
Computer Science & IT
Engineering & Technology
— — — — — — Artificial Intelligence
Computer Science & IT
Engineering & Technology
subject_facet — — — — — — Artificial Intelligence
Computer Science & IT
Engineering & Technology
title Predicting Public Transport User Honesty: A Machine Learning Approach to Lost Item Returns
title_auth Predicting Public Transport User Honesty: A Machine Learning Approach to Lost Item Returns
title_full Predicting Public Transport User Honesty: A Machine Learning Approach to Lost Item Returns
title_fullStr Predicting Public Transport User Honesty: A Machine Learning Approach to Lost Item Returns
title_full_unstemmed Predicting Public Transport User Honesty: A Machine Learning Approach to Lost Item Returns
title_short Predicting Public Transport User Honesty: A Machine Learning Approach to Lost Item Returns
title_sort predicting public transport user honesty: a machine learning approach to lost item returns
topic — — — — — — Artificial Intelligence
Computer Science & IT
Engineering & Technology
url https://www.mecs-press.org/ijisa/ijisa-v18-n2/v18n2-6.html