Ruoting Li
Fitts Ind & Syst Eng Grad Temp
Works (3)
Updated: June 18th, 2023 21:27
2023 article
Detecting Human Trafficking: Automated Classification of Online Customer Reviews of Massage Businesses
Li, R., Tobey, M., Mayorga, M. E., Caltagirone, S., & Ozaltin, O. Y. (2023, February 22). M&SOM-MANUFACTURING & SERVICE OPERATIONS MANAGEMENT, Vol. 2.
author keywords: human trafficking; massage businesses; online customer reviews; Natural Language Processing; ensemble learning
TL;DR:
The proposed models can save countless hours in IMB investigations by automatically sorting through large quantities of data to flag potential illicit activity, eliminating the need for manual screening of these reviews by law enforcement and other stakeholders.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
5. Gender Equality
(Web of Science)
16. Peace, Justice and Strong Institutions
(Web of Science)
Sources: Web Of Science, ORCID
Added: March 20, 2023
2022 article
Interpretable models for the automated detection of human trafficking in illicit massage businesses
Tobey, M., Li, R., Ozaltin, O. Y., Mayorga, M. E., & Caltagirone, S. (2022, August 28). IISE TRANSACTIONS, Vol. 8.
author keywords: Human trafficking; illicit massage; interpretable machine learning; risk scores; decision trees
TL;DR:
This work uses findings from primary stakeholder interviews with law enforcement, regulatory bodies, legitimate massage practitioners, and subject-matter experts from nonprofit organizations to identify data sources and potential indicators of illicit massage businesses (IMBs).
(via Semantic Scholar)
UN Sustainable Development Goal Categories
5. Gender Equality
(Web of Science)
16. Peace, Justice and Strong Institutions
(Web of Science)
Sources: Web Of Science, ORCID
Added: September 26, 2022
2022 journal article
Septic shock prediction and knowledge discovery through temporal pattern mining
ARTIFICIAL INTELLIGENCE IN MEDICINE, 132.
author keywords: Temporal pattern mining; Sepsis; Electronic health records; Prediction; Pattern selection
MeSH headings : Critical Care; Electronic Health Records; Humans; Knowledge Discovery; Sepsis / diagnosis; Sepsis / therapy; Shock, Septic / diagnosis; Shock, Septic / therapy
TL;DR:
This framework proposes a framework to find a small number of relevant temporal patterns in electronic health records for the early prediction of septic shock and finds that model-based feature selection approaches PST3 and PST4 yield the best prediction performance among these techniques.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
3. Good Health and Well-being
(Web of Science)
Sources: Web Of Science, ORCID
Added: October 17, 2022
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