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Sci. 12:1586438. doi: 10.3389/fvets.2025.1586438 Keywords: isolation forest; autoencoders; K-nearest neighbors; unsupervised machine learning; anomaly detection Author: Mafalda Pedro Mil-Homens Creator: LaTeX with hyperref package + hypdvips Producer: pikepdf 9.10.2 CreationDate: Sat Nov 15 08:44:26 2025 CET ModDate: Sat Nov 15 08:34:09 2025 CET Custom Metadata: yes Metadata Stream: yes Tagged: no UserProperties: no Suspects: no Form: none JavaScript: no Pages: 16 Encrypted: no Page size: 595.26 x 842.116 pts (A4) Page rot: 0 File size: 2291249 bytes Optimized: no PDF version: 1.5 name type encoding emb sub uni object ID ------------------------------------ ----------------- ---------------- --- --- --- --------- JHCDPD+MuseoSans-700 Type 1C Custom yes yes yes 28 0 JHCEAE+MuseoSans-300 Type 1C Custom yes yes yes 31 0 JHCEED+MuseoSans-300Italic Type 1C Custom yes yes yes 34 0 JHCEEE+MinionPro-Regular Type 1C Custom yes yes yes 37 0 JHCEFF+MuseoSans-500 Type 1C Custom yes yes yes 40 0 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UseOutlines Language: en Outlines: Item: Title: Leveraging productivity indicators for anomaly detection in swine breeding herds with unsupervised learning Destination: ariclecounter.1 Children: Item: Title: Introduction Destination: section*.2 Item: Title: Methods Destination: section*.3 Children: Item: Title: Overview of the study Destination: section*.4 Item: Title: Data source Destination: section*.5 Item: Title: Inclusion criteria and dataset Destination: section*.6 Item: Title: Unsupervised learning for anomaly detection Destination: section*.7 Item: Title: Isolation Forest Destination: section*.8 Item: Title: K-nearest neighbor Destination: section*.9 Item: Title: Autoencoders Destination: section*.10 Item: Title: Assessing differences among anomalous and non-anomalous weeks Destination: section*.11 Children: Item: Title: Permutation test to compare means Destination: section*.12 Item: Title: Density plots Destination: section*.13 Item: Title: Performance Destination: section*.14 Item: Title: PRRSV example line plots Destination: section*.15 Item: Title: Results Destination: section*.16 Children: Item: Title: Descriptive Destination: section*.17 Item: Title: Production System 1 Destination: section*.18 Item: Title: Production System 2 Destination: section*.19 Item: Title: Performance Destination: section*.20 Item: Title: PRRSV example line plots Destination: section*.21 Item: Title: Discussion Destination: section*.22 Item: Title: Conclusion Destination: section*.23 Item: Title: Data availability statement Destination: section*.24 Item: Title: Author contributions Destination: section*.25 Item: Title: Funding Destination: section*.26 Item: Title: Acknowledgments Destination: section*.27 Item: Title: Conflict of interest Destination: section*.28 Item: Title: Generative AI statement Destination: section*.29 Item: Title: Publisher's note Destination: section*.30 Item: Title: References Destination: section*.31 Filters: FilterPipeline: DCTDecode Images: 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Front. Vet. Sci. 12:1586438. doi: 10.3389/fvets.2025.1586438 Leveraging productivity indicators for anomaly detection in swine breeding herds with unsupervised learning Mafalda Pedro Mil-Homens Chong Wang Giovani Trevisan Fernanda Dórea Daniel C. L. Linhares Derald Holtkamp Gustavo S. Silva Copyright © 2025 Pedro Mil-Homens, Wang, Trevisan, Dórea, Linhares, Holtkamp and Silva. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. isolation forest autoencoders K-nearest neighbors unsupervised machine learning anomaly detection pikepdf 9.10.2 isolation forest; autoencoders; K-nearest neighbors; unsupervised machine learning; anomaly detection LaTeX with hyperref package + hypdvips 2025-11-15T13:04:09+05:30 2025-11-15T12:44:26+05:00 2025-11-15T13:04:09+05:30 uuid:5de96325-cf9d-4a85-a9f1-1fe651d0831d uuid:0148b9f6-7dcc-4c3f-81ac-67f36c3d5b87 True https://www.frontiersin.org/legal/copyright-statement Copyright © 2025 Pedro Mil-Homens, Wang, Trevisan, Dórea, Linhares, Holtkamp and Silva. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. 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