[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118050-en":3,"doc-seo-118050-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118050,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","Machine Learning Applications and Developments for Swine Industry - Dissertation","This dissertation explores the potential of machine learning in the swine industry, where key challenges include disease outbreaks, antimicrobial resistance, and the demand for efficient, timely decisions to support optimal production. It addresses limitations of traditional approaches by introducing data-driven methods for disease prediction, improved decision-making, and more effective antimicrobial use. The work spans six chapters covering virus sublineage classification, time-series AMR forecasting, MIC prediction from genomic representations, novelty detection, and synthetic tabular data generation.","UC Davis  \nUC Davis Electronic Theses and Dissertations  \nTitle  \nMachine Learning Applications and Developments for Swine Industry  \nPermalink  \n[https://escholarship.org/uc/item/8vv1s811](https://escholarship.org/uc/item/8vv1s811)  \nAuthor  \nKim, Jeonghoon  \nPublication Date  \n2023  \nPeer reviewed|Thesis/dissertation  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nMachine Learning Applications and Developments for Swine Industry  \nBy  \nJEONGHOON KIM  \nDISSERTATION  \nSubmitted in partial satisfaction of the requirements for the degree of DOCTOR OF PHILOSOPHY  \nin  \nApplied Mathetmatics  \nin the  \nOFFICE OF GRADUATE STUDIES  \nof the  \nUNIVERSITY OF CALIFORNIA  \nDAVIS  \nApproved:  \n\n| Xin Liu, Chair |\n| --- |\n| Beatriz Martínez-López |\n\nThomas Strohmer Committee in Charge  \n2023  \n© Jeonghoon Kim, 2023 . All rights reserved.  \nContents  \nAbstract iv  \nAcknowledgments v  \nChapter 1 . Introduction 1  \nChapter 2 . Applications of Machine Learning for the Classification of Porcine Reproductive and Respiratory Syndrome Virus Sublineages Using Amino Acid Scores of ORF5 Gene 7  \n2.1. Abstract 7  \n2.2. Introduction 8  \n2.3. Materials and methods 10  \n2.4. Results 15  \n2.5. Discussion 19  \n2.6. Conclusion 21  \n2.7. Supplements 22  \nChapter 3 . Predicting Antimicrobial Resistance of Bacterial Pathogens using Time Series Analysis 23  \n3.1. Abstract 24  \n3.2. Introduction 24  \n3.3. Materials and Methods 26  \n3.4. Results 31  \n3.5. Discussion 34  \n3.6. Conclusion 37  \n3.7. Supplements 38  \nChapter 4 . Predicting Minimal Inhibitory Concentration Values for 12 Antibiotics using 203  \nStreptococcus suis Whole Genome Sequences 43  \n4.1. Materials and Methods 43  \n4.2. Numerical Results 47  \n4.3. Discussion 48  \nChapter 5 . OAAE: Adversarial Autoencoders for Novelty Detection in Multi-modal Normality Case via Orthogonalized Latent Space 50  \n5.1. Abstract 50  \n5.2. Introduction 51  \n5.3. Related work 52  \n5.4. Proposed Method: OAAE 53  \n5.5. Experiment 55  \n5.6. Discussion 58  \n5.7. Conclusion 59  \nChapter 6 . An Empirical Study on Impact of Label Noise on Synthetic Tabular Data Generation 60  \n6.1. Abstract 60  \n6.2. Introduction 61  \n6.3. Preliminaries 63  \n6.4. Experiments 65  \n6.5. Numerical Results 69  \n6.6. Discussion 74  \n6.7. Conclusion 75  \nBibliography 79  \nAbstract  \nThis dissertation explores the potential of machine learning in the swine industry, which faces numerous challenges such as disease outbreaks, antimicrobial resistance, and the need for efficient decision-making for optimal production processes. Traditional methods may not be scalable, accurate, or timely enough to meet the demands. Machine learning provides data-driven solutions that enhance decision-making, improve disease prediction, optimize antimicrobial usage, and enhance overall production efficiency. The dissertation consists of six chapters. Chapter 1 presents an introduction and overview of the remaining chapters. Chapters 2, 3, and 4 present machine learning applications to the swine industry. Chapters 5 and 6 present the development and understanding of deep learning models, specifically for novelty detection and synthetic tabular data generation.  \nChapters 2, 3, and 4 address real-world problems in the swine industry using machine learning methods with real-world data. Each Chapter focuses on a specific issue: virus classification, antimicrobial resistance (AMR) prediction, and Minimal Inhibitory Concentration (MIC) prediction. Specifically, Chapter 2 aims to classify the Porcine Reproductive and Respiratory Syndrome Virus, a highly infectious disease of pigs, into four different sublineages via amino acid scores from Open Reading Frame 5 gene sequences. Chapter 3 focuses on predicting the future AMR burden of the bacterial pathogen through time series analysis. The goal of Chapter 4 is to predict MIC values for 12 antibiotics using Random Forest based on k-mer counting data processing approach fo","cbCaitLQaAqVTcu7","https://ap.wps.com/l/cbCaitLQaAqVTcu7","pdf",15938743,1,95,"English","en",105,"# Chapter 1. Introduction\n# Chapter 2. Applications of Machine Learning for the Classification of Porcine Reproductive and Respiratory Syndrome Virus Sublineages Using Amino Acid Scores of ORF5 Gene\n# Chapter 3. Predicting Antimicrobial Resistance of Bacterial Pathogens using Time Series Analysis\n# Chapter 4. Predicting Minimal Inhibitory Concentration Values for 12 Antibiotics using Streptococcus suis Whole Genome Sequences\n# Chapter 5. OAAE: Adversarial Autoencoders for Novelty Detection in Multi-modal Normality Case via Orthogonalized Latent Space\n# Chapter 6. An Empirical Study on Impact of Label Noise on Synthetic Tabular Data Generation\n# Bibliography","[{\"question\":\"What problems in the swine industry does this dissertation target?\",\"answer\":\"It focuses on disease outbreaks, antimicrobial resistance, and the need for efficient decision-making for production. The research emphasizes data-driven solutions to address scalability and timeliness limitations of traditional methods.\"},{\"question\":\"How does Chapter 2 classify PRRSV sublineages?\",\"answer\":\"Chapter 2 classifies Porcine Reproductive and Respiratory Syndrome Virus into four sublineages using amino acid scores derived from ORF5 gene sequences.\"},{\"question\":\"What deep learning approaches are used in Chapters 5 and 6?\",\"answer\":\"Chapter 5 proposes OAAE using adversarial autoencoders for novelty detection in multi-modal normality via an orthogonalized latent space. Chapter 6 evaluates how label noise affects synthetic tabular data generation using deep generative model-based synthesizers.\"}]","Machine Learning Applications and Developments for Swine Industry - Dissertation | PDF",1785681028,239,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"machine-learning-applications-and-developments-for-swine-industry-dissertation","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-applications-and-developments-for-swine-industry-dissertation/118050/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-02",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What problems in the swine industry does this dissertation target?","Question",{"text":76,"@type":77},"It focuses on disease outbreaks, antimicrobial resistance, and the need for efficient decision-making for production. The research emphasizes data-driven solutions to address scalability and timeliness limitations of traditional methods.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does Chapter 2 classify PRRSV sublineages?",{"text":81,"@type":77},"Chapter 2 classifies Porcine Reproductive and Respiratory Syndrome Virus into four sublineages using amino acid scores derived from ORF5 gene sequences.",{"name":83,"@type":74,"acceptedAnswer":84},"What deep learning approaches are used in Chapters 5 and 6?",{"text":85,"@type":77},"Chapter 5 proposes OAAE using adversarial autoencoders for novelty detection in multi-modal normality via an orthogonalized latent space. Chapter 6 evaluates how label noise affects synthetic tabular data generation using deep generative model-based synthesizers.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]