[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120283-en":3,"doc-seo-120283-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"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},120283,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Machine Learning Applications in Infection Prediction, Microgrid Control, and Ophthalmology","This dissertation explores applications of machine learning across three distinct domains: infection prediction in swine industry systems, electrical microgrid control, and retinal genotyping in ophthalmology. It presents a windowing-based learning model to predict infection in two real-world swine settings and identifies informative features for risk prediction. It then develops reinforcement learning methods for microgrid control under uncertainty, demonstrating improved performance versus rule-based and model predictive strategies. Finally, it uses vision transformer models to infer genotype and high-risk allele combinations from retinal fundus images and derive phenotype-genotype insights for AMD.","UC Davis  \nUC Davis Electronic Theses and Dissertations  \nTitle  \nMachine Learning Applications in Infection Prediction, Microgrid Control, and Ophthalmology  \nPermalink  \n[https://escholarship.org/uc/item/92b8f8xn](https://escholarship.org/uc/item/92b8f8xn)  \nAuthor  \nHalev, Avishai  \nPublication Date  \n2024  \nPeer reviewed|Thesis/dissertation  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nMachine Learning Applications in Infection Prediction, Microgrid Control, and Ophthalmology  \nBy  \nAVISHAI HALEV  \nDISSERTATION  \nSubmitted in partial satisfaction of the requirements for the degree of DOCTOR OF PHILOSOPHY  \nin  \nApplied Mathematics  \nin the  \nOFFICE OF GRADUATE STUDIES  \nof the  \nUNIVERSITY OF CALIFORNIA  \nDAVIS  \nApproved:  \n\n| Xin Liu, Chair |\n| --- |\n| Zhaodan Kong |\n\nThomas Strohmer Committee in Charge 2024  \n© Avishai S. Halev, 2024 . All rights reserved.  \nTo Lily, who gave unyielding love and support.  \nTo my dad, Jeff,  \nwithout whose advice I would not have succeeded.  \nTo my mom, Elyza, who believes in me like no other.  \nTo my sisters, Davida and Shoshana, who provided inspiration and encouragement.  \nTo Luka, who stuck along for the ride.  \nContents  \nAbstract v  \nAcknowledgments vi  \nChapter 1 . Introduction 1  \n1.1. Chapter Overview 3  \nChapter 2 . Infection prediction in swine populations with machine learning 5  \n2.1. Abstract 5  \n2.2. Introduction 6  \n2.3. Results 8  \n2.4. Discussion 13  \n2.5. Conclusions 18  \n2.6. Methods 19  \nChapter 3 . Microgrid control under uncertainty 30  \n3.1. Abstract 30  \n3.2. Introduction 30  \n3.3. Background 32  \n3.4. Microgrids and Uncertainty 35  \n3.5. Methods 40  \n3.6. Results 43  \n3.7. Discussion 47  \n3.8. Conclusion 49  \n3.9. Appendix 50  \nChapter 4 . Genotype prediction 59  \n4.1. Abstract 59  \n4.2. Introduction 60  \n4.3. Methods 61  \n4.4. Results 64  \n4.5. Genotype Prediction Performance 65  \n4.6. Discussion 68  \n4.7. Appendix 71  \nBibliography 74  \nAbstract  \nThis dissertation explores applications of machine learning in three unique disciplines: infection prediction in the swine industry, electrical microgrid control, and retinal genotyping in ophthalmology.  \nChapter 2 explores the problem of infection prediction in swine populations, a major concern restraining the productivity of swine farms globally. We build a windowing-based machine learning model that is able to predict infection in two real-world swine systems, and determine features that provide value in predicting infection.  \nChapter 3 considers the tertiary microgrid control problem under uncertainty. We devise reinforcement learning algorithms to control the microgrid in the context of uncertainty and show that our reinforcement learning approach outperforms rule-based control and model predictive control approaches in high uncertainty scenarios.  \nChapter 4 examines the potential of deep learning models to predict genotypes given phenotypic information contained in retinal fundus images, a task that is not quantifiable by humans. We construct vision transformer models and are able to predict the presence high-risk allele combinations in the CFH and ARMS2 genes. We further utilize our model to produce insights into the phenotype-genotype relationship in AMD.  \nAcknowledgments  \nI would like to thank my advisor, Xin Liu. You have inspired and encouraged me to become a more precise thinker and researcher. Your patience and guidance have been invaluable. Thank you for years of thinking about problems together.  \nThank you to my dissertation committee, Xin Liu, Zhaodan Kong and Thomas Strohmer. You have all helped me carved out my niche as a mathematician and researcher.  \nThank you to the members of the lab: Jeonghoon Kim, Sean Banks, Yongshuai Liu, Shahbaz Rezaei, Ha-Min Son, Ziwen Kan, Fanyu Meng, Rex Liu, Xiaoxiao Wang, Chao Huang, Taeyeong Choi and Albara Ramli. Your dedication to your work and willingness to mull over problems have made for an","cbCainNkRAgYctDK","https://ap.wps.com/l/cbCainNkRAgYctDK","pdf",2459587,1,90,"English","en",105,"# Chapter 1. Introduction\n## 1.1. Chapter Overview\n# Chapter 2. Infection prediction in swine populations with machine learning\n## 2.2. Introduction\n## 2.3. Results\n## 2.4. Discussion\n# Chapter 3. Microgrid control under uncertainty\n## 3.3. Background\n## 3.5. Methods\n## 3.6. Results\n## 3.7. Discussion\n# Chapter 4. Genotype prediction\n## 4.2. Introduction\n## 4.3. Methods\n## 4.4. Results\n## 4.6. Discussion","[{\"question\":\"What three problem areas does the dissertation address using machine learning?\",\"answer\":\"It covers infection prediction in swine populations, microgrid control under uncertainty, and retinal genotype prediction in ophthalmology.\"},{\"question\":\"How does the dissertation approach infection prediction in swine?\",\"answer\":\"It builds a windowing-based machine learning model for prediction in two real-world swine systems and determines features that contribute value for predicting infection.\"},{\"question\":\"What modeling methods are used for microgrid control and genotype prediction?\",\"answer\":\"For microgrid control, it devises reinforcement learning algorithms to act under uncertainty. For genotype prediction, it constructs vision transformer models to predict genotypes from retinal fundus images and infer high-risk allele combinations.\"}]","Machine Learning Applications in Infection Prediction, Microgrid Control, and Ophthalmology | PDF",1785729227,227,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-applications-in-infection-prediction-microgrid-control-and-ophthalmology","",{"@graph":36,"@context":85},[37,54,68],{"@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-in-infection-prediction-microgrid-control-and-ophthalmology/120283/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What three problem areas does the dissertation address using machine learning?","Question",{"text":75,"@type":76},"It covers infection prediction in swine populations, microgrid control under uncertainty, and retinal genotype prediction in ophthalmology.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the dissertation approach infection prediction in swine?",{"text":80,"@type":76},"It builds a windowing-based machine learning model for prediction in two real-world swine systems and determines features that contribute value for predicting infection.",{"name":82,"@type":73,"acceptedAnswer":83},"What modeling methods are used for microgrid control and genotype prediction?",{"text":84,"@type":76},"For microgrid control, it devises reinforcement learning algorithms to act under uncertainty. 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