[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-116941-en":3,"doc-seo-116941-105":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},116941,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",8,"Research & Report","Machine Learning for Biosensors - Problem Report","Biosensors have become widely used diagnostic tools because they can detect and quantify biological analytes across many application domains. Growing needs for faster, more reliable biosensing devices make machine learning an important approach for improving performance. The report reviews recent progress in applying machine learning to biosensors, covering advantages such as higher sensitivity, selectivity, and accuracy. It also examines key techniques including data preprocessing, feature extraction, classification, and data analysis models, alongside integration challenges related to data availability, sensor performance, and computational demands.","Graduate Theses, Dissertations, and Problem Reports  \n2023  \nMachine Learning for Biosensors  \nGayathri Anapanani [ga00014@mix.wvu.edu](ga00014@mix.wvu.edu)  \nFollow this and additional works at: [https://researchrepository.wvu.edu/etd](https://researchrepository.wvu.edu/etd)  \n Part of the Biomedical Commons, and the Other Computer Engineering Commons  \nRecommended Citation  \nAnapanani, Gayathri, \"Machine Learning for Biosensors\" (2023) . Graduate Theses, Dissertations, and Problem Reports. 12118.  \n[https://researchrepository.wvu.edu/etd/121](https://researchrepository.wvu.edu/etd/121)18  \nThis Problem/Project Report is protected by copyright and/or related rights. It has been brought to you by the The Research Repository @ WVU with permission from the rights-holder(s) . You are free to use this Problem/Project Report in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you must obtain permission from the rights-holder(s) directly, unless additional rights are indicated by a Creative Commons license in the record and/ or on the work itself. This Problem/Project Report has been accepted for inclusion in WVU Graduate Theses, Dissertations, and Problem Reports collection by an authorized administrator of The Research Repository @ WVU. For more information, please contact [researchrepository@mail.wvu.edu](researchrepository@mail.wvu.edu).  \nMachine Learning for Biosensors  \nGayathriAnapanani  \nProblem Report submitted to the  \nStatler College of Engineering and Mineral Resources at West Virginia  \nUniversity  \nIn partial fulfillment of the requirements for the degree of Master of Science in Computer Science  \nYuxin Liu, PhD., Chair  \nJeremey Dawson, Ph.D.  \nNatalia Schmid, Ph.D.  \nLane Department of Computer Science and Electrical Engineering West Virginia University  \nMorgantown, West Virginia  \n2023  \nKeywords: Plant Species Classification, FGVC, Classification, Fine-Grained Visual Classification, Optimization  \nCopyright 2023 GayathriAnapanani  \nABSTRACT  \nMachine Learning for Biosensors  \nGayathri Anapanani  \nBiosensors have become increasingly popular as diagnostic tools due to their ability to detect and quantify biological analytes in a wide range of applications. With the growing demand for faster and more reliable biosensing devices, machine learning has become a valuable tool in enhancing biosensor performance. In this report, we review recent progress in the application of machine learning to biosensors. We discuss the potential benefits of using machine learning in biosensors, including improved sensitivity, selectivity, and accuracy. We also discuss the various machine learning techniques that have been applied to biosensors, including data preprocessing, feature extraction, and classification and data analysis models. The potential benefits of machine learning in biosensors are discussed, including the ability to analyze large and complex data sets, to detect subtle changes in biomolecular interactions, and to provide real-time monitoring of biological processes. The challenges associated with the integration of machine learning and biosensors are also addressed, including data availability, sensor performance, and computational requirements. We further highlight the challenges and opportunities for the integration of machine learning and biosensors, including the development of portable and low-cost biosensors, and the use of machine learning algorithms for efficient data analysis. Finally, we provide an outlook on future trends and emerging technologies in the field, including the use of artificial intelligence and deep learning algorithms for biosensors, and the potential for creating a fully autonomous biosensing system.  \nAcknowledgements  \nMost importantly I would like to thank Dr. Yuxin Liu for giving me an opportunity to be part of her Lab. It is because of her motivation to accomplish things, constant follow-ups and engaging with the group wh","cbCaicJGBVWyJOG1","https://ap.wps.com/l/cbCaicJGBVWyJOG1","pdf",1905641,1,74,"English","en",105,"# Table of Contents\n## Chapter 1: IntroducƟon\n## Chapter 2. 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