[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121701-en":3,"doc-seo-121701-105":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},121701,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","Optimization of Machine Learning Models with segmentation to Determine the Pose of Cattle","Image pattern recognition introduces major difficulties in feature recognition, turning pose identification into a complex task for machine learning models. The study targets cow pose detection by classifying cow images into pose categories such as front, right, left, and others. With the growth of image-based applications, the work proposes an SVM and Random Forest pipeline, enhanced by data augmentation (Gaussian blur and brightness adjustments) and segmentation. Segmentation is performed using Canny and K-means, then accuracy and ROC evaluate competing models, showing strong performance with Canny-SVM and K-means-RF.","Optimization of Machine Learning Models with segmentation to  \nDetermine the Pose of Cattle  \nAmril Mutoi Siregar 1, Sony Hartono Wijaya2, Ahmad Fauzi 1, Tjong Wan Sen3 , Sutan Faisal 1, Tukino 1, Yana  \nCahyana 1  \n1Departement of Informatics, Faculty of Computer Science, University Buana Perjuangan, Karawang, Indonesia 2Departement Computer Science, Faculty of Mathematics and Natural Sciences, IPB University, Bogor, Indonesia  \n3Faculty of Computing, President University Jl. Ki Hajar Dewantara, Jababeka, Bekasi, Indonesia  \nARTICLE INFO  \nArticle history:  \nReceived July 24, 2023 Revised August 15, 2023 Published September 07, 2023  \nKeywords:  \nAugmentations;  \nCattle;  \nMachine Learning; Random Forest;  \nSegmentation;  \nSupport Vector Machine  \nCorresponding Author:  \nABSTRACT  \nImage pattern recognition poses numerous challenges, particularly in feature recognition, making it a complex problem for machine learning algorithms. This study focuses on the problem of cow pose detection, involving the classification of cow images into categories like front, right, left, and others. With the increasing popularity of image-based applications, such as object recognition in smartphone technologies, there is a growing need for accurate and efficient classification algorithms based on shape and color. In this paper, we propose a machine learning approach utilizing Support Vector Machine (SVM) and Random Forest (RF) algorithms for cow pose detection. To achieve an optimal model, we employ data augmentation techniques, including Gaussian blur, brightness adjustments, and segmentation. The proposed segmentation methods used are Canny and Kmeans. We compare several machine learning algorithms to identify the optimal approach in terms of accuracy. The success of our method is measured by accuracy and Receiver Operating Characteristic (ROC) analysis. The results indicate that using the Canny segmentation, SVM achieved 74.31% accuracy with a testing ratio of 90:10, while RF achieved 99.60% accuracy with the same testing ratio. Furthermore, testing with SVM and K-means segmentation reached an accuracy of 98.61% with a test ratio of 80:20 . The study demonstrates the effectiveness of SVM and Random Forest algorithms in cow pose detection, with Kmeans segmentation yielding highly accurate results. These findings hold promising implications for real-world applications in image-based recognition systems. Based on the results of the model obtained, it is very important in pattern recognition to use segmentation based on color eventhough shape recognition.  \nThis work is licensed under a Creative Commons Attribution-Share Alike 4.0  \nAmril Mutoi Siregar, Department of Informatics, Faculty of Computer Science, University Buana Perjuangan, Karawang, Indonesia  \nEmail: [amrilmutoi@ubpkarawang.ac.id](amrilmutoi@ubpkarawang.ac.id)  \n1. INTRODUCTION  \nPose estimation in animal husbandry is a specialized task to detect the predefined positions of humans, cows, pigs in an image. This research started in the 1990s, aimed at animal detection, face pattern estimation and motion tracking. Recently, the cost, time and technical aspects of pose estimation have become much more efficient. Now applications can gradually be integrated into aspects of life, such as gesture-based humancomputer interaction [1], gesture assessment by viewing animal and human postures in healthcare applications [2], [3], social security-adversary action detection [4] . Technological advances using machine learning and deep learning models, which have led to an increase in pose estimation work, open up topics into animal pose estimation. For computational analysis of animal behavior, pose estimation is often a critical step and deep  \nlearning-based tools have rapidly influenced neuroscience, ethology, and medicine [5], [6] . Tracking animals between frames can be difficult due to similarity in appearance, immobile behavior and possible occlusion. Based on human pose estimation research","cbCaidKFBVeLlbl9","https://ap.wps.com/l/cbCaidKFBVeLlbl9","pdf",1009670,1,11,"English","en",105,"# Introduction\n## Pose estimation in animal husbandry\n## Motivation for automatic cow pose detection\n## Prior work and research gap\n## Proposed approach and pipeline","[{\"question\":\"How does the study perform cow pose detection?\",\"answer\":\"Cow images are classified into predefined pose categories such as front, right, and left. The pipeline combines Support Vector Machine (SVM) and Random Forest (RF) with segmentation and image augmentation.\"},{\"question\":\"Which segmentation methods and data augmentations are used?\",\"answer\":\"Segmentation uses Canny and K-means. Data augmentation includes Gaussian blur and brightness adjustments, alongside segmentation to improve model training.\"},{\"question\":\"What evaluation metrics are used to assess the models?\",\"answer\":\"Model success is measured using accuracy and Receiver Operating Characteristic (ROC) analysis under different test ratios.\"}]","Optimization of Machine Learning Models with segmentation to Determine the Pose of Cattle | PDF",1785806339,28,{"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},"optimization-of-machine-learning-models-with-segmentation-to-determine-the-pose-of-cattle","",{"@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/optimization-of-machine-learning-models-with-segmentation-to-determine-the-pose-of-cattle/121701/",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","2026-08-04",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},"How does the study perform cow pose detection?","Question",{"text":75,"@type":76},"Cow images are classified into predefined pose categories such as front, right, and left. The pipeline combines Support Vector Machine (SVM) and Random Forest (RF) with segmentation and image augmentation.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which segmentation methods and data augmentations are used?",{"text":80,"@type":76},"Segmentation uses Canny and K-means. Data augmentation includes Gaussian blur and brightness adjustments, alongside segmentation to improve model training.",{"name":82,"@type":73,"acceptedAnswer":83},"What evaluation metrics are used to assess the models?",{"text":84,"@type":76},"Model success is measured using accuracy and Receiver Operating Characteristic (ROC) analysis under different test ratios.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]