[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125109-en":3,"doc-seo-125109-105":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":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},125109,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Machine Learning Classifiers Based on HoG Features Extracted from Locomotive Neutral Section Images - Comparative Study","This paper presents a comparative evaluation of machine learning classifiers for neutral section image classification using Histogram of Oriented Gradient (HoG) features extracted from a neutral section dataset. A neutral section is a phase break in the Transnet freight rail system, enabling locomotives to auto-switch between phases via induction magnets and onboard magnet detection sensors. Using the dataset with open/close markers, classifiers including Decision Tree, Discriminant Analysis, SVM, KNN, Ensemble, Naïve Bayes, and CNN are assessed with a confusion matrix, F1-measure, and computation time. MATLAB Classification Learner results indicate Linear SVM achieves the best prediction speed and strong accuracy (93.40% training, 94% test, 75 objects/s).","2022 International Conference on Engineering and Emerging Technologies (ICEET) | 978-1-6654-9 106-8/22/$31.00 ©2022 IEEE | DOI: 10. 1 109/ICEET56468 .2022. 10007093  \nProc. of the 8th International Conference on Engineering and Emerging Technologies (ICEET) 27-28 October 2022, Kuala Lumpur, Malaysia  \nMachine Learning Classifiers Based on HoG Features Extracted from Locomotive Neutral  \nSection Images  \nChristopher Thembinkosi Mcineka Dept. of Electronic & Computer Engineering  \nDurban University of Technology Durban, South Africa [21959502@dut4life.ac.za](21959502@dut4life.ac.za)  \nNelendran Pillay Dept. of Electronic & Computer  \nEngineering  \nDurban University of Technology Durban, South Africa [trevorpi@dut.ac.za](trevorpi@dut.ac.za)  \nAbstract— This paper presents a comparative study on machine learning algorithms for neutral section image classification. The classifiers are trained by employing the Histogram of Oriented Gradient features that are extracted from the neutral section dataset [1]. A neutral section is a phase break that is used on the Transnet freight rail system to separate the single-phase supply from the 25kV three-phase overhead traction supply. The 25kV is a stepped-down voltage from an 88kV three-phase supply coming from the national grid. While the main purpose of the neutral section is to separate phase voltages, electric locomotives can traverse through these phases by switching On and Off. This auto-switching is possible through induction magnets installed in between the rails and with magnet detection sensors installed underneath the locomotives. However, a computer vision model has been developed, trained, and tested with a neutral section dataset containing images having open and close markers [1]. This paper, therefore, utilises this dataset to provide performance comparison on several machine learning classification algorithms viz. Decision Tree, Discriminant Analysis, Support Vector Machine, K-Nearest Neighbors, Ensemble, Naïve Bayes, and Convolutional Neural Network. A confusion matrix, F1-measure and computation time are employed to measure the performance of each classifier. The MATLAB Classification Learner application was used to obtain the results. The results show that the Linear Support Vector Machine performs best when considering performance and prediction speed. The Linear Support Vector Machine achieved a training accuracy of 93.40% with a test accuracy reaching 94% at a prediction speed of 75 objects per second (computation time).  \nKeywords— Neutral section dataset, Machine Learning Classifiers, Histogram of Oriented Gradient, Computer Vision, MATLAB, Confusion matrix, F1-measure.  \nI. INTRODUCTION Transnet embarked on a strategy called Transnet 4.0, which aimed at aligning its strategy with the Fourth Industrial Revolution (4IR) technologies. In keeping with this ethos, Mcineka and Reddy [1] developed a model that employed Machine Learning (ML) algorithm to automatically switch off/on the electric locomotives as they traverse through the Neutral Section (NS) . Therefore, the conventional onboard switching scheme deployed in the Transnet railway lines can be replaced with a Computer Vision (CV) based system. In [1], the authors were able to achieve an overall accuracy of  \n72% on their model. ML classifiers have been deployed indifferent sectors such as in transportation viz. traffic sign detection and Automatic Number Plate Recognition (ANPR) . While several pieces of literature have employed ML classifiers, few have focused on the automatic switching of electric locomotives in railway industries [1] . Subsequently, the latter implies that there is a scarcity of available datasets that can be used to train and test any classifier being proposed for auto-switching electrical locomotives through a CV system. Artificial Intelligence (AI) technology is part of the 4IR industrial changes and CV being the field of AI is in line with the Transnet 4.0 strategy. The CV enables a compute","cbCaiu1AKrT98H4T","https://ap.wps.com/l/cbCaiu1AKrT98H4T","pdf",419238,1,6,"English","en",105,"# Abstract\n# Introduction\n# Literature Review\n## Decision Trees\n## Other Classifiers\n# Conventional Neutral Section in Transnet Freight Rail\n# Dataset Overview\n# Mathematical Model of Classifiers\n# Results and Performance Metrics\n# Conclusion","[{\"question\":\"What is the purpose of the neutral section in the Transnet freight rail system?\",\"answer\":\"It acts as a phase break that separates single-phase supply from 25kV three-phase overhead traction supply, while locomotives traverse by switching on/off between phases.\"},{\"question\":\"Which feature representation and dataset are used for training and evaluation?\",\"answer\":\"The study uses HoG features extracted from a neutral section dataset containing images with open and close markers.\"},{\"question\":\"How is classifier performance measured in the paper?\",\"answer\":\"Performance is measured using a confusion matrix, F1-measure, and computation time, with results produced in MATLAB Classification Learner.\"}]","Machine Learning Classifiers Based on HoG Features Extracted from Locomotive Neutral Section Images - Comparative Study | PDF",1785896692,15,{"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-classifiers-based-on-hog-features-extracted-from-locomotive-neutral-section-images-comparative-study","",{"@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-classifiers-based-on-hog-features-extracted-from-locomotive-neutral-section-images-comparative-study/125109/",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","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the purpose of the neutral section in the Transnet freight rail system?","Question",{"text":75,"@type":76},"It acts as a phase break that separates single-phase supply from 25kV three-phase overhead traction supply, while locomotives traverse by switching on/off between phases.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which feature representation and dataset are used for training and evaluation?",{"text":80,"@type":76},"The study uses HoG features extracted from a neutral section dataset containing images with open and close markers.",{"name":82,"@type":73,"acceptedAnswer":83},"How is classifier performance measured in the paper?",{"text":84,"@type":76},"Performance is measured using a confusion matrix, F1-measure, and computation time, with results produced in MATLAB Classification Learner.","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},[93,97,101,105,110,114,119,122,127,130,134],{"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":21,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]