[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117330-en":3,"doc-seo-117330-105":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},117330,1099514068035,"Ezra","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Simulation Prediction of Background Radiation Using Machine Learning","Simulation of natural background radiation data applies machine learning to radiation physics by training field-collected datasets with multiple algorithms. Data were obtained through a field study conducted in Gwagwalada Area, Abuja, Federal Capital Territory, Nigeria. Random Forest, Naïve-Bayes, Support Vector Machine, and Kernel Support Vector Machine are evaluated for classifying background radiation effects as harmful or harmless. Random Forest achieves the highest test accuracy (94.0%) with strong trained (98%) and K-fold cross-validation (96.9%) performance, supporting AI-based identification and categorization.","Simulation Prediction of Background Radiation Using  \nMachine Learning  \nPeter Oluwasayo Adiguna *, Tobi Titus Oyekanmib, Ayodeji Adedotun Adeniyic  \naDepartment of Computer Science, New Mexico Highlands University, 1005 Diamond St, Las Vegas, New  \nMexico, USA  \nb.cDepartment of Media, Art, and Technology, New Mexico Highlands University, 1005 Diamond St, Las Vegas,  \nNew Mexico, USA  \n[a](aEmail: poadigun@nmhu.edu)[Email: poadigun@nmhu.edu](aEmail: poadigun@nmhu.edu)  \nbEmail: [toyekanmi@live.nmhu.edu](toyekanmi@live.nmhu.edu)  \ncEmail: [aadeniyi1@live.nmhu.edu](aadeniyi1@live.nmhu.edu)  \nAbstract  \nThe simulation of the natural background radiation dataset is research that implemented the application of machine learning in radiation physics. This is achieved by training natural background radiation datasets using different machine learning algorithms. The background radiation dataset is acquired from a field study carried out in the Gwagwalada Area, Abuja, Federal Capital Territory, Nigeria. The different machine learning algorithms applied are Random Forest, Naïve-Bayes, Support Vector Machine, and Kernel Support Vector Machine. Random Forest algorithms have the best test accuracy of 94.0%, a trained score of 98%, a K-fold cross validation score of 96.9%, and efficiently classify the effect of background radiation as harmful or harmless. This result established the integrated application of artificial intelligence and therefore indicates that machine learning has the ability to classify and categorize the effect of background radiation datasets.  \nKeywords: Machine Learning (ML); Random Forest (RF); Naïve-Bayes (NB); Support Vector Machine (SVM); Kernel Support Vector Machine (KSVM) .  \nReceived: 11/23/2024  \nAccepted: 1/23/2025  \nPublished: 2/3/2025  \n* Corresponding author.  \n1. Introduction  \nIn the field of science and engineering, simulation, modeling, and prediction are pertinent approaches to describing and understanding the dynamism of the real world. Some of the systems employed in making such decisions and predictions are artificial intelligence (AI) and machine learning (ML). Machine learning (ML) is afield of inquiring, learning, and interpreting, and it is the sole aim of the system (machine) being trained for.“It is a field of inquiry devoted to understanding and building methods, methods that model data to enhance performance on some set of tasks”[1] .  \nIn some parts of the world, little attention or zero attention is given to background radiation, which is hazardous to life and our environment. Background radiation is the amount of ionizing radiation present in the environment at a particular location which is not due to the deliberate introduction of radiation sources. Background radiation is also the intensity of ionizing radiation in a particular area per unit of time (hour) . Background radiation originates from a variety of sources, both natural and artificial. These include cosmic radiation and environmental radioactivity such as naturally occurring radioactive materials (NORMs) including radon and radium, and man-made fallout from nuclear weapons testing and nuclear accidents.  \nRadionuclides or radioisotopes are the main elements that cause ionizing radiation. These radioisotopes are heavy nuclei and unstable atoms that have high amounts of energy. There are classes of nuclei which are numerous and unstable elements. They break up or disintegrate spontaneously by emitting some corpuscular or electromagnetic radiation of very high energy. In the first case, the atomic number Z or the mass number A or both, of the nucleus change, thereby producing an altogether new nucleus. In the second case, the nucleus makes a transition from a quantum state of higher energy to one of lower energy. This spontaneous transformation of a nucleus is known as radioactivity which was the first nuclear phenomenon to be discovered[2] .  \nRadionuclides or radioisotopes are the main elements that cause ionizing radiation. T","cbCais0ZMggt6Znr","https://ap.wps.com/l/cbCais0ZMggt6Znr","pdf",609626,1,26,"English","en",105,"# Introduction\n## Background radiation and its sources\n## Radioisotopes and radioactivity\n## Natural exposure levels\n## Machine learning motivation\n# Methods and Algorithms\n## Random Forest\n## Naïve Bayes\n## SVM and Kernel SVM\n# Results and Discussion\n## Accuracy and validation metrics\n## Harmful vs harmless classification\n## Implications of AI-based radiation prediction","[{\"question\":\"What dataset is used for background radiation simulation and prediction?\",\"answer\":\"The simulation uses a natural background radiation dataset collected through a field study in Gwagwalada Area, Abuja, Federal Capital Territory, Nigeria.\"},{\"question\":\"Which machine learning algorithms are compared in the study?\",\"answer\":\"The study trains and compares Random Forest, Naïve-Bayes, Support Vector Machine, and Kernel Support Vector Machine.\"},{\"question\":\"What is the best performing algorithm and its accuracy?\",\"answer\":\"Random Forest performs best, achieving 94.0% test accuracy, 98% trained score, and 96.9% K-fold cross-validation score.\"}]","Simulation Prediction of Background Radiation Using Machine Learning | PDF",1785675220,66,{"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},"simulation-prediction-of-background-radiation-using-machine-learning","",{"@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/simulation-prediction-of-background-radiation-using-machine-learning/117330/",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","2026-08-02",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 dataset is used for background radiation simulation and prediction?","Question",{"text":75,"@type":76},"The simulation uses a natural background radiation dataset collected through a field study in Gwagwalada Area, Abuja, Federal Capital Territory, Nigeria.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning algorithms are compared in the study?",{"text":80,"@type":76},"The study trains and compares Random Forest, Naïve-Bayes, Support Vector Machine, and Kernel Support Vector Machine.",{"name":82,"@type":73,"acceptedAnswer":83},"What is the best performing algorithm and its accuracy?",{"text":84,"@type":76},"Random Forest performs best, achieving 94.0% test accuracy, 98% trained score, and 96.9% K-fold cross-validation score.","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"]