Simulation Prediction of Background Radiation Using Machine Learning

Authors

  • Peter Oluwasayo Adigun Department of Computer Science, New Mexico Highlands University, 1005 Diamond St, Las Vegas, New Mexico, USA
  • Tobi Titus Oyekanmi Department of Media, Art, and Technology, New Mexico Highlands University, 1005 Diamond St, Las Vegas, New Mexico, USA
  • Ayodeji Adedotun Adeniyi Department of Media, Art, and Technology, New Mexico Highlands University, 1005 Diamond St, Las Vegas, New Mexico, USA

Keywords:

Machine Learning, random forest, Naïve Bayes Classifiers, Support Vector Machines

Abstract

The 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.

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Published

2025-02-03

How to Cite

Peter Oluwasayo Adigun, Tobi Titus Oyekanmi, & Ayodeji Adedotun Adeniyi. (2025). Simulation Prediction of Background Radiation Using Machine Learning. American Scientific Research Journal for Engineering, Technology, and Sciences, 101(1), 71–96. Retrieved from https://asrjetsjournal.org/index.php/American_Scientific_Journal/article/view/11432

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Articles