International Journal of Computer Science & Network Security

International Journal of Computer Science & Network Security (국제컴퓨터통신보호논문지학회)
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IoT Enabled Intelligent System for Radiation Monitoring and Warning Approach using Machine Learning

  • Muhammad Saifullah (Department of Computer Science, The Islamia University of Bahawalpur) ;
  • Imran Sarwar Bajwa (Department of Computer Science, The Islamia University of Bahawalpur) ;
  • Muhammad Ibrahim (Department of Computer Science, The Islamia University of Bahawalpur) ;
  • Mutyyba Asgher (Department of Computer Science, The Islamia University of Bahawalpur)
  • Received : 2023.05.05
  • Published : 2023.05.30
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Abstract

Internet of things has revolutionaries every field of life due to the use of artificial intelligence within Machine Learning. It is successfully being used for the study of Radiation monitoring, prediction of Ultraviolet and Electromagnetic rays. However, there is no particular system available that can monitor and detect waves. Therefore, the present study designed in which IOT enables intelligence system based on machine learning was developed for the prediction of the radiation and their effects of human beings. Moreover, a sensor based system was installed in order to detect harmful radiation present in the environment and this system has the ability to alert the humans within the range of danger zone with a buzz, so that humans can move to a safer place. Along with this automatic sensor system; a self-created dataset was also created in which sensor values were recorded. Furthermore, in order to study the outcomes of the effect of these rays researchers used Support Vector Machine, Gaussian Naïve Bayes, Decision Trees, Extra Trees, Bagging Classifier, Random Forests, Logistic Regression and Adaptive Boosting Classifier were used. To sum up the whole discussion it is stated the results give high accuracy and prove that the proposed system is reliable and accurate for the detection and monitoring of waves. Furthermore, for the prediction of outcome, Adaptive Boosting Classifier has shown the best accuracy of 81.77% as compared with other classifiers.

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References

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