IoT-Enabled Intelligent System for the Radiation Monitoring and Warning Approach

Abstract

Radiations are especially harmful to children and infants as their body cells divide rapidly, thus providing radiations more chances to interfere with the organs, leading to a number of diseases, especially those related to the skin. Therefore, the demand for a system that can detect harmful radiations timely and effectively becomes high. Many new and modern techniques comprising radiation protection and alerting systems are being introduced along with improvements and enhancements. This study demonstrates the practical implementation of an IoT-enabled intelligent system based on machine learning for radiation monitoring and warning by classifying radiations and their corresponding effects on infants. The proposed system alerts humans about the danger zones with audio/visual announcements or a buzz so that they can move to a safer place. Along with this automatic sensor system, a real-time dataset is also collected, in which sensor values are recorded along with their effects on infants for experiments. Additionally, the outcomes of the effect of radiations corresponding to the recorded sensor values are classified by using support vector machines, Gaussian naïve Bayes, decision trees, extra trees, bagging classifiers, random forests, logistic regression, and adaptive boosting classifiers. The experiment reveals that the adaptive boosting classifier gives the best accuracy of 81.77% compared to other classifiers.