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Children and newborns are more vulnerable to radiation exposure due to fast cell division, which may interfere with organs and cause skin problems. A system that detects hazardous radiation quickly and efficiently is in great demand. Many new and improved radiation protection and warning technologies are being offered. This research classifies radiation and its effects on newborns to show the development of an IoT-enabled system that utilizes artificial intelligence for radiation monitor and alarms. To help people escape hazardous zones, the suggested system uses audio/visual notifications. Gather the automated sensor system along with a real-time dataset of sensor values and their effects on newborns. Support vector machines, additional trees, bagged classification algorithms, random forests, logistic regression, and boosting adaptive classifiers classify radiation effects based on sensor measurement. This experiment shows the boosting adaptive classifier has the highest precision.
Sambhare et al. (Fri,) studied this question.