Abstract
The most notable health risk is the use of alcohol. Alcohol consumption, when exceeds the limit, causes an alarming situation where the person is prone to various diseases like liver cirrhosis, increase blood pressure, mental disorders, impotency, road accidents, and violence under the influence of alcohol. Although several reports have been submitted regarding the existence of addiction to alcohol among males in villages, the exact reason for alcohol addiction and its psychological, socio-economic impacts remains unknown and is a challenge for exploration by the data analyst. Various types of alcoholism and problems faced by the drinker have been analyzed using statistical data collected in the villages, Internet of Things-based alcohol monitoring on the Blynk app, and gas sensors. When a drunk person breathes near the alcohol sensor, it detects the ethanol in his breathing and provides an output based on alcohol concentration. If there is more alcohol concentration, the indication light would be brighter and vice versa. Hence, the concentration and consumption of alcohol by a person are detected and extensively studied by deploying artificial neural networks. The training and testing can be done using the multilayer perceptron model, which uses a radial basis function network.
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