Self-report on height and weight had huge result on the precision of a screen for overweight and corpulence. Real and reported body size each helps to find the underreporting body weight. Self-reports will stay significant wellbeing reconnaissance apparatus yet ought not to be depended on solely to identify weight issues. Exactness checks of self-report information might further develop reviews of weight issues in youngsters. This Classification helps to improve the understanding the underweight and the under height causes. We consider the dataset employed to isolate the underweight and under heightened individuals of both orientations as per individual levels and loads. To isolate individuals, this study utilized an assortment of regression approaches, including linear and logistic regression and classification approaches like decision tree and random Forest techniques with the help of these techniques we achieve the self-reported anthropometric measurements can be used to determine BMI for purposes of weight classification.

In today’s modern world, the Internet is highly crucial. A larger number of wired and wireless sensors characterize the Internet of Things (IoT). These sensors are networked devices that create massive amounts of data, reaction time, latency, and security vulnerabilities. These problems create delay in decision making in some situation which is very extreme in case of Healthcare, Smart vehicles, Industry 4.0 etc., the data moving from the sensor devices have to reach the cloud and from there it has to reach back to receivers. Therefore, in order deal with such huge data we use fog computing, a well-known distributed architecture. Fog computing works with the aim to enhance the processing, intelligence, and accumulation of data closer to the Edge devices. The proposed framework helps in reduce latency as we place a Fog node device between the cloud and the edge device where data is generated and sent to the cloud and retrieved from the cloud. The framework is designed in such way that the different sensor devices can be placed on it and collects the sensor data from them. To test the framework functioning, without the fognode and with the fognode, comparing the latency with packet transfer rate from sensor devices to the cloud and vice versa. In this paper, we are considering two different case studies to test the proposed functioning of the framework with the following case studies i) Related to medical are like diabetes and cardiovascular illnesses are considered for prediction based on patient health records and ii) Vehicle theft where the owner get an alert of the theft of the vehicle within few seconds of the theft. In Medical case study initially, patient health data is acquired and stored using Sensor devices.