Fingerprint Automated System Using Internet of Things (IOT)

Abstract

In the present-day scenario, engineering pupils are facing issues in understanding the topics, henceforth the amount plus the quality of training is constantly declining. Among the probable because of this particular drop in quality of training is pupils can't recognize and understand the subject. A fix to this particular issue is suggested in this paper. It's dependent on the theory of 3 preferential learning styles of a person namely visual, kinaesthetic and auditory (VAK Theory). And so the present method is grounded on classifying the pupils dependent on their learning styles and also allotting them tasks as per their preferential mastering types appropriately. The proposed system applies forwards an IOT based method that will automatically categorize pupils, hold the history of their performance, assignment, submission reputation, etc. This can aid in reducing manpower of faculty members and also improve the functionality of pupils from 4 to 42 % by custom developing tasks as per the innate learning style.