Reduction of Human Error through Deep Learning Model using Internet of Things

A wide range of IoT applications for the industry has been developed, in recent years has been expanded. To understand the development of the internet of things in the industry. Internet of things (IoT) is to build a strong industrial automation system, in addition to the radio frequency identification, wireless, mobile, offers a promising opportunity to utilize the growing popularity of the sensor equipment. Using machine learning model to identify the error like Decision Tree (DT) and Support Vector Machine (SVM), have been used in automated production design, this method, since the automated system, delay or interruption of the error is observed, increase the complexity of the production plant it has suffered thorugh humar error or automation erros.To overcome the problems of this method, a method using the apparatus of the IoT terminal system using a Recurrent Neural Network (RNN). In the proposed global Internet of things architecture is implemented to find the error though data analysis model with observed sensor unit with components of the Internet of things in smart terminal system and product design, analyze their main needs, some of the latest smart clothing of application. In this method, eciency to provide smart error prediction gives support to provide clothing that is connected to error monitoring of things of the network, the future development of smart industry, the smart industry in the current It will guide the object.