REVIEW THE DEEP LEARNING TECHNIQUE FOR MISSING DATA CLASSIFICATION IN IOT APPLICATIONS FOR NETWORK PERFORMANCE IMPROVEMENT

Abstract

In order to ensure product safety and increase production quality, The construction of mine Internet of Things networks continues to accelerate mining enterprises. Given the large increase in the number of networked devices connectivity capability in the mine, there is considerable strain on the mine network communication facilities. We suggest an Innovative Solution Using Deep Learning for Missing Data Classification in IoT Network Performance Enhancement System Market Classifier based on neural networks to improve the quality of service in the connectivity infrastructure of mine networks. The classifier uses a transformation wavelet to delete the data flow and to build compliance characteristics to identify the market categories of the system.Owing to the findings of the classification, the system changes the specifications of the network services given to the terminal equipment in a versatile manner. In this way, the system's network capacity can be fairly distributed. We assess the output of the classifier model using the test data collection. We review the deep learning technique in IoT applications for Network Improvement for missing data classification