Missing Data Classification in IoT Applications for Network Performance Improvement Using Artificial Intelligence

Abstract

Many new IoT products have recently been commonly used across various industries and sectors, including medical care, smart homes, smart mills, and so on. Using data generated by IoT devices in intelligent spaces, a variety of key technology studies such as context recognition, vice recommendations, and the lack of IoT application data classification have been carried out. The data on the IoT device is expected to be complete during these tests. However, data from IoT devices can be incomplete due to a variety of factors such as stocking failures, defective IoT devices, and network instability. Incomplete data provided by a variety of IoT devices may lead to the discovery of noisy and redundant values. Missing values, in particular, are a common cause of IoT issues. The accuracy and reliability of IoT applications such as context awareness, real-time decision making, service instructions, and abnormality detection suffer from a lack of relevance. The strategy is proposed. The scientific method in cross-neural data classification IoT network performance has not improve.