Framework for Securing Quality Control Process in Engineering Using IOT based Machine Learning Models

The study aims to investigate how combining many technologies may improve the scalability, adaptability, and security of network engineering systems. Some of the technologies that come under this category include the Internet of Things (IoT), Network Function Virtualization (NFV), Machine Learning (ML), and Software Defined Networking (SDN). Examining potential effective merger strategies for these technologies is one of the study's objectives. Our study has shown that our approach improves organizational effectiveness, defect finding, and engineering productivity. This is accomplished via the use of machine learning (ML) and the Internet of Things (IoT) to real-time data collecting, predictive analytics, and quality control assurance. By making use of these two technologies, this assignment may be successfully completed. The case studies demonstrate the potential of our suggested machine learning models to decrease training time and improve engineering quality control assessment accuracy. The fact that our models can do both of these tasks in comparison to earlier machine learning models serves as evidence for this.