Incorporating Computer Networks and Machine Learning for AI-Driven Network Operators

An intelligent network operator (INO), working as an artificial entity intelligence (AI) model, has been detailed and analyzed in this paper and involves an innovative approach, which is merging computer networks and artificial neural networks (ANNs). The operator first scans the network's state information into embedding vectors for matching embedding vectors of different types of information at each point of the network's operation. The fact that the operator has successfully cleared all testing phases with a 100% accuracy rate is mitigating the risks during the operation. Furthermore, extending the identification and resolution of major training losses is also introduced as a novel technique to maximize the overall efficiency of the operator's training process. A specific sealed computer network simulator has been designed to facilitate a teaching and testing environment. It also serves as a simulator that allows data collection, training, and testing procedures to be automated. This essay outlines succinctly the principal advancements used in this study through the use of the novel methodologies employed in the design, development and testing of the AI-based network operator. Both of these contributions are very important for the area and indicate that there exists the potential to improve network operations and training and testing frameworks through the application of AI.