Looking at the issues of low efficiency, poor control performance, and difficult access control of the traditional role-based access control model, an artificial intelligence technique-based power information system access control model has been designed. The detector is designed by artificial intelligence technology, combining artificial neural network, and artificial immune algorithm, which provide the basis for checking the access request module. It has been proved that the design model can effectively support the access and modification of legitimate users and prevent illegal users from accessing, and the control accuracy is high. The use of artificial intelligence (AI) in the power sector is now reaching emerging markets, where it may have a critical impact, as clean, cheap, and reliable energy is essential to development. Artificial intelligence can be proven very efficient for resolving the control and decision-making issues in high complex systems.

This Data mining is a technique for extracting useful information from large amounts of data. In large databases, enormous patterns may be examined and evaluated utilizing statistics and artificial intelligence. Data mining can be used to anticipate future trends or uncover hidden patterns. Classification, clustering, association rules, regression, and outlier identification are examples of data mining techniques. The data mining technology is receiving a lot of traction in the healthcare industry. In the discipline of bioinformatics, several researchers are using data mining techniques. Bioinformatics is the science of storing, retrieving, organizing, interpreting, and exploiting data from biological sequences and molecules. A prediction is a statement regarding a future event based on the current condition. The major intend of this work is to predict the microarray cancer using machine learning (ML) algorithms. Different phases are comprised in the prediction of microarray cancer. This research makes the implementation of voting-based classification algorithm. The suggested algorithm assists in optimizing the performance up to 2% while predicting the microarray cancer.