Communication Fault Maintenance Decision of Information System Based on Inverse Symmetry Algorithm In view of the characteristics of various types of information system integrated hardware and software systems, complex network topology, complex causes of fault alarm and uncertainty, this paper studies the communication fault maintenance decisionmaking based on the inverse symmetry algorithm. Based on the principle of the inverse symmetry algorithm, the modulation and demodulation process of information system is determined, the redundant system attributes in the information system are reduced by rough set, and the Bayesian network model with minimum diagnosis set is obtained by combining the prior knowledge in the operation process of the information system. The input and output of the network are the condition attributes and decision attributes of the decision table, respectively. Through the above process, the optimal fault diagnosis rules are established. After the communication fault is diagnosed, different communication fault maintenance decisions of the information system are used to eliminate the fault. The experimental results show that this method can effectively diagnose the communication fault of the information system and make effective maintenance decisions. The average accuracy of data transmission of the information system using this method is over 99.5% under different operating distances, which has better communication performance.

A DecisionTheoretic Rough Set Approach to LatticeValued Information System The decisiontheoretic rough set utilizes Bayesian decision to interpret the thresholds of probabilistic rough set model. That provides a novel semantic description for rough regions in the viewpoint of threeway decision theory and has been applied to numerous fields. However, it lacks the ability to deal with latticevalued information system (LvIS), in which the condition attribute set consists of multiple types of attributes and their domain constitute lattice. Therefore, this study concentrates on the decisiontheoretic rough approach in a LvIS. Then, the total decision cost associated with rough regions is addressed and an attribute reduction algorithm will be designed based on minimum decision cost. Finally, a case study on medical diagnosis is conducted to illustrate the decision procedure and attribute reduction approach.