Improving data quality in a network of optimizers

In the process of data generation or transmission, the quality of data may degrade and not meet the required level for subsequent processing steps. Improving data quality is one of the crucial steps that needs to be taken to obtain accurate information hidden within the data in any field. Researchers have proposed various methods to perform this process, which differ based on the type of data. However, it is important to note that often these methods do not consider the existing similarities in different dimensions of the data simultaneously. This can have an undesirable or detrimental impact on certain parts of the data and may not improve the damaged segments. As a result, the obtained output will not contain all the desired information. In this paper, a new method is introduced in which data quality improvement is carried out using a set of collaborative nodes in an interactive network structure. This method enhances resistance against various types of degradation by employing a set of nodes. The performance of the proposed method is compared with six other state-of-the-art data quality improvement methods on real degraded datasets. The results obtained from the simulation show that the proposed method outperforms the other compared methods.