Fake news has become a major problem affecting people, society, the economy and national security. This work proposes a combined deep learning model based on the ideal distance weighting method for fake news detection. The proposed model was validated on the ISOT and COVID-19 fake news datasets. Firstly, the ISOT and COVID-19 fake news datasets were collected. Secondly, the training-based models were used to provide accuracy values. After that, these values were transformed into criteria weights using the new ideal distance weighting method. Finally, the prediction value of the proposed model is calculated by the criteria weights. The results show that the proposed method is effective to distinguish the fake news datasets.

In this paper, a novel combination method is offered to integrate the results of two new relative closeness models, called relative closeness benevolent (RCB) and relative closeness aggressive (RCA) models, for ranking all DMUs. To prove the applicability of the proposed method, it is examined in three numerical examples, performance assessment problem, six nursing homes and fourteen international passenger airlines. Firstly, RCB and RCA models were formulated in order to generate the cross-efficiency intervals matrix (CEIM). After obtaining CEIM, the RC index was utilized to generate a combined cross-efficiency matrix (combined CEM). In combined CEM, target DMUs were viewed as criteria and DMUs were viewed as alternatives. After that, the weights of each criterion were generated using a new weighting method based on standard deviation technique (MSDT). Finally, all DMUs were evaluated and ranked. Comparison with existing cross-efficiency models indicates the more reliable results through the use of the proposed method.