The majority of evidential fusion models presented in the literature is based on optimistic assumptions about the reliability of the models producing beliefs and assumes that they are equally reliable. At the same time, the belief models used in combination may have some limitations and may result in different reliabilities, which may decrease the performance of the combination. One way to confront this problem is to consider a discount rule utilizing reliability coefficients. One of the problems of using discounting is the way of modeling reliability coefficients. This paper proposes modeling reliability coefficients by considering a new effective measure of belief uncertainty. The new reliability coefficients are introduced in a multilayer decision fusion-based Convolutional Neural Network (CNN) architecture built within the Transferable Belief Model, as well as in a multimodal deep learning scenario. Case study results demonstrate the feasibility of representing reliability by the belief uncertainty measure considered.
