Deep Fuzzy Models and the Realm of Applications

The recent days have seen huge developments in deep learning with specific reference to artificial neural networks (ANN). However, ANNs cannot address when data is impregnated with ambiguity, uncertainty of non statistical kind, vagueness, and noise. These factors are detrimental to efficient learning of deep networks. It is exactly here that the role of deep fuzzy models comes to play. These models can effectively capture the mentioned vagaries of data and are the best to accommodate humanistic notions, approximations, and tolerance to imprecision. The fruitions of the capabilities of deep fuzzy notions has led to development of models. In this direction, this paper makes an overall view of ongoing research work related to deep fuzzy models in the individual capacity and hybridized models. This article explores application of the concept in the realm of data processing, fault diagnosis, image processing, Robotics, vulnerability detection systems, and many more. It is hoped that this article of review will facilitate the novice researchers who have set forth in this direction to apply deep fuzzy concepts to achieve high accuracy in conventional as well as widely used learning tasks such as object recognition, computer vision, and in certain AI applications within a short time.