![]() The application of the MWWO algorithm helps to tune the parameters of the DBN model, and the choice of optimal feature subset from the EHO algorithm leads to enhanced classification performance. The EHO algorithm is applied as a feature selector, and MWWO-DBN is utilized for the classification process. In this view, this article introduces a new FS using elephant herd optimization (EHO) with modified water wave optimization (MWWO) algorithm-based deep belief network (DBN) for FCP. Besides, computational intelligence techniques can be used as a classification model to determine the financial crisis of an organization. This is termed as a feature selection (FS) issue that assists to improvise the classifier results. An essential process toward the design of a precise financial crisis prediction (FCP) approach comprises the choice of proper variables (features) that are related to the issues at hand. ![]() A diverse set of data classifier approaches were presented for forecasting the financial crisis of an institution using the past data. At present times, financial decisions are mainly based on the classifier technique, which is utilized to allocate a collection of observations into fixed groups. ![]()
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