Abstract:

In view of the shortcomings of the existing Vague soft set extension model, three models including covering rough set, Vague soft set and soft expert set are merged and expanded, and a new mathematical model for dealing with uncertain problems is proposed in this study, namely covering rough Vague soft expert set, and related properties are studied. On this basis, this study presents an assisted diagnosis algorithm for animal diseases based on a rough set of Vague soft experts. The algorithm calculates the upper and lower approximation operators covering the rough Vague soft expert set, establishes the relationship between the disease degree and the disease level through the membership function, and makes an auxiliary diagnosis. The diagnostic results of African swine fever disease show that the ADADA_CRVSES algorithm proposed in the study is an effective auxiliary diagnosis algorithm for animal diseases, and the accuracy rate of disease diagnosis is more than 90%.

Key words: Vague set, soft set, Vague soft set, soft expert set, covering rough Vague set, covering rough Vague soft expert set, upper and lower approximation operator, disease diagnosis algorithm