Hybrid data fusion model for restricted information using Dempster–Shafer and adaptive neuro-fuzzy inference (DSANFI) system

Information fusion is the crux of data fusion which is used to compare large numerical data using normalization and aggregate function. The recent trends in information fusion have the limitations in fusing the important details in order to overcome the security issues in both low-level and high-level information fusion systems. The information from heterogeneous sources is different from one another like conceptual, contextual and graphical. This information is included for the fusion process, but the ancient approach has the limitation, and this research work proposes a new model of information fusion for the restricted content for various types of information. In this research work, an algorithm for information fusion is implemented for decision making based on Dempster–Shafer and adaptive neuro-fuzzy inference (DSANFI) system. Proposed hybrid work is applicable in data fusion process based on the theoretical approach of Dempster–Shafer and then into ANFIS. The proposed data fusion method is employed in a wide range of fields which include robotics, statistics, estimation and control.