A new double relaxed inertial viscosity-type algorithm for split equilibrium problems and its application to detecting osteoporosis health problems

Abstract

This article proposes a new double relaxed inertial viscosity-type algorithm for solving split equilibrium problems; this algorithm is flexible to use by the relaxed extrapolation parameters in real numbers. We obtain a strong convergence theorem under suitable assumptions of two bifunctions in real Hilbert spaces. In data classification, we apply our proposed algorithm for finding optimal output weight in an extreme learning machine. The primary features of the Osteoporosis dataset from Harvard Dataverse are used for the algorithm’s experiments. The right extrapolation parameters show that the algorithm converges faster than the standard algorithm. The comparison with the existing algorithm is demonstrated for our proposed algorithm’s high efficiency. Finally, our algorithm’s accuracy and loss plots are presented, obtaining our good-fitting model.