Use Artificial Bee Colony to Determine the Optimal Stratigraphic Boundaries for a Threshold Geometric Stochastic Process

Abstract

        The Artificial Bee Colony (ABC) algorithm was used in this study as a representation of a developmental artificial intelligence technique to identify the best class boundaries for modeling data on the number of daily infections with the Coronavirus (Covid-19) epidemic and for the three governorates of Iraq (Baghdad, Erbil, and Basra). The number of daily infection cases during the outbreak of a particular epidemic disease, such as the emerging acute respiratory syndrome coronavirus, frequently exhibits multiple trends: a steady increase during the epidemic's growth phase or outbreak, followed by a stable phase in the number of daily infection cases (referred to as the stabilization phase by some sources), which involves controlling the epidemic to eradicate it, and a subsequent decline, decline, or disappearance phase. In order to handle this type of data, a random stochastic model known as the geometric stochastic process model with an intelligent threshold was put forth. This model uses an intelligent technique to identify the data's turning points, or inversions.