Beschreibung:
SI is a relatively recent technology that was inspired by observations of natural social insects and artificial systems. This system comprises multiple individual agents who rely on collective behavior in decentralized and self-organized networks. One of the biggest difficulties for existing computer techniques is learning from such large datasets, which is addressed utilizing big data. Big data-based categorization refers to the challenge of determining which set of classifications a new discovery belongs to. This classification is based on a training set of data that comprises observations that have been assigned to a certain category. In this paper, CIN-big data value calculation based on particle swarm optimization (BD-PSO) algorithm is proposed by operating in local optima and to improve the operating efficiency. The convergence speed of the particle swarm optimization (PSO), which operates in the local optima, is improved by big data-based particle swarm optimization (BD-PSO). It improves computing efficiency by improving the method, resulting in a reduction in calculation time. The performance of the BD-PSO is tested on four benchmark dataset, which is taken from the UCI. The datasets used for evaluation are wine, iris, blood transfusion, and zoo. SVM and CG-CNB are the two existing methods used for the comparison of BD-PSO. It achieves 92% of accuracy, 92% of precision, 92% of recall, and 1.34 of F1 measure, and time taken for execution is 149 ms, which in turn outperforms the existing approaches. It achieves robust solutions and identifies appropriate intelligent technique related to the optimization problem.