A Hybrid Approach to Classifying Crime Big Data

Abstract

Big data opens up new possibilities in a variety of areas. In some instances, having a large dataset available may be beneficial. However, processing an Exabyte of data in its entirety is difficult. This article uses Hybrid Enhanced Artificial Bee Colony to create a hybrid algorithm for obtaining timely and actionable knowledge to expand and use comprehensive extensive data modelling systems and analytics algorithms. Hybrid Enhanced Artificial Bee Colony (HEABC) algorithm Support Vector Machine (SVM) classifiers have been contemplatedto build a hybrid algorithm for obtaining timely and actionable information. Resolving this issue would permit high exactitude data classification, which is particularly important for Big Data, to be completed in a reasonable amount of time. The hybrid algorithm computes an s. The hybrid algorithm simultaneously seeks the kernel function's form. The concept of bees inspired the hybrid EABC algorithm. During the algorithm's implementation, some bees alter the shape of their kernel function to correspond to the particle with the most significant classification accuracy. The proposed EABC algorithm is a mathematical formula that reduces the time spent creating SVM classifiers, which is crucial for solving the big data classification problem. SVM classification work focused on the hybrid EABC algorithm. Experimentation has shown that the Big Data classification approaches provided are adequate.