Illicit Events Evaluation Using NSGA-2 Algorithms Based on Energy Consumption

The proposed work objective is to adopt the non-dominated sorting genetic algorithm II (NSGA-II), a type of MOEA (multi-objective evolutionary algorithms), to reduce the dimensionality and identify the most relevant features. The objectives and hypotheses tested using NSGA-II and MOEA are to develop predictive models that best fit the problem of traffic analysis, analyse the set of features associated with the dataset, and apply NSGA techniques to reduce the data dimensionality.The work used the datasets and considered the Transnational Terrorist Hostage Event (TTHE) and Counter Trafficking Data Collaborative (CTDC) datasets, which relate to global human trafficking (HTr).The method used is focused on analysing the energy consumption (Econ) with the carbon footprint (CFP) of the proposed algorithm for different values of the main parameters on different hardware platforms, where the quality of each individual is computed based on ML algorithms.The result represents the "F-measure” metric obtained from support vector machines (SVM) and the number of features in order to determine the dimensionality, the execution time, the Econ, and the CFP during the feature engineering process. To estimate performance, the parameter values are computed using SVM.The proposed work concludes that cybercrime employs and represents susceptible behaviour on the OSN Channels network. We identified the threat patterns by using NSGA-II and Econ analysis to establish connections between the variables in the dataset.