An XGBoost-based casualty prediction method for terrorist attacks

Abstract

Terrorist attacks have been becoming one of the severe threats to national public security and world peace. Ascertaining whether the behaviors of terrorist attacks will threaten the lives of innocent people is vital in dealing with terrorist attacks, which has a profound impact on the resource optimization configuration. For this purpose, we propose an XGBoost-based casualty prediction algorithm, namely RP-GA-XGBoost, to predict whether terrorist attacks will cause the casualties of innocent civilians. In the proposed RP-GA-XGBoost algorithm, a novel method that incorporates random forest (RF) and principal component analysis (PCA) is devised for selecting features, and a genetic algorithm is used to tune the hyperparameters of XGBoost. The proposed method is evaluated on the public dataset (Global Terrorism Database, GTD) and the terrorist attack dataset in China. Experimental results demonstrate that the proposed algorithm achieves area under curve (AUC) of 87.00%, and accuracy of 86.33% for the public dataset, and sensitivity of 94.00%, AUC of 94.90% for the terrorist attack dataset in China, which proves the superiority and higher generalization ability of the proposed algorithm. Our study, to the best of our knowledge, is the first to apply machine learning in the management of terrorist attacks, which can provide early warning and decision support information for terrorist attack management.

RETRACTED ARTICLE: Towards secure deep learning architecture for smart farming-based applications

“…Several agriculture farmers and researchers revamp to smart farming technology for determining soil condition and crops status at real time and also it could be used in sprinkling pesticides with the help of assisted drones, thereby protruding its multi-purposes [1]. On the other hand, the introduction of several communication modules and deep learning algorithms makes the system vulnerable to cyber-security [2] and threats in the smart farming infrastructure. This could lower the economy of a particular country, which predominately relies on the agricultural firm.…”

“…The differential privacy was introduced in the year 2006, and accepted as a de facto standard in preserving private data. Generally, differential privacy can be inferred with two settings: (1) global-real data are collected by a trusted central authority and then secure them with the privacy-preserving mode; (2) local-no such authority is supported, but the user secures their own data with the private version. Here, the local setting is highly preferred.…”

An effective matching algorithm with adaptive tie-breaking strategy for online food delivery problem

“…As a member of tree algorithms, XGBoost aims at improving the traditional gradient boosting decision tree in both model performance and computational speed. XGBoost reduces the look-up times of creating individual trees and supports parallel computing [11,27], which makes it much faster than many other existing algorithms. Besides, due to its portability, scalability and flexibility, XGBoost can be operated on various platforms and provide multiple interfaces for users to define necessary parameters.…”

Research on Rockburst Prediction Classification Based on GA-XGB Model

“…The AUC represents the area under the ROC curve and can be used to quantitatively evaluate the classification effect. The closer the ROC curve to the (0,1) point, the better the classification performance of the model, suggesting that the larger the AUC value, the better [46].…”