A novel classification model for cotton yarn quality based on trained neural network using genetic algorithm

GGA-MLP: A Greedy Genetic Algorithm to Optimize Weights and Biases in Multilayer Perceptron

The task of designing an Artificial Neural Network (ANN) can be thought of as an optimization problem that involves many parameters whose optimal value needs to be computed in order to improve the classification accuracy of an ANN. Two of the major parameters that need to be determined during the design of an ANN are weights and biases. Various gradient-based optimization algorithms have been proposed by researchers in the past to generate an optimal set of weights and biases. However, due to the tendency of gradient-based algorithms to get trapped in local minima, researchers have started exploring metaheuristic algorithms as an alternative to the conventional techniques. In this paper, we propose the GGA-MLP (Greedy Genetic Algorithm-Multilayer Perceptron) approach, a learning algorithm, to generate an optimal set of weights and biases in multilayer perceptron (MLP) using a greedy genetic algorithm. The proposed approach increases the performance of the traditional genetic algorithm (GA) by using a greedy approach to generate the initial population as well as to perform crossover and mutation. To evaluate the performance of GGA-MLP in classifying nonlinear input patterns, we perform experiments on datasets of varying complexities taken from the University of California, Irvine (UCI) repository. The experimental results of GGA-MLP are compared with the existing state-of-the-art techniques in terms of classification accuracy. The results show that the performance of GGA-MLP is better than or comparable to the existing state-of-the-art techniques.

An XGBoost-based casualty prediction method for terrorist attacks

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.