Militarized Conflict Modeling Using Computational Intelligence

Abstract

The computational intelligence (CI) methods such as artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) have many applications in chemistry, oil and gas, electronics, financial, telecommunications, and many others. In this article, ANN and ANFIS are used to model and predict the biodiesel conversion under several conditions. The inputs of the proposed CI models are oil type, catalyst type, calcination temperature, catalyst concentration, methanol-to-oil ratio, n-hexane-to-oil volume ratio, reaction time, and reaction temperature and the output is biodiesel conversion. Experimental data of available literature are used to train and test the CI models in MATLAB 7.0.4 software. Comparison between the proposed ANN and ANFIS models and the experimental data show that the proposed CI models are very efficient and fast tools, and there is a good agreement between them and the experimental data with a minimum error. Also, it can be found that the introduced ANN model is more accurate than the ANFIS model. The proposed ANN model has overall MRE% (mean relative error percentage) <1.5%, RMSE (root mean square error) <1.34, R (correlation coefficient) >0.9995, and MAE (mean absolute error) <0.9.

Introduction to Computational Intelligence

“…In the literature, there are thousands of (if not more) books, conference proceedings, and edited monographs are devoted to CI and its corresponding vast amount of applications, e.g., (Wang and Kusiak 2001;Engelbrecht 2007;Fink and Rothlauf 2008;Marwala 2009Marwala , 2010Marwala , 2012Rutkowski 2008;Sumathi and Paneerselvam 2010;Marwala and Lagazio 2011;Xing and Gao 2014;Chatterjee and Siarry 2013;Yang 2008Yang , 2010aEberhart and Shi 2007;Fulcher and Jain 2008;Mumford and Jain 2009;Michalewicz 1996), to name just a few.…”

Modelling militarized interstate disputes using data mining techniques: Prevention and prediction of conflicts

“…One of the more elaborate pieces of research on predicting the incidence of violent international conflict was conducted by Marwala and Lagazio. 1 In their book, the authors examine a broad range of computational intelligence algorithms developed and applied in various disciplines for the task of predicting interstate conflict. They propose and evaluate methods based on various principles such as neural networks, Bayesian approaches, support vector machines, fuzzy sets, particle swarm optimization, simulated annealing, or genetic algorithms.…”

“…The timely prediction of a conflict via an early warning system may lead to its prevention or an early intervention, thus increasing peace in the international system. 1…”

What is a Causal Machine?

“…The MLP propagates information from the cause to the effect and thus is regarded to follow the projectile theory of causality. More details on interstate conflict are found in Marwala and Lagazio (2011). In the identification of the causal link between the MID and the conflict status, the MID variables which are used are the following:…”