Artificial intelligence for conflict management
| dc.contributor.author | Habtemariam, Eyasu A. | |
| dc.date.accessioned | 2006-10-31T08:43:47Z | |
| dc.date.available | 2006-10-31T08:43:47Z | |
| dc.date.issued | 2006-10-31T08:43:47Z | |
| dc.description | Student Number : 0213053E MSc research report - School of Electrical and Information Engineering - Faculty of Engineering and the Built Environment | en |
| dc.description.abstract | One of the risks that have a great impact on society is military con- °ict. Militarised Interstate Dispute (MID) is de¯ned as an outcome of interstate interactions which result in either peace or con°ict. E®ective prediction of the possibility of con°ict between states is a good decision support tool. Neural networks (NNs) have been implemented to predict militarised interstate disputes before Marwala and Lagazio [2004]. Sup- port Vector Machines (SVMs) have proven to be very good prediction techniques in many other real world problems Chen and Odobez [2002]; Pires and Marwala [2004]. In this research we introduce SVMs to predict MID. The results found show that SVM is better in predicting con°ict cases (true positives) without e®ectively reducing the number of correctly classi¯ed peace (true negatives) than NN. A sensitivity analysis for the in°uence of the dyadic (explanatory) variables shows that NN gives more consistent and easy to interpret results than SVM. Further investigation is required with regards to the sensitivity analysis of SVM. | en |
| dc.format.extent | 320707 bytes | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.uri | http://hdl.handle.net/10539/1515 | |
| dc.language.iso | en | en |
| dc.subject | Artifcial Intelligence | en |
| dc.subject | Artifcial Neural Networks | en |
| dc.subject | pattern recognition | en |
| dc.subject | MID Data | en |
| dc.title | Artificial intelligence for conflict management | en |
| dc.type | Thesis | en |