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A Model for Conflicts’ Prediction using Deep Neural Network

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2021
Olabanji B. Olaide, Adebola K. Ojo

Olabanji B Olaide and Adebola K Ojo. A Model for Conflicts’ Prediction using Deep Neural Network. International Journal of Computer Applications 183(29):8-12, October 2021. BibTeX

	author = {Olabanji B. Olaide and Adebola K. Ojo},
	title = {A Model for Conflicts’ Prediction using Deep Neural Network},
	journal = {International Journal of Computer Applications},
	issue_date = {October 2021},
	volume = {183},
	number = {29},
	month = {Oct},
	year = {2021},
	issn = {0975-8887},
	pages = {8-12},
	numpages = {5},
	url = {},
	doi = {10.5120/ijca2021921667},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Conflict is part of human social interaction, which may occur from a mere misunderstanding among groups of settlers. In recent times, advanced Machine Learning (ML) techniques have been applied to conflict prediction. Strategic frameworks for improving ML settings in conflict research are emerging and are being tested with new algorithm-based approaches. These developments have given rise to the need to develop a Deep Neural Network model that predicts conflicts. Hence, in this study, two Artificial Neural Network models were developed, the dataset which was extracted from by the Armed Conflict Location and Event Data Project (ACLED), in four separate CSV files (January 2015 to December 2018). The dataset for the year 2015 has 2697 instances and 28 features, for 2016 was 2233 with the same feature, for 2017 has 2669 instances with the same features, and 2018 has 1651 instances. After the development of the models: the baseline Artificial Neural Network achieved an accuracy of 95% and a loss of 5% on the training data and an accuracy of 90% and 10% loss on the test set. The Deep Neural Network Model achieved 98% accuracy and 2% loss on the training set, with 89% accuracy and 11% loss on the test set. It was concluded that to further improve the prediction of conflict, there is a need to address the issue of the dataset, in developing a better and more robust model.


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Artificial Neural Network, Conflict, Deep Neural Network, Multi-Class Target Label,Prediction, Model.