Title

Intelligent Systems – I: Using predictive analytics to forecast drone attacks in Pakistan

Abstract/Description

Drones are autonomous aircrafts employed in conditions where manned flight is perilous. Drone-based attacks are made in Northern Pakistan with the intention of eliminating terrorists (in the context of US-led war of terror). In June 2004, the first drone strike killed one militant and four civilians; since then hundreds of attacks have killed thousands of people including accidental deaths of innocent children and women. To gauge the impact of future drone attacks, we apply time series forecasting on drone attack data to predict the frequency of different types of future attacks. On a reliable drone attack data set, we use IBM SPSS tool to learn four predictive models: 1) number of drone attacks, 2) number of militant casualties, 3) number of civilian casualties, and 4) number of injuries. Over our actual dataset, the prediction accuracy is maximized when we allow SPSS to automatically select the forecasting algorithm, as compared to a manual selection and configuration. We use automated selection to predict our four types of data for the six months, July 2013 till December 2013.

Location

Room M1

Session Theme

Intelligent Systems – I

Session Type

Other

Session Chair

Dr. Sajjad Haider

Start Date

14-12-2013 3:30 PM

End Date

14-12-2013 4:00 PM

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Dec 14th, 3:30 PM Dec 14th, 4:00 PM

Intelligent Systems – I: Using predictive analytics to forecast drone attacks in Pakistan

Room M1

Drones are autonomous aircrafts employed in conditions where manned flight is perilous. Drone-based attacks are made in Northern Pakistan with the intention of eliminating terrorists (in the context of US-led war of terror). In June 2004, the first drone strike killed one militant and four civilians; since then hundreds of attacks have killed thousands of people including accidental deaths of innocent children and women. To gauge the impact of future drone attacks, we apply time series forecasting on drone attack data to predict the frequency of different types of future attacks. On a reliable drone attack data set, we use IBM SPSS tool to learn four predictive models: 1) number of drone attacks, 2) number of militant casualties, 3) number of civilian casualties, and 4) number of injuries. Over our actual dataset, the prediction accuracy is maximized when we allow SPSS to automatically select the forecasting algorithm, as compared to a manual selection and configuration. We use automated selection to predict our four types of data for the six months, July 2013 till December 2013.