Evaluating the Impact of Drone Strikes on Terrorism Dynamics

Yadav, Dixita (2024) Evaluating the Impact of Drone Strikes on Terrorism Dynamics. Masters thesis, Dublin, National College of Ireland.

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Abstract

Terrorism is a threat to the world that results in thousands of casualties, assaults, overnight political shifts, downfalls in economies and much more. Governments and its defence bodies are trying their best to counter against terrorism by adopting latest technological tools such as UAV’s (Unmanned aerial vehicle) i.e. Drone strikes, to attack terrorist occupied regions. It has emerged as a best tool of counterattack against terrorism while involving minimal risk of military personnel. It is debatable on the fact that how much of retaliation is observed on a successful drone strike and does drone strikes affect the frequency of terrorist attacks in that region. To study this, research is carried out by using two different open-source datasets i.e. Global Terrorism Database (GTD) and Drone Wars Data. This research aims to evaluate the impact of drone strikes on terrorism to measure the changes in terrorist activities and their motives. Post evaluation an ensemble technique (Voting Classifier) is used on the overlayed data using geospatial proximity and modelled using SVC and Random Forest as base models and Logistic regression as meta model. The implementation helps to understand the classification of severity of the drone strikes.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Anant, Aaloka UNSPECIFIED
Uncontrolled Keywords:	Terrorism; Drone Strike; Voting Classifier; Logistic regression; Random Forest; SVC; Confusion Matrix
Subjects:	H Social Sciences > HV Social pathology. Social and public welfare Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	08 Sep 2025 09:10
Last Modified:	08 Sep 2025 09:10
URI:	https://norma.ncirl.ie/id/eprint/8837

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