Terör Saldırılarını İçeren Büyük Verinin Makine Öğrenmesi Teknikleri ile Analizi

Mustafa ULAŞ; Bariş KARABAY

Article Details

Terör Saldırılarını İçeren Büyük Verinin Makine Öğrenmesi Teknikleri ile Analizi

Authors:

Mustafa ULAŞ | Bariş KARABAY

Publisher:

Fırat University

Main Topics:

Engineering and applied operations

Journal:

Fırat University Journal of Engineering Science 2020 Vol.32 Issue 1, pp.267-277

Document Type:

Article

Publication Date:

3/2020

Document Language:

Turkish

Document ID (Org No.):

2000432548

pISSN:

1308-9072

Abstract:

 


This type of information gathered from various news
sources, internationally trusted news agencies, and the terror information
collected between 1970 and 2017, and discussed the data set called Global
Terrorism Database (GTD) with PGIS. Analysis and classification decision of
terrorist incidents by using big data technology techniques by applying
algorithms to classify machine learning methods to terrorist data datasets,
large data processing tool which predicts which terrorist group is performed by
a terrorist event is made here. In the event of a terrorist attack, the type of
attack, country, region, target audience and type of weapons to capture such
information is estimated to seize. Apache Spark framework and Python
programming language for the development of big data tool. Classification
algorithms from several different machine learning methods were applied to the
top 10 terrorist organizations that carried out the most attacks from the GTD
dataset. Performance among these algorithms was shown. The algorithms applied
are for the highest value with 98.2% of the K-Nearest Neighbor (KNN) algorithm
as the highest accuracy rate of sorting. The logistic regression (LR) algorithm
was specified according to the situation appropriate for the big data set.


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Article Details

Terör Saldırılarını İçeren Büyük Verinin Makine Öğrenmesi Teknikleri ile Analizi

Authors: Mustafa ULAŞ | Bariş KARABAY

Publisher: Fırat University

Main Topics: Engineering and applied operations

Journal: Fırat University Journal of Engineering Science 2020 Vol.32 Issue 1, pp.267-277

Document Type: Article

Publication Date: 3/2020

Document Language: Turkish

Document ID (Org No.): 2000432548

pISSN: 1308-9072

Issues

Authors:

Mustafa ULAŞ | Bariş KARABAY

Publisher:

Fırat University

Main Topics:

Engineering and applied operations

Journal:

Fırat University Journal of Engineering Science 2020 Vol.32 Issue 1, pp.267-277

Document Type:

Article

Publication Date:

3/2020

Document Language:

Turkish

Document ID (Org No.):

2000432548

pISSN:

1308-9072