Socio-economic Inequality or Demographic Conditions: A Micro-level Analysis of Terrorism in Jharkhand

Ouassini, Nabil; Verma, Arvind

doi:10.1177/2516606918765494

Cited by 7 publications

(4 citation statements)

References 14 publications

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“…e output of the hidden layer is passed to output layer with W and b and computed as given in equation (3). From the output layer, loss function is computed as given in equation (4). During the backpropagation process, weights are updated, taking into account the error of the actual Y and predicted Y, as shown in the following equation:…”

Section: Dealing With Unbalanced Classesmentioning

confidence: 99%

“…It is equally important that the emotional reactions of the population is understood in regard to terrorist events so that we are able to design assistance to effectively help those who are suffering from these issues or they do not react to carry out another terrorist activity as a revenge. Terrorism has been studied for decades to understand the major factors causing the act of terrorism or understanding how to perform counterterrorism or understanding the social and economic effects of terrorism [3,4]. However, because of the complex nature of terrorism, it is difficult to find an effective solution that can be used as a counterterrorism to protect the lives of individuals.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Future Terrorist Activities Using Deep Neural Networks

et al. 2020

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One of the most important threats to today’s civilization is terrorism. Terrorism not only disturbs the law and order situations in a society but also affects the quality of lives of humans and makes them suppressed physically and emotionally and deprives them of enjoying life. The more the civilizations have advanced, the more the people are working towards exploring different mechanisms to protect the mankind from terrorism. Different techniques have been used as counterterrorism to protect the lives of individuals in society and to improve the quality of life in general. Machine learning methods have been recently explored to develop techniques for counterterrorism based on artificial intelligence (AI). Since deep learning has recently gained more popularity in machine learning domain, in this paper, these techniques are explored to understand the behavior of terrorist activities. Five different models based on deep neural network (DNN) are created to understand the behavior of terrorist activities such as is the attack going to be successful or not? Or whether the attack is going to be suicide or not? Or what type of weapon is going to be used in the attack? Or what type of attack is going to be carried out? Or what region is going to be attacked? The models are implemented in single-layer neural network (NN), five-layer DNN, and three traditional machine learning algorithms, i.e., logistic regression, SVM, and Naïve Bayes. The performance of the DNN is compared with NN and the three machine learning algorithms, and it is demonstrated that the performance in DNN is more than 95% in terms of accuracy, precision, recall, and F1-Score, while ANN and traditional machine learning algorithms have achieved a maximum of 83% accuracy. This concludes that DNN is a suitable model to be used for predicting the behavior of terrorist activities. Our experiments also demonstrate that the dataset for terrorist activities is big data; therefore, a DNN is a suitable model to process big data and understand the underlying patterns in the dataset.

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Section: Dealing With Unbalanced Classesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction of Future Terrorist Activities Using Deep Neural Networks

et al. 2020

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“…It analyzes performance of classifiers (Multilayer Perceptron, Lazy Tree, Naïve Bayes, Multiclass) for detection of trends in terrorist attacks world GTD database [9]. N. Ouassini and A. Verma illustrated association between social, economic, and demographic indices and left-wing intolerance in state of Jharkhand in India [10]. R. Alhamdani, M. Abdullah, exploits deep learning techniques to identify the terrorist attacks behavior and distribution online misinformation using different forms of social media by employing global terrorist database [11].…”

Section: Literature Reviewmentioning

confidence: 99%

Machine Learning and Statistical Analysis Techniques on Terrorism

Rajesh¹,

Donepudi²,

Alam³

et al. 2020

Fuzzy Systems and Data Mining VI

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Terrorism is a major issue facing the world today. It has negative impact on the economy of the nation suffering terrorist attacks from which it takes years to recover. Many developing countries are facing threats from terrorist groups and organizations. This paper examines various terrorist factors using data mining from the historical data to predict the terrorist groups most likely to attack a nation. In this paper we focus on sampled data primarily from India for the past two decades and also consider International database. To create meaningful insights, data mining, machine learning techniques and algorithms such as Decision Tree, Naïve Bayes, Support Vector Machine, Ensemble methods, Random Forest Classification are implemented to analyze comparative based classification results. Patterns and predictions are represented in the form of visualizations with the help of Python and Jupyter Notebook. This analysis will help to take appropriate preventive measures to stop Terrorism attacks and to increase investments, to grow the economy and tourism.

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“…Terör olaylarının insanlar için önemli bir tehdit olduğu bilinmekte ve engellemek için çeşitli çabalar sarf edilmektedir [1][2][3]. Bu sorunu çözmek için terör olaylarını anlamak veya nasıl gerçekleştirileceğini tahmin etmek üzerine [4][5] çeşitli disiplinlerde çalışılmaktadır [6][7][8][9]. Bilişim alanında ise son yıllarda terör olaylarını engellemek için makine öğrenmesi algoritmaları kullanılmaktadır [10][11][12].…”

Section: Introductionunclassified

Dinamit Destekli Terör Faaliyetlerinin Önlenmesi İçin Derin Öğrenme Temelli Güvenlik Destek Sistemi

Kaya¹,

Baran²,

Tuncer³

2021

European Journal of Science and Technology

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ÖzGünümüz toplumunda, insanları tehdit eden en önemli etmenlerden birisi terörizmdir. Terörizm bir toplumda, insanların düzen durumlarını bozarak, yaşam kalitesini etkilemektedir. Devletler ise terörle mücadele etmek için sürekli farklı yöntemler geliştirmektedir. Bu yöntemlerden birisi de terörle mücadele için makine öğrenmesinin bir alt alanı olan derin öğrenmenin kullanılmasıdır. Derin öğrenme, makine öğrenmesi alanında son yıllarda oldukça popülerlik kazanmıştır. Bu çalışmada, terör faaliyetlerini fark etmek ve önlemek için derin öğrenmeye dayalı VGG-16 mimarisi temel alınarak yeni bir model önerilmektedir. Önerilen model ile güvenlik kontrollerinde kullanılan kamera görüntülerinden alınan görüntülerde, insan ya da tren rayları üzerinde dinamit tespit edildiğinde, durumu hızla belirlemek ve uygun önlemleri almak için güvenlik görevlilerini uyaran bir sistem gerçekleştirilmiştir. Çalışmada kullanılan veri seti, internet ortamından indirilen dinamit resimleri düzenlenerek oluşturulmuştur. Önerilen modelin performansını değerlendirmek için, insan ya da tren rayları üzerinde bulunan dinamit resimleri test edilerek, %98,4'lük başarı doğruluğu ve 0,024 kayıp oranıyla dinamit görüntüleri tespit edilmektedir.

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Socio-economic Inequality or Demographic Conditions: A Micro-level Analysis of Terrorism in Jharkhand

Cited by 7 publications

References 14 publications

Prediction of Future Terrorist Activities Using Deep Neural Networks

Prediction of Future Terrorist Activities Using Deep Neural Networks

Machine Learning and Statistical Analysis Techniques on Terrorism

Dinamit Destekli Terör Faaliyetlerinin Önlenmesi İçin Derin Öğrenme Temelli Güvenlik Destek Sistemi

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