An ensemble machine learning model for the prediction of danger zones: Towards a global counter-terrorism

Olabanjo, Olusola; Aribisala, Benjamin S.; Mazzara, Manuel; Wusu, Ashiribo Senapon

doi:10.1016/j.socl.2021.100020

Cited by 17 publications

(9 citation statements)

References 15 publications

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“…Olabanjo et al [29] created an ensemble ML model incorporating an SVM and KNN. To predict continents susceptible to terrorism from GTD, two feature selection techniques, Chi-squared, Information Gain, and a hybrid of both, were applied to the dataset.…”

Section: A Machine Learning Approach For Enhancing Defense Against Gl...mentioning

confidence: 99%

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Terrorism Attack Classification Using Machine Learning: The Effectiveness of Using Textual Features Extracted from GTD Dataset

Abdalsalam,

Li,

Dahou

et al. 2024

CMES

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One of the biggest dangers to society today is terrorism, where attacks have become one of the most significant risks to international peace and national security. Big data, information analysis, and artificial intelligence (AI) have become the basis for making strategic decisions in many sensitive areas, such as fraud detection, risk management, medical diagnosis, and counter-terrorism. However, there is still a need to assess how terrorist attacks are related, initiated, and detected. For this purpose, we propose a novel framework for classifying and predicting terrorist attacks. The proposed framework posits that neglected text attributes included in the Global Terrorism Database (GTD) can influence the accuracy of the model's classification of terrorist attacks, where each part of the data can provide vital information to enrich the ability of classifier learning. Each data point in a multiclass taxonomy has one or more tags attached to it, referred as "related tags." We applied machine learning classifiers to classify terrorist attack incidents obtained from the GTD. A transformer-based technique called DistilBERT extracts and learns contextual features from text attributes to acquire more information from text data. The extracted contextual features are combined with the "key features" of the dataset and used to perform the final classification. The study explored different experimental setups with various classifiers to evaluate the model's performance. The experimental results show that the proposed framework outperforms the latest techniques for classifying terrorist attacks with an accuracy of 98.7% using a combined feature set and extreme gradient boosting classifier.

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Section: A Machine Learning Approach For Enhancing Defense Against Gl...mentioning

confidence: 99%

“…The difficulties related to training ML models due to high dimensional data. [29] Ensemble ML model which combines SVM and KNN…”

Section: Gtdmentioning

confidence: 99%

Terrorism Attack Classification Using Machine Learning: The Effectiveness of Using Textual Features Extracted from GTD Dataset

Abdalsalam,

Li,

Dahou

et al. 2024

CMES

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“…Por otra parte, los métodos de ensamble se han aplicado a temas como la predicción de ataques terroristas tal como en Olabanjo et al (2021) se propone un método de ensamble para la predicción de zonas de peligro en lucha contra el terrorismo global. El objetivo de este trabajo fue desarrollar un modelo de aprendizaje automático que combinó Support Vector Machine y K Nearest Neighbours para la predicción de continentes susceptibles al terrorismo.…”

Section: Trabajos Relacionadosunclassified

Predicción de factores clave en el aumento de la demografía en Colombia a través del ensamble de modelos de Machine Learning

2022

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El envejecimiento de la población es considerado uno de los fenómenos sociales más significativos que está transformando las economías y las sociedades en todo el mundo. Según la Organización Mundial de la Salud (OMS) el envejecimiento está en aumento. En Colombia, el crecimiento demográfico presenta un incremento natural que muestra una notable diferencia entre las tasas de natalidad y las de mortalidad general. Según el DANE, en Colombia, las tasas de crecimiento natural denotan un vertiginoso declive a lo largo del tiempo. El gobierno central y los gobiernos locales pueden ayudar en la toma de decisiones para establecer políticas de salud sexual y reproductiva. Como herramienta de apoyo aparece el Machine Learning (ML), en el cual existen algoritmos que permiten crear modelos para aprender de los datos e identificar patrones que sirven para apoyar a entes gubernamentales en el proceso de toma de decisiones. Con base en lo anterior, este trabajo propone un método de ensamble de algoritmos de ML que apoye la toma de decisiones respecto al control demográfico enfocado en natalidad. El método de predicción permitió evidenciar que la disminución de nacimientos en Colombia durante los últimos años se debe al cambio en las prioridades de mujeres y hombres. Las mujeres enfrentan discriminación y dificultad en el acceso y la permanencia del empleo a causa de la maternidad. Como consecuencia, se les dificulta articular su vida profesional con el mercado laboral. Las mujeres tienen que asumir una carga desproporcionada de cuidado, por la cual quieren tener menos hijos, es decir uno o máximo dos.

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“…Data features that are ambiguous, such as columns with similar features, will be collided or selected so that only one column will remain [21]. Empty-valued features will also be deleted in preprocessing.…”

Section: Preprocessing Datasetmentioning

confidence: 99%

Linkage Detection of Features that Cause Stroke using Feyn Qlattice Machine Learning Model

Purwono

Ma’arif

Negara

et al. 2021

Jurnal Ilmiah Teknik Elektro Komputer Dan Informatika

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Stroke is a disease caused by brain tissue damage because of blockage in the cerebrovascular system that disrupts body sensory and motoric systems Stroke disease is one of the highest death cause in the world. Data collection from Electronic Health Records (EHR) is increasing and has been included in the health service big data. It can be processed and analyzed using machine learning to determine the risk group of stroke disease. Machine learning can be used as a predictor of stroke causes, while the predictor clarifies the influence of each cause factor of the disease. Our contribution in this research is to evaluate Feyn Qlattice machine learning models to detect the influence of stroke disease's main cause features. We attempt to obtain a correlation between features of the stroke disease, especially on the gender as a feature, whether any other features can influence the gender feature. This research utilizes 4908 data of the disease predictor using the Feyn Qlattice model. The result implies that gender highly impacts age and hypertension on stroke disease causes. Autorun in Feyn Qlattice model was run with ten epochs, resulting in 17596 test models at 57s. Query string parameter that was focused on age and hypertension features resulted in 1245 models at 4s. An increase of accuracy was found in training metrics from 0.723 to 0.732 and in testing metrics from 0.695 to 0.708. Evaluation results showed that the model is reasonably good as a predictor of stroke disease, indicated with blue lines of AUC in training and testing metrics close to ROC's left side peak curve.

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An ensemble machine learning model for the prediction of danger zones: Towards a global counter-terrorism

Cited by 17 publications

References 15 publications

Terrorism Attack Classification Using Machine Learning: The Effectiveness of Using Textual Features Extracted from GTD Dataset

Terrorism Attack Classification Using Machine Learning: The Effectiveness of Using Textual Features Extracted from GTD Dataset

Predicción de factores clave en el aumento de la demografía en Colombia a través del ensamble de modelos de Machine Learning

Linkage Detection of Features that Cause Stroke using Feyn Qlattice Machine Learning Model

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