| Title | Predicting Terror Attacks Using Neo4j Sandbox and Machine Learning Algorithms |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Raj, Ankit, Somani, Sunil B. |
| Conference Name | 2022 6th International Conference On Computing, Communication, Control And Automation (ICCUBEA |
| Date Published | aug |
| Keywords | composability, Computational modeling, Deep Learning, graph embedding, Human Behavior, machine learning algorithms, Machine Learning Graph Database, Metrics, Neo4j Sandbox, Predictive models, pubcrawl, relational database security, relational databases, resilience, Resiliency, Support vector machines, Terrorism |
| Abstract | Terrorism, and radicalization are major economic, political, and social issues faced by the world in today's era. The challenges that governments and citizens face in combating terrorism are growing by the day. Artificial intelligence, including machine learning and deep learning, has shown promising results in predicting terrorist attacks. In this paper, we attempted to build a machine learning model to predict terror activities using a global terrorism database in both relational and graphical forms. Using the Neo4j Sandbox, you can create a graph database from a relational database. We used the node2vec algorithm from Neo4j Sandbox's graph data science library to convert the high-dimensional graph to a low-dimensional vector form. In order to predict terror activities, seven machine learning models were used, and the performance parameters that were calculated were accuracy, precision, recall, and F1 score. According to our findings, the Logistic Regression model was the best performing model which was able to classify the dataset with an accuracy of 0.90, recall of 0.94 precision of 0.93, and an F1 score of 0.93. |
| Notes | ISSN: 2771-1358 |
| DOI | 10.1109/ICCUBEA54992.2022.10010980 |
| Citation Key | raj_predicting_2022 |
Predicting Terror Attacks Using Neo4j Sandbox and Machine Learning Algorithms