Automating Global Threat-Maps Generation via Advancements of News Sensors and AI

Sufi, Fahim; Alsulami, Musleh; Gutub, Adnan

doi:10.1007/s13369-022-07250-1

Cited by 30 publications

(12 citation statements)

References 42 publications

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“…Certainly, as our analysis suggests, the observability and measurability of food safety risks are crucial. In the future, there will be a need to consider leveraging digital technologies to optimize regulations, such as using IoT, machine learning, and visualization techniques [ 43 , 44 ] to expedite the research and resolution of food safety decisions.…”

Section: Discussionmentioning

confidence: 99%

Research on safety risk control of prepared foods from the perspective of supply chain

Gong,

Sun,

et al. 2024

Heliyon

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Section: Discussionmentioning

confidence: 99%

Research on safety risk control of prepared foods from the perspective of supply chain

Gong,

Sun,

et al. 2024

Heliyon

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“…Some researchers suggested systems based on AI that the intelligence officer or strategic decisionmaker might conduct a multi-dimensional analysis in an interactive way to benefit from in the fight against terrorism. Security services can make the most of the outputs of those systems that depend on them [3]. The application of methods based on it has recently been applied to develop AI-based counter-terrorism technologies.…”

Section: Introductionmentioning

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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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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“…As an exemplification, social media are real-world sensors that can be employed to evaluate the pulse of societies by collecting real-time data and information from online human interactions [ 2 , 3 ]. The social sensor integrated with application programming interfaces for sensing news (i.e., the news sensor) combined sentiment analysis to extract sentiment information from global news and generate an interactive global threat map using geographic data [ 4 ]. This system may provide crucial decision support and intelligent early warning, allowing decision-makers to monitor the situation to identify potential hazards and improve area security by monitoring environmental conditions in real time through sensors connected to the IoT [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Cross-Domain Sentiment Analysis Based on Feature Projection and Multi-Source Attention in IoT

Kong,

Xu,

Mei

2023

Sensors

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Social media is a real-time social sensor to sense and collect diverse information, which can be combined with sentiment analysis to help IoT sensors provide user-demanded favorable data in smart systems. In the case of insufficient data labels, cross-domain sentiment analysis aims to transfer knowledge from the source domain with rich labels to the target domain that lacks labels. Most domain adaptation sentiment analysis methods achieve transfer learning by reducing the domain differences between the source and target domains, but little attention is paid to the negative transfer problem caused by invalid source domains. To address these problems, this paper proposes a cross-domain sentiment analysis method based on feature projection and multi-source attention (FPMA), which not only alleviates the effect of negative transfer through a multi-source selection strategy but also improves the classification performance in terms of feature representation. Specifically, two feature extractors and a domain discriminator are employed to extract shared and private features through adversarial training. The extracted features are optimized by orthogonal projection to help train the classification in multi-source domains. Finally, each text in the target domain is fed into the trained module. The sentiment tendency is predicted in the weighted form of the attention mechanism based on the classification results from the multi-source domains. The experimental results on two commonly used datasets showed that FPMA outperformed baseline models.

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Automating Global Threat-Maps Generation via Advancements of News Sensors and AI

Cited by 30 publications

References 42 publications

Research on safety risk control of prepared foods from the perspective of supply chain

Research on safety risk control of prepared foods from the perspective of supply chain

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

Cross-Domain Sentiment Analysis Based on Feature Projection and Multi-Source Attention in IoT

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