Mohammed Abdalsalam^1,*, Chunlin Li¹, Abdelghani Dahou², Natalia Kryvinska³

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1427-1467, 2024, DOI:10.32604/cmes.2023.029911 - 17 November 2023

Abstract 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… More >

Samuel Kofi Akpatsa^1,*, Hang Lei¹, Xiaoyu Li¹, Victor-Hillary Kofi Setornyo Obeng¹, Ezekiel Mensah Martey¹, Prince Clement Addo², Duncan Dodzi Fiawoo³

Journal of Quantum Computing, Vol.4, No.1, pp. 1-11, 2022, DOI:10.32604/jqc.2022.026658 - 12 August 2022

Abstract The ability of pre-trained BERT model to achieve outstanding performances on many Natural Language Processing (NLP) tasks has attracted the attention of researchers in recent times. However, the huge computational and memory requirements have hampered its widespread deployment on devices with limited resources. The concept of knowledge distillation has shown to produce smaller and faster distilled models with less trainable parameters and intended for resource-constrained environments. The distilled models can be fine-tuned with great performance on a wider range of tasks, such as sentiment classification. This paper evaluates the performance of DistilBERT model and other More >