Background/Aims On 17 September 2024, over 3000 pager devices containing explosives were remotely detonated across Lebanon in ...

The successful application of large-scale transformer models in Natural Language Processing (NLP) is often hindered by the substantial computational cost and data requirements of full fine-tuning.

The application of data science in agriculture enables the analysis of diverse datasets using methods such as machine learning, deep learning, computer vision, text mining (Drury and Roche, 2019), and ...

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This project implements a sophisticated text classification system to detect AI-generated content using BERT (Bidirectional Encoder Representations from Transformers). The system can distinguish ...

Abstract: Deep learning models have greatly improved various natural language processing tasks. However, their effectiveness depends on large data sets, which can be difficult to acquire. To mitigate ...

This project demonstrates the use of Long Short-Term Memory (LSTM) networks for classifying text messages as spam or ham (non-spam). By combining Natural Language Processing (NLP) techniques with deep ...

ABSTRACT: Since transformer-based language models were introduced in 2017, they have been shown to be extraordinarily effective across a variety of NLP tasks including but not limited to language ...