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Explainable detection: a transformer-based language modeling approach for Bengali news title classification with

Md Julkar Naeen¹, Sourav Kumar Das¹, Sakib Alam Jisan¹

¹Department of Computer Science and Engineering, Daffodil International University, Dhaka, Bangladesh.

Frontiers in Artificial Intelligence

|November 24, 2025

Summary

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Classification of Signals

Classification of Signals

In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...

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This study enhances Bengali text classification using deep learning models like XLM-RoBERTa, achieving 0.91 accuracy. Explainable AI techniques, including LIME, were used to ensure transparency and validate results for low-resource languages.

Area of Science:

Natural Language Processing (NLP)
Machine Learning (ML)
Deep Learning

Background:

Bengali text classification presents challenges due to scattered data and noise.
Low-resource languages require specialized NLP approaches for effective text analysis.

Keywords:

Bengali news titles LIME classification deep learning explainable AI long short-term memory machine learning transformer model

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