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publications

A Comparative Analysis for the Detection of COVID-19 from Chest X-ray Dataset

Published in International Conference on Recent Progresses in Science, Engineering and Technology, 2022

We compare multiple deep CNN architectures on a large chest X-ray dataset (COVIDx CXR-3) for COVID-19 detection, finding EfficientNet-B3 yields the best balance of accuracy and sensitivity.

Recommended citation: Anirban Saha Anik, Kowshik Chakraborty, Bishowjit Datta, Abdul Kader, MD. Kishor Morol. "A Comparative Analysis for the Detection of COVID-19 from Chest X-ray Dataset." International Conference on Recent Progresses in Science, Engineering and Technology (ICRPSET 2022). DOI: 10.1109/ICRPSET57982.2022.10188570.
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A Study on Future Lockdown Predictions Using ANN

Published in International Conference on Next-Generation Computing, IoT and Machine Learning, 2023

We use modified artificial neural networks to predict future lockdown timings based on epidemiological and socio-economic features, achieving high accuracy on a curated pandemic dataset.

Recommended citation: Shuvra Smaran Das, Anirban Saha Anik, Md. Muzakker Hossain, Md. Kishor Morol, Fariha Jahan, Md. Abdullah Al-Jubair. "A Study on Future Lockdown Predictions Using ANN." International Conference on Next-Generation Computing, IoT and Machine Learning (NCIM 2023). DOI: 10.1109/NCIM59001.2023.10212686.
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Outcome-Based Education: Evaluating Students’ Perspectives Using Transformer

Published in 27th International Conference on Computer and Information Technology, 2024

We apply transformer-based NLP to analyze student feedback and sentiment to assess Outcome-Based Education (OBE) effectiveness and surface actionable insights for improving educational outcomes.

Recommended citation: Das, Shuvra Smaran and Anik, Anirban Saha and Morol, Md Kishor and Mahmood, Mohammad Sakib. "Outcome-Based Education: Evaluating Students Perspectives Using Transformer." 2024 27th International Conference on Computer and Information Technology (ICCIT).
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Multi-Agent Retrieval-Augmented Framework for Evidence-Based Counterspeech Against Health Misinformation

Published in Second Conference on Language Modeling, 2025

We propose a multi-agent retrieval-augmented framework that orchestrates specialized LLM agents for evidence retrieval, summarization, generation, and refinement to produce evidence-based counterspeech that outperforms RAG baselines.

Recommended citation: Anirban Saha Anik, Xiaoying Song, Elliott Wang, Bryan Wang, Bengisu Yarimbas, Lingzi Hong. (2025). "Multi-Agent Retrieval-Augmented Framework for Evidence-Based Counterspeech Against Health Misinformation." Conference on Language Models (COLM 2025).
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Published in , 1900

A Hybrid Attention-Guided Fusion Network with Grad-CAM for MPox Skin Lesion Classification

Published in 2025 IEEE 4th International Conference on Computing and Machine Intelligence (ICMI), 2025

We introduce a hybrid attention-guided fusion network with Grad-CAM for interpretable and accurate MPox skin lesion classification, demonstrating high diagnostic performance on a curated dataset.

Recommended citation: M. Arman, N. Prottush, M. A. Rusho, A. Datta, Anirban Saha Anik, D. M. Dohan, M. A. U. I. Sajid, I. A. Sheikh, and M. K. Jahna. "A Hybrid Attention-Guided Fusion Network with Grad-CAM for MPox Skin Lesion Classification." 2025 IEEE 4th International Conference on Computing and Machine Intelligence (ICMI).
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Dynamic Fusion of Large Language Models for Crisis Communication

Published in International Conference on Information Systems for Crisis Response and Management, 2025

We propose a dynamic fusion approach that integrates multiple LLMs and retrieval to generate timely, professional, and actionable crisis responses for social media contexts.

Recommended citation: Hong, L., Song, X., Saha Anik, A., Frias-Martinez, V. (2025). "Dynamic Fusion of Large Language Models for Crisis Communication." Proceedings of the International ISCRAM Conference.
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A Dynamic Fusion Model for Consistent Crisis Response

Published in The 2025 Conference on Empirical Methods in Natural Language Processing (EMNLP 2025 Findings), 2025

We introduce a dynamic fusion framework that combines multiple LLMs and retrieval to produce consistent, professional, and actionable crisis responses, and propose a consistency metric to measure stylistic stability across outputs.

Recommended citation: Song, Xiaoying, Anirban Saha Anik, Eduardo Blanco, Vanessa Frias-Martinez, and Lingzi Hong. "A Dynamic Fusion Model for Consistent Crisis Response." Findings of the Association for Computational Linguistics: EMNLP 2025.
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ClaimIQ at CheckThat! 2025: Comparing Prompted and Fine-Tuned Language Models for Verifying Numerical Claims

Published in Working Notes of the Conference and Labs of the Evaluation Forum, 2025

We compare prompted and fine-tuned language models for verifying numerical claims in the CheckThat! Lab Task 3 challenge, exploring retrieval and model adaptation strategies to improve numerical claim verification performance.

Recommended citation: Anirban Saha Anik, Md Fahimul Kabir Chowdhury, Andrew Wyckoff, Sagnik Ray Choudhury. "ClaimIQ at CheckThat! 2025: Comparing Prompted and Fine-Tuned Language Models for Verifying Numerical Claims." Working Notes of the Conference and Labs of the Evaluation Forum (CLEF 2025).
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teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015