Md. Tauhid Bin Iqbal

Work place: East West University, Dhaka, 1212, Bangladesh

E-mail: tauhid.iqbal@ewubd.edu

Website: https://orcid.org/0000-0001-7126-8969

Research Interests:

Biography

Md. Tauhid Bin Iqbal was born on December 31, 1991, in Mymensingh, Bangladesh. He received the Bache- lor’s degree in information technology from the University of Dhaka, Bangladesh, in 2012, and the Ph.D. degree in computer science and engineering from Kyung Hee University, South Korea, in 2019.
He conducted postdoctoral research at the Machine Learning and Visual Computing (MLVC) Laboratory, Kyung Hee University, South Korea. He is currently an Assistant Professor with the Department of Computer Science and Engineering, East West University, Dhaka, Bangladesh. His research interests include deep biomet- ric analysis, medical AI, explainable AI, and image processing.

Author Articles
Impact of 2023 Turkey Earthquake Price Hikes: Insightful Socio-Economic Analysis Using Transformer Models and Explainable AI

By Muhammed Yaseen Morshed Adib Md. Tauhid Bin Iqbal Farig Yousuf Sadeque

DOI: https://doi.org/10.5815/ijieeb.2025.05.05, Pub. Date: 8 Oct. 2025

Natural disasters cause economic instability, leading to severe financial hardships for affected communities. The rapid surge in essential goods prices during such events significantly burdens vulnerable populations, highlighting the critical need for timely policy interventions. While understanding public sentiment on economic distress is crucial for effective data-driven policy generation, research specifically analyzing public sentiment on price hikes in such contexts remains limited, often due to a lack of dedicated datasets. To address this, this paper first introduces a novel dataset of social media comments on price hikes related to the 2023 Turkey earthquake. Second, to support data-driven policy-making by quantifying public sentiment, we applied a range of AI models and identified transformer-based models like DistilBERT as particularly effective for sentiment classification. Furthermore, we employ Explainable AI techniques to enhance model trust, enabling policymakers to confidently use these insights to support disaster recovery and economic stabilization in affected regions.

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