no code implementations • SMM4H (COLING) 2022 • Afrin Sultana, Nihad Karim Chowdhury, Abu Nowshed Chy
SMM4H 2022 Task 3 introduced a shared task focusing on the identification of non-persistent patients from tweets and WebMD reviews.
no code implementations • 22 Oct 2023 • Abdul Aziz, Nihad Karim Chowdhury, Muhammad Ashad Kabir, Abu Nowshed Chy, Md. Jawad Siddique
In this research, we have proposed a unified multimodal transformer-based framework with image-text pair settings to identify human desire, sentiment, and emotion.
no code implementations • 1 Oct 2021 • Nihad Karim Chowdhury, Muhammad Ashad Kabir, Md. Muhtadir Rahman
To address this issue, in this paper, we propose an ensemble-based multi-criteria decision making (MCDM) method for selecting top performance machine learning technique(s) for COVID-19 cough classification.
no code implementations • 25 Jul 2021 • Aishwarza Panday, Muhammad Ashad Kabir, Nihad Karim Chowdhury
The purpose of this study is to systematically review, assess, and synthesize research articles that have used different machine learning techniques to detect and diagnose COVID-19 from chest X-ray and CT scan images.
no code implementations • 24 Sep 2020 • Nihad Karim Chowdhury, Muhammad Ashad Kabir, Md. Muhtadir Rahman, Noortaz Rezoana
This paper proposed an ensemble of deep convolutional neural networks (CNN) based on EfficientNet, named ECOVNet, to detect COVID-19 using a large chest X-ray data set.
no code implementations • 29 Jul 2020 • Nihad Karim Chowdhury, Md. Muhtadir Rahman, Muhammad Ashad Kabir
The experimental results demonstrate that our proposed method significantly improves performance metrics: accuracy, precision, recall, and F1 scores reach 96. 58%, 96. 58%, 96. 59%, and 96. 58%, respectively, which is comparable or enhanced compared with the state-of-the-art methods.