no code implementations • 30 Mar 2024 • Md Abrar Jahin, Md Sakib Hossain Shovon, M. F. Mridha
Sentiment analysis is crucial for understanding public opinion and consumer behavior.
no code implementations • 22 Mar 2024 • Md Abrar Jahin, Saleh Akram Naife, Fatema Tuj Johora Lima, M. F. Mridha, Jungpil Shin
Domestic violence, which is often perceived as a gendered issue among female victims, has gained increasing attention in recent years.
no code implementations • 4 Mar 2024 • Anik Kumar Saha, Md Abrar Jahin, Md. Rafiquzzaman, M. F. Mridha
The one-way ANOVA test with a significance level of 5% also showed a significant difference between proposed and existing furniture dimensions.
no code implementations • 2 Feb 2024 • Kazrin Ahmad, Md. Saiful Islam, Md Abrar Jahin, M. F. Mridha
The research findings provide industry stakeholders, governments, and organizations with significant drivers of IoT adoption to overcome these barriers and optimize the utilization of IoT technology to improve the effectiveness and reliability of the cold supply chain.
no code implementations • 12 Dec 2023 • Md Abrar Jahin, Saleh Akram Naife, Anik Kumar Saha, M. F. Mridha
Supply chain risk assessment (SCRA) has witnessed a profound evolution through the integration of artificial intelligence (AI) and machine learning (ML) techniques, revolutionizing predictive capabilities and risk mitigation strategies.
no code implementations • 1 Nov 2023 • Md Farhan Ishmam, Md Sakib Hossain Shovon, M. F. Mridha, Nilanjan Dey
The multimodal task of Visual Question Answering (VQA) encompassing elements of Computer Vision (CV) and Natural Language Processing (NLP), aims to generate answers to questions on any visual input.
no code implementations • 25 Oct 2023 • Osim Kumar Pal, Md Sakib Hossain Shovon, M. F. Mridha, Jungpil Shin
In recent years, the combination of artificial intelligence (AI) and unmanned aerial vehicles (UAVs) has brought about advancements in various areas.
1 code implementation • 13 Oct 2023 • Abdullah Al Imran, Md Sakib Hossain Shovon, M. F. Mridha
This study presents a large multi-modal Bangla YouTube clickbait dataset consisting of 253, 070 data points collected through an automated process using the YouTube API and Python web automation frameworks.
no code implementations • 1 Aug 2023 • Md Sakib Hossain Shovon, M. F. Mridha, Khan Md Hasib, Sultan Alfarhood, Mejdl Safran, Dunren Che
An ensemble approach integrated with threshold filtered single instance evaluation (SIE) technique has been proposed in this study to diagnose BC from the multi-categorical expression of HER2 subtypes.
no code implementations • 24 Jul 2023 • Md Abrar Jahin, Md Sakib Hossain Shovon, Md. Saiful Islam, Jungpil Shin, M. F. Mridha, Yuichi Okuyama
Our proposed model, QAmplifyNet, employs quantum-inspired techniques within a quantum-classical neural network to predict backorders effectively on short and imbalanced datasets.
no code implementations • 24 Jul 2023 • Md Abrar Jahin, Md Sakib Hossain Shovon, Jungpil Shin, Istiyaque Ahmed Ridoy, M. F. Mridha
This article intends to systematically identify and comparatively analyze state-of-the-art supply chain (SC) forecasting strategies and technologies.
no code implementations • 19 Nov 2022 • M. F. Mridha, Md. Kishor Morol, Md. Asraf Ali, Md Sakib Hossain Shovon
The outcomes of this study determined that the HER2 cancer detecting rates of the convoHER2 model are much enough to provide better diagnosis to the patient for recovering their HER2 breast cancer in future.
no code implementations • 27 Sep 2021 • M. F. Mridha, Abu Quwsar Ohi, Md. Abdul Hamid, Muhammad Mostafa Monowar
Speech recognition is a language-dependent system constructed directly based on the linguistic and textual properties of any language.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +1
1 code implementation • 9 May 2021 • Farisa Benta Safir, Abu Quwsar Ohi, M. F. Mridha, Muhammad Mostafa Monowar, Md. Abdul Hamid
Further, we experiment with two different recurrent neural networks (RNN) methods, LSTM and GRU.
Optical Character Recognition Optical Character Recognition (OCR)
1 code implementation • 7 Feb 2021 • M. F. Mridha, Abu Quwsar Ohi, Muhammad Mostafa Monowar, Md. Abdul Hamid, Md. Rashedul Islam, Yutaka Watanobe
The robustness of a speaker recognition system mainly depends on the extraction process of speech embeddings, which are primarily pre-trained on a large-scale dataset.
1 code implementation • 14 Jan 2021 • Abu Quwsar Ohi, M. F. Mridha, Md. Abdul Hamid, Muhammad Mostafa Monowar, Faris A Kateb
The FabricNet can recognize a large scale of fibers by only utilizing a surface image of fabric.
no code implementations • 3 Jan 2021 • Md. Mohsin Kabir, Abu Quwsar Ohi, Md. Saifur Rahman, M. F. Mridha
Deep neural networks have been demonstrated as very powerful systems for facing the challenge of object classification from high-resolution images, but deploying such object classification networks on the embedded device remains challenging due to the high computational and memory requirements.
1 code implementation • 15 Nov 2020 • M. F. Mridha, Abu Quwsar Ohi, M. Ameer Ali, Mazedul Islam Emon, Muhammad Mohsin Kabir
This article presents a Bangla handwriting dataset named BanglaWriting that contains single-page handwritings of 260 individuals of different personalities and ages.
no code implementations • 10 Nov 2020 • Muhammad Mohsin Kabir, Abu Quwsar Ohi, M. F. Mridha
The experimental results validate that the Xception and DenseNet architectures perform better in multi-label plant disease classification tasks.
no code implementations • 12 Oct 2020 • Abu Quwsar Ohi, M. F. Mridha, Md. Abdul Hamid, Muhammad Mostafa Monowar, Dongsu Lee, Jinsul Kim
It also introduces new features that are used for both speaker verification and identification tasks.
no code implementations • 21 Jul 2020 • M. F. Mridha, Md. Saifur Rahman, Abu Quwsar Ohi
To the best of our knowledge, this is the first attempt in developing a text based human abnormality detection system.
1 code implementation • Knowledge-Based Systems 2020 • Abu Quwsar Ohi, M. F. Mridha, Farisa Benta Safir, Md. Abdul Hamid, Muhammad Mostafa Monowar
Deep clustering method downsamples high dimensional data, which may also relate clustering loss.