ORCID Profile
0000-0003-2565-5321
Current Organisations
BRAC University
,
Curtin University
,
Murdoch University
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Publisher: IEEE
Date: 05-01-2021
Publisher: IEEE
Date: 26-02-2022
Publisher: Association for Computational Linguistics
Date: 2023
Publisher: Springer Nature Singapore
Date: 2022
Publisher: Elsevier BV
Date: 06-2023
Publisher: SCITEPRESS - Science and Technology Publications
Date: 2022
Publisher: IEEE
Date: 2020
Publisher: IEEE
Date: 18-12-2021
Publisher: IEEE
Date: 06-09-2023
Publisher: Springer Science and Business Media LLC
Date: 06-01-2023
DOI: 10.1186/S12859-022-05127-6
Abstract: With the global spread of COVID-19, the world has seen many patients, including many severe cases. The rapid development of machine learning (ML) has made significant disease diagnosis and prediction achievements. Current studies have confirmed that omics data at the host level can reflect the development process and prognosis of the disease. Since early diagnosis and effective treatment of severe COVID-19 patients remains challenging, this research aims to use omics data in different ML models for COVID-19 diagnosis and prognosis. We used several ML models on omics data of a large number of in iduals to first predict whether patients are COVID-19 positive or negative, followed by the severity of the disease. On the COVID-19 diagnosis task, we got the best AUC of 0.99 with our multilayer perceptron model and the highest F1-score of 0.95 with our logistic regression (LR) model. For the severity prediction task, we achieved the highest accuracy of 0.76 with an LR model. Beyond classification and predictive modeling, our study founds ML models performed better on integrated multi-omics data, rather than single omics. By comparing top features from different omics dataset, we also found the robustness of our model, with a wider range of applicability in erse dataset related to COVID-19. Additionally, we have found that omics-based models performed better than image or physiological feature-based models, proving the importance of the omics-based dataset for future model development. This study diagnoses COVID-19 positive cases and predicts accurate severity levels. It lowers the dependence on clinical data and professional judgment, by leveraging the utilization of state-of-the-art models. our model showed wider applicability across different omics dataset, which is highly transferable in other respiratory or similar diseases. Hospital and public health care mechanisms can optimize the distribution of medical resources and improve the robustness of the medical system.
Publisher: IEEE
Date: 05-2019
Publisher: IEEE
Date: 06-09-2023
Publisher: Elsevier BV
Date: 2023
Publisher: Institution of Engineering and Technology (IET)
Date: 06-10-2021
DOI: 10.1049/TJE2.12082
Publisher: IEEE
Date: 16-10-2022
Publisher: IEEE
Date: 12-2020
Publisher: IEEE
Date: 06-2022
Publisher: Wiley
Date: 20-07-2022
DOI: 10.1111/EXSY.13045
Abstract: Although speech recognition has achieved significant success using integrated and efficient models, still some series of challenges remain as linguistic‐acoustic patterns are perturbed by speakers' in idual articulation gestures and environmental noises. Due to dynamic changes in the vocal tract cavity, word utterances yield temporal and perturbed linguistic‐acoustic features, whereas vowel utterances yield less‐perturbed quasi‐stationary features. To recognize patterns as in vowels and words, the basic feedforward neural network (NN), among other methods, responds to these vocal tract‐induced variabilities and has shown promising results because of its simple yet effective modelling of nonlinear data. We, therefore, present a comprehensive study on how these variabilities of acoustical features affect the speech token classification performances using NNs. We chose vocal tract resonance (formant frequency) as linguistic‐acoustic feature. Our statistical evaluation of vocal tract‐induced variabilities in seven Bengali vowels and words revealed that words have more variations than vowels. We used four‐fold cross‐validation in an NN with Adam optimizer to compute classification performances using five different metrics. Our experiments found that formant transitions and dispersions do not contribute to classification, and five‐hidden‐layered NN is optimum. In all different test cases, we justified our hypothesis—word classification falls behind vowel classification due to the variability induced by vocal tract dynamics. The optimum NN with 28,263 trainable parameters achieved the highest accuracy and AUC scores: 0.89 and 0.99 in vowels, and 0.64 and 0.91 in words.
Location: Bangladesh
No related grants have been discovered for Rakibul Hasan.