Zobrazeno 1 - 10
of 232
pro vyhledávání: '"KABIR, Muhammad Ashad"'
This research presents a robust approach to classifying COVID-19 cough sounds using cutting-edge machine-learning techniques. Leveraging deep neural decision trees and deep neural decision forests, our methodology demonstrates consistent performance
Externí odkaz:
http://arxiv.org/abs/2501.01117
BeliN: A Novel Corpus for Bengali Religious News Headline Generation using Contextual Feature Fusion
Automatic text summarization, particularly headline generation, remains a critical yet underexplored area for Bengali religious news. Existing approaches to headline generation typically rely solely on the article content, overlooking crucial context
Externí odkaz:
http://arxiv.org/abs/2501.01069
As the use of web browsers continues to grow, the potential for cybercrime and web-related criminal activities also increases. Digital forensic investigators must understand how different browsers function and the critical areas to consider during we
Externí odkaz:
http://arxiv.org/abs/2410.12605
Artificial intelligence (AI) has emerged as a promising tool for predicting COVID-19 from medical images. In this paper, we propose a novel continual learning-based approach and present the design and implementation of a mobile application for screen
Externí odkaz:
http://arxiv.org/abs/2410.12589
Autor:
Rahman, Md. Sohanur, Chowdhury, Muhammad E. H., Rahman, Hasib Ryan, Ahmed, Mosabber Uddin, Kabir, Muhammad Ashad, Roy, Sanjiban Sekhar, Sarmun, Rusab
In this study, we propose a novel and robust framework, Self-DenseMobileNet, designed to enhance the classification of nodules and non-nodules in chest radiographs (CXRs). Our approach integrates advanced image standardization and enhancement techniq
Externí odkaz:
http://arxiv.org/abs/2410.12584
Analysis of child development facts and myths using text mining techniques and classification models
Publikováno v:
Heliyon, 2024, 10 (17)
The rapid dissemination of misinformation on the internet complicates the decision-making process for individuals seeking reliable information, particularly parents researching child development topics. This misinformation can lead to adverse consequ
Externí odkaz:
http://arxiv.org/abs/2408.13091
Publikováno v:
Heliyon, 2024, 10 (17)
Assistive technologies have been developed to enhance blind users' typing performance, focusing on speed, accuracy, and effort reduction. One such technology is word prediction software, designed to minimize keystrokes required for text input. This s
Externí odkaz:
http://arxiv.org/abs/2408.10791
Autor:
Islam, Sheikh Mohammed Shariful, Abrar, Moloud, Tegegne, Teketo, Loranjo, Liliana, Karmakar, Chandan, Awal, Md Abdul, Hossain, Md. Shahadat, Kabir, Muhammad Ashad, Mahmud, Mufti, Khosravi, Abbas, Siopis, George, Moses, Jeban C, Maddison, Ralph
Machine learning models have the potential to identify cardiovascular diseases (CVDs) early and accurately in primary healthcare settings, which is crucial for delivering timely treatment and management. Although population-based CVD risk models have
Externí odkaz:
http://arxiv.org/abs/2407.16721
Autor:
Kabir, Muhammad Ashad, Rahman, Sheikh Sowmen, Islam, Mohammad Mainul, Ahmed, Sayed, Laird, Craig
Publikováno v:
JMIR mHealth and uHealth, Vol 9, Iss 3, p e24202 (2021)
BackgroundAs the use of smartphones increases globally across various fields of research and technology, significant contributions to the sectors related to health, specifically foot health, can be observed. Numerous smartphone apps are now being use
Externí odkaz:
https://doaj.org/article/133c9d25cc9b4467babc47bf46e10ebd
Publikováno v:
Heliyon, 2024, 10 (14)
The COVID-19 pandemic has had adverse effects on both physical and mental health. During this pandemic, numerous studies have focused on gaining insights into health-related perspectives from social media. In this study, our primary objective is to d
Externí odkaz:
http://arxiv.org/abs/2402.09897