i Worksafe: Towards Healthy Workplaces During COVID-19 With an Intelligent Phealth App for Industrial Settings
M. Shamim Kaiser,Mufti Mahmud,Manan Binth Taj Noor,Nusrat Zerin Zenia,Shamim Al Mamun,K. M. Abir Mahmud,Saiful Azad,V. N. Manjunath Aradhya,Punitha Stephan,Thompson Stephan,Ramani Kannan,Mohammed Hanif,Tamanna Sharmeen,Tianhua Chen,Amir Hussain +14 more
TLDR
In this paper, a mobile app-based intelligent portable healthcare (pHealth) tool, called ${i}$ WorkSafe, is presented to assist industries in detecting possible suspects for COVID-19 infection among their employees who may need primary care.Abstract:
The recent outbreak of the novel Coronavirus Disease (COVID-19) has given rise to diverse health issues due to its high transmission rate and limited treatment options. Almost the whole world, at some point of time, was placed in lock-down in an attempt to stop the spread of the virus, with resulting psychological and economic sequela. As countries start to ease lock-down measures and reopen industries, ensuring a healthy workplace for employees has become imperative. Thus, this paper presents a mobile app-based intelligent portable healthcare (pHealth) tool, called ${i}$ WorkSafe, to assist industries in detecting possible suspects for COVID-19 infection among their employees who may need primary care. Developed mainly for low-end Android devices, the ${i}$ WorkSafe app hosts a fuzzy neural network model that integrates data of employees’ health status from the industry’s database, proximity and contact tracing data from the mobile devices, and user-reported COVID-19 self-test data. Using the built-in Bluetooth low energy sensing technology and K Nearest Neighbor and K-means techniques, the app is capable of tracking users’ proximity and trace contact with other employees. Additionally, it uses a logistic regression model to calculate the COVID-19 self-test score and a Bayesian Decision Tree model for checking real-time health condition from an intelligent e-health platform for further clinical attention of the employees. Rolled out in an apparel factory on 12 employees as a test case, the pHealth tool generates an alert to maintain social distancing among employees inside the industry. In addition, the app helps employees to estimate risk with possible COVID-19 infection based on the collected data and found that the score is effective in estimating personal health condition of the app user.read more
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Journal ArticleDOI
Security Threats and Artificial Intelligence Based Countermeasures for Internet of Things Networks: A Comprehensive Survey
Shakila Zaman,Khaled Alhazmi,Mohammed Aseeri,Muhammad R. Ahmed,Risala Tasin Khan,M. Shamim Kaiser,Mufti Mahmud +6 more
TL;DR: In this paper, a comprehensive layer-wise survey on IoT security threats, and the AI-based security models to impede security threats is presented, and open challenges and future research directions are addressed for the safeguard of the IoT network.
Journal ArticleDOI
Forecasting major impacts of COVID-19 pandemic on country-driven sectors: challenges, lessons, and future roadmap.
Saket Kumar,Rajkumar Viral,Vikas Deep,Purushottam Sharma,Manoj Kumar,Mufti Mahmud,Thompson Stephan +6 more
TL;DR: The impact of this pandemic on country-driven sectors is evaluated and some strategies to lessen these impacts on a country’s economy are recommended.
Journal ArticleDOI
COVID-19 Infection Detection from Chest X-Ray Images Using Hybrid Social Group Optimization and Support Vector Classifier.
TL;DR: In this article, the authors proposed a pipeline that uses CXR images to detect COVID-19 infection using Hybrid Social Group Optimization algorithm and achieved a classification accuracy of 99.65% using support vector classifier, which outperforms other state-of-the-art deep learning algorithms for binary and multi-class classification.
Journal ArticleDOI
Attention-Based Bi-Directional Long-Short Term Memory Network for Earthquake Prediction
Md. Hasan Al Banna,Tapotosh Ghosh,Md. Jaber Al Nahian,Kazi Abu Taher,M. Shamim Kaiser,Mufti Mahmud,Mohammad Shahadat Hossain,Karl Andersson +7 more
TL;DR: In this article, an earthquake occurrence and location prediction model is proposed, which is composed of combinations of various LSTM architectures and dense layers, and an attention mechanism was added to the LSTMs architecture to improve the model's earthquake occurrence prediction accuracy.
Book ChapterDOI
Artificial Intelligence and Machine Learning for Ensuring Security in Smart Cities
Sabbir Ahmed,Md. Farhad Hossain,M. Shamim Kaiser,Manan Binth Taj Noor,Mufti Mahmud,Chinmay Chakraborty +5 more
TL;DR: This chapter addresses security issues in smart city applications; and corresponding countermeasures using artificial intelligence and machine learning.
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