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A lightweight and cost effective edge intelligence architecture based on containerization technology
Al-Rakhami Mabrook Gumaei Abdu Alsahli Mohammed Hassan Mohammad Mehedi Alamri Atif Guerrieri Antonio Fortino Giancarlo 《World Wide Web》2020,23(2):1341-1360
World Wide Web - The integration of Cloud computing and Internet of Things led to rapid growth in the edge computing field. This would not be achievable without combining the data centers’... 相似文献
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Abdu Gumaei Mabrook Al-Rakhami Mohamad Mahmoud Al Rahhal Fahad Raddah H. Albogamy Eslam Al Maghayreh Hussain AlSalman 《计算机、材料和连续体(英文)》2021,66(1):315-329
The fast spread of coronavirus disease (COVID-19) caused by SARSCoV-2 has become a pandemic and a serious threat to the world. As of May 30,
2020, this disease had infected more than 6 million people globally, with hundreds
of thousands of deaths. Therefore, there is an urgent need to predict confirmed cases
so as to analyze the impact of COVID-19 and practice readiness in healthcare systems.
This study uses gradient boosting regression (GBR) to build a trained model to predict
the daily total confirmed cases of COVID-19. The GBR method can minimize the loss
function of the training process and create a single strong learner from weak learners.
Experiments are conducted on a dataset of daily confirmed COVID-19 cases from January 22, 2020, to May 30, 2020. The results are evaluated on a set of evaluation performance measures using 10-fold cross-validation to demonstrate the effectiveness of
the GBR method. The results reveal that the GBR model achieves 0.00686 root mean
square error, the lowest among several comparative models. 相似文献
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Syed Sajid Ullah Saddam Hussain Abdu Gumaei Mohsin S. Alhilal Bader Fahad Alkhamees Mueen Uddin Mabrook Al-Rakhami 《计算机、材料和连续体(英文)》2022,70(1):233-247
Nowadays, healthcare has become an important area for the Internet of Things (IoT) to automate healthcare facilities to share and use patient data anytime and anywhere with Internet services. At present, the host-based Internet paradigm is used for sharing and accessing healthcare-related data. However, due to the location-dependent nature, it suffers from latency, mobility, and security. For this purpose, Named Data Networking (NDN) has been recommended as the future Internet paradigm to cover the shortcomings of the traditional host-based Internet paradigm. Unfortunately, the novel breed lacks a secure framework for healthcare. This article constructs an NDN-Based Internet of Medical Things (NDN-IoMT) framework using a lightweight certificateless (CLC) signature. We adopt the Hyperelliptic Curve Cryptosystem (HCC) to reduce cost, which provides strong security using a smaller key size compared to Elliptic Curve Cryptosystem (ECC). Furthermore, we validate the safety of the proposed scheme through AVISPA. For cost-efficiency, we compare the designed scheme with relevant certificateless signature schemes. The final result shows that our proposed scheme uses minimal network resources. Lastly, we deploy the given framework on NDN-IoMT. 相似文献
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Abdu Gumaei Mabrook Al-Rakhami Hussain AlSalman Sk. Md. Mizanur Rahman Atif Alamri 《计算机、材料和连续体(英文)》2020,65(2):1033-1057
Human activity recognition is commonly used in several Internet of Things
applications to recognize different contexts and respond to them. Deep learning has
gained momentum for identifying activities through sensors, smartphones or even
surveillance cameras. However, it is often difficult to train deep learning models on
constrained IoT devices. The focus of this paper is to propose an alternative model by
constructing a Deep Learning-based Human Activity Recognition framework for edge
computing, which we call DL-HAR. The goal of this framework is to exploit the
capabilities of cloud computing to train a deep learning model and deploy it on lesspowerful edge devices for recognition. The idea is to conduct the training of the model in
the Cloud and distribute it to the edge nodes. We demonstrate how the DL-HAR can
perform human activity recognition at the edge while improving efficiency and accuracy.
In order to evaluate the proposed framework, we conducted a comprehensive set of
experiments to validate the applicability of DL-HAR. Experimental results on the
benchmark dataset show a significant increase in performance compared with the state-of-the-art models. 相似文献
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