Intelligent Control and Security of Fog Resources in Healthcare Systems via a Cognitive Fog Model

Al-Khafajiy, Mohammed; Otoum, Safa; Baker, Thar; Asim, Muhammad; Maamar, Zakaria; Aloqaily, Moayad; Taylor, Mark; Randles, Martin

doi:10.1145/3382770

Cited by 34 publications

(21 citation statements)

References 45 publications

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“…There are various systems for storage virtualization, one of the most famous being system-based virtualization by methods for storage territory systems. This model utilizes a system available gadget through an enormous transfer speed association with give storerooms [144] .…”

Section: Incorporating Cloud Computing In Combating Covid-19mentioning

confidence: 99%

Notice of Retraction: AI Techniques for COVID-19

Hussain¹,

et al. 2020

Self Cite

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Artificial Intelligence (AI) intent is to facilitate human limits. It is getting a standpoint on human administrations, filled by the growing availability of restorative clinical data and quick progression of insightful strategies. Motivated by the need to highlight the need for employing AI in battling the COVID-19 Crisis, this survey summarizes the current state of AI applications in clinical administrations while battling COVID-19. Furthermore, we highlight the application of Big Data while understanding this virus. We also overview various intelligence techniques and methods that can be applied to various types of medical information-based pandemic. We classify the existing AI techniques in clinical data analysis, including neural systems, classical SVM, and edge significant learning. Also, an emphasis has been made on regions that utilize AI-oriented cloud computing in combating various similar viruses to COVID-19. This survey study is an attempt to benefit medical practitioners and medical researchers in overpowering their faced difficulties while handling COVID-19 big data. The investigated techniques put forth advances in medical data analysis with an exactness of up to 90%. We further end up with a detailed discussion about how AI implementation can be a huge advantage in combating various similar viruses.

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Section: Incorporating Cloud Computing In Combating Covid-19mentioning

confidence: 99%

Notice of Retraction: AI Techniques for COVID-19

Hussain¹,

et al. 2020

Self Cite

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“…One of the important issues with MLP is its convergence rate. The impact of convergence behaviour on neural network performance has been confirmed in previous researches [2][3][4][5][6]. In this paper, to employ an adaptive learning rate has been proposed to inrease the eneralization and convrgence rate of the proposed mulitple MLP in medical environment.…”

Section: Multiple Mlp Designsupporting

confidence: 53%

“…Therefore, there is an urgent need to develop an intelligent medical diagnostic system to identify and diagnose autism disorder in early stages. Providing a computer aided diagnosis (CAD) system with the capability of automatic autism detection and prediction is an important challenge in big data of medical engineering [5,6].…”

mentioning

confidence: 99%

Autism Spectrum Diagnosis using Adaptive Learning Algorithm for Multiple MLP Classifier

Safara¹

2021

JISIoT

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A medical condition that causes disability and early neurological and cognitive condition is autism spectrum disorder (ASD). Gene expression and environment have an impact on this medical condition. Development of diagnostic instruments and skills improved the autism recognition and increased the society awareness about it. To cope with this disorder collaboration between families, service providers, and autistic individuals is a necessity. Early diagnosis of ASD could help in lessening stress, increase adaptation, and support welfare in healthcare systems. Therefore, a large body of research is attempting to provide an intelligent medical diagnostic system to identify and diagnose ASD in early stages using machine learning methods. In this paper, several multilayer perceptron neural network is proposed for ASD detection in healthcare systems. The learning rate is adaptively tuned to achieve the best results. The results show that the approach proposed in this study achieved 99.6% accuracy, which indicates the superiority of the proposed method in identifying and detecting autism disorder in comparison with similar previous methods.

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“…The Long Short-Term Memory approach was selected since it achieved an accuracy rate of 96 per cent. Authors in [42][43][44] performed a similar study in terms of healthcare applications.…”

Section: Literature Reviewmentioning

confidence: 99%

Discrete Human Activity Recognition and Fall Detection by Combining FMCW RADAR Data of Heterogeneous Environments for Independent Assistive Living

Saeed

Shah

et al. 2021

Electronics

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Human activity monitoring is essential for a variety of applications in many fields, particularly healthcare. The goal of this research work is to develop a system that can effectively detect fall/collapse and classify other discrete daily living activities such as sitting, standing, walking, drinking, and bending. For this paper, a publicly accessible dataset is employed, which is captured at various geographical locations using a 5.8 GHz Frequency-Modulated Continuous-Wave (FMCW) RADAR. A total of ninety-nine participants, including young and elderly individuals, took part in the experimental campaign. During data acquisition, each aforementioned activity was recorded for 5–10 s. Through the obtained data, we generated the micro-doppler signatures using short-time Fourier transform by exploiting MATLAB tools. Subsequently, the micro-doppler signatures are validated, trained, and tested using a state-of-the-art deep learning algorithm called Residual Neural Network or ResNet. The ResNet classifier is developed in Python, which is utilised to classify six distinct human activities in this study. Furthermore, the metrics used to analyse the trained model’s performance are precision, recall, F1-score, classification accuracy, and confusion matrix. To test the resilience of the proposed method, two separate experiments are carried out. The trained ResNet models are put to the test by subject-independent scenarios and unseen data of the above-mentioned human activities at diverse geographical spaces. The experimental results showed that ResNet detected the falling and rest of the daily living human activities with decent accuracy.

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Intelligent Control and Security of Fog Resources in Healthcare Systems via a Cognitive Fog Model

Cited by 34 publications

References 45 publications

Notice of Retraction: AI Techniques for COVID-19

Notice of Retraction: AI Techniques for COVID-19

Autism Spectrum Diagnosis using Adaptive Learning Algorithm for Multiple MLP Classifier

Discrete Human Activity Recognition and Fall Detection by Combining FMCW RADAR Data of Heterogeneous Environments for Independent Assistive Living

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