IoMT-Based Smart Monitoring Hierarchical Fuzzy Inference System for Diagnosis of COVID-19

Khan, Tauseef; Abbas, Sagheer; Ditta, Allah; Khan, Muhammad Ahsan; Alquhayz, Hani; Fatima, Areej; Khan, Mikael

doi:10.32604/cmc.2020.011892

Cited by 17 publications

(16 citation statements)

References 7 publications

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“…Te IoMT and smart living are becoming important gadgets in the healthcare system. Te smart healthcare network structure is made up of several tiny embedded computers that are linked together and connected to a range of IoMT devices [22] over an Internet. Wired networking services have been phased out in favor of wireless networking services [23].…”

Section: Related Workmentioning

confidence: 99%

Fused Weighted Federated Deep Extreme Machine Learning Based on Intelligent Lung Cancer Disease Prediction Model for Healthcare 5.0

Abbas

Issa

Fatima

et al. 2023

International Journal of Intelligent Systems

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In the era of advancement in information technology and the smart healthcare industry 5.0, the diagnosis of human diseases is still a challenging task. The accurate prediction of human diseases, especially deadly cancer diseases in the smart healthcare industry 5.0, is of utmost importance for human wellbeing. In recent years, the global Internet of Medical Things (IoMT) industry has evolved at a dizzying pace, from a small wristwatch to a big aircraft. With this advancement in the healthcare industry, there also rises the issue of data privacy. To ensure the privacy of patients’ data and fast data transmission, federated deep extreme learning entangled with the edge computing approach is considered in this proposed intelligent system for the diagnosis of lung disease. Federated deep extreme machine learning is applied for the prediction of lung disease in the proposed intelligent system. Furthermore, to strengthen the proposed model, a fused weighted deep extreme machine learning methodology is adopted for better prediction of lung disease. The MATLAB 2020a tool is used for simulation and results. The proposed fused weighted federated deep extreme machine learning model is used for the validation of the best prediction of cancer disease in the smart healthcare industry 5.0. The result of the proposed fused weighted federated deep extreme machine learning approach achieved 97.2%, which is better than the state-of-the-art published methods.

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Section: Related Workmentioning

confidence: 99%

Fused Weighted Federated Deep Extreme Machine Learning Based on Intelligent Lung Cancer Disease Prediction Model for Healthcare 5.0

Abbas

Issa

Fatima

et al. 2023

International Journal of Intelligent Systems

Self Cite

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“…Data fusion and deep ensemble learning were adopted for prediction. The IoMT-based smart monitoring [20] system was presented to diagnose COVID 19. The lightweight encryption technique [21] is used for IoMT applications to enhance the security of the medical image.…”

Section: Introductionmentioning

confidence: 99%

Secure IoMT for Disease Prediction Empowered With Transfer Learning in Healthcare 5.0, the Concept and Case Study

et al. 2023

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Identifying human diseases remains a difficult process, even in the age of advanced information technology and the smart healthcare industry 5.0. In the smart healthcare industry 5.0, precise prediction of human diseases, particularly lethal cancer diseases, is critical for human well-being. The global Internet of Medical Things sector has advanced at a breakneck pace in recent years, from small wristwatches to large aircraft. The critical aspects of the Internet of Medical Things include security and privacy, owing to the massive scale and deployment of the Internet of Medical Things networks. Transfer learning with a secure IoMTbased approach is considered. The google net deep machine-learning model is used for accurate disease prediction in the smart healthcare industry 5.0. We can easily and reliably anticipate the lethal cancer disease in the human body by using the secure IoMT-based transfer learning approach. Furthermore, the results of the proposed secure IoMT-based Transfer learning techniques are used to validate the best cancer disease prediction in the smart healthcare industry 5.0. The proposed secure IoMT-based transfer learning methodology reached 98.8%, better than the state-of-the-art methodologies used previously for cancer disease prediction in the smart healthcare industry 5.0.INDEX TERMS IoMT, transfer learning, deep machine learning, histopathology, image processing, lung cancer.

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“…[ 3 ] In a very short time, the virus went beyond the epidemic and became a pandemic. [ 4 ] COVID-19 belongs to the family of Respiratory Syndrome Middle East and Respiratory Syndrome viruses. [ 5 ] Among the most widely used modeling methods, we can refer to artificial neural networks (ANNs), which are based on the learning model of the human nervous system.…”

Section: Introductionmentioning

confidence: 99%

“…presented internet-based hierarchical inference systems, objects, and medicine for the diagnosis of COVID-19 using input-input variables in two layers. [ 4 ] In another study, Dehghandar et al . designed a fuzzy expert system for the diagnosis of COVID-19 using twelve input variables and extracting 29 rules using a Look-up Table.…”

Section: Introductionmentioning

confidence: 99%

Classification of COVID-19 Individuals Using Adaptive Neuro-Fuzzy Inference System

Dehghandar

Rezvani

2022

J Med Signals Sens

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IoMT-Based Smart Monitoring Hierarchical Fuzzy Inference System for Diagnosis of COVID-19

Cited by 17 publications

References 7 publications

Fused Weighted Federated Deep Extreme Machine Learning Based on Intelligent Lung Cancer Disease Prediction Model for Healthcare 5.0

Fused Weighted Federated Deep Extreme Machine Learning Based on Intelligent Lung Cancer Disease Prediction Model for Healthcare 5.0

Secure IoMT for Disease Prediction Empowered With Transfer Learning in Healthcare 5.0, the Concept and Case Study

Classification of COVID-19 Individuals Using Adaptive Neuro-Fuzzy Inference System

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