Multi-technique object tracking approach- A reinforcement paradigm

Samuel, Oluwarotimi Williams; Asogbon, Grace Mojisola; Sangaiah, Arun Kumar; Li, Guanglin

doi:10.1016/j.compeleceng.2017.02.002

Cited by 5 publications

(2 citation statements)

References 14 publications

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“…Securityissueswereconsideredinmanyrecentpapers (Samuel,O.W.,2019),butsomeofthe existingworksarenotfocused.LookingatthesecurityandconfidentialityinIoTresources,Acloud-basedRHMIoTmodel,dataencryption,anddecryptionmethodsarepresentedinourwork,whereas otherswerenotconsideredwhichisshowninTable4.…”

Section: Security Solutions For Iot Medical Datamentioning

confidence: 99%

An RHMIoT Framework for Cardiovascular Disease Prediction and Severity Level Using Machine Learning and Deep Learning Algorithms

Patro

Padhy

2022

International Journal of Ambient Computing and Intelligence

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Cardiovascular disease is one of the deadliest diseases in the world. Accurate analysis and prediction for real-time heart disease are highly significant. To address this challenge, a novel IoT-based automated function monitoring system to promote the e-healthcare system is proposed. The proposed remote healthcare monitoring system uses an IoT framework (RHMIoT) using deep learning and auto encoder-based machine learning algorithms to accurately predict the presence of heart disease. The RHMIoT framework contains two phases: the first phase is to monitor the severity level of the heart disease patient in real-time, and the second phase is used in the medical decision support system to predict the accuracy level of heart disease. To train and test the open-access Framingham dataset, various deep learning and auto encoder-based machine learning techniques are used. The proposed system obtains an accuracy of 0.8714% using the auto encoder-based kernel SVM algorithm compared to other approaches.

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Section: Security Solutions For Iot Medical Datamentioning

confidence: 99%

An RHMIoT Framework for Cardiovascular Disease Prediction and Severity Level Using Machine Learning and Deep Learning Algorithms

Patro

Padhy

2022

International Journal of Ambient Computing and Intelligence

View full text Add to dashboard Cite

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“…Modern-day robots are designed to operate in a structured environment. Due to the complexity and unpredictability of the external environment and the limitations of existing vision systems, current intelligent robots are unable to completely achieve their intended objectives [10][11][12][13]. In addition, unlike the for human vision, it is difficult for a robotic system to track and detect multiple objects simultaneously [9].…”

Section: Introductionmentioning

confidence: 99%