IoT Based Healthcare System for Emergency Medical Services

Asokan, Anju; Priyanka, B.; Thenmozhi, S.; Sumathi, K.

doi:10.1109/iceca52323.2021.9675896

Cited by 4 publications

(3 citation statements)

References 16 publications

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“…The collected data are stored in cloud to be used for analysis by different machine learning techniques like Naïve Bayes Multinomial (NBM), Random Forest(RF), Logistic Model Tree(LMT) and Decision Tree(DT) to predict the heart disease attack in advance so that optimum care of human body can be taken. The parameter selection is done as per the requirement in different machine learning models [15] so that the prediction of diseases can be done with more accuracy and less error. The prediction in different models like Naïve Bayes Multinomial (NBM), Random Forest(RF), Logistic Model Tree(LMT) and Decision Tree(DT) are compared on the basis of the accuracy and different error parameters and it is found that Decision Tree(DT) performs the best.…”

Section: Discussionmentioning

confidence: 99%

“…Jayasingh et al [15] have demonstrated a novel approach for data classification using neural network. They have described the different soft computing models which are helpful for data classification.Jayasingh et al [16] have made the smart weather prediction using different machine learning techniques.…”

Section: International Journal On Recent and Innovation Trends In Com...mentioning

confidence: 99%

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Application of IoT Framework for Prediction of Heart Disease using Machine Learning

Swain,

Behera,

Swain

et al. 2023

IJRITCC

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Prognosis of illnesses is a difficult problem these days throughout the globe. Elder people of twenty years and over are taken into consideration to be laid low with this sickness now a days. For example, human beings having HbA1c level more than 6.5% are diagnosed as infected with diabetic diseases. This paper uses IoT to evaluate threat factors which have been similar to heart diseases which are not treated properly. Diagnosis, prevention of heart disease may be done by use of machine learning (ML). There has been an extensive disconnect among Machine Learning architects, health care researchers, patients and physicians in their technology. This paper intends to perform an in-intensity evaluation on Machine Learning to make us of new advance technologies. Latest advances within the development of IoT implanted devices and other medicine delivery gadgets, disease diagnostic methods and other medical research have considerably helped human beings diagnosed heart diseases. New soft computing models can be helpful for remedy of various heart diseases. The Food and Drug Administration (FDA) employs several particularly creative thoughts to get their capsules to the client. Artificial Neural Community offers a first-rate chance to deal with heart diseases with advance IoT and cloud applications.

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Section: Discussionmentioning

confidence: 99%

Section: International Journal On Recent and Innovation Trends In Com...mentioning

confidence: 99%

Application of IoT Framework for Prediction of Heart Disease using Machine Learning

Swain,

Behera,

Swain

et al. 2023

IJRITCC

View full text Add to dashboard Cite

show abstract

“…Along with blockchain, IoT is the evolutionary technology of this era. IoT technology has already been implemented in various fields including healthcare (Haghi et al, 2020;Asokan et al, 2021;Bhuiyan et al, 2021;Mehmood et al, 2021;Sharma et al, 2022); smart cities (Zafeiriou, 2020;Hajam and Sofi, 2021;Kaushik and Bagga, 2021;Nadeem et al, 2021); security (Siddiqui et al, 2019(Siddiqui et al, , 2020; business (Aagaard et al, 2019;Ali et al, 2019Ali et al, , 2020aFattouch et al, 2020;Torres et al, 2020); and agriculture (Azfar et al, 2018;Kassim, 2020;Syed et al, 2020) etc.…”

Section: Iot As Energy Trading Enabling Technologymentioning

confidence: 99%

A survey on peer-to-peer energy trading for local communities: Challenges, applications, and enabling technologies

Nadeem

2022

Front. Comput. Sci.

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Electricity is still a highly demanding resource in developing countries, especifically in rural areas. Local communities in developing countries are now focusing on generating their own electricity using solar and trading the energy which is in access to the supplier. Energy trading at the peer-to-peer level is a novel paradigm. It allows customers to contribute energy to the grid and power generation and supply companies. In this paper, we investigate the energy trading enabling technologies, their applications, challenges, and benefits to the local communities from various aspects. We analyse how blockchain and IoT technologies can help energy trading at the customer level from recent research proposals. We also present our analysis of recent research on the implementation of energy trading technologies and future directions.

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