Genomic sequence analysis of lung infections using artificial intelligence technique

Kumar, Rajagopal; Al‐Turjman, Fadi; Anand, L.; Kumar, Abhishek; Magesh, S.; Vengatesan, K.; Sitharthan, R.; Rajesh, M.

doi:10.1007/s12539-020-00414-3

Cited by 9 publications

(6 citation statements)

References 27 publications

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“…When the COVID-19 episode has influenced our day by day lives, IoT and AI have become significant weapons in following and following cases. Actualizing these advances have brought about adjusting the need to vanquish the infection with the clashing need to ensure singular protection [ 21 ]. It is basic to get that while there is a desperate need of investing amounts of energy to go up against the pandemic, it is immensely significant that such apparatuses should be restricted being used, both regarding reason and time and that individual rights to security, non-segregation, the insurance of editorial sources and different opportunities are thoroughly ensured [ 22 – 25 ].…”

Section: Role Of Iot and Ai In Health Carementioning

confidence: 99%

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RETRACTED ARTICLE: Application of machine learning (ML) and internet of things (IoT) in healthcare to predict and tackle pandemic situation

Sitharthan

Rajesh²

2021

Distrib Parallel Databases

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The pandemic situation has pretentious the habitual life of the human, it also has surpassed the regional, social, business activities and forced human society to live in a limited boundary. In this paper, the application of the internet of things (IoT) and machine learning (ML) based system to combat pandemic situation in health care application has been discussed. The developed ML and IoT based monitoring system help in tracking the infected persons from the previous data and makes them get isolate from the non-infected person. The developed ML combined IoT system uses parallel computing in tracking the pandemic disease and also in the prevention of pandemic disease by predicting and analysing the data using artificial intelligence. The implementation of ML-based IoT in the pandemic situation in healthcare application has proved its performance in tracking and prevents the spreading of pandemic disease. It also further has a positive impact on reducing medical costs and has recorded improved treatment for infected patients. The proposed methodology has an accuracy of 93 % in monitoring and tracking. The result obtained help in preventing the spread of the pandemic and provide support to the healthcare system.

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Section: Role Of Iot and Ai In Health Carementioning

confidence: 99%

“…In [ 20 ], 5G based industrial internet of things (IIoT) has been used to interact with serval sensor in the application of health care systems. In [ 21 ], AI is used in the analysis of lung infection, this methodology can be used to predict the existence and growth of COVID-19 in the lungs.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Application of machine learning (ML) and internet of things (IoT) in healthcare to predict and tackle pandemic situation

Sitharthan

Rajesh²

2021

Distrib Parallel Databases

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“…From the analysis, it is seen that the proposed prediction technique takes the lower computation time of 438 s. Further, it uses a rule-based technique, which reduces the complexity when compared to LR and MLR techniques. The P-SVM used in genomic sequence analysis [ 24 ] obtained a prediction accuracy of 84% and is also purely rule-based. The prediction accuracy obtained through the proposed ANFIS technique is 86%.…”

Section: Resultsmentioning

confidence: 99%

“…Hence, the rule formation is simple and has higher accuracy; ANFIS can be used for predicting a profound epidemic disaster. Kumar et al [ 24 ] discuss the machine learning algorithm for COVID-19 estimation for lung infected patients. Jeon et al [ 25 ] developed an LQR controller based on fuzzy logic for wind turbines.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, a profound analysis is carried out in India to predict the possible COVID-19 outbreak using Adaptive Neuro-fuzzy Inference System (ANFIS)-based machine learning technique. Generally, Machine learning models are deployed for feature prediction that involves risk and also for epidemic analysis [ 24 ]. The country India has been chosen in this analysis as the COVID-19 cases get rapidly increases across the states of India and have a higher population rate.…”

Section: Introductionmentioning

confidence: 99%

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ANFIS for prediction of epidemic peak and infected cases for COVID-19 in India

Kumar¹,

Al‐Turjman²,

Srinivas

et al. 2021

Neural Comput & Applic

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Corona Virus Disease 2019 (COVID-19) is a continuing extensive incident globally affecting several million people's health and sometimes leading to death. The outbreak prediction and making cautious steps is the only way to prevent the spread of COVID-19. This paper presents an Adaptive Neuro-fuzzy Inference System (ANFIS)-based machine learning technique to predict the possible outbreak in India. The proposed ANFIS-based prediction system tracks the growth of epidemic based on the previous data sets fetched from cloud computing. The proposed ANFIS technique predicts the epidemic peak and COVID-19 infected cases through the cloud data sets. The ANFIS is chosen for this study as it has both numerical and linguistic knowledge, and also has ability to classify data and identify patterns. The proposed technique not only predicts the outbreak but also tracks the disease and suggests a measurable policy to manage the COVID-19 epidemic. The obtained prediction shows that the proposed technique very effectively tracks the growth of the COVID-19 epidemic. The result shows the growth of infection rate decreases at end of 2020 and also has delay epidemic peak by 40–60 days. The prediction result using the proposed ANFIS technique shows a low Mean Square Error (MSE) of 1.184 × 10 –3 with an accuracy of 86%. The study provides important information for public health providers and the government to control the COVID-19 epidemic.

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