Towards applying internet of things and machine learning for the risk prediction of COVID-19 in pandemic situation using Naive Bayes classifier for improving accuracy

Deepa, N.; Priya, J. Sathya; Devi, T

doi:10.1016/j.matpr.2022.03.345

Cited by 22 publications

(7 citation statements)

References 9 publications

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“…A detailed study on machine learning, systems biology and bioinformatics is done thereby creating a disease model and host-microbe interaction disease network. The study can be utilized by clinicians and researchers to improve the prognosis, detection and treatment of post-COVID-19 mucormycosis that requires further evidence and testimonials in post-COVID-19 mucormycosis cases [16].The authors in [8] proposes an integrated method for the monitoring of Covid-19 by incorporating machine learning algorithms and IoT device. The model predicts the risk associated with COVID-19 pandemic by using ML algorithms such as Random forest (RF) and Naive Bayes (NB) classifier.…”

Section: Iot-based Technologies the Authors Inmentioning

confidence: 99%

“…Technology Target population Covid/Post-Covid Sharma et.al [10] IoT, Mobile Computing, Cloud Computing Elderly people Covid Taiwo et.al [11] IoT General Both Verma et.al [16] ML, Bioinformatics General Postcovid Deepa et.al [17] ML,Random Forest,Naïve Bayes,IoT General Covid Boussen, et.al [20] Unsupervised ML, clustering Generic covid Born et.al [21] AI, Data Analysis General Covid Ieracitano et.al [22] AI General Covid Nabavi, et.al [23] ML, AI General Covid Pahar et.al [24] Transfer learning,ML, Deep learning General Covid-19 Khanday et.al [26] ML, Deep Learning. General Covid Alhomdy et.al [27] Cloud Computing Generic Covid Moorthy.et.al [28] Cloud computing, wearables.…”

Section: Referencesmentioning

confidence: 99%

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Post-Covid Remote Patient Monitoring using Medical Internet of Things and Machine Learning Analytics

Rahman

Parveen

Sofi

et al. 2023

SCPE

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The Covid-19 pandemic disturbed the smooth functioning of healthcare services throughout the world. New practices such as masking, social distancing and so on were followed to prevent the spread. Further, the severity of the problem increases for the elderly people and people having co-morbidities as proper medical care was not possible and as a result many deaths were recorded. Even for those patients who recovered from Covid could not get proper health monitoring in the Post-Covid phase as a result many deaths and severity in health conditions were reported after the Covid recovery i.e., the Post-Covid era. Technical interventions like the Internet of Things (IoT) based remote patient monitoring using Medical Internet of Things (M-IoT) wearables is one of the solutions that could help in the Post-Covid scenarios. The paper discusses a proposed framework where in a variety of IoT sensing devices along with ML algorithms are used for patient monitoring by utilizing aggregated data acquired from the registered Post-Covid patients. Thus, by using M-IoT along with Machine Learning (ML) approaches could help us in monitoring Post-Covid patients with co-morbidities for and immediate medical help.

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Section: Iot-based Technologies the Authors Inmentioning

confidence: 99%

Section: Referencesmentioning

confidence: 99%

Post-Covid Remote Patient Monitoring using Medical Internet of Things and Machine Learning Analytics

Rahman

Parveen

Sofi

et al. 2023

SCPE

View full text Add to dashboard Cite

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“…Naï ve Bayes algorithm has been used in the study conducted by different authors [7]- [16]. Also, in another study conducted by [17] Naive Bayes classifier are used for the prediction of COVID-19. The accuracy of NB got a 99%.…”

Section: Related Workmentioning

confidence: 99%

Determining Factors Resulting to Employee Attrition Using Data Mining Techniques

Repaso¹,

Capariño²,

Hermogenes³

et al. 2022

IJEME

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Business Process Outsourcing is a budding industry which currently employs millions of workers in the Philippines which draws applicants from undergraduate to professionals. It provides high-quality, well-paying jobs to millions of Filipinos while inspiring economic activity and investments all around. However, attrition rate of around 50 percent in the current year is a big challenge to predict employee turnover. This study came up with a model that can be adopted in the organization to predict possible attrition and guide the employers particularly the HR team in determining first-hand the type of applicant that they have by applying Data Mining techniques. The authors extracted significant predictors among the given data from a BPO company. Fast Correlation-Based Filtering Algorithm was performed to remove irrelevant data and increase learning accuracy. 1470 records with 21 attributes were initially provided and 17 were identified as significant after filtering and preprocessing of data was performed. The preprocessed data was used for model building with the application of Naï ve Bayes Algorithm. The resulting model predicted percentage probability of hoppers and stayers. Among the 17 given variables, Total Working Years, Marital Status and Age ranked as the top predictors in determining possibility of attrition. The data was split into 60% training data or a total of 882 records and 40% testing data or a total of 588 records. The predicted number of stayers is 542 or 92.2% and the predicted hoppers or who likely to resign are 46 or 7.8% The model was tested and evaluated to check accuracy of result through confusion matrix cross validation technique which yielded an accuracy percentage of 84.69%.

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“…Xbox live, Google Stadia's were major gaming projects launched on the cloud platform that enabled users to play games without having them on their personal computer. These projects work under "pay-per-use" or subscription based [1,2]. This saves the cost for buying high end graphic cards or gaming consoles.…”

Section: Introductionmentioning

confidence: 99%

Towards applying FCM with DBSCAN for Detecting DDoS Attack in Cloud Infrastructure to Improve Data Transmission Rate

Devi

Deepa

Karthikeyan

et al. 2022

ijcci

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Cloud is a pay-to-use technology which can be used to offer IT resources instead of buying computer hardware. It is time saving and cheaper technology. This paper analyzes the DDoS attack on cloud infrastructure and can be detected by using FCM with DBSCAN hybrid algorithm that classifies the clusters of data packets and detects the outlier in that particular data packet. The experimental outcome shows that the enhanced hybrid approach has better results in detecting the DDoS attack. The DDoS attack targets the main host of the cloud infrastructure by sending unwanted packets. This attack is a major threat to the network security. The FCM with DBSCAN hybrid approach detects outliers and also assigns one specific data point in clusters to detect DDoS attack in cloud infrastructure. By using this hybrid approach the data can be grouped as clusters and the data beyond the noise level can also be detected. This algorithm helps in identifying the data that are vulnerable to DDoS attack. This detection helps in improving the data transmission rate.

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Towards applying internet of things and machine learning for the risk prediction of COVID-19 in pandemic situation using Naive Bayes classifier for improving accuracy

Cited by 22 publications

References 9 publications

Post-Covid Remote Patient Monitoring using Medical Internet of Things and Machine Learning Analytics

Post-Covid Remote Patient Monitoring using Medical Internet of Things and Machine Learning Analytics

Determining Factors Resulting to Employee Attrition Using Data Mining Techniques

Towards applying FCM with DBSCAN for Detecting DDoS Attack in Cloud Infrastructure to Improve Data Transmission Rate

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