Published: Feb 16, 2023
Converted to Gold OA:
DOI: 10.4018/IJRQEH.318090
Volume 12
Shivani Sharma, Bipin Kumar Rai, Mahak Gupta, Muskan Dinkar
An illness that lasts longer and has continual repercussions is known as a chronic illness. Adults all across the world die as a result of chronic sickness. Diabetes disease prediction by...
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An illness that lasts longer and has continual repercussions is known as a chronic illness. Adults all across the world die as a result of chronic sickness. Diabetes disease prediction by improvising support vector machine is a platform that predicts diabetes based on the data entered into the system and offers reliable results based on that data. Earlier, the dataset consisted of a smaller number of features comprising the patients' medical details that were useful in determining the patient's health condition and was mainly focused on gestational diabetes, which only deals with pregnant women. In this work, the authors build a system that is more efficient than the previous system because of these reasons. It provides more accurate results by improvising the support vector machine, which includes more datasets and can predict the possibility of diabetes disease in both males and females.
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MLA
Sharma, Shivani, et al. "DDPIS: Diabetes Disease Prediction by Improvising SVM." IJRQEH vol.12, no.2 2023: pp.1-11. http://doi.org/10.4018/IJRQEH.318090
APA
Sharma, S., Rai, B. K., Gupta, M., & Dinkar, M. (2023). DDPIS: Diabetes Disease Prediction by Improvising SVM. International Journal of Reliable and Quality E-Healthcare (IJRQEH), 12(2), 1-11. http://doi.org/10.4018/IJRQEH.318090
Chicago
Sharma, Shivani, et al. "DDPIS: Diabetes Disease Prediction by Improvising SVM," International Journal of Reliable and Quality E-Healthcare (IJRQEH) 12, no.2: 1-11. http://doi.org/10.4018/IJRQEH.318090
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Published: Feb 10, 2023
Converted to Gold OA:
DOI: 10.4018/IJRQEH.318129
Volume 12
Bipin Kumar Rai, Shivya Srivastava, Shruti Arora
In the healthcare industry, providing a vital backbone for services is critical. The supply chain is a complex network that crosses organizational and geographical borders. In the healthcare...
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In the healthcare industry, providing a vital backbone for services is critical. The supply chain is a complex network that crosses organizational and geographical borders. In the healthcare business, counterfeit pills are one of the primary reasons for the harmful impact on human health and financial loss. Thus, pharmaceutical supply chains and end-to-end tracking systems are the recent research in healthcare. In this paper, the authors propose blockchain-based traceability of counterfeited drugs (BBTCD) that implements tracking of counterfeited drugs using smart contracts on the Ethereum blockchain. They offer a solution to fully decentralize the tracking by storing BBTCD on IPFS (inter planetary file system) to provide transparency and cost-effectiveness.
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Rai, Bipin Kumar, et al. "Blockchain-Based Traceability of Counterfeited Drugs." IJRQEH vol.12, no.2 2023: pp.1-12. http://doi.org/10.4018/IJRQEH.318129
APA
Rai, B. K., Srivastava, S., & Arora, S. (2023). Blockchain-Based Traceability of Counterfeited Drugs. International Journal of Reliable and Quality E-Healthcare (IJRQEH), 12(2), 1-12. http://doi.org/10.4018/IJRQEH.318129
Chicago
Rai, Bipin Kumar, Shivya Srivastava, and Shruti Arora. "Blockchain-Based Traceability of Counterfeited Drugs," International Journal of Reliable and Quality E-Healthcare (IJRQEH) 12, no.2: 1-12. http://doi.org/10.4018/IJRQEH.318129
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Published: Mar 24, 2023
Converted to Gold OA:
DOI: 10.4018/IJRQEH.320480
Volume 12
Arvind Yadav, Vinod Kumar, Devendra Joshi, Dharmendra Singh Rajput, Haripriya Mishra, Basavaraj S. Paruti
COVID-19 prediction models are highly welcome and necessary for authorities to make informed decisions. Traditional models, which were used in the past, were unable to reliably estimate death rates...
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COVID-19 prediction models are highly welcome and necessary for authorities to make informed decisions. Traditional models, which were used in the past, were unable to reliably estimate death rates due to procedural flaws. The genetic algorithm in association with an artificial neural network (GA-ANN) is one of the suitable blended AI strategies that can foretell more correctly by resolving this difficult COVID-19 phenomena. The genetic algorithm is used to simultaneously optimise all of the ANN parameters. In this work, GA-ANN and ANN models were performed by applying historical daily data from sick, recovered, and dead people in India. The performance of the designed hybrid GA-ANN model is validated by comparing it to the standard ANN and MLR approach. It was determined that the GA-ANN model outperformed the ANN model. When compared to previous examined models for predicting mortality rates in India, the hypothesized hybrid GA-ANN model is the most competent. This hybrid AI (GA-ANN) model is suggested for the prediction due to reasonably better performance and ease of implementation.
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MLA
Yadav, Arvind, et al. "Hybrid Artificial Intelligence-Based Models for Prediction of Death Rate in India Due to COVID-19 Transmission." IJRQEH vol.12, no.2 2023: pp.1-15. http://doi.org/10.4018/IJRQEH.320480
APA
Yadav, A., Kumar, V., Joshi, D., Rajput, D. S., Mishra, H., & Paruti, B. S. (2023). Hybrid Artificial Intelligence-Based Models for Prediction of Death Rate in India Due to COVID-19 Transmission. International Journal of Reliable and Quality E-Healthcare (IJRQEH), 12(2), 1-15. http://doi.org/10.4018/IJRQEH.320480
Chicago
Yadav, Arvind, et al. "Hybrid Artificial Intelligence-Based Models for Prediction of Death Rate in India Due to COVID-19 Transmission," International Journal of Reliable and Quality E-Healthcare (IJRQEH) 12, no.2: 1-15. http://doi.org/10.4018/IJRQEH.320480
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