COVID-19 Outbreak Prediction Using Quantum Neural Networks

Kairon, Pranav; Bhattacharyya, Siddhartha

doi:10.1007/978-981-15-9290-4_12

Cited by 17 publications

(10 citation statements)

References 12 publications

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“…Exploratory Data Analysis is a vital process that entails performing preliminary analyses on data to uncover patterns, identify anomalies, test hypotheses, and verify assumptions using summary statistics and graphical representations. Some of the critical steps in exploratory data analysis are importing the data set in which we will get two data frames; one consisting of the data to be trained and the other for predicting the target value, identifying the number of features and columns, identifying the qualities or cues, identifying the data types of components, identifying the number of observations, checking if the dataset has empty cells or samples, identifying the number of empty cells by features or columns, and exploring categorical features [ 29 ].…”

Section: Methodsmentioning

confidence: 99%

Machine Learning and Deep Learning Based Time Series Prediction and Forecasting of Ten Nations’ COVID-19 Pandemic

et al. 2022

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In the paper, the authors investigated and predicted the future environmental circumstances of a COVID-19 to minimize its effects using artificial intelligence techniques. The experimental investigation of COVID-19 instances has been performed in ten countries, including India, the United States, Russia, Argentina, Brazil, Colombia, Italy, Turkey, Germany, and France using machine learning, deep learning, and time series models. The confirmed, deceased, and recovered datasets from January 22, 2020, to May 29, 2021, of Novel COVID-19 cases were considered from the Kaggle COVID dataset repository. The country-wise Exploratory Data Analysis visually represents the active, recovered, closed, and death cases from March 2020 to May 2021. The data are pre-processed and scaled using a MinMax scaler to extract and normalize the features to obtain an accurate prediction rate. The proposed methodology employs Random Forest Regressor, Decision Tree Regressor, K Nearest Regressor, Lasso Regression, Linear Regression, Bayesian Regression, Theilsen Regression, Kernel Ridge Regressor, RANSAC Regressor, XG Boost, Elastic Net Regressor, Facebook Prophet Model, Holt Model, Stacked Long Short-Term Memory, and Stacked Gated Recurrent Units to predict active COVID-19 confirmed, death, and recovered cases. Out of different machine learning, deep learning, and time series models, Random Forest Regressor, Facebook Prophet, and Stacked LSTM outperformed to predict the best results for COVID-19 instances with the lowest root-mean-square and highest R 2 score values.

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Section: Methodsmentioning

confidence: 99%

Machine Learning and Deep Learning Based Time Series Prediction and Forecasting of Ten Nations’ COVID-19 Pandemic

et al. 2022

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“… Ensemble learning model(SVM and Random Forest) had better performance than individual models. [35] 2020 comparative analysis of quantum backpropagation multilayer perceptron (QBMLP) and continuous variable quantum neural networks Promising results on convoluted and sporadic data. [36] 2020 Analysis on the largest English Twitter depression dataset(COVID 19) Pre-trained transformer classification models BERT, RoBERTa and XLNet ware used.…”

Section: Related Workmentioning

confidence: 99%

COVID-19 outbreak: An ensemble pre-trained deep learning model for detecting informative tweets

Malla

P.J.A.

2021

Applied Soft Computing

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“…In the current critical situation, it seems important to establish an effective supply chain network for medicines that can provide COVID‐19 patients with suitable medicines in a coordinated manner 57 . In such cases, inventory control and management of the distribution of medicines due to the perishability of medicines can also reduce human losses and improve patient health 58…”

Section: Numerical Application Of Proposed Algorithmmentioning

confidence: 99%

Hospital admission and care of COVID‐19 patients problem based on spherical hesitant fuzzy decision support system

et al. 2021

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The emergency response to the health care management in the hospital do not have enough systems for providing medical service to the COVID19 patients (e.g., scheduled or nonemergency). Therefore, in this paper, we developed an emergency decision support model for consideration of patients care and admission scheduling (PCAS). The complex decision support model assigns a set of patients into a number of restricted resources like rooms, time slots, and beds depending on satisfying a number of predefined constraints such as disease severity, waiting time, and disease types. This is a crucial issue with multi‐criteria decision making (MCDM). In this paper, we first begin an assessment into the admission and care to tackle this issue and collect four factors effecting the admission and care of COVID‐19 patients that form a system of criteria. While there is a lot of vague and uncertain data that can be effectively depicted for these indicators by the spherical hesitant fuzzy set, then, we implement a strong MCDM method based on list of aggregation operators to address the patients' hospital admission and care. Last of all, a numerical real‐life application about PCAS is provided to demonstrate the validity of the proposed approaches along with relevant discussions, the merits of proposed approaches are also analyzed by validity test. The proposed methodology has been shown to help hospitals manage the admissions and care of COVID‐19 patients in a flexible manner.

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COVID-19 Outbreak Prediction Using Quantum Neural Networks

Cited by 17 publications

References 12 publications

Machine Learning and Deep Learning Based Time Series Prediction and Forecasting of Ten Nations’ COVID-19 Pandemic

Machine Learning and Deep Learning Based Time Series Prediction and Forecasting of Ten Nations’ COVID-19 Pandemic

COVID-19 outbreak: An ensemble pre-trained deep learning model for detecting informative tweets

Hospital admission and care of COVID‐19 patients problem based on spherical hesitant fuzzy decision support system

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