Modeling and Prediction of COVID-19 in India Using Machine Learning

2021 ◽  
pp. 59-67
Author(s):  
Arindam Ghosh ◽  
Arnab Sadhu
Keyword(s):  
Machine Learning ◽  
Modeling And Prediction

scholarly journals Evaluation of F10.7, Sunspot Number and Photon Flux Data for Ionosphere TEC Modeling and Prediction Using Machine Learning Techniques

Atmosphere ◽  
2021 ◽  
Vol 12 (9) ◽  
pp. 1202
Author(s):  
Andres Gilberto Machado da Silva Benoit ◽  
Adriano Petry
Keyword(s):  
Machine Learning ◽  
Sunspot Number ◽  
Polynomial Regression ◽  
Learning Curves ◽  
Machine Learning Techniques ◽  
Photon Flux ◽  
Electron Content ◽  
Flux Data ◽  
Modeling And Prediction ◽  
Learning Techniques

Considering the growing volumes and varieties of ionosphere data, it is expected that automation of analytical model building using modern technologies could lead to more accurate results. In this work, machine learning techniques are applied to ionospheric modeling and prediction using sun activity data. We propose Total Electron Content (TEC) spectral analysis, using discrete cosine transform (DCT) to evaluate the relation to the solar features F10.7, sunspot number and photon flux data. The ionosphere modeling procedure presented is based on the assessment of a six-year period (2014–2019) of data. Different multi-dimension regression models were considered in experiments, where each geographic location was independently evaluated using its DCT frequency components. The features correlation analysis has shown that 5-year data seem more adequate for training, while learning curves revealed overfitting for polynomial regression from the 4th to 7th degrees. A qualitative evaluation using reconstructed TEC maps indicated that the 3rd degree polynomial regression also seems inadequate. For the remaining models, it can be noted that there is seasonal variation in root-mean-square error (RMSE) clearly related to the equinox (lower error) and solstice (higher error) periods, which points to possible seasonal adjustment in modeling. Elastic Net regularization was also used to reduce global RMSE values down to 2.80 TECU for linear regression.

scholarly journals Machine learning for radiation outcome modeling and prediction

2020 ◽  
Vol 47 (5) ◽  
Author(s):  
Yi Luo ◽  
Shifeng Chen ◽  
Gilmer Valdes
Keyword(s):  
Machine Learning ◽  
Modeling And Prediction

scholarly journals Modeling and Prediction of Daily Traffic Patterns—WASK and SIX Case Study

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1637
Author(s):  
Róża Goścień ◽  
Aleksandra Knapińska ◽  
Adam Włodarczyk
Keyword(s):  
Machine Learning ◽  
High Efficiency ◽  
Network Flows ◽  
Telecommunication Networks ◽  
Traffic Modeling ◽  
Average Error ◽  
Traffic Patterns ◽  
Average Prediction Error ◽  
Modeling And Prediction ◽  
The Fourier Transform

The paper studies efficient modeling and prediction of daily traffic patterns in transport telecommunication networks. The investigation is carried out using two historical datasets, namely WASK and SIX, which collect flows from edge nodes of two networks of different size. WASK is a novel dataset introduced and analyzed for the first time in this paper, while SIX is a well-known source of network flows. For the considered datasets, the paper proposes traffic modeling and prediction methods. For traffic modeling, the Fourier Transform is applied. For traffic prediction, two approaches are proposed—modeling-based (the forecasting model is generated based on historical traffic models) and machine learning-based (network traffic is handled as a data stream where chunk-based regression methods are applied for forecasting). Then, extensive simulations are performed to verify efficiency of the approaches and their comparison. The proposed modeling method revealed high efficiency especially for the SIX dataset, where the average error was lower than 0.1%. The efficiency of two forecasting approaches differs with datasets–modeling-based methods achieved lower errors for SIX while machine learning-based for WASK. The average prediction error for SIX reached 3.36% while forecasting for WASK turned out extremely challenging.

scholarly journals Risk factors analysis of COVID-19 patients with ARDS and prediction based on machine learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Wan Xu ◽  
Nan-Nan Sun ◽  
Hai-Nv Gao ◽  
Zhi-Yuan Chen ◽  
Ya Yang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Clinical Characteristics ◽  
Prediction Models ◽  
Clinical Manifestations ◽  
Diagnostic System ◽  
Machine Learning Algorithms ◽  
Human Beings ◽  
Factors Analysis ◽  
Modeling And Prediction

AbstractCOVID-19 is a newly emerging infectious disease, which is generally susceptible to human beings and has caused huge losses to people's health. Acute respiratory distress syndrome (ARDS) is one of the common clinical manifestations of severe COVID-19 and it is also responsible for the current shortage of ventilators worldwide. This study aims to analyze the clinical characteristics of COVID-19 ARDS patients and establish a diagnostic system based on artificial intelligence (AI) method to predict the probability of ARDS in COVID-19 patients. We collected clinical data of 659 COVID-19 patients from 11 regions in China. The clinical characteristics of the ARDS group and no-ARDS group of COVID-19 patients were elaborately compared and both traditional machine learning algorithms and deep learning-based method were used to build the prediction models. Results indicated that the median age of ARDS patients was 56.5 years old, which was significantly older than those with non-ARDS by 7.5 years. Male and patients with BMI > 25 were more likely to develop ARDS. The clinical features of ARDS patients included cough (80.3%), polypnea (59.2%), lung consolidation (53.9%), secondary bacterial infection (30.3%), and comorbidities such as hypertension (48.7%). Abnormal biochemical indicators such as lymphocyte count, CK, NLR, AST, LDH, and CRP were all strongly related to the aggravation of ARDS. Furthermore, through various AI methods for modeling and prediction effect evaluation based on the above risk factors, decision tree achieved the best AUC, accuracy, sensitivity and specificity in identifying the mild patients who were easy to develop ARDS, which undoubtedly helped to deliver proper care and optimize use of limited resources.

scholarly journals Evolution of the Computational Pharmaceutics Approaches in the Modeling and Prediction of Drug Payload in Lipid and Polymeric Nanocarriers

2021 ◽  
Vol 14 (7) ◽  
pp. 645
Author(s):  
Shaymaa A. Abd-algaleel ◽  
Hend M. Abdel-Bar ◽  
Abdelkader A. Metwally ◽  
Rania M. Hathout
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Molecular Dynamic ◽  
Docking Studies ◽  
Molecular Dynamic Simulations ◽  
Dynamic Simulations ◽  
Polymeric Nanocarriers ◽  
Modeling And Prediction ◽  
Wet Lab ◽  
Key Aspects

This review describes different trials to model and predict drug payload in lipid and polymeric nanocarriers. It traces the evolution of the field from the earliest attempts when numerous solubility and Flory-Huggins models were applied, to the emergence of molecular dynamic simulations and docking studies, until the exciting practically successful era of artificial intelligence and machine learning. Going through matching and poorly matching studies with the wet lab-dry lab results, many key aspects were reviewed and addressed in the form of sequential examples that highlighted both cases.

Sign in / Sign up

Export Citation Format

Share Document