scholarly journals Prediction of the Infectious Outbreak COVID-19 and Prevalence of Anxiety: Global Evidence

2021 ◽  
Vol 13 (20) ◽  
pp. 11339
Author(s):  
Daniyal Alghazzawi ◽  
Atika Qazi ◽  
Javaria Qazi ◽  
Khulla Naseer ◽  
Muhammad Zeeshan ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Disease Outbreaks ◽  
Epidemiological Data ◽  
World Health ◽  
Series Data ◽  
Systems Science ◽  
Support Vector ◽  
Twitter Users ◽  
Health Organization

Forecasting disease outbreaks in real-time using time-series data can help for the planning of public health interventions. We used a support vector machine (SVM) model using epidemiological data provided by Johns Hopkins University Centre for Systems Science and Engineering (JHU CCSE), World Health Organization (WHO), and the Centers for Disease Control and Prevention (CDC) to predict upcoming records before the WHO made an official declaration. Our study, conducted on the time series data available from 22 January till 10 March 2020, revealed that COVID-19 was spreading at an alarming rate and progressing towards a pandemic. The initial insight that confirmed COVID-19 cases were increasing was because these received the highest number of effects for our selected dataset from 22 January to 10 March 2020, i.e., 126,344 (64%). The recovered cases were 68289 (34%), and the death rate was around 2%. Moreover, we classified the tweets from 22 January to 15 April 2020 into positive and negative sentiments to identify the emotions (stress or relaxed) posted by Twitter users related to the COVID-19 pandemic. Our analysis identified that tweets mostly conveyed a negative sentiment with a high frequency of words for #coronavirus and #lockdown amid COVID-19. However, these anxiety tweets are an alarm for healthcare authorities to devise plans accordingly.

Role of machine learning algorithms in timely prediction of the infectious outbreak: COVID-19 (Preprint)

2020 ◽  
Author(s):  
Atika Qazi ◽  
Khulla Naseer ◽  
Javaria Qazi ◽  
Muhammad Abo
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Time Series Data ◽  
Learning Algorithms ◽  
Epidemiological Data ◽  
Machine Learning Algorithms ◽  
World Health ◽  
Series Data ◽  
Systems Science ◽  
Support Vector

UNSTRUCTURED Well-timed forecast of infectious outbreaks using time-series data can help in proper planning of public health measures. If the forecasts are generated from machine learning algorithms, they can be used to manage resources where most needed. Here we present a support vector machine (SVM) model using epidemiological data provided by Johns Hopkins University Centre for Systems Science and Engineering (JHU CCSE), world health organization (WHO), Center for Disease Control and Prevention (CDC) to predict upcoming data before official declaration by WHO. Our study conducted on the time series data available from 22nd January till 10th March 2020 reveals that COVID-19 was spreading at an alarming rate and progressing towards a pandemic. If machine learning algorithms are used to predict the dynamics of an infectious outbreak future strategies can help in better management. Besides exploratory data analysis (EDA) highlights the importance of quarantine measures taken at the onset of this endemic by China and world leadership in containing the initial COVID-19 transmission. Nevertheless, when quarantine measures were relaxed due to extreme scrutiny a sharp upsurge was seen in COVID-19 transmission. The initial insight that confirmed COVID-19 cases are increasing as these got the highest number of effects for our selected dataset from 22nd January-10th March 2020 i.e. 126,344 (64%). The recovered cases are 68289 (34%) and the death rate is around 2%. The model presented here is flexible and can include uncertainty about outbreak dynamics and can be a significant tool for combating future outbreaks.

scholarly journals COVID 19: Real-time Forecasts of confirmed cases, active cases, and health infrastructure requirements for India and its states using the ARIMA model

2020 ◽  
Author(s):  
Rishabh Tyagi ◽  
Mahadev Bramhankar ◽  
Mohit Pandey ◽  
M Kishore
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Arima Model ◽  
Public Health Care ◽  
World Health ◽  
Series Data ◽  
Health Infrastructure ◽  
Future Demands ◽  
Health Organization

AbstractBackgroundCOVID-19 is an emerging infectious disease which has been declared a Pandemic by the World Health Organization (WHO) on 11th March 2020. The Indian public health care system is already overstretched, and this pandemic is making things even worse. That is why forecasting cases for India is necessary to meet the future demands of the health infrastructure caused due to COVID-19.ObjectiveOur study forecasts the confirmed and active cases for COVID-19 until July mid, using time series Autoregressive Integrated Moving Average (ARIMA) model. Additionally, we estimated the number of isolation beds, Intensive Care Unit (ICU) beds and ventilators required for the growing number of COVID-19 patients.MethodsWe used ARIMA model for forecasting confirmed and active cases till the 15th July. We used time-series data of COVID-19 cases in India from 14th March to 22nd May. We estimated the requirements for ICU beds as 10%, ventilators as 5% and isolation beds as 85% of the active cases forecasted using the ARIMA model.ResultsOur forecasts indicate that India will have an estimated 7,47,772 confirmed cases (95% CI: 493943, 1001601) and 296,472 active cases (95% CI:196820, 396125) by 15th July. While Maharashtra will be the most affected state, having the highest number of active and confirmed cases, Punjab is expected to have an estimated 115 active cases by 15th July. India needs to prepare 2,52,001 isolation beds (95% CI: 167297, 336706), 29,647 ICU beds (95% CI: 19682, 39612), and 14,824 ventilator beds (95% CI: 9841, 19806).ConclusionOur forecasts show an alarming situation for India, and Maharashtra in particular. The actual numbers can go higher than our estimated numbers as India has a limited testing facility and coverage.

scholarly journals Implementation of IoT Framework with Data Analysis Using Deep Learning Methods for Occupancy Prediction in a Building

2021 ◽  
Vol 13 (3) ◽  
pp. 67
Author(s):  
Eric Hitimana ◽  
Gaurav Bajpai ◽  
Richard Musabe ◽  
Louis Sibomana ◽  
Jayavel Kayalvizhi
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Deep Learning ◽  
Time Series Data ◽  
Multivariate Time Series ◽  
Machine Learning Algorithms ◽  
Series Data ◽  
Support Vector ◽  
Human Beings ◽  
Feed Forward Network

Many countries worldwide face challenges in controlling building incidence prevention measures for fire disasters. The most critical issues are the localization, identification, detection of the room occupant. Internet of Things (IoT) along with machine learning proved the increase of the smartness of the building by providing real-time data acquisition using sensors and actuators for prediction mechanisms. This paper proposes the implementation of an IoT framework to capture indoor environmental parameters for occupancy multivariate time-series data. The application of the Long Short Term Memory (LSTM) Deep Learning algorithm is used to infer the knowledge of the presence of human beings. An experiment is conducted in an office room using multivariate time-series as predictors in the regression forecasting problem. The results obtained demonstrate that with the developed system it is possible to obtain, process, and store environmental information. The information collected was applied to the LSTM algorithm and compared with other machine learning algorithms. The compared algorithms are Support Vector Machine, Naïve Bayes Network, and Multilayer Perceptron Feed-Forward Network. The outcomes based on the parametric calibrations demonstrate that LSTM performs better in the context of the proposed application.

Prediction and Analysis of Gold Prices using Ensemble Machine Learning Algorithms

2021 ◽  
Vol 9 (VI) ◽  
pp. 4367-4374
Author(s):  
Gudipally Chandrashakar
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Time Series Data ◽  
Gold Price ◽  
Machine Learning Algorithms ◽  
Series Data ◽  
Gradient Boosting ◽  
Support Vector ◽  
Average Value ◽  
Ensemble Machine Learning

In this article, we used historical time series data up to the current day gold price. In this study of predicting gold price, we consider few correlating factors like silver price, copper price, standard, and poor’s 500 value, dollar-rupee exchange rate, Dow Jones Industrial Average Value. Considering the prices of every correlating factor and gold price data where dates ranging from 2008 January to 2021 February. Few algorithms of machine learning are used to analyze the time-series data are Random Forest Regression, Support Vector Regressor, Linear Regressor, ExtraTrees Regressor and Gradient boosting Regression. While seeing the results the Extra Tree Regressor algorithm gives the predicted value of gold prices more accurately.

Forecasting Rice Production in West Bengal State in India

2013 ◽  
Vol 4 (4) ◽  
pp. 68-91 ◽  
Author(s):  
Arindam Chaudhuri
Keyword(s):  
Time Series ◽  
Multiple Regression ◽  
Computational Intelligence ◽  
Time Series Data ◽  
Rice Production ◽  
Productivity Index ◽  
Series Data ◽  
Support Vector ◽  
Production Time ◽  
Inference System

Forecasting rice production is a challenging problem in agricultural statistics. The inherent difficulty lies in demand and supply affected by many uncertain factors viz. economic policies, agricultural factors, credit measures, foreign trade etc. which interact in a complex manner. Since last few decades, Statistical techniques are used for developing predictive models to estimate required parameters. Determination of nature of rice production time series data is difficult, expensive, time consuming and involves tedious tests. In this paper, we use Interval Type Fuzzy Auto Regressive Integrated Moving Average (ITnARIMA), Adaptive Neuro Fuzzy Inference System (ANFIS) and Modified Regularized Least Squares Fuzzy Support Vector Regression (MRLSFSVR) for prediction of Productivity Index percent (PI %) of rice production time series data and compare it with traditional Statistical tool of Multiple Regression. The accuracies of ITnARIMA and ANFIS techniques are evaluated as relatively similar. It is found that ANFIS exhibits high performance than ITnARIMA, MRLSFSVR and Multiple Regression for predicting PI %. The performance comparison shows that Computational Intelligence paradigm is a promising tool for minimizing uncertainties in rice production data. Further Computational Intelligence techniques also minimize potential inconsistency of correlations.

scholarly journals Prediction of COVID-19 corona virus pandemic based on time series data using support vector machine

2020 ◽  
Vol 23 (8) ◽  
pp. 1583-1597
Author(s):  
Vijander Singh ◽  
Ramesh Chandra Poonia ◽  
Sandeep Kumar ◽  
Pranav Dass ◽  
Pankaj Agarwal ◽  
...  
Keyword(s):  
Time Series ◽  
Support Vector Machine ◽  
Time Series Data ◽  
Series Data ◽  
Support Vector ◽  
Corona Virus

scholarly journals Efficient time series data classification using sliding window technique based improved association rule mining with enhanced support vector machine

2018 ◽  
Vol 7 (3.3) ◽  
pp. 218 ◽  
Author(s):  
D Senthil ◽  
G Suseendran
Keyword(s):  
Time Series ◽  
Support Vector Machine ◽  
Association Rule ◽  
Association Rule Mining ◽  
Time Series Data ◽  
Sliding Window ◽  
Series Data ◽  
Support Vector ◽  
Rule Mining ◽  
Window Technique

Time series analysis is an important and complex problem in machine learning and statistics. In the existing system, Support Vector Machine (SVM) and Association Rule Mining (ARM) is introduced to implement the time series data. However it has issues with lower accuracy and higher time complexity. Also it has issue with optimal rules discovery and segmentation on time series data. To avoid the above mentioned issues, in the proposed research Sliding Window Technique based Improved ARM with Enhanced SVM (SWT-IARM with ESVM) is proposed. In the proposed system, the preprocessing is performed using Modified K-Means Clustering (MKMC). The indexing process is done by using R-tree which is used to provide faster results. Segmentation is performed by using SWT and it reduces the cost complexity by optimal segments. Then IARM is applied on efficient rule discovery process by generating the most frequent rules. By using ESVM classification approach, the rules are classified more accurately.  

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