scholarly journals The Role of Ambient parameters on Transmission Rates of the COVID-19 Outbreak: A machine learning model

2021 ◽  
Author(s):  
Amir Jamshidnezhad ◽  
Seyed Ahmad Hosseini ◽  
Seyed Mahdi Hossaeini Marashic ◽  
Leila Ibrahimi Ghavamabadi ◽  
Hediye Mousavi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Climatic Factors ◽  
Ambient Air ◽  
Coefficient Of Determination ◽  
Ann Model ◽  
Machine Learning Model ◽  
Testing Stage ◽  
Artificial Neural Network Ann ◽  
Air Conditioning Systems

Abstract The relation between ambient air temperature and prevalence of viral infection has been under investigation in recent years. The study was aimed at providing the statistical and machine learning-based analysis to investigate the influence of climatic factors on frequency of COVID-19 confirmed cases in Iran. The data of confirmed cases of COVID-19 and some climatic factors related to 31 provinces of Iran during 04/03/2020 to 05/05/2020 was gathered from the official resources. In order to investigate the important climatic factors on the frequency of confirmed cases of COVID-19 in all studied cities, a model based on an artificial neural network (ANN) was developed. The proposed ANN model showed accuracy rates of 87.25% and 86.4% in the training and testing stage, respectively, for classification of COVID-19 confirmed cases. The results showed that in the city of Ahvaz, despite the increase in temperature, the coefficient of determination R2 has been increasing. This study clearly showed that, with increasing outdoor temperature, the use of air conditioning systems to set a comfort zone temperature is unavoidable; thus, the number of positive cases of COVID-19 increases. Also, this study shows the role of closed-air cycle condition in the indoor environment of tropical cities.

scholarly journals The role of ambient parameters on transmission rates of the COVID-19 outbreak: A machine learning model

Work ◽  
2021 ◽  
pp. 1-9
Author(s):  
Amir Jamshidnezhad ◽  
Seyed Ahmad Hosseini ◽  
Leila Ibrahimi Ghavamabadi ◽  
Seyed Mahdi Hossaeini Marashi ◽  
Hediye Mousavi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Climatic Factors ◽  
Ambient Air ◽  
Coefficient Of Determination ◽  
Ann Model ◽  
Machine Learning Model ◽  
Testing Stage ◽  
Artificial Neural Network Ann ◽  
Air Conditioning Systems

BACKGROUND: In recent years the relationship between ambient air temperature and the prevalence of viral infection has been under investigation. OBJECTIVE: The study was aimed at providing the statistical and machine learning-based analysis to investigate the influence of climatic factors on frequency of COVID-19 confirmed cases in Iran. METHOD: The data of confirmed cases of COVID-19 and some climatic factors related to 31 provinces of Iran between 04/03/2020 and 05/05/2020 was gathered from official resources. In order to investigate the important climatic factors on the frequency of confirmed cases of COVID-19 in all studied cities, a model based on an artificial neural network (ANN) was developed. RESULTS: The proposed ANN model showed accuracy rates of 87.25%and 86.4%in the training and testing stage, respectively, for classification of COVID-19 confirmed cases. The results showed that in the city of Ahvaz, despite the increase in temperature, the coefficient of determination R2 has been increasing. CONCLUSION: This study clearly showed that, with increasing outdoor temperature, the use of air conditioning systems to set a comfort zone temperature is unavoidable. Thus, the number of positive cases of COVID-19 increases. Also, this study shows the role of closed-air cycle condition in the indoor environment of tropical cities.

scholarly journals Ambient temperature change Vs confirmed cases of COVID-19: A machine learning model

2020 ◽  
Author(s):  
Amir Jamshidnezhad ◽  
Seyed Ahmad Hosseini ◽  
Leila Ibrahimi Ghavamabadi ◽  
Marzieh Zilae ◽  
Hediye Mousavi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Climatic Factors ◽  
Linear Regression Analysis ◽  
Ambient Air ◽  
Multiple Linear Regression Analysis ◽  
Coefficient Of Determination ◽  
Ann Model ◽  
Testing Stage ◽  
Artificial Neural Network Ann ◽  
The Impact

Abstract Background The relation between ambient air temperature and prevalence of viral infection has been under investigation in recent years. The present study aimed at providing the statistical and machine learning based analysis to investigate the influence of climatic factors on frequency of COVID-19 confirmed cases in Iran.Method The data of confirmed cases of COVID-19 as well as some climatic factors related to 31 provinces of Iran, during 04/03/2020 to 05/05/2020 were gathered from the official resources. In order to investigate the important climatic factors on the frequency of confirmed cases of Covid-19 in all studied cities, a model based on an artificial neural network (ANN) was developed. Moreover, the statistical analysis were used to assess the trend of positive cases in comparison with the fluctuations of some climatic factors. Results The proposed ANN model showed the accuracy rate of 87.25% and 86.4% in the training and testing stage, respectively for classification of COVID-19 confirmed cases. Moreover, multiple linear regression analysis was obtained the R2 equal to 0.40 and 0.68 in two cities of Qom and Ahvaz, respectively. The results showed that in the city of Ahvaz, despite the increase in temperature, the coefficient of determination R2 has been increasing. Conclusion This study clearly showed that with increasing outdoor temperature, the use of air conditioning systems to set a comfort zone temperature is unavoidable; thus the number of positive cases of COVID-19 increases. Also, this study shows the role of closed air cycle condition in indoor environment of tropical cities, along with the impact of climatic factors in the frequency of positive cases of COVID-19 and the capacity of ANN classification in the surveys.

A comparative study and combined application of RSM and ANN in adsorptive removal of diuron using biomass ashes

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Sunil K. Deokar ◽  
Nachiket A. Gokhale ◽  
Sachin A. Mandavgane
Keyword(s):  
Three Dimensional ◽  
Current Approach ◽  
Coefficient Of Determination ◽  
Ann Model ◽  
Operational Parameters ◽  
Initial Concentration ◽  
Combined Application ◽  
Artificial Neural Network Ann ◽  
Relationship Of ◽  
Biomass Ashes

Abstract Biomass ashes like rice husk ash (RHA), bagasse fly ash (BFA), were used for aqueous phase removal of a pesticide, diuron. Response surface methodology (RSM) and artificial neural network (ANN) were successfully applied to estimate and optimize the conditions for the maximum diuron adsorption using biomass ashes. The effect of operational parameters such as initial concentration (10–30 mg/L); contact time (0.93–16.07 h) and adsorbent dosage (20–308 mg) on adsorption were studied using central composite design (CCD) matrix. Same design was also employed to gain a training set for ANN. The maximum diuron removal of 88.95 and 99.78% was obtained at initial concentration of 15 mg/L, time of 12 h, RHA dosage of 250 mg and at initial concentration of 14 mg/L, time of 13 h, BFA dosage of 60 mg respectively. Estimation of coefficient of determination (R 2) and mean errors obtained for ANN and RSM (R 2 RHA = 0.976, R 2 BFA = 0.943) proved ANN (R 2 RHA = 0.997, R 2 BFA = 0.982) fits better. By employing RSM coupled with ANN model, the qualitative and quantitative activity relationship of experimental data was visualized in three dimensional spaces. The current approach will be instrumental in providing quick preliminary estimations in process and product development.

scholarly journals Prediction of Healing Performance of Autogenous Healing Concrete Using Machine Learning

Materials ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4068
Author(s):  
Xu Huang ◽  
Mirna Wasouf ◽  
Jessada Sresakoolchai ◽  
Sakdirat Kaewunruen
Keyword(s):  
Machine Learning ◽  
Search Algorithm ◽  
Weather Conditions ◽  
Prediction Performance ◽  
Machine Learning Algorithms ◽  
Coefficient Of Determination ◽  
Gradient Boosting ◽  
Support Vector ◽  
Self Healing ◽  
Artificial Neural Network Ann

Cracks typically develop in concrete due to shrinkage, loading actions, and weather conditions; and may occur anytime in its life span. Autogenous healing concrete is a type of self-healing concrete that can automatically heal cracks based on physical or chemical reactions in concrete matrix. It is imperative to investigate the healing performance that autogenous healing concrete possesses, to assess the extent of the cracking and to predict the extent of healing. In the research of self-healing concrete, testing the healing performance of concrete in a laboratory is costly, and a mass of instances may be needed to explore reliable concrete design. This study is thus the world’s first to establish six types of machine learning algorithms, which are capable of predicting the healing performance (HP) of self-healing concrete. These algorithms involve an artificial neural network (ANN), a k-nearest neighbours (kNN), a gradient boosting regression (GBR), a decision tree regression (DTR), a support vector regression (SVR) and a random forest (RF). Parameters of these algorithms are tuned utilising grid search algorithm (GSA) and genetic algorithm (GA). The prediction performance indicated by coefficient of determination (R2) and root mean square error (RMSE) measures of these algorithms are evaluated on the basis of 1417 data sets from the open literature. The results show that GSA-GBR performs higher prediction performance (R2GSA-GBR = 0.958) and stronger robustness (RMSEGSA-GBR = 0.202) than the other five types of algorithms employed to predict the healing performance of autogenous healing concrete. Therefore, reliable prediction accuracy of the healing performance and efficient assistance on the design of autogenous healing concrete can be achieved.

scholarly journals Development of a methodology to backcalculate pavement layer moduli using the traffic speed deflectometer

2018 ◽  
Vol 45 (5) ◽  
pp. 377-385 ◽  
Author(s):  
Omar Elbagalati ◽  
Momen Mousa ◽  
Mostafa A. Elseifi ◽  
Kevin Gaspard ◽  
Zhongjie Zhang
Keyword(s):  
Traffic Control ◽  
Coefficient Of Determination ◽  
Falling Weight Deflectometer ◽  
Ann Model ◽  
Acceptable Accuracy ◽  
Traffic Speed ◽  
Artificial Neural Network Ann ◽  
Falling Weight ◽  
Good Agreement

Backcalculation analysis of pavement layer moduli is typically conducted based on falling weight deflectometer (FWD) measurements; however, the stationary nature of FWD requires lane closure and traffic control. To overcome these limitations, a number of continuous deflection devices were introduced in recent years. The objective of this study was to develop a methodology to incorporate traffic speed deflectometer (TSD) measurements in the backcalculation analysis. To achieve this objective, TSD and FWD measurements were used to train and to validate an artificial neural network (ANN) model that would convert TSD deflection measurements to FWD deflection measurements. The ANN model showed acceptable accuracy with a coefficient of determination of 0.81 and a good agreement between the backcalculated moduli from FWD and TSD measurements. Evaluation of the model showed that the backcalculated layer moduli from TSD could be used in pavement analysis and in structural health monitoring with a reasonable level of accuracy.

New recommendation to predict export value using big data and machine learning technique

2021 ◽  
pp. 1-14
Author(s):  
Rani Nooraeni ◽  
Jimmy Nickelson ◽  
Eko Rahmadian ◽  
Nugroho Puspito Yudho
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Big Data ◽  
Ann Model ◽  
Machine Learning Technique ◽  
Current Period ◽  
Learning Technique ◽  
Monthly Period ◽  
Conventional Machine ◽  
Artificial Neural Network Ann

Official statistics on monthly export values have a publicity lag between the current period and the published publication. None of the previous researchers estimated the value of exports for the monthly period. This circumstance is due to limitations in obtaining supporting data that can predict the criteria for the current export value of goods. AIS data is one type of big data that can provide solutions in producing the latest indicators to forecast export values. Statistical Methods and Conventional Machine Learning are implemented as forecasting methods. Seasonal ARIMA and Artificial Neural Network (ANN) methods are both used in research to forecast the value of Indonesia’s exports. However, ANN has a weakness that requires high computational costs to obtain optimal parameters. Genetic Algorithm (GA) is effective in increasing ANN accuracy. Based on these backgrounds, this paper aims to develop and select an AIS indicator to predict the monthly export value in Indonesia and optimize ANN performance by combining the ANN algorithm with the genetic algorithm (GA-ANN). The research successfully established five indicators that can be used as predictors in the forecasting model. According to the model evaluation results, the genetic algorithm has succeeded in improving the performance of the ANN model as indicated by the resulting RMSE GA-ANN value, which is smaller than the RMSE of the ANN model.

Power Forecasting from Solar Panels Using Artificial Neural Network in UTHM Parit Raja

2021 ◽  
Vol 02 (01) ◽  
Author(s):  
Natasha Munirah Mohd Fahmi ◽  
◽  
Nor Aira Zambri ◽  
Norhafiz Salim ◽  
Sim Sy Yi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Climatic Factors ◽  
Activation Function ◽  
Solar Panels ◽  
Ann Model ◽  
Power Characteristics ◽  
Step Procedure ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

This paper presents a step-by-step procedure for the simulation of photovoltaic modules with numerical values, using MALTAB/Simulink software. The proposed model is developed based on the mathematical model of PV module, which based on PV solar cell employing one-diode equivalent circuit. The output current and power characteristics curves highly depend on some climatic factors such as radiation and temperature, are obtained by simulation of the selected module. The collected data are used in developing Artificial Neural Network (ANN) model. Multilayer Perceptron (MLP) and Radial Basis Function (RBF) are the techniques used to forecast the outputs of the PV. Various types of activation function will be applied such as Linear, Logistic Sigmoid, Hyperbolic Tangent Sigmoid and Gaussian. The simulation results show that the Logistic Sigmoid is the best technique which produce minimal root mean square error for the system.

The role of cognitive functions in the diagnosis of bipolar disorder: A machine learning model

2021 ◽  
Vol 145 ◽  
pp. 104311
Author(s):  
Harun Olcay Sonkurt ◽  
Ali Ercan Altınöz ◽  
Emre Çimen ◽  
Ferdi Köşger ◽  
Gürkan Öztürk
Keyword(s):  
Machine Learning ◽  
Bipolar Disorder ◽  
Cognitive Functions ◽  
Learning Model ◽  
Machine Learning Model

scholarly journals Investigating the Applications of Machine Learning Techniques to Predict the Rock Brittleness Index

2020 ◽  
Vol 10 (5) ◽  
pp. 1691 ◽  
Author(s):  
Deliang Sun ◽  
Mahshid Lonbani ◽  
Behnam Askarian ◽  
Danial Jahed Armaghani ◽  
Reza Tarinejad ◽  
...  
Keyword(s):  
Machine Learning ◽  
Point Load ◽  
P Wave ◽  
Machine Learning Techniques ◽  
Brittleness Index ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Ann Model ◽  
Learning Techniques ◽  
Rock Brittleness

Despite the vast usage of machine learning techniques to solve engineering problems, a very limited number of studies on the rock brittleness index (BI) have used these techniques to analyze issues in this field. The present study developed five well-known machine learning techniques and compared their performance to predict the brittleness index of the rock samples. The comparison of the models’ performance was conducted through a ranking system. These techniques included Chi-square automatic interaction detector (CHAID), random forest (RF), support vector machine (SVM), K-nearest neighbors (KNN), and artificial neural network (ANN). This study used a dataset from a water transfer tunneling project in Malaysia. Results of simple rock index tests i.e., Schmidt hammer, p-wave velocity, point load, and density were considered as model inputs. The results of this study indicated that while the RF model had the best performance for training (ranking = 25), the ANN outperformed other models for testing (ranking = 22). However, the KNN model achieved the highest cumulative ranking, which was 37. The KNN model showed desirable stability for both training and testing. However, the results of validation stage indicated that RF model with coefficient of determination (R2) of 0.971 provides higher performance capacity for prediction of the rock BI compared to KNN model with R2 of 0.807 and ANN model with R2 of 0.860. The results of this study suggest a practical use of the machine learning models in solving problems related to rock mechanics specially rock brittleness index.

scholarly journals Impact of biomass burning emission on total peroxy nitrates: fire plume identification during the BORTAS campaign

2016 ◽  
Vol 9 (11) ◽  
pp. 5591-5606 ◽  
Author(s):  
Eleonora Aruffo ◽  
Fabio Biancofiore ◽  
Piero Di Carlo ◽  
Marcella Busilacchio ◽  
Marco Verdecchia ◽  
...  
Keyword(s):  
Biomass Burning ◽  
Atmospheric Pressure ◽  
Thermal Dissociation ◽  
Ann Model ◽  
Fire Plume ◽  
Pressure Threshold ◽  
Peroxy Nitrates ◽  
Artificial Neural Network Ann ◽  
The Impact

Abstract. Total peroxy nitrate ( ∑ PN) concentrations have been measured using a thermal dissociation laser-induced fluorescence (TD-LIF) instrument during the BORTAS campaign, which focused on the impact of boreal biomass burning (BB) emissions on air quality in the Northern Hemisphere. The strong correlation observed between the  ∑ PN concentrations and those of carbon monoxide (CO), a well-known pyrogenic tracer, suggests the possible use of the  ∑ PN concentrations as marker of the BB plumes. Two methods for the identification of BB plumes have been applied: (1)  ∑ PN concentrations higher than 6 times the standard deviation above the background and (2)  ∑ PN concentrations higher than the 99th percentile of the  ∑ PNs measured during a background flight (B625); then we compared the percentage of BB plume selected using these methods with the percentage evaluated, applying the approaches usually used in literature. Moreover, adding the pressure threshold ( ∼  750 hPa) as ancillary parameter to  ∑ PNs, hydrogen cyanide (HCN) and CO, the BB plume identification is improved. A recurrent artificial neural network (ANN) model was adapted to simulate the concentrations of  ∑ PNs and HCN, including nitrogen oxide (NO), acetonitrile (CH3CN), CO, ozone (O3) and atmospheric pressure as input parameters, to verify the specific role of these input data to better identify BB plumes.

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