An Accurate Estimation of Interstate Traffic of Metro City Using Linear Regression Model of Machine Learning

2020 ◽  
Author(s):  
Apurv Chandel
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Accurate Estimation

scholarly journals Predicting the COVID-19 positive cases in India with concern to Lockdown by using Mathematical and Machine Learning based Models

2020 ◽  
Author(s):  
Ajit Kumar Pasayat ◽  
Satya Narayan Pati ◽  
Aashirbad Maharana
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Regression Model ◽  
Growth Model ◽  
Linear Regression Model ◽  
Exponential Growth ◽  
Time Window ◽  
Epidemic Curve ◽  
First Case ◽  
Exponential Growth Model

In this study, we analyze the number of infected positive cases of COVID-19 outbreak with concern to lockdown in India in the time window of February 11th 2020 to Jun 30th 2020. The first case in India was reported in Kerala on January 30th 2020. To break the chain of spreading, Government announced a nationwide lockdown on March 24th 2020, which is increased two times. The Ongoing lockdown 3.0 is over on May 18th, 2020. We derived how the lockdown relaxation is going to impact on containment of the outbreak. Here the Exponential Growth Model has been used to derive the epidemic curve based on the data collected from February 11th 2020, to May 11th 2020, and the Machine Learning based Linear Regression model that gives the epidemic curve to predict the cases with the continuous flow of the lockdown. We estimate that if the lockdown is continuing with more relaxation, then the estimated infected cases reach up to 1.16 crores by June 30th 2020, and the lockdown would persist with current restriction, then the expected predicted infected cases are 5.69 lacs. The Exponential Growth Model and the Linear Regression Model are advantageous to predict the number of affected cases of COVID-19. These models can be used for forecasting in long term intervals. It shows from our result that lockdown with certain restriction has a vital role in preventing the spreading of this epidemic in this current situation.

scholarly journals Relationship between the Condylion–Gonion–Menton Angle and Dentoalveolar Heights

2020 ◽  
Vol 17 (9) ◽  
pp. 3309 ◽  
Author(s):  
Rosa Valletta ◽  
Roberto Rongo ◽  
Ada Carolina Pango Madariaga ◽  
Roberta Baiano ◽  
Gianrico Spagnuolo ◽  
...  
Keyword(s):  
Retrospective Study ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Southern Italy ◽  
Positive Association ◽  
Negative Association ◽  
Accurate Estimation ◽  
Strongly Correlated ◽  
Facial Growth

An accurate estimation of both facial growth and the dentoalveolar dimension is key to successful treatment. The aim of this study was to analyze the relation between the Condylion–Gonion–Menton angle (CoGoMe^) and dentoalveolar heights in a population of patients from southern Italy. This retrospective study analyzed 270 cephalograms of 115 males (42.1%, mean age 15.5 ± 5.2 years) and 155 females (57.9%, mean age 15.6 ± 5.9 years). The facial divergency was evaluated with the Sella–Nasion and Gonion–Gnation angle (SN^GoGn), mandibular structure with the CoGoMe^, and dentoalveolar heights were assessed in four measurements: upper anterior (UADH), lower anterior (LADH), upper posterior (UPDH), and lower posterior (LPDH). Data were analyzed by means of Pearson’s correlation and linear regression model (p < 0.05). All the dentoalveolar heights were strongly correlated among them (p < 0.001). The UADH was correlated with the SN^GoGn (r = 0.145; p = 0.017), while the LPDH was correlated with the CoGoMe^ (r = −0.183; p = 0.003). Moreover, there was a positive association between the UADH and the SN^GoGn (B = 0.08; 95% CI: 0.014–0.144; p = 0.017), and a negative association between the CoGoMe^ and the LPDH (B = −0.098; 95% CI: −0.161–0.035; p = 0.003). Facial divergency and mandibular structure are associated with dentoalveolar heights.

scholarly journals Comparative study of linear regression and SIR models of COVID-19 propagation in Ukraine before vaccination

2021 ◽  
pp. 5-18
Author(s):  
Alireza Mohammadi ◽  
Ievgen Meniailov ◽  
Kseniia Bazilevych ◽  
Sergey Yakovlev ◽  
Dmytro Chumachenko
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Compartment Model ◽  
Sir Model ◽  
Research Subjects ◽  
Compartment Models ◽  
Epidemic Process ◽  
Death Cases

The global COVID-19 pandemic began in December 2019 and spread rapidly around the world. Worldwide, more than 230 million people fell ill, 4.75 million cases were fatal. In addition to the threat to health, the pandemic resulted in social problems, an economic crisis and the transition of an ordinary life to a "new reality". Mathematical modeling is an effective tool for controlling the epidemic process of COVID-19 in specified territories. Modeling makes it possible to predict the future dynamics of the epidemic process and to identify the factors that affect the increase in incidence in the greatest way. The simulation results enable public health professionals to take effective evidence-based responses to contain the epidemic. The study aims to develop machine learning and compartment models of COVID-19 epidemic process and to investigate experimental results of simulation. The object of research is COVID-19 epidemic process and its dynamics in territory of Ukraine. The research subjects are methods and models of epidemic process simulation, which include machine learning methods and compartment models. To achieve this aim of the research, we have used machine learning forecasting methods and have built COVID-19 epidemic process linear regression model and COVID-19 epidemic process compartment model. Because of experiments with the developed models, the predictive dynamics of the epidemic process of COVID-19 for 30 days were obtained for confirmed cases, recovered and death. For ‘Confirmed’, ‘Recovered’ and ‘Death’ cases mean errors have almost 1.15, 0.037 and 1.39 percent deviant, respectively, with a linear regression model. For ‘Confirmed’, ‘Recovered’ and ‘Death’ cases mean errors have almost 3.29, 1.08, and 0.71 percent deviant, respectively, for the SIR model. Conclusions. At this stage in the development of the epidemic process of COVID-19, it is more expedient to use a linear model to predict the incidence rate, which has shown higher accuracy and efficiency, the reason for that lies on the fact that the used linear regression model for this research was implemented on merely 30 days (from fifteen days before 2nd of March) and not the whole dataset of COVID-19. Also, it is expected that if we try to forecast in longer time ranges, the linear regression model will lose precision. Alternatively, since SIR model is more comprised in including more factors, the model is expected to perform better in fore-casting longer time ranges.

scholarly journals Simulation-Driven Machine Learning for Predicting Stent Expansion in Calcified Coronary Artery

2020 ◽  
Vol 10 (17) ◽  
pp. 5820
Author(s):  
Pengfei Dong ◽  
Guochang Ye ◽  
Mehmet Kaya ◽  
Linxia Gu
Keyword(s):  
Machine Learning ◽  
Coronary Artery ◽  
Linear Regression ◽  
Regression Model ◽  
Cross Sections ◽  
Linear Regression Model ◽  
Patient Specific ◽  
Support Vector ◽  
Stent Expansion ◽  
Mechanistic Understanding

In this work, we integrated finite element (FE) method and machine learning (ML) method to predict the stent expansion in a calcified coronary artery. The stenting procedure was captured in a patient-specific artery model, reconstructed based on optical coherence tomography images. Following FE simulation, eight geometrical features in each of 120 cross sections in the pre-stenting artery model, as well as the corresponding post-stenting lumen area, were extracted for training and testing the ML models. A linear regression model and a support vector regression (SVR) model with three different kernels (linear, polynomial, and radial basis function kernels) were adopted in this work. Two subgroups of the eight features, i.e., stretch features and calcification features, were further assessed for the prediction capacity. The influence of the neighboring cross sections on the prediction accuracy was also investigated by averaging each feature over eight neighboring cross sections. Results showed that the SVR models provided better predictions than the linear regression model in terms of bias. In addition, the inclusion of stretch features based on mechanistic understanding could provide a better prediction, compared with the calcification features only. However, there were no statistically significant differences between neighboring cross sections and individual ones in terms of the prediction bias and range of error. The simulation-driven machine learning framework in this work could enhance the mechanistic understanding of stenting in calcified coronary artery lesions, and also pave the way toward precise prediction of stent expansion.

Design Repository Effectiveness for 3D Convolutional Neural Networks: Application to Additive Manufacturing

2019 ◽  
Vol 141 (11) ◽  
Author(s):  
Glen Williams ◽  
Nicholas A. Meisel ◽  
Timothy W. Simpson ◽  
Christopher McComb
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Additive Manufacturing ◽  
Linear Regression ◽  
Regression Model ◽  
Convolutional Neural Network ◽  
Linear Regression Model ◽  
Multiple Linear Regression Model ◽  
Build Time

Abstract Machine learning can be used to automate common or time-consuming engineering tasks for which sufficient data already exist. For instance, design repositories can be used to train deep learning algorithms to assess component manufacturability; however, methods to determine the suitability of a design repository for use with machine learning do not exist. We provide an initial investigation toward identifying such a method using “artificial” design repositories to experimentally test the extent to which altering properties of the dataset impacts the assessment precision and generalizability of neural networks trained on the data. For this experiment, we use a 3D convolutional neural network to estimate quantitative manufacturing metrics directly from voxel-based component geometries. Additive manufacturing (AM) is used as a case study because of the recent growth of AM-focused design repositories such as GrabCAD and Thingiverse that are readily accessible online. In this study, we focus only on material extrusion, the dominant consumer AM process, and investigate three AM build metrics: (1) part mass, (2) support material mass, and (3) build time. Additionally, we compare the convolutional neural network accuracy to that of a baseline multiple linear regression model. Our results suggest that training on design repositories with less standardized orientation and position resulted in more accurate trained neural networks and that orientation-dependent metrics were harder to estimate than orientation-independent metrics. Furthermore, the convolutional neural network was more accurate than the baseline linear regression model for all build metrics.

scholarly journals A Linear Regression Model for Global Solar Radiation on Horizontal Surfaces at Warri, Nigeria

2013 ◽  
Vol 2 (3) ◽  
pp. 121-126 ◽  
Author(s):  
Michael S. Okundamiya ◽  
Israel E. Okpamen
Keyword(s):  
Renewable Energy ◽  
Linear Regression ◽  
Solar Radiation ◽  
Solar Energy ◽  
Regression Model ◽  
Linear Regression Model ◽  
Global Solar Radiation ◽  
Mean Bias Error ◽  
Accurate Estimation ◽  
Radiation Data

The growing anxiety on the negative effects of fossil fuels on the environment and the global emission reduction targets call for a more extensive use of renewable energy alternatives. Efficient solar energy utilization is an essential solution to the high atmospheric pollution caused by fossil fuel combustion. Global solar radiation (GSR) data, which are useful for the design and evaluation of solar energy conversion system, are not measured at the forty-five meteorological stations in Nigeria. The dearth of the measured solar radiation data calls for accurate estimation. This study proposed a temperature-based linear regression, for predicting the monthly average daily GSR on horizontal surfaces, at Warri (latitude 5.020N and longitude 7.880E) an oil city located in the south-south geopolitical zone, in Nigeria. The proposed model is analyzed based on five statistical indicators (coefficient of correlation, coefficient of determination, mean bias error, root mean square error, and t-statistic), and compared with the existing sunshine-based model for the same study. The results indicate that the proposed temperature-based linear regression model could replace the existing sunshine-based model for generating global solar radiation data. Keywords: air temperature; empirical model; global solar radiation; regression analysis; renewable energy; Warri

scholarly journals A Comparison of Multiple Machine Learning Algorithms to Predict Whole-Body Vibration Exposure of Dumper Operators in Iron Ore Mines in India

2021 ◽  
Vol 10 ◽  
pp. 169-182
Author(s):  
Rahul Upadhyay ◽  
Amrites Senapati ◽  
Ashis Bhattacherjee ◽  
Aditya Kumar Patra ◽  
Snehamoy Chatterjee ◽  
...  
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Iron Ore ◽  
Whole Body Vibration ◽  
Whole Body ◽  
Road Condition ◽  
Haul Road ◽  
Awkward Posture

Background: This study deals with some factors that influence the exposure of whole-body vibration (WBV) of dumper operators in surface mines. The study also highlights the approach to improve the multivariate linear analysis outcomes when collinearity exists between certain factor pairs. Material and Methods: A total number of 130 vibration readings was taken from two adjacent surface iron ore mines. The frequency-weighted RMS acceleration was used for the WBV exposure assessment of the dumper operators. The factors considered in this study are age, weight, seat backrest height, awkward posture, the machine age, load tonnage, dumper speed and haul road condition. Four machine learning models were explored through the empirical training-testing approach. Results: The bootstrap linear regression model was found to be the best model based on performance and predictability when compared to multiple linear regression, LASSO regression, and decision tree. Results revealed that multiple factors influence WBV exposure. The significant factors are: weight of operators (regression coefficient β=-0.005, p<0.001), awkward posture (β=0.033, p<0.001), load tonnage (β=-0.026, p<0.05), dumper speed (β=0.008, p<0.001) and poor haul road condition (β=0.015, p<0.001). Conclusion: The bootstrap linear regression model produced efficient results for the dataset which was characterized by collinearity. WBV exposure is multifactorial. Regular monitoring of WBV exposure and corrective actions through appropriate prevention programs including the ergonomic design of the seat would increase the health and safety of operators.

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