Comparison of Multivariate Linear Regression and Machine Learning Algorithm Developed for Prediction of Precision Warfarin Dosing in a Korean Population

This study aims to predict the compressive strength of concrete using a machine-learning algorithm with linear regression analysis and to evaluate its accuracy. The open-source software library TensorFlow was used to develop the machine-learning algorithm. In the machine-earning algorithm, a total of seven variables were set: water, cement, fly ash, blast furnace slag, sand, coarse aggregate, and coarse aggregate size. A total of 4297 concrete mixtures with measured compressive strengths were employed to train and testing the machine-learning algorithm. Of these, 70% were used for training, and 30% were utilized for verification. For verification, the research was conducted by classifying the mixtures into three cases: the case where the machine-learning algorithm was trained using all the data (Case-1), the case where the machine-learning algorithm was trained while maintaining the same number of training dataset for each strength range (Case-2), and the case where the machine-learning algorithm was trained after making the subcase of each strength range (Case-3). The results indicated that the error percentages of Case-1 and Case-2 did not differ significantly. The error percentage of Case-3 was far smaller than those of Case-1 and Case-2. Therefore, it was concluded that the range of training dataset of the concrete compressive strength is as important as the amount of training dataset for accurately predicting the concrete compressive strength using the machine-learning algorithm.

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Oxidation States of Binary Oxides from Data Analytics of the Electronic Structure

10.26434/chemrxiv.7234658.v1 ◽

2018 ◽

Author(s):

Sergei Posysaev ◽

Olga Miroshnichenko ◽

Matti Alatalo ◽

Duy Le ◽

Talat S. Rahman

Keyword(s):

Machine Learning ◽

Electronic Structure ◽

Linear Regression ◽

Data Analytics ◽

Learning Algorithm ◽

Mixed Valence ◽

Oxidation States ◽

Machine Learning Algorithm ◽

Binary Oxides ◽

Mixed Valence Compounds

<p>A connection between the oxidation state (OS) and Bader charge has been missing so far. To our knowledge, all previous work tried to connect OS with Bader charges only with few compounds. The aim of this work was to find a dependency between OS and Bader charge, using <a>a large number of compounds from an open database</a>. We show that a <a>correlation indeed exists between OSs and Bader charges</a> using the simplest machine learning algorithm, linear regression. The applicability of determining OS by Bader charges in mixed-valence compounds and surfaces is considered.</p>

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Motion Detection and Prediction Using Machine Learning Algorithm

Issue 4 - Journal of Science and Technology ◽

10.46243/jst.2020.v5.i5.pp220-226 ◽

2020 ◽

pp. 220-226

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Logistic Regression ◽

Linear Regression ◽

21St Century ◽

Motion Detection ◽

Data Analytics ◽

Learning Algorithm ◽

Machine Learning Algorithm ◽

Processing Data

Machine learning is a branch of Artificial Intelligence which is gaining importance in the 21st century with increasing processing speeds and miniaturization of sensors, the applications of Artificial Intelligence and cognitive technologies are growing rapidly. An array of ultrasonic sensors i.e., HCSR-04 is placed at different directions, collecting data for a particularinterval of a period during a particular day. The acquired sensor values are subjected to pre-processing, data analytics, and visualization. The prepared data is now split into test and train. A prediction model is designed using logistic regression and linear regression and checked for accuracy, F1 score, and precision compared.

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Oxidation States of Binary Oxides from Data Analytics of the Electronic Structure

10.26434/chemrxiv.7234658 ◽

2018 ◽

Author(s):

Sergei Posysaev ◽

Olga Miroshnichenko ◽

Matti Alatalo ◽

Duy Le ◽

Talat S. Rahman

Keyword(s):

Machine Learning ◽

Electronic Structure ◽

Linear Regression ◽

Data Analytics ◽

Learning Algorithm ◽

Mixed Valence ◽

Oxidation States ◽

Machine Learning Algorithm ◽

Binary Oxides ◽

Mixed Valence Compounds

<p>A connection between the oxidation state (OS) and Bader charge has been missing so far. To our knowledge, all previous work tried to connect OS with Bader charges only with few compounds. The aim of this work was to find a dependency between OS and Bader charge, using <a>a large number of compounds from an open database</a>. We show that a <a>correlation indeed exists between OSs and Bader charges</a> using the simplest machine learning algorithm, linear regression. The applicability of determining OS by Bader charges in mixed-valence compounds and surfaces is considered.</p>

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A machine learning-based diagnostic model associated with knee osteoarthritis severity

Scientific Reports ◽

10.1038/s41598-020-72941-4 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Soon Bin Kwon ◽

Yunseo Ku ◽

Hy uk-soo Han ◽

Myung Chul Lee ◽

Hee Chan Kim ◽

...

Keyword(s):

Machine Learning ◽

Linear Regression ◽

Regression Models ◽

Learning Algorithm ◽

T Test ◽

Machine Learning Algorithm ◽

Linear Regression Models ◽

Estimation Model ◽

Student’S T ◽

Student’S T Test

Abstract Knee osteoarthritis (KOA) is characterized by pain and decreased gait function. We aimed to find KOA-related gait features based on patient reported outcome measures (PROMs) and develop regression models using machine learning algorithms to estimate KOA severity. The study included 375 volunteers with variable KOA grades. The severity of KOA was determined using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). WOMAC scores were used to classify disease severity into three groups. A total of 1087 features were extracted from the gait data. An ANOVA and student’s t-test were performed and only features that were significant were selected for inclusion in the machine learning algorithm. Three WOMAC subscales (physical function, pain and stiffness) were further divided into three classes. An ANOVA was performed to determine which selected features were significantly related to the subscales. Both linear regression models and a random forest regression was used to estimate patient the WOMAC scores. Forty-three features were selected based on ANOVA and student’s t-test results. The following number of features were selected from each joint: 12 from hip, 1 feature from pelvic, 17 features from knee, 9 features from ankle, 1 feature from foot, and 3 features from spatiotemporal parameters. A significance level of < 0.0001 and < 0.00003 was set for the ANOVA and t-test, respectively. The physical function, pain, and stiffness subscales were related to 41, 10, and 16 features, respectively. Linear regression models showed a correlation of 0.723 and the machine learning algorithm showed a correlation of 0.741. The severity of KOA was predicted by gait analysis features, which were incorporated to develop an objective estimation model for KOA severity. The identified features may serve as a tool to guide rehabilitation and progress assessments. In addition, the estimation model presented here suggests an approach for clinical application of gait analysis data for KOA evaluation.

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Expenditure Predicting using Machine Learning

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.a4720.119119 ◽

2019 ◽

Vol 9 (1) ◽

pp. 2169-2172

Keyword(s):

Machine Learning ◽

Linear Regression ◽

Stock Market ◽

Learning Algorithm ◽

Machine Learning Algorithm ◽

Personal Finances ◽

Learning Machine ◽

A Company

As we know in today’s world managing expenses is a very challenging thing. By analyzing our previous expenses, we can predict our upcoming expenses. Now digitalization is everywhere so we can get bank transaction history easily, just by getting the data from transaction history we can predict the estimation of upcoming expense. We can do this using machine learning, machine learning is used in many things one of them is prediction. We are using linear regression algorithm, it is a machine learning algorithm used in prediction. The main aim of this project is to build a system that helps in managing personal finances of the user. This project has mainly three modules, first is to collect the data and prepare it to be used in algorithm, next is to build a network between the algorithm and the dataset. The last one is prediction in which system is going to predict the expenses. Particularly we are predicting the expense of next month. We can also use this system in stock market for predicting the next step if stocks of a company will rise or fall do, this can help us in making money from stock market and manage our expense.

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A hybrid analysis model supported by machine learning algorithm and multiple linear regression to find reasons for unemployment of programmers in Iraq

TELKOMNIKA (Telecommunication Computing Electronics and Control) ◽

10.12928/telkomnika.v19i2.16738 ◽

2021 ◽

Vol 19 (2) ◽

pp. 444

Author(s):

Mohamed A. Abdulhamed ◽

Hadeel I. Mustafa ◽

Zainab I. Othman

Keyword(s):

Machine Learning ◽

Linear Regression ◽

Multiple Linear Regression ◽

Learning Algorithm ◽

Machine Learning Algorithm ◽

Analysis Model ◽

Hybrid Analysis

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HR Analytics using R-Machine Learning Algorithm: Multiple Linear Regression Analysis

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.e2789.039520 ◽

2020 ◽

Vol 9 (5) ◽

pp. 1179-1183

Keyword(s):

Machine Learning ◽

Regression Analysis ◽

Linear Regression ◽

Multiple Linear Regression ◽

Learning Algorithm ◽

Linear Regression Analysis ◽

Multiple Linear Regression Analysis ◽

Machine Learning Algorithm ◽

Relevant Topic ◽

The Impact

The purpose of empirical research study to know the impact of various HRD practices and its impact on predictor (job satisfaction). The structured survey research instrument was used to gather the data from 500 sample respondents. The questionnaire was validated with pilot study and data was with crone Bach’s alpha reliability test. The results of the outcome validated with R-Machine Learning Algorithm, multiple linear regression analysis with the help of train data and test data (30:70) ratio. Furthermore results reveals corrgram plot, matrix correlation plot and validation of data with validation match test among various HRD practices and it’s inter relationship. The analysis supported with various reviews which include both western and Indian reviews. The study can be generalized to any sector wherever HRD practices can be implemented. The study feasible/applicable to social implications and employee concern problems and related productivity. The study provides new insights to the readers and analysis which was not published by any other in the relevant topic related machine learning algorithm in analytics world.

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Stock Prediction Analysis by using Linear Regression Machine Learning Algorithm

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.d1110.029420 ◽

2020 ◽

Vol 9 (4) ◽

pp. 841-844

Keyword(s):

Machine Learning ◽

Linear Regression ◽

Stock Market ◽

Learning Algorithm ◽

Machine Learning Algorithms ◽

Government Policies ◽

Machine Learning Algorithm ◽

Machine Learn ◽

Market Sentiment ◽

Industry Performance

Stock market is varying day to day. Many factors such as government policies, industry performance, market sentiment etc are the main cause of up and downs in stock market. To invest money in stock market, study and analysis of stock market is essential. This type of analysis can be done by using Machine learning algorithms. The main objective of this paper is to predict the stock market future values by using linear regression machine learn algorithms based on past values. The methodology is developed and implemented in python on APPLE and TSLA stock.

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