scholarly journals Study on Influence of Range of Data in Concrete Compressive Strength with Respect to the Accuracy of Machine Learning with Linear Regression

2021 ◽  
Vol 11 (9) ◽  
pp. 3866
Author(s):  
Jun-Ryeol Park ◽  
Hye-Jin Lee ◽  
Keun-Hyeok Yang ◽  
Jung-Keun Kook ◽  
Sanghee Kim
Keyword(s):  
Machine Learning ◽  
Compressive Strength ◽  
Linear Regression ◽  
Coarse Aggregate ◽  
Learning Algorithm ◽  
Linear Regression Analysis ◽  
Aggregate Size ◽  
Training Dataset ◽  
Machine Learning Algorithm ◽  
Concrete Compressive Strength

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.

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

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.

Effect of coarse aggregate size on strength and workability of concrete

2006 ◽  
Vol 33 (2) ◽  
pp. 206-213 ◽  
Author(s):  
Peter J Tumidajski ◽  
B Gong
Keyword(s):  
Compressive Strength ◽  
Water Demand ◽  
Coarse Aggregate ◽  
Cement Content ◽  
Aggregate Size ◽  
Concrete Compressive Strength ◽  
Aggregate Fraction ◽  
Maximum Compressive Strength

The properties of concrete were studied when the proportions of 37.5 and 19.5 mm stone in the coarse aggregate were varied. With the cement content of 160 kg/m3 and the ratio of water/cement (w/c) greater than 0.9, the compressive strength is maximum at 25 percent by weight (w/o) of 37.5 mm stone. Conversely, for the cement content of 350 kg/m3 and w/c ratios of less than 0.50, maximum compressive strength is substantively reduced. For both 160 kg/m3 and 350 kg/m3 cement contents, workability improves slightly as the proportion of the 37.5 mm stone is increased. For 100 mm fixed slumps and cement content of less than 160 kg/m3, there was little change in compressive strength as the proportion of 37.5 mm stone increased. However, when cement content was increased from 190 to 350 kg/m3, maximum compressive strength was observed, which shifted downward from 50 w/o to 25 w/o of 37.5 mm stone. In general, to maintain a 100 mm slump, water demand decreased as the proportion of 37.5 mm stone in the coarse aggregate fraction increased.Key words: concrete, compressive strength, workability, slump, aggregate, size, cement.

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

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>

scholarly journals Machine Learning Approaches for Early Prediction of Gestational Diabetes Mellitus Based on Prospective Cohort Study

2021 ◽  
Author(s):  
Jingyuan Wang ◽  
Xiujuan Chen ◽  
Yueshuai Pan ◽  
Kai Chen ◽  
Yan Zhang ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Machine Learning ◽  
Cohort Study ◽  
Gestational Diabetes ◽  
Learning Algorithm ◽  
Training Dataset ◽  
Machine Learning Algorithm ◽  
Early Prediction ◽  
Test Dataset ◽  
Svm Model

Abstract Purpose: To develop and verify an early prediction model of gestational diabetes mellitus (GDM) using machine learning algorithm.Methods: The dataset collected from a pregnant cohort study in eastern China, from 2017 to 2019. It was randomly divided into 75% as the training dataset and 25% as the test dataset using the train_test_split function. Based on Python, four classic machine learning algorithm and a New-Stacking algorithm were first trained by the training dataset, and then verified by the test dataset. The four models were Logical Regression (LR), Random Forest (RT), Artificial Neural Network (ANN) and Support Vector Machine (SVM). The sensitivity, specificity, accuracy, and area under the Receiver Operating Characteristic Curve (AUC) were used to analyse the performance of models.Results: Valid information from a total of 2811 pregnant women were obtained. The accuracies of the models ranged from 80.09% to 86.91% (RF), sensitivities ranged from 63.30% to 81.65% (SVM), specificities ranged from 79.38% to 97.53% (RF), and AUCs ranged from 0.80 to 0.82 (New-Stacking).Conclusion: This paper successfully constructed a New-Stacking model theoretically, for its better performance in specificity, accuracy and AUC. But the SVM model got the highest sensitivity, the SVM model was recommends as the prediction model for clinical.

Overdischarge Detection and Prevention with Temperature Monitoring of Li-ion Batteries and Linear Regression-based Machine Learning

2021 ◽  
pp. 1-11
Author(s):  
Bing Li ◽  
Casey Jones ◽  
Vikas Tomar
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Learning Algorithm ◽  
Linear Regression Analysis ◽  
Peak Temperature ◽  
Training Data ◽  
Battery Management System ◽  
Cell Temperature ◽  
Li Ion ◽  
Cutoff Voltage

Abstract This work focuses on the use of linear regression analysis-based machine learning for the prediction of the end of discharge of a commercial prismatic lithium (Li)-ion cell. The cell temperature was recorded during the cycling of Li-ion cells and the relation between the open circuit voltage and cell temperature was used in the development of the linear regression-based machine learning algorithm. The peak temperature was selected as the indicator of battery end of discharge. A battery management system using a pyboard microcontroller was constructed to monitor the temperature of the cell under test, and was also used to control a MOSFET that acted as a switch to disconnect the cell from the circuit. The method used an initial 10 charge and discharge cycles at a rate of 1C as the training data, then another charge and discharge cycle for the testing data. During the test cycling, the discharge was continued beyond the cutoff voltage to initiate an overdischarge while the temperature of the cell was continuously monitored. The experiment was performed on 3 different cells, and the overdischarge for each was secured within 0.1 V of the cutoff voltage. The results of these experiments show that a linear regression-based analysis can be implemented to detect an overdischarge condition of a cell based on the anticipated peak temperature during discharge.

scholarly journals Motion Detection and Prediction Using Machine Learning Algorithm

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.

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

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>

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

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.

scholarly journals Expenditure Predicting using Machine Learning

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.

scholarly journals A Prognostic Three-Axis Coordination Model for Supply Chain Regulation Using Machine Learning Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Hariprasath Manoharan ◽  
Yuvaraja Teekaraman ◽  
Ramya Kuppusamy ◽  
Arun Radhakrishnan ◽  
Mohamed Yaseen Jabarulla
Keyword(s):  
Machine Learning ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Learning Algorithm ◽  
Training Dataset ◽  
Machine Learning Algorithm ◽  
Chain Management ◽  
Coordination Model ◽  
Effective Manner ◽  
Supply Chain Management System

In this article, data processing of a supply chain management system has been monitored using the Internet of Space (IoS) which can be able to create possessions for managing the business process. In modern circumstances, many business inventiveness are trading and exporting products on their possession, but in many cases, information on such manufactured products is not monitored in an effective manner. To overcome the abovementioned issue, a precise model of monitoring several distributed products in supply chain management has been introduced with high sustainability error reduction. The framed model in the management process has been integrated with the boosting algorithm, a type of machine learning algorithm where training dataset has been introduced appropriately. This variation in the incorporation of the boosting optimization process not only increases the efficiency of the proposed model but also attempts to prove the success strategy under five different scenarios, where after sequential tests IoS model delivers high improvement in the distribution process for an average percentile of 67% than the existing methods.

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