scholarly journals A Machine Learning-Based Prediction Model of LCCO2 for Building Envelope Renovation in Taiwan

2021 ◽  
Vol 13 (15) ◽  
pp. 8209
Author(s):  
Yaw-Shyan Tsay ◽  
Chiu-Yu Yeh ◽  
Yu-Han Chen ◽  
Mei-Chen Lu ◽  
Yu-Chen Lin
Keyword(s):  
Machine Learning ◽  
Energy Consumption ◽  
Carbon Emissions ◽  
Construction Projects ◽  
Building Design ◽  
Learning Model ◽  
Building Envelope ◽  
Construction Methods ◽  
Machine Learning Model ◽  
Residential And Commercial Sectors

In 2015, Taiwan’s government announced the “Greenhouse Gas Reduction and Management Act”, the goal of which was a 50% reduction in carbon emissions by 2050, compared with 2005. The residential and commercial sectors produce approximately one third of all carbon emissions in Taiwan, and the number of construction renovation projects is much larger than that of new construction projects. In this paper, we considered the life-cycle CO2 (LCCO2) of a building envelope renovation project in Tainan and focused on local construction methods for typical row houses. The LCCO2 of 744 cases with various climate zones, orientations, and insulation and glazing types was calculated via EnergyPlus, SimaPro, and a local database (LCBA database), and the results were then used to develop a machine learning model. Our findings showed that the machine learning model was capable of predicting annual energy consumption and LCCO2. With regard to annual energy consumption, the RMSE was 227.09 kW·h (per year) and the R2 was 0.992. For LCCO2, the RMSE was 2792.47 kgCO2eq and the R2 was 0.989, which indicates a high-confidence process for decision making in the early stages of building design and renovation.

Machine Learning Accelerated Genetic Algorithms for Computational Materials Search

2018 ◽  
Author(s):  
Steen Lysgaard ◽  
Paul C. Jennings ◽  
Jens Strabo Hummelshøj ◽  
Thomas Bligaard ◽  
Tejs Vegge
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Au Nanoparticles ◽  
Learning Model ◽  
Energy Calculations ◽  
Atomic Distribution ◽  
Machine Learning Model ◽  
Fold Reduction ◽  
Computational Materials

A machine learning model is used as a surrogate fitness evaluator in a genetic algorithm (GA) optimization of the atomic distribution of Pt-Au nanoparticles. The machine learning accelerated genetic algorithm (MLaGA) yields a 50-fold reduction of required energy calculations compared to a traditional GA.

AN EFFICIENT MACHINE LEARNING MODEL FOR PREDICTION OF ACUTE MYOCARDIAL INFARCTION

2020 ◽  
Vol 13 ◽  
Author(s):  
Dhilsath Fathima.M ◽  
S. Justin Samuel ◽  
R. Hari Haran
Keyword(s):  
Machine Learning ◽  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Logistic Regression ◽  
Decision Tree ◽  
Learning Model ◽  
Training Dataset ◽  
Data Set ◽  
Machine Learning Model ◽  
Proposed Model

Aim: This proposed work is used to develop an improved and robust machine learning model for predicting Myocardial Infarction (MI) could have substantial clinical impact. Objectives: This paper explains how to build machine learning based computer-aided analysis system for an early and accurate prediction of Myocardial Infarction (MI) which utilizes framingham heart study dataset for validation and evaluation. This proposed computer-aided analysis model will support medical professionals to predict myocardial infarction proficiently. Methods: The proposed model utilize the mean imputation to remove the missing values from the data set, then applied principal component analysis to extract the optimal features from the data set to enhance the performance of the classifiers. After PCA, the reduced features are partitioned into training dataset and testing dataset where 70% of the training dataset are given as an input to the four well-liked classifiers as support vector machine, k-nearest neighbor, logistic regression and decision tree to train the classifiers and 30% of test dataset is used to evaluate an output of machine learning model using performance metrics as confusion matrix, classifier accuracy, precision, sensitivity, F1-score, AUC-ROC curve. Results: Output of the classifiers are evaluated using performance measures and we observed that logistic regression provides high accuracy than K-NN, SVM, decision tree classifiers and PCA performs sound as a good feature extraction method to enhance the performance of proposed model. From these analyses, we conclude that logistic regression having good mean accuracy level and standard deviation accuracy compared with the other three algorithms. AUC-ROC curve of the proposed classifiers is analyzed from the output figure.4, figure.5 that logistic regression exhibits good AUC-ROC score, i.e. around 70% compared to k-NN and decision tree algorithm. Conclusion: From the result analysis, we infer that this proposed machine learning model will act as an optimal decision making system to predict the acute myocardial infarction at an early stage than an existing machine learning based prediction models and it is capable to predict the presence of an acute myocardial Infarction with human using the heart disease risk factors, in order to decide when to start lifestyle modification and medical treatment to prevent the heart disease.

Smart-ML: A System for Machine Learning Model Exploration using Pipeline Graph

2020 ◽  
Author(s):  
Dhaval Patel ◽  
Shrey Shrivastava ◽  
Wesley Gifford ◽  
Stuart Siegel ◽  
Jayant Kalagnanam ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Model ◽  
Machine Learning Model

Machine Learning Model for the Detection of Electric Energy Fraud using an Edge-Fog Computing Architecture

2020 ◽  
Author(s):  
Juan C. Olivares-Rojas ◽  
Enrique Reyes-Archundia ◽  
Noel E. Rodriiguez-Maya ◽  
Jose A. Gutierrez-Gnecchi ◽  
Ismael Molina-Moreno ◽  
...  
Keyword(s):  
Machine Learning ◽  
Fog Computing ◽  
Electric Energy ◽  
Learning Model ◽  
Computing Architecture ◽  
Machine Learning Model
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