Application of Machine Learning Model and Hybrid Model in Retail Sales Forecast

2021 ◽  
Author(s):  
Haichen Jiang ◽  
Jiatong Ruan ◽  
Jianmin Sun
Keyword(s):  
Machine Learning ◽  
Hybrid Model ◽  
Learning Model ◽  
Retail Sales ◽  
Sales Forecast ◽  
Machine Learning Model

Development of a Diabetes Melitus Detection and Prediction Model Using Light Gradient Boosting Machine and K-Nearest Neighbour

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
B. A Omodunbi
Keyword(s):  
Diabetes Mellitus ◽  
Machine Learning ◽  
Hybrid Model ◽  
Learning Model ◽  
Experimental Result ◽  
Gradient Boosting ◽  
Light Gradient ◽  
Machine Learning Model ◽  
Gradient Boosting Machine ◽  
Receiver Operating

Diabetes mellitus is a health disorder that occurs when the blood sugar level becomes extremely high due to body resistance in producing the required amount of insulin. The aliment happens to be among the major causes of death in Nigeria and the world at large. This study was carried out to detect diabetes mellitus by developing a hybrid model that comprises of two machine learning model namely Light Gradient Boosting Machine (LGBM) and K-Nearest Neighbor (KNN). This research is aimed at developing a machine learning model for detecting the occurrence of diabetes in patients. The performance metrics employed in evaluating the finding for this study are Receiver Operating Characteristics (ROC) Curve, Five-fold Cross-validation, precision, and accuracy score. The proposed system had an accuracy of 91% and the area under the Receiver Operating Characteristic Curve was 93%. The experimental result shows that the prediction accuracy of the hybrid model is better than traditional machine learning

scholarly journals Short-term electricity grid maximum demand forecasting with the ARIMAX-SVR Machine Learning Hybrid Model

2021 ◽  
Vol 28 (1) ◽  
pp. 22-30
Author(s):  
Hon Fung Chow
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Hybrid Model ◽  
Regression Models ◽  
Demand Forecasting ◽  
Learning Model ◽  
Support Vector ◽  
Linear Regression Models ◽  
Short Term ◽  
Machine Learning Model

This paper proposes and discusses the viability of a short-term grid maximum demand forecasting model combining autoregressive integrated moving average with regressors (ARIMAX) and support vector regression (SVR). Grid demand forecasting is essential to generation unit scheduling, maintenance planning and system security. Traditionally, grid demand is forecasted using multivariate linear regression models with parameters adjusted to past data. A disadvantage of the linear regression model is that the parameters require regular adjustment, otherwise the prediction accuracy will deteriorate over time. With recent advances in the field of machine learning and lower computational costs, the usage of machine learning in the power industry becomes increasingly practicable. The proposed model is a machine learning model that combines ARIMAX and SVR to exploit their respective effectiveness in predicting linear and non-linear data. In contrast to linear regression models, the machine learning model automatically updates itself when new data is included. The hybrid model is benchmarked against other forecasting models and demonstrated a marked improvement in accuracy, achieving RMSE of 67.7MW and MAPE of 1.32% in a seven-day forecast.

scholarly journals Estimation of PM<sub>2.5</sub> Concentration in China Using Linear Hybrid Machine Learning Model

2021 ◽  
Author(s):  
Zhihao Song ◽  
Bin Chen ◽  
Yue Huang ◽  
Li Dong ◽  
Tingting Yang
Keyword(s):  
Machine Learning ◽  
Hybrid Model ◽  
Mixed Model ◽  
Meteorological Data ◽  
Learning Model ◽  
Average Concentration ◽  
Gradient Boosting ◽  
Spatiotemporal Resolution ◽  
Machine Learning Model ◽  
Hybrid Machine

Abstract. The satellite remote-sensing aerosol optical depth (AOD) and meteorological elements were employed to invert PM2.5 in order to control air pollution more effectively. This paper proposes a restricted gradient-descent linear hybrid machine learning model (RGD–LHMLM) by integrating a random forest (RF), a gradient boosting regression tree (GBRT), and a deep neural network (DNN) to estimate the concentration of PM2.5 in China in 2019. The research data included Himawari-8 AOD with high spatiotemporal resolution, ERA-5 meteorological data, and geographic information. The results showed that, in the hybrid model developed by linear fitting, the DNN accounted for the largest proportion, whereas the weight coefficient was 0.62. The R2 values of RF, GBRT, and DNN were reported 0.79, 0.81, and 0.8, respectively. Preferably, the generalization ability of the mixed model was better than that of each sub-model, and R2 reached 0.84, whereas RMSE and MAE were reported 12.92 µg/m3 and 8.01 µg/m3, respectively. For the RGD-LHMLM, R2 was above 0.7 in more than 70 % of the sites, whereas RMSE and MAE were below 20 µg/m3 and 15 µg/m3, respectively, in more than 70 % of the sites due to the correlation coefficient having seasonal difference between the meteorological factor and PM2.5. Furthermore, the hybrid model performed best in winter (mean R2 was 0.84) and worst in summer (mean R2 was 0.71). The spatiotemporal distribution characteristics of PM2.5 in China were then estimated and analyzed. According to the results, there was severe pollution in winter with an average concentration of PM2.5 being reported 62.10 µg/m3. However, there was slight pollution in summer with an average concentration of PM2.5 being reported 47.39 µg/m3. The findings also indicate that North China and East China are more polluted than other areas and that their average annual concentration of PM2.5 was reported 82.68 µg/m3. Moreover, there was relatively low pollution in Inner Mongolia, Qinghai, and Tibet, for their average PM2.5 concentrations were reported below 40 µg/m3.

scholarly journals Estimation of PM<sub>2.5</sub> concentration in China using linear hybrid machine learning model

2021 ◽  
Vol 14 (8) ◽  
pp. 5333-5347
Author(s):  
Zhihao Song ◽  
Bin Chen ◽  
Yue Huang ◽  
Li Dong ◽  
Tingting Yang
Keyword(s):  
Machine Learning ◽  
Hybrid Model ◽  
Mixed Model ◽  
Meteorological Data ◽  
Learning Model ◽  
Average Concentration ◽  
Gradient Boosting ◽  
Spatiotemporal Resolution ◽  
Machine Learning Model ◽  
Hybrid Machine

Abstract. Satellite remote sensing aerosol optical depth (AOD) and meteorological elements were employed to invert PM2.5 (the fine particulate matter with a diameter below 2.5 µm) in order to control air pollution more effectively. This paper proposes a restricted gradient-descent linear hybrid machine learning model (RGD-LHMLM) by integrating a random forest (RF), a gradient boosting regression tree (GBRT), and a deep neural network (DNN) to estimate the concentration of PM2.5 in China in 2019. The research data included Himawari-8 AOD with high spatiotemporal resolution, ERA5 meteorological data, and geographic information. The results showed that, in the hybrid model developed by linear fitting, the DNN accounted for the largest proportion, and the weight coefficient was 0.62. The R2 values of RF, GBRT, and DNN were reported as 0.79, 0.81, and 0.8, respectively. Preferably, the generalization ability of the mixed model was better than that of each sub-model, and R2 (determination coefficient) reached 0.84, and RMSE (root mean square error) and MAE (mean absolute error) were reported as 12.92 and 8.01 µg m−3, respectively. For the RGD-LHMLM, R2 was above 0.7 in more than 70 % of the sites and RMSE and MAE were below 20 and 15 µg m−3, respectively, in more than 70 % of the sites due to the correlation coefficient having a seasonal difference between the meteorological factor and PM2.5. Furthermore, the hybrid model performed best in winter (mean R2 was 0.84) and worst in summer (mean R2 was 0.71). The spatiotemporal distribution characteristics of PM2.5 in China were then estimated and analyzed. According to the results, there was severe pollution in winter with an average concentration of PM2.5 being reported as 62.10 µg m−3. However, there was only slight pollution in summer with an average concentration of PM2.5 being reported as 47.39 µg m−3. The period from 10:00 to 15:00 LT (Beijing time, UTC+8 every day is the best time for model inversion; at this time the pollution is also high. The findings also indicate that North China and East China are more polluted than other areas, and their average annual concentration of PM2.5 was reported as 82.68 µg m−3. Moreover, there was relatively low pollution in Inner Mongolia, Qinghai, and Tibet, for their average PM2.5 concentrations were reported below 40 µg m−3.

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
Sign in / Sign up

Export Citation Format

Share Document