Improved support vector machine model in the prediction of tuberculosis incidence

2021 ◽  
pp. 1-9
Author(s):  
Zhikai Li ◽  
Chen Hao
Keyword(s):  
Support Vector ◽  
Physical Damage ◽  
Combined Model ◽  
Machine Model ◽  
Model Research ◽  
Common Infectious Disease ◽  
Single Data ◽  
Using Data ◽  
Model Algorithm ◽  
Improved Support Vector Machine

Tuberculosis, as a more common infectious disease with serious physical damage to humans, has been relatively vacant in predictive model research. In order to improve the accuracy of pulmonary tuberculosis, this study combined the incidence of tuberculosis, collected data using data collection methods, used a single data model for predictive analysis, and compared with the actual situation. At the same time, through the comparative analysis, the paper draws the shortcomings of the traditional single model algorithm, constructs a combined model for the prediction of tuberculosis, and collects the incidence of tuberculosis. In addition, this paper draws it into a statistical chart, and analyzes its pathological characteristics and the dynamic trend of the onset. Through experimental research, it can be seen that the prediction accuracy of the combined model of this study is high, which can provide theoretical reference for subsequent related research.

Prediction for Burned Area of Forest Fires Based on SVM Model

2014 ◽  
Vol 513-517 ◽  
pp. 4084-4089 ◽  
Author(s):  
Dao Wen Xie ◽  
Shi Liang Shi
Keyword(s):  
Forest Fires ◽  
Fuel Load ◽  
Burned Area ◽  
Support Vector ◽  
Machine Model ◽  
Guangzhou City ◽  
Svm Model ◽  
Forest Fuel ◽  
Using Data ◽  
Fuel Moisture Content

Forest fire spreading is a complex burning phenomenon, and it is difficult to build a general spreading model for the fires occurred in different area over the world, even in the same country. Accordingly, predicting the burned area of forest fires is also a challenging task. In this work, five attributes (i.e. forest fuel moisture content, forest fuel inflammability, forest fuel load ,slope and burning time) are selected as input to predict burned area of forest fires occurred in the area of Guangzhou City in China. Next, using Data Mining (DM) technique, an SVM (Support Vector Machine) model was built and applied to deal with this type of a regression task, predicting burned area. Results showed that the selection of input attributes was reasonable, and the proposed SVM model was suitable for prediction of burned area, with higher precision, better generalization. This work provided a new way to deal with predictions for burned area of forest fires.

scholarly journals Method for Predicting Failure Rate of Airborne Equipment Based on Optimal Combination Model

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
WenQiang Li ◽  
Ning Hou ◽  
XiangKun Sun
Keyword(s):  
Failure Rate ◽  
Health Management ◽  
Multiple Linear Regression Model ◽  
Optimal Combination ◽  
Support Vector ◽  
Equipment Failure ◽  
Combined Model ◽  
Machine Model ◽  
Partial Least Squares Model ◽  
Comparison Functions

Accurate prediction of airborne equipment failure rate can provide correct repair and maintenance decisions and effectively establish a health management mechanism. This plays an important role in ensuring the safe use of the aircraft and flight safety. This paper proposes an optimal combination forecasting model, which mixes five single models (Multiple Linear Regression model (MLR), Gray model GM (1, N), Partial Least Squares model (PLS), Artificial Neural Network model (BP), and Support Vector Machine model (SVM)). The combined model and its single model are compared with the other three algorithms. Seven classic comparison functions are used for predictive performance evaluation indicators. The research results show that the combined model is superior to other models in terms of prediction accuracy. This paper provides a practical and effective method for predicting the airborne equipment failure rate.

Support vector approach using data augmentation with copula function applied to load forecasting

2019 ◽  
Author(s):  
Gabriel Ribeiro ◽  
Marcos Yamasaki ◽  
Helon Vicente Hultmann Ayala ◽  
Leandro Coelho ◽  
Viviana Mariani
Keyword(s):  
Data Augmentation ◽  
Load Forecasting ◽  
Copula Function ◽  
Support Vector ◽  
Using Data ◽  
Vector Approach

scholarly journals Predicting Ink Transfer Rate of 3D Additive Printing Using EGBO Optimized Least Squares Support Vector Machine Model

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Shengpu Li ◽  
Yize Sun
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Least Squares ◽  
Transfer Rate ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Support Vector ◽  
Experimental Sample ◽  
Machine Model ◽  
Ink Transfer

Ink transfer rate (ITR) is a reference index to measure the quality of 3D additive printing. In this study, an ink transfer rate prediction model is proposed by applying the least squares support vector machine (LSSVM). In addition, enhanced garden balsam optimization (EGBO) is used for selection and optimization of hyperparameters that are embedded in the LSSVM model. 102 sets of experimental sample data have been collected from the production line to train and test the hybrid prediction model. Experimental results show that the coefficient of determination (R2) for the introduced model is equal to 0.8476, the root-mean-square error (RMSE) is 6.6 × 10 (−3), and the mean absolute percentage error (MAPE) is 1.6502 × 10 (−3) for the ink transfer rate of 3D additive printing.

scholarly journals An Improved Whale Algorithm for Support Vector Machine Prediction of Photovoltaic Power Generation

Symmetry ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 212
Author(s):  
Yu-Wei Liu ◽  
Huan Feng ◽  
Heng-Yi Li ◽  
Ling-Ling Li
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Output Power ◽  
Input Data ◽  
Support Vector Machine Model ◽  
Weather Conditions ◽  
Clean Energy ◽  
Support Vector ◽  
Machine Model ◽  
Photovoltaic Power

Accurate prediction of photovoltaic power is conducive to the application of clean energy and sustainable development. An improved whale algorithm is proposed to optimize the Support Vector Machine model. The characteristic of the model is that it needs less training data to symmetrically adapt to the prediction conditions of different weather, and has high prediction accuracy in different weather conditions. This study aims to (1) select light intensity, ambient temperature and relative humidity, which are strictly related to photovoltaic output power as the input data; (2) apply wavelet soft threshold denoising to preprocess input data to reduce the noise contained in input data to symmetrically enhance the adaptability of the prediction model in different weather conditions; (3) improve the whale algorithm by using tent chaotic mapping, nonlinear disturbance and differential evolution algorithm; (4) apply the improved whale algorithm to optimize the Support Vector Machine model in order to improve the prediction accuracy of the prediction model. The experiment proves that the short-term prediction model of photovoltaic power based on symmetry concept achieves ideal accuracy in different weather. The systematic method for output power prediction of renewable energy is conductive to reducing the workload of predicting the output power and to promoting the application of clean energy and sustainable development.

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