- 标题
- 摘要
- 关键词
- 实验方案
- 产品
-
[Communications in Computer and Information Science] Advances in Signal Processing and Intelligent Recognition Systems Volume 968 (4th International Symposium SIRS 2018, Bangalore, India, September 19–22, 2018, Revised Selected Papers) || A Novel Method for Stroke Prediction from Retinal Images Using HoG Approach
摘要： Stroke is one of the principal reasons for adult impairment worldwide. Retinal fundus images are analyzed for the detection of various cardiovascular diseases like Stroke. Stroke is mainly characterized by soft and hard exudates, artery or vein occlusion and alterations in retinal vasculature. In this research work, Histogram of Oriented Gradients (HoG) has been implemented to extract features from the region of interest of retinal fundus images. This innovative method is assessed for the computer aided diagnosis of normal healthy and abnormal images of stroke patients. A comparative analysis has been made between the extracted HoG features and Haralick features. HoG features extracted from the region of interest, when given to a Na?ve Bayes classifier provides an accuracy of 93% and a Receiver Operating Characteristic (ROC) curve area of 0.979.
关键词： Haralick features,Na?ve Bayes classifier,Histogram of Oriented Gradients (HoG),Stroke
更新于2025-09-23 15:23:52
-
Weighting Factor Selection of the Ensemble Model for Improving Forecast Accuracy of Photovoltaic Generating Resources
摘要： Among renewable energy sources, solar power is rapidly growing as a major power source for future power systems. However, solar power has uncertainty due to the effects of weather factors, and if the penetration rate of solar power in the future increases, it could reduce the reliability of the power system. A study of accurate solar power forecasting should be done to improve the stability of the power system operation. Using the empirical data from solar power plants in South Korea, the short-term forecasting of solar power outputs were carried out for 2016. We performed solar power forecasting with the support vector regression (SVR) model, the na?ve Bayes classifier (NBC), and the hourly regression model. We proposed the ensemble method including the selection of weighting factors for each model to improve forecasting accuracy. The forecasted solar power generation error was indicated using normalized mean absolute error (NMAE) compared to the plant capacity. For the ensemble method, the results of each forecasting model were weighted with the reciprocal of the standard deviation of the forecast error, thus improving the solar power outputs forecast accuracy.
关键词： support vector regression,na?ve Bayes classifier,solar power forecasting,machine learning,ensemble,day ahead power forecasting
更新于2025-09-11 14:15:04