In agriculture sector, machine learning has been widely used by researchers for crop yield prediction. However, it is quite difficult to identify the most critical features from a dataset. Feature selection techniques...In agriculture sector, machine learning has been widely used by researchers for crop yield prediction. However, it is quite difficult to identify the most critical features from a dataset. Feature selection techniques allow us to remove the extraneous and noisy features from the original feature set. The feature selection techniques help the model to focus only on the important features of the data, thus reducing execution time and improving efficiency of the model. The aim of this study is to determine relevant subset features for achieving high predictive performance by using different feature selection techniques like Filter methods, Wrapper methods and embedded methods. In this work, different feature selection techniques like Rank-based feature selection technique, weighted feature selection technique and Hybrid Feature Selection Technique have been applied to the agricultural data. The optimal feature set returned by different feature selection techniques is used for yield prediction using Linear regression, Random Forest, and Decision Tree Regressor. The accuracy of prediction obtained using the above three methods has been analyzed by using different evaluation parameters. This study helps in increasing predictive accuracy with the minimum number of features.展开更多
针对车载智能监控的要求,设计一种嵌入式车体移动报警安全监控系统。系统以TMS320DM642芯片为核心,采用简化的Itti模型来提取监控画面的颜色和亮度特征,并检测和分割出感兴趣区域(Regions of Interest)。根据ROI质心相似度的变化来判断...针对车载智能监控的要求,设计一种嵌入式车体移动报警安全监控系统。系统以TMS320DM642芯片为核心,采用简化的Itti模型来提取监控画面的颜色和亮度特征,并检测和分割出感兴趣区域(Regions of Interest)。根据ROI质心相似度的变化来判断车辆状态。实验结果表明:该系统较为准确地检测和分割感兴趣区域,并能对其进行实时地监控;系统对车辆状态的判断和报警可靠性较高。展开更多
文摘In agriculture sector, machine learning has been widely used by researchers for crop yield prediction. However, it is quite difficult to identify the most critical features from a dataset. Feature selection techniques allow us to remove the extraneous and noisy features from the original feature set. The feature selection techniques help the model to focus only on the important features of the data, thus reducing execution time and improving efficiency of the model. The aim of this study is to determine relevant subset features for achieving high predictive performance by using different feature selection techniques like Filter methods, Wrapper methods and embedded methods. In this work, different feature selection techniques like Rank-based feature selection technique, weighted feature selection technique and Hybrid Feature Selection Technique have been applied to the agricultural data. The optimal feature set returned by different feature selection techniques is used for yield prediction using Linear regression, Random Forest, and Decision Tree Regressor. The accuracy of prediction obtained using the above three methods has been analyzed by using different evaluation parameters. This study helps in increasing predictive accuracy with the minimum number of features.
文摘针对车载智能监控的要求,设计一种嵌入式车体移动报警安全监控系统。系统以TMS320DM642芯片为核心,采用简化的Itti模型来提取监控画面的颜色和亮度特征,并检测和分割出感兴趣区域(Regions of Interest)。根据ROI质心相似度的变化来判断车辆状态。实验结果表明:该系统较为准确地检测和分割感兴趣区域,并能对其进行实时地监控;系统对车辆状态的判断和报警可靠性较高。
