Artificial intelligence(AI)is a sophisticated technology that investigates and formulates theories,methods,techniques,and application systems designed to emulate,expand,and enhance human intelligence[1].In recent year...Artificial intelligence(AI)is a sophisticated technology that investigates and formulates theories,methods,techniques,and application systems designed to emulate,expand,and enhance human intelligence[1].In recent years,the rapid advancement of key AI technologies,including image recognition,machine learning,neural networks and robotics,has significantly propelled multidisciplinary integration and development within the medical field[2].The considerable potential of AI in the field of medicine,as evidenced by its formidable data processing and analytical capabilities,has been demonstrated in a number of ways.展开更多
目的:对都匀毛尖茶汤品质进行数字化评价。方法:首先应用感官审评方法对茶汤品质进行评价,然后测定茶汤中内含成分含量,利用主成分分析法和相关系数法综合筛选与茶汤品质密切相关的内含成分,最后应用偏最小二乘回归方法(partial least s...目的:对都匀毛尖茶汤品质进行数字化评价。方法:首先应用感官审评方法对茶汤品质进行评价,然后测定茶汤中内含成分含量,利用主成分分析法和相关系数法综合筛选与茶汤品质密切相关的内含成分,最后应用偏最小二乘回归方法(partial least squares regression,PLS)和人工神经网络方法(backpropagation artificial neural network,BP-ANN)尝试建立茶汤品质评价模型。结果:茶汤内含成分的前3个主成分累计方差贡献率为97.85%,筛选出7种反映茶汤品质的内含成分,分别为氨基酸、茶多酚、水浸出物、儿茶素总量、表没食子儿茶素没食子酸酯、表儿茶素没食子酸酯和表没食子儿茶素。在建立的2种都匀毛尖茶汤品质预测模型中,线性偏最小二乘方法得到的结果一般,验证集决定系数和预测均方根误差分别为0.788和1.264,而非线性人工神经网络方法建立的模型结果最佳,验证集决定系数和预测均方根误差分别为0.962和0.516,模型具有很好的稳定性。结论:应用人工神经网络方法结合主成分分析和相关分析方法实现了对都匀毛尖茶汤品质的快速、准确数字化评价,研究方法可为其他茶类茶汤品质评价提供一定程度的参考。展开更多
A novel spatial interpolation method based on integrated radial basis function artificial neural networks （IRBFANNs） is proposed to provide accurate and stable predictions of heavy metals concentrations in soil at u...A novel spatial interpolation method based on integrated radial basis function artificial neural networks （IRBFANNs） is proposed to provide accurate and stable predictions of heavy metals concentrations in soil at un- sampled sites in a mountain region. The IRBFANNs hybridize the advantages of the artificial neural networks and the neural networks integration approach. Three experimental projects under different sampling densities are carried out to study the performance of the proposed IRBFANNs-based interpolation method. This novel method is compared with six peer spatial interpolation methods based on the root mean square error and visual evaluation of the distribution maps of Mn elements. The experimental results show that the proposed method performs better in accuracy and stability. Moreover, the proposed method can provide more details in the spatial distribution maps than the compared interpolation methods in the cases of sparse sampling density.展开更多
基金supported by the National Natural Science Foundation of China(No.82205271)the Chinese Medicine Research Project supported by the Hubei Administration of Traditional Chinese Medicine(No.ZY2025L183)the Graduate Innovation Projects of Hebei University of Chinese Medicine(No.XCXZZBS2024005).
文摘Artificial intelligence(AI)is a sophisticated technology that investigates and formulates theories,methods,techniques,and application systems designed to emulate,expand,and enhance human intelligence[1].In recent years,the rapid advancement of key AI technologies,including image recognition,machine learning,neural networks and robotics,has significantly propelled multidisciplinary integration and development within the medical field[2].The considerable potential of AI in the field of medicine,as evidenced by its formidable data processing and analytical capabilities,has been demonstrated in a number of ways.
基金The National Natural Science Foundation of China(No.61261007,61062005)the Key Program of Yunnan Natural Science Foundation(No.2013FA008)
文摘A novel spatial interpolation method based on integrated radial basis function artificial neural networks （IRBFANNs） is proposed to provide accurate and stable predictions of heavy metals concentrations in soil at un- sampled sites in a mountain region. The IRBFANNs hybridize the advantages of the artificial neural networks and the neural networks integration approach. Three experimental projects under different sampling densities are carried out to study the performance of the proposed IRBFANNs-based interpolation method. This novel method is compared with six peer spatial interpolation methods based on the root mean square error and visual evaluation of the distribution maps of Mn elements. The experimental results show that the proposed method performs better in accuracy and stability. Moreover, the proposed method can provide more details in the spatial distribution maps than the compared interpolation methods in the cases of sparse sampling density.
