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Identification of Gravesa?? ophthalmology by laser-induced breakdown spectroscopy combined with machine learning method
摘要： Diagnosis of the Graves’ ophthalmology remains a significant challenge. We identified between Graves’ ophthalmology tissues and healthy controls by using laser-induced breakdown spectroscopy (LIBS) combined with machine learning method. In this work, the paraffin-embedded samples of the Graves’ ophthalmology were prepared for LIBS spectra acquisition. The metallic elements (Na, K, Al, Ca), non-metallic element (O) and molecular bands ((C-N), (C-O)) were selected for diagnosing Graves’ ophthalmology. The selected spectral lines were inputted into the supervised classification methods including linear discriminant analysis (LDA), support vector machine (SVM), k-nearest neighbor (kNN), and generalized regression neural network (GRNN), respectively. The results showed that the predicted accuracy rates of LDA, SVM, kNN, GRNN were 76.33%, 96.28%, 96.56%, and 96.33%, respectively. The sensitivity of four models were 75.89%, 93.78%, 96.78%, and 96.67%, respectively. The specificity of four models were 76.78%, 98.78%, 96.33%, and 96.00%, respectively. This demonstrated that LIBS assisted with a nonlinear model can be used to identify Graves’ ophthalmopathy with a higher rate of accuracy. The kNN had the best performance by comparing the three nonlinear models. Therefore, LIBS combined with machine learning method can be an effective way to discriminate Graves’ ophthalmology.
关键词： support vector machine (SVM),linear discriminant analysis (LDA),Graves’ ophthalmology,laser-induced breakdown spectroscopy (LIBS),k-nearest neighbor (kNN),generalized regression neural network (GRNN)
更新于2025-09-23 15:21:01
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Application of Generalized Regression Neural Network in Predicting the Performance of Solar Photovoltaic Thermal Water Collector
摘要： Solar photovoltaic thermal water collector (SPV/T-WC) is a hybrid device which converts power from the solar energy in to thermal and electrical simultaneously. The performance of such SPV/T-WC mainly depends on its electrical and thermal power output. Besides the performance of SPV/T-WC, is more sensitive to the transient nature of electrical and thermal power output. Thus a demand for predicting the performance variations in the SPV/T-WC is demand by users. Only limited performance prediction based research works are attempted in the performance prediction of the SPV/T-WC either numerically or by using cognitive models. In this study, two generalized regression neural network (GRNN) models are proposed to predict the transient performance variations in the SPV/T-WC. The two individual objectives of the ?rst and second model include the prediction of overall power output and the overall ef?ciency delivered by an SPV/T-WC system. Both the GRNN models proposed in this study consist of two inputs and single output. In order to train this GRNN model, real time experiments are conducted with stand-alone SPV/T-WC for four continuous days. Then based on such experimental data sets, GRNN models are trained, tested, and validated. The results predicted by the both GRNN models are in good agreement with the real time experimental results. The overall accuracy of the proposed GRNN models in predicting the performance is 95.36% and 96.22% respectively.
关键词： Solar,Water,Collector,Thermal,accuracy,Photovoltaic,GRNN,Prediction
更新于2025-09-23 15:21:01