To improve the accuracy of predicting non-invasive blood glucose concentration in the near-infrared spectrum, we utilized the Particle Swarm Optimization (PSO) algorithm to optimize hyperparameters for the Multi-Kerne...To improve the accuracy of predicting non-invasive blood glucose concentration in the near-infrared spectrum, we utilized the Particle Swarm Optimization (PSO) algorithm to optimize hyperparameters for the Multi-Kernel Learning Support Vector Machine (MKL-SVR). With these optimized hyperparameters, we established a non-invasive blood glucose regression model, referred to as the PSO-MKL-SVR model. Subsequently, we conducted a comparative analysis between the PSO-MKL-SVR model and the PSO-SVR model. In a dataset comprising ten volunteers, the PSO-MKL-SVR model exhibited significant precision improvements, including a 16.03% reduction in Mean Square Error and a 0.29% increase in the Squared Correlation Coefficient. Moreover, there was a 0.14% higher probability of the Clark’s Error Grid Analysis falling within Zone A. Additionally, the PSO-MKL-SVR model demonstrated a faster operational speed compared to the PSO-SVR model.展开更多
Aiming at the problem of open set voiceprint recognition, this paper proposes an adaptive threshold algorithm based on OTSU and deep learning. The bottleneck technology of open set voiceprint recognition lies in the c...Aiming at the problem of open set voiceprint recognition, this paper proposes an adaptive threshold algorithm based on OTSU and deep learning. The bottleneck technology of open set voiceprint recognition lies in the calculation of similarity values and thresholds of speakers inside and outside the set. This paper combines deep learning and machine learning methods, and uses a Deep Belief Network stacked with three layers of Restricted Boltzmann Machines to extract deep voice features from basic acoustic features. And by training the Gaussian Mixture Model, this paper calculates the similarity value of the feature, and further determines the threshold of the similarity value of the feature through OTSU. After experimental testing, the algorithm in this paper has a false rejection rate of 3.00% for specific speakers, a false acceptance rate of 0.35% for internal speakers, and a false acceptance rate of 0 for external speakers. This improves the accuracy of traditional methods in open set voiceprint recognition. This proves that the method is feasible and good recognition effect.展开更多
文摘To improve the accuracy of predicting non-invasive blood glucose concentration in the near-infrared spectrum, we utilized the Particle Swarm Optimization (PSO) algorithm to optimize hyperparameters for the Multi-Kernel Learning Support Vector Machine (MKL-SVR). With these optimized hyperparameters, we established a non-invasive blood glucose regression model, referred to as the PSO-MKL-SVR model. Subsequently, we conducted a comparative analysis between the PSO-MKL-SVR model and the PSO-SVR model. In a dataset comprising ten volunteers, the PSO-MKL-SVR model exhibited significant precision improvements, including a 16.03% reduction in Mean Square Error and a 0.29% increase in the Squared Correlation Coefficient. Moreover, there was a 0.14% higher probability of the Clark’s Error Grid Analysis falling within Zone A. Additionally, the PSO-MKL-SVR model demonstrated a faster operational speed compared to the PSO-SVR model.
文摘Aiming at the problem of open set voiceprint recognition, this paper proposes an adaptive threshold algorithm based on OTSU and deep learning. The bottleneck technology of open set voiceprint recognition lies in the calculation of similarity values and thresholds of speakers inside and outside the set. This paper combines deep learning and machine learning methods, and uses a Deep Belief Network stacked with three layers of Restricted Boltzmann Machines to extract deep voice features from basic acoustic features. And by training the Gaussian Mixture Model, this paper calculates the similarity value of the feature, and further determines the threshold of the similarity value of the feature through OTSU. After experimental testing, the algorithm in this paper has a false rejection rate of 3.00% for specific speakers, a false acceptance rate of 0.35% for internal speakers, and a false acceptance rate of 0 for external speakers. This improves the accuracy of traditional methods in open set voiceprint recognition. This proves that the method is feasible and good recognition effect.
