Optoacoustic quantitative
            <i>in vitro</i>
            detection of diabetes mellitus involving the comprehensive impacts based on improved quantum particle swarm optimized wavelet neural network

Ren, Zhong; Liu, Tao; Xiong, Chengxin; Peng, Wenping; Wu, Junli; Liang, Gaoqiang; Sun, Bingheng

doi:10.1080/15599612.2023.2185714

Cited by 2 publications

(2 citation statements)

References 45 publications

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“…After 500 times training cycles for the training set samples, 125 randomly selected samples from the testing set were input into the trained 1DCNN-SAM-LSTM model. The results demonstrate that 1DCNN-SAM-LSTM achieved the superior performance with MSE C of 0.034317 mmol/L on the training set and MSE P of 0.14320 mmol/L on the testing set when NumberHeads is 32 and NumChannels of SAM module is 128, which is better than that of the previous results [ 17 , 20 ]. The clarke error grid graph of BGC for 125 testing set samples with the synthetical influences of various factors is depicted in Fig.…”

Section: Quantitative Predictionmentioning

confidence: 65%

“…Han [ 19 ] performed the noninvasive blood glucose detection based on near-infrared spectroscopy combined with linear PLSR and the nonlinear stacked auto-encoder deep neural network. Ren [ 20 ] proposed PAS combined with a kind of improved quantum particle swarm optimized wavelet neural network (QPSO-WNN) to quantitatively in vitro detection of diabetes. Under the optimal parameters of improved QPSO-WNN algorithm, the MSE of BGC reached 0.3088 mmol/L.…”

Section: Introductionmentioning

confidence: 99%

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Quantitative blood glucose detection influenced by various factors based on the fusion of photoacoustic temporal spectroscopy with deep convolutional neural networks

Xiong,

Ren,

Liu

2024

Biomed. Opt. Express

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In order to efficiently and accurately monitor blood glucose concentration (BGC) synthetically influenced by various factors, quantitative blood glucose in vitro detection was studied using photoacoustic temporal spectroscopy (PTS) combined with a fusion deep neural network (fDNN). Meanwhile, a photoacoustic detection system influenced by five factors was set up, and 625 time-resolved photoacoustic signals of rabbit blood were collected under different influencing factors.In view of the sequence property for temporal signals, a dimension convolutional neural network (1DCNN) was established to extract features containing BGC. Through the parameters optimization and adjusting, the mean square error (MSE) of BGC was 0.51001 mmol/L for 125 testing sets. Then, due to the long-term dependence on temporal signals, a long short-term memory (LSTM) module was connected to enhance the prediction accuracy of BGC. With the optimal LSTM layers, the MSE of BGC decreased to 0.32104 mmol/L. To further improve prediction accuracy, a self-attention mechanism (SAM) module was coupled into and formed an fDNN model, i.e., 1DCNN-SAM-LSTM. The fDNN model not only combines the advantages of temporal expansion of 1DCNN and data long-term memory of LSTM, but also focuses on the learning of more important features of BGC. Comparison results show that the fDNN model outperforms the other six models. The determination coefficient of BGC for the testing set was 0.990, and the MSE reached 0.1432 mmol/L. Results demonstrate that PTS combined with 1DCNN-SAM-LSTM ensures higher accuracy of BGC under the synthetical influence of various factors, as well as greatly enhances the detection efficiency.

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Section: Quantitative Predictionmentioning

confidence: 65%

Section: Introductionmentioning

confidence: 99%

Quantitative blood glucose detection influenced by various factors based on the fusion of photoacoustic temporal spectroscopy with deep convolutional neural networks

Xiong,

Ren,

Liu

2024

Biomed. Opt. Express

Self Cite

View full text Add to dashboard Cite

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Optimization of geometric parameters of hydraulic turbine runner in turbine mode based on ISMA and BPNN

Wang,

Chen,

Zhang

et al. 2023

Energy Science & Engineering

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The hydraulic turbine in turbine mode (TMHT) has significant advantages in residual energy recovery, but rapid optimization of its runner parameters has always been a challenge. To address this issue, the optimization algorithm ISMA‐BPNN is proposed based on the improved slime mold algorithm (ISMA) and back propagation neural network (BPNN). First, an efficiency characteristic neural network (ECNN) and a water head characteristic neural network (HCNN), which take the geometric parameters of the runner as input, and the efficiency and water head as outputs, respectively, are constructed by combining the orthogonal test‐based sample data, computational fluid dynamics (CFD), and BPNN. Then, the slime mold algorithm is improved and the optimal runner geometric parameters are obtained based on the ISMA, ECNN, and HCNN, to achieve rapid optimization of the TMHT runner. Finally, the CFD‐based numerical calculation accuracy is verified through real machine tests, and the feasibility of the ISMA‐BPNN‐based rapid optimization strategy for TMHT performance is further verified through CFD numerical simulation.

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Optoacoustic quantitative in vitro detection of diabetes mellitus involving the comprehensive impacts based on improved quantum particle swarm optimized wavelet neural network

Cited by 2 publications

References 45 publications

Quantitative blood glucose detection influenced by various factors based on the fusion of photoacoustic temporal spectroscopy with deep convolutional neural networks

Quantitative blood glucose detection influenced by various factors based on the fusion of photoacoustic temporal spectroscopy with deep convolutional neural networks

Optimization of geometric parameters of hydraulic turbine runner in turbine mode based on ISMA and BPNN

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