LSTM-convolutional-BLSTM encoder-decoder network for minimum mean-square error approach to speech enhancement

Wang, Zeyu; Zhang, Tao; Shao, Yangyang; Ding, Biyun

doi:10.1016/j.apacoust.2020.107647

Cited by 40 publications

(20 citation statements)

References 20 publications

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“…LSTM is a kind of RNN, which can handle time series dependent events effectively [ 27 , 28 ]. Each LSTM unit contains an input gate, an output gate, and several forgetting gates.…”

Section: Research Materials and Methodsmentioning

confidence: 99%

Screening and functional prediction of differentially expressed genes in walnut endocarp during hardening period based on deep neural network under agricultural internet of things

Guo

et al. 2022

PLoS ONE

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The deep neural network is used to establish a neural network model to solve the problems of low accuracy and poor accuracy of traditional algorithms in screening differentially expressed genes and function prediction during the walnut endocarp hardening stage. The paper walnut is used as the research object to analyze the biological information of paper walnut. The changes of lignin deposition during endocarp hardening from 50 days to 90 days are observed by microscope. Then, the Convolutional Neural Network (CNN) and Long and Short-term Memory (LSTM) network model are adopted to construct an expression gene screening and function prediction model. Then, the transcriptome and proteome sequencing and biological information of walnut endocarp samples at 50, 57, 78, and 90 days after flowering are analyzed and taken as the training data set of the CNN + LSTM model. The experimental results demonstrate that the endocarp of paper walnut began to harden at 57 days, and the endocarp tissue on the hardened inner side also began to stain. This indicates that the endocarp hardened laterally from outside to inside. The screening and prediction results show that the CNN + LSTM model’s highest accuracy can reach 0.9264. The Accuracy, Precision, Recall, and F1-score of the CNN + LSTM model are better than the traditional machine learning algorithm. Moreover, the Receiver Operating Curve (ROC) area enclosed by the CNN + LSTM model and coordinate axis is the largest, and the Area Under Curve (AUC) value is 0.9796. The comparison of ROC and AUC proves that the CNN + LSTM model is better than the traditional algorithm for screening differentially expressed genes and function prediction in the walnut endocarp hardening stage. Using deep learning to predict expressed genes’ function accurately can reduce the breeding cost and significantly improve the yield and quality of crops. This research provides scientific guidance for the scientific breeding of paper walnut.

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“…LSTM is a kind of RNN, which can handle time series dependent events effectively [ 27 , 28 ]. Each LSTM unit contains an input gate, an output gate, and several forgetting gates.…”

Section: Research Materials and Methodsmentioning

confidence: 99%

Screening and functional prediction of differentially expressed genes in walnut endocarp during hardening period based on deep neural network under agricultural internet of things

Guo

et al. 2022

PLoS ONE

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“…e principle of accuracy priority is that when the forecast model is used for inventory demand forecasting, the result of the forecast during the forecast period is smaller than the actual value, and the accuracy is higher. is paper chooses the mean square error (MSE) as the indicator to measure [16]:…”

Section: Establishment Of a Combination Forecasting Model Ofmentioning

confidence: 99%

Optimal Allocation of Resources for the Integrated Medical Care and Elderly Care Model under Dynamic Monitoring

Jun-xia

2022

Scientific Programming

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Aiming at the problems of poor fairness of resource allocation and long time-consuming allocation of traditional methods, a method of resource optimization allocation under dynamic monitoring of medical care combined with elderly care mode is proposed. The integrated medical and elderly care model is analyzed, the role of dynamic monitoring in the optimal allocation of resources in the integrated medical and elderly care model is discussed, and a general model for optimal allocation of medical and elderly resources based on the analysis results is established. Based on the criterion of “minimum variance of estimation error,” a combined prediction model is established to obtain the combined prediction results. The investment objects of the optimal allocation of resources in the elderly care model are clustered in advance by using the clustering method, and the investment objects of the optimal allocation of resources in the elderly care model are comprehensively evaluated by using the principal component analysis method. According to the actual results of the comprehensive evaluation, the improved whale algorithm is used to allocate pension resources reasonably, and the fairness and applicability of the allocation results are analyzed experimentally. The results show that the method proposed in this paper has strong rationality and can effectively realize the rational distribution of pensions.

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“…And LSTM is used to realize the conversion of input data by constructing targeted modules. Compared with the RNN structure, the independent module structure in the LSTM framework is more complicated, and the number of adjustable parameters and threshold units is relatively large [29]. The special feature of LSTM lies in the additional setting of cell state, and a threepart structure of input gate, output gate and forget gate [30].…”

Section: Lstmmentioning

confidence: 99%

Effect Evaluation and Intelligent Prediction of Power Substation Project Considering New Energy

Wu¹,

Zou²,

Yao³

et al. 2022

Computer Modeling in Engineering &Amp; Sciences

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The evaluation of the implementation effect of the power substation project can find out the problems of the project more comprehensively, which has important practical significance for the further development of the power substation project. To ensure accuracy and real-time evaluation, this paper proposes a novel hybrid intelligent evaluation and prediction model based on improved TOPSIS and Long Short-Term Memory (LSTM) optimized by a Sperm Whale Algorithm (SWA). Firstly, under the background of considering the development of new energy, the influencing factors of power substation project implementation effect are analyzed from three aspects of technology, economy and society. Moreover, an evaluation model based on improved TOPSIS is constructed. Then, an intelligent prediction model based on SWA optimized LSTM is designed. Finally, the scientificity and accuracy of the proposed model are verified by empirical analysis, and the important factors affecting the implementation effect of power substation projects are pointed out.

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LSTM-convolutional-BLSTM encoder-decoder network for minimum mean-square error approach to speech enhancement

Cited by 40 publications

References 20 publications

Screening and functional prediction of differentially expressed genes in walnut endocarp during hardening period based on deep neural network under agricultural internet of things

Screening and functional prediction of differentially expressed genes in walnut endocarp during hardening period based on deep neural network under agricultural internet of things

Optimal Allocation of Resources for the Integrated Medical Care and Elderly Care Model under Dynamic Monitoring

Effect Evaluation and Intelligent Prediction of Power Substation Project Considering New Energy

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