Application of Dual-Channel Convolutional Neural Network Algorithm in Semantic Feature Analysis of English Text Big Data.

Hard disk drive (HDD) failure prediction is critical for data center maintenance. The conventional long short-term memory (LSTM) neural networks have been successfully used to predict the failure of HDD. However, the short degradation cycle of the disk leads to a severe imbalance in the ratio of healthy data to failure data, which degrades the performance of LSTM neural networks. Moreover, the complexity of LSTM neural networks is also very high. This paper proposes a disk failure prediction approach based on an intelligent attribute gated recurrent unit (GRU) neural network and TimeGAN adversarial network, the GRU neural network can adapt to the impact of long hard disk data sequences, while the TimeGAN can address the data imbalance problem. Experiments performed on Backblaze data demonstrate that our proposed approach achieves better performances in terms of failure detection rate than then conventional LSTM.

With the steep rise in the development of smart grids and the current advancement in developing measuring infrastructure, short term power consumption forecasting has recently gained increasing attention. In fact, the prediction of future power loads turns out to be a key issue to avoid energy wastage and to build effective power management strategies. Furthermore, energy consumption information can be considered historical time series data that are required to extract all meaningful knowledge and then forecast the future consumption. In this work, we aim to model and to compare three different machine learning algorithms in making a time series power forecast. The proposed models are the Long Short-Term Memory (LSTM), the Gated Recurrent Unit (GRU) and the Drop-GRU. We are going to use the power consumption data as our time series dataset and make predictions accordingly. The LSTM neural network has been favored in this work to predict the future load consumption and prevent consumption peaks. To provide a comprehensive evaluation of this method, we have performed several experiments using real data power consumption in some French cities. Experimental results on various time horizons show that the LSTM model produces a better result than the GRU and the Drop-GRU forecasting methods. There are fewer prediction errors and its precision is finer. Therefore, these predictions based on the LSTM method will allow us to make decisions in advance and trigger load shedding in cases where consumption exceeds the authorized threshold. This will have a significant impact on planning the power quality and the maintenance of power equipment.

Solar desalination is one of the renewable energy techniques by which freshwater can be obtained economically. Solar desalination experiments are time and resource-consuming methods; hence there is a need for a robust system to identify the serviceability of the solar still in a specific region. The objective of the present study is to develop a forecasting model using artificial neural networks to predict freshwater productivity. Specifically, the study aims to compare the accuracy of Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural networks in forecasting the productivity of Conventional Solar Stills (CSS) and Solar Stills with Phase Change Material (SSPCM). Additionally, the research seeks to analyze the impact of advanced materials, such as phase change materials (PCM) and pin fins, on solar still productivity; determine which neural network model provides more accurate and reliable forecasts for different solar still designs; and contribute to the optimization of solar still implementations by developing predictive models applicable to diverse geographical regions. The current investigation involved analysing the experimental outcomes of a solar still that employed phase change material (PCM) and pin fins. Palmitic acid was implemented as the energy storage material and was placed beneath the absorber plate. The neural network model was trained and validated using time-series solar still experimental data. Different statistical measures were utilised to evaluate the accuracy of Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU). The results indicate that the freshwater productivity forecasted by LSTM exhibited greater accuracy than GRU. Specifically, the coefficient of determination values for LSTM were 0.96 and 0.98 for the CSS and SSPCM, respectively, which were higher than the corresponding values for GRU.

This paper proposes the application of artificial intelligence to forecast the generation capacity of wind power plants by processing data through noise reduction and filtering. It subsequently employs Long-Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural networks for training, testing, and evaluation. Processing the initial data will help minimize noise and reduce the data space. The study focuses on preprocessing methods and selecting the appropriate neural network between LSTM and GRU. The initial data processing will assess the similarity through the Spearman rank correlation coefficient. The data used in the paper is taken from local wind turbines. The processed data will be input into the neural network for evaluation based on Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Percent Error (PE), and training time. The entire network simulation and evaluation process is performed using MATLAB software. The simulation results show the feasibility and suitability of the GRU network model combined with noise filtering methods, bringing high accuracy and less training time compared to the LSTM network. Specifically, the analysis contrasting the GRU network with the optimized dataset and the LSTM with the unprocessed dataset is more effective than a difference in RMSE of 15.592 and MAPE of 611.047%. Moreover, the time difference between the GRU and LSTM networks with the same dataset has a much earlier time difference from 6 to 28 seconds.

Molecular dynamics (MD) simulations are a widely used technique in modeling complex nanoscale interactions of atoms and molecules. These simulations can provide detailed insight into how molecules behave under certain environmental conditions. This work explores a machine learning (ML) solution to predicting long-term properties of SARS-CoV-2 spike glycoproteins (S-protein) through the analysis of its nanosecond backbone RMSD (root-mean-square deviation) MD simulation data at varying temperatures. The simulation data were denoised with fast Fourier transforms. The performance of the models was measured by evaluating their mean squared error (MSE) accuracy scores in recurrent forecasts for long-term predictions. The models evaluated include k-nearest neighbors (kNN) regression models, as well as GRU (gated recurrent unit) neural networks and LSTM (long short-term memory) autoencoder models. Results demonstrated that the kNN model achieved the greatest accuracy in forecasts with MSE scores over around 0.01 nm less than those of the GRU model and the LSTM autoencoder. Furthermore, it demonstrated that the kNN model accuracy increases with data size but can still forecast relatively well when trained on small amounts of data, having achieved MSE scores of around 0.02 nm when trained on 10,000 ns of simulation data. This study provides valuable information on the feasibility of accelerating the MD simulation process through training and predicting supervised ML models, which is particularly applicable in time-sensitive studies.Graphic abstractSARS-CoV-2 spike glycoprotein molecular dynamics simulation. Extraction and denoising of backbone RMSD data. Evaluation of k-nearest neighbors regression, GRU neural network, and LSTM autoencoder models in recurrent forecasting for long-term property predictions.

The constitutive modelling of granular soils has been a long-standing research subject in geotechnical engineering, and machine learning (ML) has recently emerged as a promising tool for achieving this goal. This paper proposes two recurrent neural networks, namely, the Gated Recurrent Unit Neural Network (GRU-NN) and the Long Short-Term Memory Neural Network (LSTM-NN), which utilize input parameters such as the initial void ratio, initial fabric anisotropy, uniformity coefficient, mean particle size, and confining pressure to establish the high-dimensional relationships of granular soils from micro to macro levels subjected to triaxial shearing. The research methodology consists of several steps. Firstly, 200 numerical triaxial tests on idealized granular soils comprising polydisperse spherical particles are performed using the discrete element method (DEM) simulation to generate datasets and to train and test the proposed neural networks. Secondly, LSTM-NN and GRU-NN are constructed and trained, and their prediction performance is evaluated by the mean absolute percentage error (MAPE) and R-square against the DEM-based datasets. The extremely low error values obtained by both LSTM-NN and GRU-NN indicate their outstanding capability in predicting the constitutive behaviour of idealized granular soils. Finally, the trained ML-based models are applied to predict the constitutive behaviour of a miniature glass bead sample subjected to triaxial shearing with in situ micro-CT, as well as to two extrapolated test sets with different initial parameters. The results show that both methods perform well in capturing the mechanical responses of the idealized granular soils.

Accurate ROP (rate of penetration) prediction contributes to better production task planning, ensuring efficient production line operation, and reducing production costs. ROP prediction is influenced by multiple factors, making accurate prediction challenging. Current research primarily relies on historical data for training and modeling, lacking methods for real-time ROP prediction. This paper introduces a GRU-Informer model for real-time ROP prediction. The model employs GRU (Gated Recurrent Unit) neural networks at the lower level to capture short-term correlations in drilling parameters and uses the Informer model at the top to address long-term dependencies among drilling parameters. Thus, the GRU-Informer can capture both short-term and long-term time dependencies, providing better ROP predictions. This paper constructs a dataset using historical data from a southwestern Chinese oil field for experimentation. RMSE (Root Mean Square Error), MAE (mean absolute error) and R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${R}^{2}$$\\end{document} (Coefficient of Determination) are employed as evaluation metrics for the model. Experimental results demonstrate that the GRU-Informer outperforms traditional recurrent neural networks like LSTM (Long Short-Term Memory), GRU neural networks and Informer in real-time ROP prediction, indicating its practical value.

To predict the areas of Oncomelania hupensis snail spread in Anhui Province from 1977 to 2023 using machine learning models, and to compare the effectiveness of different machine learning models for prediction of areas of O. hupensis snail spread, so as to provide insights into investigating the trends in areas of O. hupensis snail spread. Data pertaining to O. hupensis snail spread in Anhui Province from 1977 to 2023 were collected and a database was created. Five machine learning models were created using the software Matlab R2019b, including support vector regression (SVR), nonlinear autoregressive (NAR) neural network, back propagation (BP) neural network, gated recurrent unit (GRU) neural network and long short-term memory (LSTM) neural network models, and the model fitting effect was evaluated with mean absolute error (MAE), root mean squared error (RMSE) and coefficient of determination (R2). Following model training, the areas of O. hupensis snail spread were predicted in Anhui Province from 2024 to 2030. The cumulative areas of O. hupensis snail spread were 40 241.32 hm2 in Anhui Province from 1977 to 2023, and the area of O. hupensis snail spread varied greatly among years, with a periodic peak every 4 to 6 years. The fitting curves of SVR, NAR neural network, BP neural network, GRU neural network and LSTM neural network models were increasingly closer to the real curves for areas of O. hupensis snail spread in Anhui Province. The trends in areas of O. hupensis snail spread in Anhui Province from 2024 to 2030 appeared approximately "M"-shaped curves by SVR and NAR neural network models, approximately "W"-shaped curves by BP and GRU neural network models, and a unimodal conical curve by the LSTM neural network model. The LSTM neural network model had the best effect for predicting areas of O. hupensis snail spread in Anhui Province, with the RMSE of 1 277 480, MAE of 797 422 and R2 of 0.978 9, respectively. Among the five models, The LSTM neural network model has a high efficiency for predicting areas of O. hupensis snail spread in Anhui Province, which may serve as a tool to investigate the trends in areas of O. hupensis snail spread.

The research aims to compare the effectiveness of Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural networks in predicting hyperglycemic and hypoglycemic events in diabetes patients using wearable sensor data, to identify the more effective model for near-term blood glucose forecasting. Effective prediction of blood glucose levels is crucial for Type 1 diabetes management, as extreme glucose levels can cause fatal hyperglycemic and hypoglycemic incidents. Choosing between LSTM and GRU models can significantly impact predictive accuracy and patient outcomes. The OpenD1NAMO public dataset was used as a training set to compare the efficacies of LSTM and GRU models in predicting near-term glucose levels. Artificial features such as lagged features and rolling averaged lagged features were created to facilitate glucose forecasting comparisons. Various permutations of hyperparameters were then compared to find the most effective set for the provided sensor data. GRU and LSTM models trained on a portion of the preprocessed dataset were compiled and the average of the minimum MSEs per epoch were compared. On average, the LSTM model was 53.5% more accurate than the GRU model for the dataset provided, forecasting blood glucose values with a 15-minute interval. The minimum average MSE of the LSTM model was 2.043 mmol2/L2 while the minimum average MSE of the GRU model was 3.137 mmol2/L2. Given the models’ performances on the dataset, it suggests that LSTM models will likely be more accurate than GRU models when predicting near-term blood glucose levels using live wearable sensor data.

A comparative study on long short-term memory and gated recurrent unit neural networks in fault diagnosis for chemical processes using visualization

Intelligent hybrid approaches utilizing time series forecasting error for enhanced structural health monitoring

In order to accurately predict the oil temperature of the transformer top layer, based on the traditional neural network algorithm, this paper proposes a transformer top oil temperature prediction method based on VMD (Variational Modal Decomposition) and GRU (Gated Recurrent Unit) neural network. Traditional prediction methods have less consideration of the interaction between different trend items in the oil temperature series, and the oil temperature prediction problem is a time series problem, so the historical oil temperature at the previous time also needs to be considered. In response to the above problems, VMD is introduced to decompose the original sequence into different modalities, and then use the superiority and efficiency of the GRU neural network in timing analysis to establish an oil temperature prediction model, which can avoid the gradient disappearance problem and ensure Prediction accuracy. The results of the calculation examples show that, compared with the traditional prediction method, this method has higher prediction accuracy and higher efficiency than the traditional neural network method.

With the rapid development of smart grid, to solve the power enterprises’ requirement in short-term load forecasting, this paper proposes a short-term electrical load forecasting method based on stacked auto-encoding and GRU (Gated recurrent unit) neural network. Firstly, the method input historical data which contains power load, weather information, and holiday information, and use auto-encoding to compress the historical data; and then, the multi-layer GRU is used to construct the model to predict the power load. The experiment results show, compared with traditional models, the proposed method can effectively predict the daily variation of power load and have lower prediction error and higher precision.

Artificial neural network is widely used in the financial time series, but Long short-term memory (LSTM) neural network is rarely used in the futures market in China. In this paper, the LSTM neural network is studied by using futures data. The daily trading data of four groups of futures such as silver, copper, lithium and coking coal from December 2014 to December 2018 are used as the training object to make short-term prediction of the closing price. By comparing the Back Propagation (BP) neural network, general multi-layer LSTM neural network, and using the attention mechanism optimization LSTM contrast test, the result of the experiment shows that the futures price trend forecast time sequence, attention mechanism to promote significant effect of time sequence, and LSTM combined effect, by adjusting the parameters setting, using the improved LSTM neural network for time series prediction accuracy is higher, better generalization ability.

The field of music generation has witnessed remarkable advancements in recent years, thanks to the emergence of deep learning techniques. In this paper, we present a novel music generation system utilizing a character level Recurrent Neural Network (char RNN) empowered by a hybrid architecture of Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) cells. Our system is trained on a comprehensive dataset consisting of 340 tunes represented in ABC notation, a text based format for musical compositions. By predicting the subsequent character in a sequence of ABC notation, our model adeptly generates new and captivating melodies. Extensive evaluations are conducted, employing a variety of metrics, to assess the system’s performance, revealing its ability to generate coherent and musically plausible music. Furthermore, we demonstrate the versatility of our proposed system, highlighting its potential applications in music composition, accompaniment, and real time improvisation. The results substantiate the effectiveness of employing char RNN with LSTM GRU cells for music generation, thereby opening up intriguing avenues for future research in this evolving field.