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1
Hong, Binjie, Zhijie Yan, Yingxi Chen, and Xiaobo-Jin. "Long Memory Gated Recurrent Unit for Time Series Classification." Journal of Physics: Conference Series 2278, no. 1 (2022): 012017. http://dx.doi.org/10.1088/1742-6596/2278/1/012017.
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Abstract Time series analysis is an important and challenging problem in data mining, where time series is a class of temporal data objects. In the classification task, the label is dependent on the features from the last moments. Due to the time dependency, the recurrent neural networks, as one of the prevalent learning-based architectures, take advantage of the relation among history data. The Long Short-Term Memory Network (LSTM) and Gated Recurrent Unit (GRU) are two popular artificial recurrent neural networks used in the field of deep learning. LSTM designed a gate-like method to control the short and long historical information, and GRU simplified those gates to obtain more efficient training. In our work, we propose a new model called as Long Memory Gated Recurrent Unit (LMGRU) based on such two remarkable models, where the reset gate is introduced to reset the stored value of the cell in Long Short-Term Memory (LSTM) model but the forget gate and the input gate are omitted. The experimental results on several time series benchmarks show that LMGRU achieves better effectiveness and efficiency than LSTM and GRU.
2
Arfianti, Unix Izyah, Dian Candra Rini Novitasari, Nanang Widodo, Moh Hafiyusholeh, and Wika Dianita Utami. "Sunspot Number Prediction Using Gated Recurrent Unit (GRU) Algorithm." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 15, no. 2 (2021): 141. http://dx.doi.org/10.22146/ijccs.63676.
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Sunspot is an area on photosphere layer which is dark-colored. Sunspot is very important to be researched because sunspot is affected by sunspot numbers, which present the level of solar activity. This research was conducted to make prediction on sunspot numbers using Gated Recurrent Unit (GRU) algorithm. The work principle of GRU is similar to Long short-term Memory (LSTM) method: the information from the previous memory is processed through two gates, that is update gate and reset gate, then the output generated will be input for the next process. The purpose of predicting sunspot numbers was to find out the information of sunspot numbers in the future, so that if there is a significant increase in sunspot numbers, it can inform other physical consequences that may be caused. The data used was the data of monthly sunspot numbers obtained from SILSO website. The data division and parameters used were based on the results of the trials resulted in the smallest MAPE value. The smallest MAPE value obtained from the prediction was 7.171% with 70% training data, 30% testing data, 150 hidden layer, 32 batch size, 100 learning rate drop.
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Subair, Hilma, R. Pangayar Selvi, R. Vasanthi, S. Kokilavani, and V. Karthick. "Minimum Temperature Forecasting Using Gated Recurrent Unit." International Journal of Environment and Climate Change 13, no. 9 (2023): 2681–88. http://dx.doi.org/10.9734/ijecc/2023/v13i92499.
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Aim: To forecast the monthly average Minimum Temperature (ºC) in Coimbatore district.
 Study Design: Gated Recurrent Unit (GRU) has been employed to forecast the Minimum Temperature.
 Place and Duration of Study: Time series data for average month wise Minimum Temperature from January 1982 to September 2022 was collected from Agro Climate Research Centre, TNAU for Coimbatore District.
 Methodology: GRU which belongs to the field of deep learning has been employed to anticipate the average monthly Minimum Temperature by analyzing time series data from January 1982 to September 2022 in the district of Coimbatore. The model was trained using data from 1982 January to 2019 December and tested on data from 2020 January to 2022 September. After training and testing the algorithm was deployed to forecast Minimum Temperature for the lead time ahead.
 Results: The GRU model generated RMSE and MAE scores of 0.694ºC and 0.523ºC, respectively, for Minimum Temperature. GRU model had a Willmott’s Index of Agreement (WI) value as 0.943 that is very close to 1. This demonstrates the effectiveness of the model built to effectively predict the Minimum Temperature. The study's evaluation of the RMSE, MAE, and Willmott Index value made it readily evident that the GRU model performed quite accurately for forecasting Minimum Temperature. Gated Recurrent Unit algorithm was used to forecast the Minimum Temperature from October 2022 till December 2023 that is for the next 15 months.
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Achmad, Rizkial, Yokelin Tokoro, Jusuf Haurissa, and Andik Wijanarko. "Recurrent Neural Network-Gated Recurrent Unit for Indonesia-Sentani Papua Machine Translation." Journal of Information Systems and Informatics 5, no. 4 (2023): 1449–60. http://dx.doi.org/10.51519/journalisi.v5i4.597.
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The Papuan Sentani language is spoken in the city of Jayapura, Papua. The law states the need to preserve regional languages. One of them is by building an Indonesian-Sentani Papua translation machine. The problem is how to build a translation machine and what model to choose in doing so. The model chosen is Recurrent Neural Network – Gated Recurrent Units (RNN-GRU) which has been widely used to build regional languages in Indonesia. The method used is an experiment starting from creating a parallel corpus, followed by corpus training using the RNN-GRU model, and the final step is conducting an evaluation using Bilingual Evaluation Understudy (BLEU) to find out the score. The parallel corpus used contains 281 sentences, each sentence has an average length of 8 words. The training time required is 3 hours without using a GPU. The result of this research was that a fairly good BLEU score was obtained, namely 35.3, which means that the RNN-GRU model and parallel corpus produced sufficient translation quality and could still be improved.
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Annisa, Darmawahyuni, Nurmaini Siti, Naufal Rachmatullah Muhammad, Firdaus Firdaus, and Tutuko Bambang. "Unidirectional-bidirectional recurrent networks for cardiac disorders classification." TELKOMNIKA (Telecommunication, Computing, Electronics and Control) 19, no. 3 (2021): 902–10. https://doi.org/10.12928/telkomnika.v19i3.18876.
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The deep learning approach of supervised recurrent network classifiers model, i.e., recurrent neural networks (RNNs), long short-term memory (LSTM), and gated recurrent units (GRUs) are used in this study. The unidirectional and bidirectional for each cardiac disorder (CDs) class is also compared. Comparing both phases is needed to figure out the optimum phase and the best model performance for ECG using the Physionet dataset to classify five classes of CDs with 15 leads ECG signals. The result shows that the bidirectional RNNs method produces better results than the unidirectional method. In contrast to RNNs, the unidirectional LSTM and GRU outperformed the bidirectional phase. The best recurrent network classifier performance is unidirectional GRU with average accuracy, sensitivity, specificity, precision, and F1-score of 98.50%, 95.54%, 98.42%, 89.93%, 92.31%, respectively. Overall, deep learning is a promising improved method for ECG classification.
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Vinod Prakash and Dharmender Kumar. "A Modified Gated Recurrent Unit Approach for Epileptic Electroencephalography Classification." Journal of Information and Communication Technology 22, no. 4 (2023): 587–617. http://dx.doi.org/10.32890/jict2023.22.4.3.
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Epilepsy is one of the most severe non-communicable brain disorders associated with sudden attacks. Electroencephalography (EEG), a non-invasive technique, records brain activities, and these recordings are routinely used for the clinical evaluation of epilepsy. EEG signal analysis for seizure identification relies on expert manual examination, which is labour-intensive, time-consuming, and prone to human error. To overcome these limitations, researchers have proposed machine learning and deep learning approaches. Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) have shown significant results in automating seizure prediction, but due to complex gated mechanisms and the storage of excessive redundant information, these approaches face slow convergence and a low learning rate. The proposed modified GRU approach includes an improved update gate unit that adjusts the update gate based on the output of the reset gate. By decreasing the amount of superfluous data in the reset gate, convergence is speeded, which improves both learning efficiency and the accuracy of epilepsy seizure prediction. The performance of the proposed approach is verified on a publicly available epileptic EEG dataset collected from the University of California, Irvine machine learning repository (UCI) in terms of performance metrics such as accuracy, precision, recall, and F1 score when it comes to diagnosing epileptic seizures. The proposed modified GRU has obtained 98.84% accuracy, 96.9% precision, 97.1 recall, and 97% F1 score. The performance results are significant because they could enhance the diagnosis and treatment of neurological disorders, leading to better patient outcomes.
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Jeong, Myeong-Hun, Tae-Young Lee, Seung-Bae Jeon, and Minkyo Youm. "Highway Speed Prediction Using Gated Recurrent Unit Neural Networks." Applied Sciences 11, no. 7 (2021): 3059. http://dx.doi.org/10.3390/app11073059.
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Movement analytics and mobility insights play a crucial role in urban planning and transportation management. The plethora of mobility data sources, such as GPS trajectories, poses new challenges and opportunities for understanding and predicting movement patterns. In this study, we predict highway speed using a gated recurrent unit (GRU) neural network. Based on statistical models, previous approaches suffer from the inherited features of traffic data, such as nonlinear problems. The proposed method predicts highway speed based on the GRU method after training on digital tachograph data (DTG). The DTG data were recorded in one month, giving approximately 300 million records. These data included the velocity and locations of vehicles on the highway. Experimental results demonstrate that the GRU-based deep learning approach outperformed the state-of-the-art alternatives, the autoregressive integrated moving average model, and the long short-term neural network (LSTM) model, in terms of prediction accuracy. Further, the computational cost of the GRU model was lower than that of the LSTM. The proposed method can be applied to traffic prediction and intelligent transportation systems.
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Dutta, Aniruddha, Saket Kumar, and Meheli Basu. "A Gated Recurrent Unit Approach to Bitcoin Price Prediction." Journal of Risk and Financial Management 13, no. 2 (2020): 23. http://dx.doi.org/10.3390/jrfm13020023.
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In today’s era of big data, deep learning and artificial intelligence have formed the backbone for cryptocurrency portfolio optimization. Researchers have investigated various state of the art machine learning models to predict Bitcoin price and volatility. Machine learning models like recurrent neural network (RNN) and long short-term memory (LSTM) have been shown to perform better than traditional time series models in cryptocurrency price prediction. However, very few studies have applied sequence models with robust feature engineering to predict future pricing. In this study, we investigate a framework with a set of advanced machine learning forecasting methods with a fixed set of exogenous and endogenous factors to predict daily Bitcoin prices. We study and compare different approaches using the root mean squared error (RMSE). Experimental results show that the gated recurring unit (GRU) model with recurrent dropout performs better than popular existing models. We also show that simple trading strategies, when implemented with our proposed GRU model and with proper learning, can lead to financial gain.
9
Zainuddin, Z., Akhir E. A. P., and M. H. Hasan. "Predicting machine failure using recurrent neural network-gated recurrent unit (RNN-GRU) through time series data." Bulletin of Electrical Engineering and Informatics 10, no. 2 (2021): 870~878. https://doi.org/10.11591/eei.v10i2.2036.
Full textAbstract:
Time series data often involves big size environment that lead to high dimensionality problem. Many industries are generating time series data that continuously update each second. The arising of machine learning may help in managing the data. It can forecast future instance while handling large data issues. Forecasting is related to predicting task of an upcoming event to avoid any circumstances happen in current environment. It helps those sectors such as production to foresee the state of machine in line with saving the cost from sudden breakdown as unplanned machine failure can disrupt the operation and loss up to millions. Thus, this paper offers a deep learning algorithm named recurrent neural network-gated recurrent unit (RNN-GRU) to forecast the state of machines producing the time series data in an oil and gas sector. RNN-GRU is an affiliation of recurrent neural network (RNN) that can control consecutive data due to the existence of update and reset gates. The gates decided on the necessary information to be kept in the memory. RNN-GRU is a simpler structure of long short-term memory (RNN-LSTM) with 87% of accuracy on prediction.
10
Lu, Yi-Wei, Chia-Yu Hsu, and Kuang-Chieh Huang. "An Autoencoder Gated Recurrent Unit for Remaining Useful Life Prediction." Processes 8, no. 9 (2020): 1155. http://dx.doi.org/10.3390/pr8091155.
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With the development of smart manufacturing, in order to detect abnormal conditions of the equipment, a large number of sensors have been used to record the variables associated with production equipment. This study focuses on the prediction of Remaining Useful Life (RUL). RUL prediction is part of predictive maintenance, which uses the development trend of the machine to predict when the machine will malfunction. High accuracy of RUL prediction not only reduces the consumption of manpower and materials, but also reduces the need for future maintenance. This study focuses on detecting faults as early as possible, before the machine needs to be replaced or repaired, to ensure the reliability of the system. It is difficult to extract meaningful features from sensor data directly. This study proposes a model based on an Autoencoder Gated Recurrent Unit (AE-GRU), in which the Autoencoder (AE) extracts the important features from the raw data and the Gated Recurrent Unit (GRU) selects the information from the sequences to forecast RUL. To evaluate the performance of the proposed AE-GRU model, an aircraft turbofan engine degradation simulation dataset provided by NASA was used and a comparison made of different recurrent neural networks. The results demonstrate that the AE-GRU is better than other recurrent neural networks, such as Long Short-Term Memory (LSTM) and GRU.
11
Wibawa, Aji Prasetya, Alfiansyah Putra Pertama Triono, Andien Khansa’a Iffat Paramarta, et al. "Gated Recurrent Unit (GRU) for Forecasting Hourly Energy Fluctuations." Frontier Energy System and Power Engineering 5, no. 1 (2024): 16. http://dx.doi.org/10.17977/um049v5i1p16-25.
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In the current digital era, energy use undeniably supports economic growth, increases social welfare, and encourages technological progress. Energy-related information is often presented in complex time series data, such as energy consumption data per hour or in seasonal patterns. Deep learning models are used to analyze the data. The right choice of normalization method has great potential to improve the performance of deep learning models significantly. Deep learning models generally use several normalization methods, including min-max and z-score. In this research, the deep learning model chosen is Gated Recurrent Unit (GRU) because the computational load on GRU is lighter, so it doesn't require too much memory. In addition, the GRU data is easier to train, so that it can save training time. This research phase adopts the CRISP-DM methodology in data mining as a solution commonly used in business and research. This methodology involves six stages: Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment. In this research, the model was obtained using five attribute selection, which applied 2 normalization methods: min-max and z-score. With this normalization, the GRU model produces the best MAPE of 3.9331%, RMSE of 0.9022, and R2 of 0.9022. However, when using z-score normalization, the model performance decreases with MAPE of 10.4332%, RMSE of 0.7602, and R2 of 0.4213. Overall, min-max normalization provides better performance in multivariate time series data analysis.
12
Liu, Xiao, Rongxi Zhou, Daifeng Qi, and Yahui Xiong. "A Novel Methodology for Credit Spread Prediction: Depth-Gated Recurrent Neural Network with Self-Attention Mechanism." Mathematical Problems in Engineering 2022 (August 9, 2022): 1–12. http://dx.doi.org/10.1155/2022/2557865.
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This paper develops a depth-gated recurrent neural network (DGRNN) with self-attention mechanism (SAM) based on long-short-term memory (LSTM)\gated recurrent unit (GRU) \Just Another NETwork (JANET) neural network to improve the accuracy of credit spread prediction. The empirical results of the U.S. bond market indicate that the DGRNN model is more effective than traditional machine learning methods. Besides, we discovered that the Depth-JANET model with one gated unit performs better than Depth-GRU and Depth-LSTM models with more gated units. Furthermore, comparative analyses reveal that SAM significantly improves DGRNN’s prediction performance. The results show that Depth-JANET neural network with SAM outperforms most other methods in credit spread prediction.
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Han, Shipeng, Zhen Meng, Xingcheng Zhang, and Yuepeng Yan. "Hybrid Deep Recurrent Neural Networks for Noise Reduction of MEMS-IMU with Static and Dynamic Conditions." Micromachines 12, no. 2 (2021): 214. http://dx.doi.org/10.3390/mi12020214.
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Micro-electro-mechanical system inertial measurement unit (MEMS-IMU), a core component in many navigation systems, directly determines the accuracy of inertial navigation system; however, MEMS-IMU system is often affected by various factors such as environmental noise, electronic noise, mechanical noise and manufacturing error. These can seriously affect the application of MEMS-IMU used in different fields. Focus has been on MEMS gyro since it is an essential and, yet, complex sensor in MEMS-IMU which is very sensitive to noises and errors from the random sources. In this study, recurrent neural networks are hybridized in four different ways for noise reduction and accuracy improvement in MEMS gyro. These are two-layer homogenous recurrent networks built on long short term memory (LSTM-LSTM) and gated recurrent unit (GRU-GRU), respectively; and another two-layer but heterogeneous deep networks built on long short term memory-gated recurrent unit (LSTM-GRU) and a gated recurrent unit-long short term memory (GRU-LSTM). Practical implementation with static and dynamic experiments was carried out for a custom MEMS-IMU to validate the proposed networks, and the results show that GRU-LSTM seems to be overfitting large amount data testing for three-dimensional axis gyro in the static test. However, for X-axis and Y-axis gyro, LSTM-GRU had the best noise reduction effect with over 90% improvement in the three axes. For Z-axis gyroscope, LSTM-GRU performed better than LSTM-LSTM and GRU-GRU in quantization noise and angular random walk, while LSTM-LSTM shows better improvement than both GRU-GRU and LSTM-GRU networks in terms of zero bias stability. In the dynamic experiments, the Hilbert spectrum carried out revealed that time-frequency energy of the LSTM-LSTM, GRU-GRU, and GRU-LSTM denoising are higher compared to LSTM-GRU in terms of the whole frequency domain. Similarly, Allan variance analysis also shows that LSTM-GRU has a better denoising effect than the other networks in the dynamic experiments. Overall, the experimental results demonstrate the effectiveness of deep learning algorithms in MEMS gyro noise reduction, among which LSTM-GRU network shows the best noise reduction effect and great potential for application in the MEMS gyroscope area.
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Z., Zainuddin, P. Akhir E. A., and Hasan M. H. "Predicting machine failure using recurrent neural network-gated recurrent unit (RNN-GRU) through time series data." Bulletin of Electrical Engineering and Informatics 10, no. 2 (2021): 870–78. http://dx.doi.org/10.11591/eei.v10i2.2036.
Full textAbstract:
Time series data often involves big size environment that lead to high dimensionality problem. Many industries are generating time series data that continuously update each second. The arising of machine learning may help in managing the data. It can forecast future instance while handling large data issues. Forecasting is related to predicting task of an upcoming event to avoid any circumstances happen in current environment. It helps those sectors such as production to foresee the state of machine in line with saving the cost from sudden breakdown as unplanned machine failure can disrupt the operation and loss up to millions. Thus, this paper offers a deep learning algorithm named recurrent neural network-gated recurrent unit (RNN-GRU) to forecast the state of machines producing the time series data in an oil and gas sector. RNN-GRU is an affiliation of recurrent neural network (RNN) that can control consecutive data due to the existence of update and reset gates. The gates decided on the necessary information to be kept in the memory. RNN-GRU is a simpler structure of long short-term memory (RNN-LSTM) with 87% of accuracy on prediction.
15
Wang, Yusen, Wenlong Liao, and Yuqing Chang. "Gated Recurrent Unit Network-Based Short-Term Photovoltaic Forecasting." Energies 11, no. 8 (2018): 2163. http://dx.doi.org/10.3390/en11082163.
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Photovoltaic power has great volatility and intermittency due to environmental factors. Forecasting photovoltaic power is of great significance to ensure the safe and economical operation of distribution network. This paper proposes a novel approach to forecast short-term photovoltaic power based on a gated recurrent unit (GRU) network. Firstly, the Pearson coefficient is used to extract the main features that affect photovoltaic power output at the next moment, and qualitatively analyze the relationship between the historical photovoltaic power and the future photovoltaic power output. Secondly, the K-means method is utilized to divide training sets into several groups based on the similarities of each feature, and then GRU network training is applied to each group. The output of each GRU network is averaged to obtain the photovoltaic power output at the next moment. The case study shows that the proposed approach can effectively consider the influence of features and historical photovoltaic power on the future photovoltaic power output, and has higher accuracy than the traditional methods.
16
Kang, Qiao, Jing Kan, Fangyan Dong, and Kewei Chen. "Semantic Similarity Analysis via Syntax Dependency Structure and Gate Recurrent Unit." Journal of Advanced Computational Intelligence and Intelligent Informatics 28, no. 1 (2024): 179–85. http://dx.doi.org/10.20965/jaciii.2024.p0179.
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Sentences are composed of words, phrases, and clauses. The relationship between them is usually tree-like. In the hierarchical structure of the sentence, the dependency relationships between different components affect the syntactic structure. Syntactic structure is very important for understanding the meaning of the whole sentence. However, the gated recursive unit (GRU) models cannot fully encode hierarchical syntactic dependencies, which leads to its poor performance in various natural language tasks. In this paper, a model called relative syntactic distance bidirectional gated recursive unit (RSD-BiGRU) is constructed to capture syntactic structure dependencies. The model modifies the gating mechanism in GRU through relative syntactic distance. It also offers a transformation gate to model the syntactic structure more directly. Embedding sentence meanings with sentence structure dependency into dense vectors. This model is used to conduct semantic similarity experiments on the QQP and SICK datasets. The results show that the sentence representation obtained by RSD-BiGRU model contains more semantic information. This is helpful for semantic similarity analysis tasks.
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Muhammad, Ukasha, and Gbolagade Morufat Damola. "Cryptocurrencies Price Prediction Using Deep Learning Models (Gated Recurrent Unit And Recurrent Neural Network))." Kasu Journal of Computer Science 1, no. 3 (2024): 544–52. http://dx.doi.org/10.47514/kjcs/2024.1.3.0011.
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Background: With their volatile prices, cryptocurrencies have become valuable assets in the financial market. Predicting cryptocurrency prices accurately is essential for making well-informed investment decisions. Time series prediction models, like Gated Recurrent Unit (GRU) and Recurrent Neural Networks (RNN), are popular tools for financial data forecasting because they can capture sequential dependencies in data. Aim: This study aims to predict the average monthly closing prices of five major cryptocurrencies—Bitcoin (BTC), Ethereum (ETH), Binance Coin (BNB), Litecoin (LTC), and Ripple (XRP)—using GRU and RNN models and evaluate their performance in forecasting these prices. Method: Time series input sequences were produced and historical price data for the chosen cryptocurrencies were preprocessed using Min-Max Scaling. This data was divided into training and test sets, and it was used to train both the GRU and RNN models. The Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE) were used to assess the performance of the model. Results: For the majority of cryptocurrencies, the RNN model exhibited better predicted accuracy and consistently outperformed the GRU model. For instance, the RMSE for Ripple was 0.06 for the RNN model and 0.09 for GRU. In a similar vein, the RNN model outperformed the GRU model with a MAPE of 12.97% for Ethereum. These results imply that RNN models are more suitable for financial forecasting in this sector, as they yield more accurate predictions for cryptocurrency values.
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Ardana, Aulia Riefqi, and Yuliant Sibaroni. "HATE SPEECH DETECTION USING GLOVE WORD EMBEDDING AND GATED RECURRENT UNIT." Jurnal Teknik Informatika (Jutif) 5, no. 6 (2024): 1759–67. https://doi.org/10.52436/1.jutif.2024.5.6.2557.
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Social media has become a tool that makes it easier for people to exchange information. The freedom to share information has opened the door for increased incidents of hate speech on social media. Hate speech detection is an interesting topic because with the increasing use of social media, hate speech can quickly spread and trigger significant negative impacts, discrimination, and social conflict. This research aims to see the effect of GRU method, GloVe word embedding and word modifier algorithm in detecting hate speech. GRU and GloVe are used in this research for the hate speech detection system, where deep learning with a Gated Recurrent Unit (GRU) and Word Embedding with the Global Vector model (GloVe) converts words in text into numerical vectors that represent the meaning and context of the words. GRU is chosen due to its ability to capture long-term dependencies in textual data with higher computational efficiency compared to Long Short-Term Memory (LSTM). Gated Recurrent Unit (GRU) model processes the sequence of words to understand the sentence structure. GRU model processes the sequence of words to understand the sentence structure. The evaluation results for the classification of hate speech using GRU and GloVe are 90.7% accuracy and 91% F1 score. With the combination of informal word modifier algorithms there is an increase with a value of 92.8% F1 and 92.4% accuracy. in conclusion, the use of informal word modifier algorithms can increase the evaluation value in detecting hate speech.
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Hamzah, Hamzah, Sugeng Winardi, Poly Endrayanto Eko Chrismawan, and Rainbow Tambunan. "Robust Stock Price Prediction using Gated Recurrent Unit (GRU)." International Journal of Informatics and Computation 5, no. 1 (2023): 29. http://dx.doi.org/10.35842/ijicom.v5i1.56.
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Forecasting the direction of price movement of the stock market could yield significant profits. Traders use technical analysis, which is the study of price by scrutinizing past prices, to forecast the future price of the nickel stock price. Therefore, in this study, we propose Gated Recurrent Units (GRU) to predict nickel stock price trends. This research aims to produce an accurate nickel stock price trend prediction model. The research method utilized historical data on nickel stock prices from Yahoo Finance. The research results show that the model developed accurately predicted nickel stock price trends. From the RMSE, MAE, and MSE analysis results, the RMSE value was 0.0123, the MAE value was 0.0089, and the MSE value was 0.0002 on the test data.
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Ding, Chen, Zhouyi Zheng, Sirui Zheng, et al. "Accurate Air-Quality Prediction Using Genetic-Optimized Gated-Recurrent-Unit Architecture." Information 13, no. 5 (2022): 223. http://dx.doi.org/10.3390/info13050223.
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Air pollution is becoming a serious concern with the development of society and urban expansion, and predicting air quality is the most pressing problem for human beings. Recently, more and more machine-learning-based methods are being used to solve the air-quality-prediction problem, and gated recurrent units (GRUs) are a representative method because of their advantage for processing time-series data. However, in the same air-quality-prediction task, different researchers have always designed different structures of the GRU due to their different experiences. Data-adaptively designing a GRU structure has thus become a problem. In this paper, we propose an adaptive GRU to address this problem, and the adaptive GRU structures are determined by the dataset, which mainly contributes with three steps. Firstly, an encoding method for the GRU structure is proposed for representing the network structure in a fixed-length binary string; secondly, we define the reciprocal of the sum of the loss of each individual as the fitness function for the iteration computation; thirdly, the genetic algorithm is used for computing the data-adaptive GRU network structure, which can enhance the air-quality-prediction result. The experiment results from three real datasets in Xi’an show that the proposed method achieves better effectiveness in RMSE and SAMPE than the existing LSTM-, SVM-, and RNN-based methods.
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Ding, Chen, Zhouyi Zheng, Sirui Zheng, et al. "Accurate Air-Quality Prediction Using Genetic-Optimized Gated-Recurrent-Unit Architecture." Information 13, no. 5 (2022): 223. http://dx.doi.org/10.3390/info13050223.
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Air pollution is becoming a serious concern with the development of society and urban expansion, and predicting air quality is the most pressing problem for human beings. Recently, more and more machine-learning-based methods are being used to solve the air-quality-prediction problem, and gated recurrent units (GRUs) are a representative method because of their advantage for processing time-series data. However, in the same air-quality-prediction task, different researchers have always designed different structures of the GRU due to their different experiences. Data-adaptively designing a GRU structure has thus become a problem. In this paper, we propose an adaptive GRU to address this problem, and the adaptive GRU structures are determined by the dataset, which mainly contributes with three steps. Firstly, an encoding method for the GRU structure is proposed for representing the network structure in a fixed-length binary string; secondly, we define the reciprocal of the sum of the loss of each individual as the fitness function for the iteration computation; thirdly, the genetic algorithm is used for computing the data-adaptive GRU network structure, which can enhance the air-quality-prediction result. The experiment results from three real datasets in Xi’an show that the proposed method achieves better effectiveness in RMSE and SAMPE than the existing LSTM-, SVM-, and RNN-based methods.
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Arwansyah, Arwansyah, Suryani Suryani, Hasyrif SY, Usman Usman, Ahyuna Ahyuna, and Samsu Alam. "Time Series Forecasting Menggunakan Deep Gated Recurrent Units." Digital Transformation Technology 4, no. 1 (2022): 410–16. http://dx.doi.org/10.47709/digitech.v4i1.4141.
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Time series forecasting memiliki aplikasi yang luas dalam berbagai bidang seperti keuangan, ekonomi, meteorologi, dan pengelolaan rantai pasokan. Penelitian ini bertujuan untuk mengevaluasi kinerja model Gated Recurrent Unit (GRU) yang mendalam dalam konteks prediksi data time series. Akurasi prediksi time series sangat penting untuk pengambilan keputusan yang lebih baik dan efisien. GRU diperkenalkan sebagai varian Recurrent Neural Networks (RNN) yang lebih efisien dan efektif dalam menangani data time series. RNN sering digunakan untuk tugas ini, namun memiliki kelemahan dalam mempelajari ketergantungan jangka panjang akibat masalah vanishing gradient. Penelitian ini mengusulkan penggunaan model GRU mendalam untuk meningkatkan akurasi prediksi data time series. Arsitektur model yang diusulkan terdiri dari tiga lapisan GRU dengan 64 unit masing-masing, diikuti oleh satu lapisan output dense. Dataset yang digunakan dalam penelitian ini adalah dataset time series yang terdiri dari AQI, Weather, Exchange Rate, dan ETT. Model dilatih menggunakan optimizer Adam dan loss function mean squared error, dengan jumlah epoch sebanyak 100 dan batch size 32. Data dibagi menjadi set training dan testing dengan rasio 80:20. Kinerja model dievaluasi menggunakan metrik Mean Absolute Error (MAE) dan Root Mean Squared Error (RMSE). Hasil eksperimen menunjukkan bahwa model GRU mendalam menghasilkan prediksi yang lebih akurat dibandingkan dengan model ARIMA, RNN, dan LSTM. Model GRU mendalam menunjukkan kemampuan yang lebih baik dalam mempelajari ketergantungan jangka panjang dalam data time series, sehingga meningkatkan akurasi prediksi. Hasil penelitian ini memberikan kontribusi signifikan dalam bidang time series forecasting dan dapat menjadi alternatif yang lebih efektif dibandingkan model tradisional dan model machine learning lainnya.
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Yang, Shuang, and Xiangyang Zeng. "Combination of gated recurrent unit and Network in Network for underwater acoustic target recognition." INTER-NOISE and NOISE-CON Congress and Conference Proceedings 263, no. 6 (2021): 486–92. http://dx.doi.org/10.3397/in-2021-1490.
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Underwater acoustic target recognition is an important part of underwater acoustic signal processing and an important technical support for underwater acoustic information acquisition and underwater acoustic information confrontation. Taking into account that the gated recurrent unit (GRU) has an internal feedback mechanism that can reflect the temporal correlation of underwater acoustic target features, a model with gated recurrent unit and Network in Network (NIN) is proposed to recognize underwater acoustic targets in this paper. The proposed model introduces NIN to compress the hidden states of GRU while retaining the original timing characteristics of underwater acoustic target features. The higher recognition rate and faster calculation speed of the proposed model are demonstrated with experiments for raw underwater acoustic signals comparing with the multi-layer stacked GRU model.
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Holle, Alfransis Perugia Bennybeng, and Warih Maharani. "DEPRESSION DETECTION ON TWITTER USING GATED RECURRENT UNIT." Jurnal Teknik Informatika (Jutif) 5, no. 1 (2024): 121–28. https://doi.org/10.52436/1.jutif.2024.5.1.1187.
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In the present era, technological advancements have significantly impacted society, particularly in the use of social media. One popular social media platform is Twitter, where people could share moments, thoughts, and statuses. However, since the COVID-19 pandemic, the usage of Twitter increased, and some users began exhibiting symptoms of depression. The condition of depression required a means to channel emotions that could assist users in coping. By employing the GRU method and Word2Vec feature extraction, we developed a depression detection system capable of analyzing users' Twitter posts and identifying potential signs of depression. The dataset used in this research was obtained from 165 participants who agreed to utilize their personal Twitter data and completed a questionnaire based on the Depression Anxiety and Stress Scales-42 (DASS-42). The questionnaire results served as labels that were processed for Word2Vec feature extraction and subsequently fed into the GRU model. The evaluation revealed an accuracy rate of 57.58% and an f1-score of 56.25. By using the bidirectional layer in the model, there is an improvement in precision, recall, and f1-score values.
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Mukherjee, Partha, Youakim Badr, Srushti Karvekar, and Shanmugapriya Viswanathan. "Coronavirus Genome Sequence Similarity and Protein Sequence Classification." Journal of Digital Science 3, no. 2 (2021): 3–18. http://dx.doi.org/10.33847/2686-8296.3.2_1.
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The world currently is going through a serious pandemic due to the coronavirus disease (COVID-19). In this study, we investigate the gene structure similarity of coronavirus genomes isolated from COVID-19 patients, Severe Acute Respiratory Syndrome (SARS) patients and bats genes. We also explore the extent of similarity between their genome structures to find if the new coronavirus is similar to either of the other genome structures. Our experimental results show that there is 82.42% similarity between the CoV-2 genome structure and the bat genome structure. Moreover, we have used a bidirectional Gated Recurrent Unit (GRU) model as the deep learning technique and an improved variant of Recurrent Neural networks (i.e., Bidirectional Long Short Term Memory model) to classify the protein families of these genomes to isolate the prominent protein family accession. The accuracy of Gated Recurrent Unit (GRU) is 98% for labeled protein sequences against the protein families. By comparing the performance of the Gated Recurrent Unit (GRU) model with the Bidirectional Long Short Term Memory (Bi-LSTM) model results, we found that the GRU model is 1.6% more accurate than the Bi-LSTM model for our multiclass protein classification problem. Our experimental results would be further support medical research purposes in targeting the protein family similarity to better understand the coronavirus genomic structure.
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Fayyad, Muhammad Fauzi, Viki Kurniawan, Muhammad Ridho Anugrah, Baihaqi Hilmi Estanto, and Tasnim Bilal. "Application of Recurrent Neural Network Bi-Long Short-Term Memory, Gated Recurrent Unit and Bi-Gated Recurrent Unit for Forecasting Rupiah Against Dollar (USD) Exchange Rate." Public Research Journal of Engineering, Data Technology and Computer Science 2, no. 1 (2024): 1–10. http://dx.doi.org/10.57152/predatecs.v2i1.1094.
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Foreign exchange rates have a crucial role in a country's economic development, influencing long-term investment decisions. This research aims to forecast the exchange rate of Rupiah to the United States Dollar (USD) by using deep learning models of Recurrent Neural Network (RNN) architecture, especially Bi-Long Short-Term Memory (Bi-LSTM), Gated Recurrent Unit (GRU), and Bi-Gated Recurrent Unit (Bi-GRU). Historical daily exchange rate data from January 1, 2013 to November 3, 2023, obtained from Yahoo Finance, was used as the dataset. The model training and evaluation process was performed based on various parameters such as optimizer, batch size, and time step. The best model was identified by minimizing the Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). Among the models tested, the GRU model with Nadam optimizer, batch size 16, and timestep 30 showed the best performance, with MSE 3741.6999, RMSE 61.1694, MAE 45.6246, and MAPE 0.3054%. The forecast results indicate a strengthening trend of the Rupiah exchange rate against the USD in the next 30 days, which has the potential to be taken into consideration in making investment decisions and shows promising economic growth prospects for Indonesia.
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Prawira, Dian, and Ilhamsyah Ilhamsyah. "NEXT WORD PREDICTION PADA REKAM MEDIS ELEKTRONIK MENGGUNAKAN GATED RECURRENT UNIT." Jurnal Khatulistiwa Informatika 12, no. 2 (2024): 107–11. https://doi.org/10.31294/jki.v12i2.23669.
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Tujuan utama dari penelitian ini adalah untuk membuat model Gated Recurrent Unit (GRU) yang dirancang khusus untuk meramalkan kata berikutnya dalam rekam medis elektronik (RME). Pembangunan model ini melibatkan serangkaian proses, seperti membangun struktur dan parameter, serta melakukan pelatihan menggunakan dataset yang berisi 169.544 token. Desainnya terdiri dari beberapa lapisan GRU yang berurutan dengan karakteristik tertentu. Data pelatihan dibagi menjadi rasio pelatihan 80% dan rasio pengujian 20%. Evaluasi kinerja dilakukan selama rentang waktu 90 epoch, menghasilkan penurunan loss function dari 2,2836 menjadi 0,800 dan peningkatan akurasi dari 60,26% menjadi 80,59% di seluruh epoch. Model GRU memiliki akurasi prediksi yang mengesankan sebesar 87,04% pada dataset pengujian, yang menunjukkan kemampuannya untuk mengidentifikasi pola dalam data medis secara efektif dan potensinya untuk meningkatkan manajemen RME dalam pengaturan perawatan kesehatan.
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Adelakun, Najeem Olawale, and Mariam Adenike Lasisi. "Deep Learning-Based Sentiment Analysis In Financial Markets Using Gated Recurrent Unit." Andalasian International Journal of Applied Science, Engineering and Technology 5, no. 1 (2025): 27–38. https://doi.org/10.25077/aijaset.v5i1.217.
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In today’s volatile financial markets, investor sentiment plays a crucial role in shaping market dynamics and influencing investment decisions. Traditional analytical methods often fail to capture the subtle emotional cues embedded in vast amounts of unstructured textual data derived from news articles, social media, and financial reports. This study addresses this challenge by employing a deep learning-based approach using Gated Recurrent Units (GRU) for sentiment analysis, thereby enhancing the accuracy of financial market predictions. The research employs a systematic methodology that begins with data collection from various financial sources. This is followed by rigorous preprocessing, including data cleaning, tokenization, and downsampling to balance sentiment classes. Sentiment labeling and feature engineering, utilizing word embeddings, convert textual data into a format suitable for deep learning. The Gated Recurrent Unit (GRU) model is then trained on these features, and its performance is evaluated using metrics such as accuracy, precision, recall, and F1-score. Results indicate that while the Gated Recurrent Unit (GRU) model effectively captures neutral sentiments, it struggles to accurately classify negative and positive sentiments, highlighting areas for improvement. These findings underscore the potential of GRU-based models in financial sentiment analysis while emphasizing the need for refined techniques to enhance classification accuracy. Future research should investigate hybrid architectures, integrate attention mechanisms, and leverage real-time data to enhance the robustness and comprehensiveness of market forecasting. These insights strongly advocate for ongoing advancements in deep learning strategies to refine sentiment classification and financial prediction models.
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Karyadi, Yadi. "Prediksi Kualitas Udara Dengan Metoda LSTM, Bidirectional LSTM, dan GRU." JATISI (Jurnal Teknik Informatika dan Sistem Informasi) 9, no. 1 (2022): 671–84. http://dx.doi.org/10.35957/jatisi.v9i1.1588.
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Kualitas udara menjadi salah satu masalah utama di kota besar. Salah satu cara pengendalian kualitas udara adalah dengan cara memprediksi beberapa parameter utama dengan menggunakan algoritma deep learning. Penelitian ini menggunakan metoda deep learning yang merupakan bagian dari Recurrent Neural network yaitu Long Short Term Memory, Bidirectional Long Short Term Memory, dan Gated Recurrent Unit yang diterapkan pada permasalahan memprediksi data time series kualitas udara dengan parameter suhu, kelembaban, particular matter PM10, dan Indeks Standar Pencemar Udara (ISPU). Dari hasil pengujian 3 jenis model prediksi terhadap 4 variabel berdasarkan kreteria penilain menggunakan RMSE dari data testing dan dibandingkan dengan standard deviasi, maka model LSTM dan LSTM Bidirectional telah menunjukan hasil yang bagus untuk permasalahan data yang bersifat time series kualitas udara, Sedangkan model Gated Recurrent Unit (GRU) menampilkan hasil yang kurang bagus.
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Putera Khano, Muhammad Nazhif Abda, Dewi Retno Sari Saputro, Sutanto Sutanto, and Antoni Wibowo. "SENTIMENT ANALYSIS WITH LONG-SHORT TERM MEMORY (LSTM) AND GATED RECURRENT UNIT (GRU) ALGORITHMS." BAREKENG: Jurnal Ilmu Matematika dan Terapan 17, no. 4 (2023): 2235–42. http://dx.doi.org/10.30598/barekengvol17iss4pp2235-2242.
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Sentiment analysis is a form of machine learning that functions to obtain emotional polarity values or data tendencies from data in the form of text. Sentiment analysis is needed to analyze opinions, sentiments, reviews, and criticisms from someone for a product, service, organization, topic, etc. Recurrent Neural Network (RNN) is one of the Natural Language Processing (NLP) algorithms that is used in sentiment analysis. RNN is a neural network that can use internal memory to process input. RNN itself has a weakness in Long-Term Memory (LTM). Therefore, this article examines the combination of Long-Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) algorithms. GRU is an algorithm that is used to make each recurrent unit able to record adaptively at different time scales. Meanwhile, LSTM is a network architecture with the advantage of learning long-term dependencies on data. LSTM can remember long-term memory information, learn long-sequential data, and form information relation data in LTM. The combination of LSTM and GRU aims to overcome RNN’s weakness in LTM. The LSTM-GRU is combined by adding GRU to the data generated from LSTM. The combination of LSTM and GRU creates a better performance algorithm for addressing the LTM problem.
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Zulqarnain, Muhammad, Rozaida Ghazali, Yana Mazwin Mohmad Hassim, and Muhammad Rehan. "Text classification based on gated recurrent unit combines with support vector machine." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 4 (2020): 3734. http://dx.doi.org/10.11591/ijece.v10i4.pp3734-3742.
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As the amount of unstructured text data that humanity produce largely and a lot of texts are grows on the Internet, so the one of the intelligent technique is require processing it and extracting different types of knowledge from it. Gated recurrent unit (GRU) and support vector machine (SVM) have been successfully used to Natural Language Processing (NLP) systems with comparative, remarkable results. GRU networks perform well in sequential learning tasks and overcome the issues of “vanishing and explosion of gradients in standard recurrent neural networks (RNNs) when captureing long-term dependencies. In this paper, we proposed a text classification model based on improved approaches to this norm by presenting a linear support vector machine (SVM) as the replacement of Softmax in the final output layer of a GRU model. Furthermore, the cross-entropy function shall be replaced with a margin-based function. Empirical results present that the proposed GRU-SVM model achieved comparatively better results than the baseline approaches BLSTM-C, DABN.
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Muhammad, Zulqarnain, Ghazali Rozaida, Mazwin Mohmad Hassim Yana, and Rehan Muhammad. "Text classification based on gated recurrent unit combines with support vector machine." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 4 (2020): 3734–42. https://doi.org/10.11591/ijece.v10i4.pp3734-3742.
Full textAbstract:
As the amount of unstructured text data that humanity produce largely and a lot of texts are grows on the Internet, so the one of the intelligent technique is require processing it and extracting different types of knowledge from it. Gated recurrent unit (GRU) and support vector machine (SVM) have been successfully used to Natural Language Processing (NLP) systems with comparative, remarkable results. GRU networks perform well in sequential learning tasks and overcome the issues of “vanishing and explosion of gradients in standard recurrent neural networks (RNNs) when captureing long-term dependencies. In this paper, we proposed a text classification model based on improved approaches to this norm by presenting a linear support vector machine (SVM) as the replacement of Softmax in the final output layer of a GRU model. Furthermore, the cross-entropy function shall be replaced with a margin-based function. Empirical results present that the proposed GRU-SVM model achieved comparatively better results than the baseline approaches BLSTM-C, DABN.
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Yasir, Muhammad, Li Chen, Amna Khatoon, Muhammad Amir Malik, and Fazeel Abid. "Mixed Script Identification Using Automated DNN Hyperparameter Optimization." Computational Intelligence and Neuroscience 2021 (December 10, 2021): 1–13. http://dx.doi.org/10.1155/2021/8415333.
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Mixed script identification is a hindrance for automated natural language processing systems. Mixing cursive scripts of different languages is a challenge because NLP methods like POS tagging and word sense disambiguation suffer from noisy text. This study tackles the challenge of mixed script identification for mixed-code dataset consisting of Roman Urdu, Hindi, Saraiki, Bengali, and English. The language identification model is trained using word vectorization and RNN variants. Moreover, through experimental investigation, different architectures are optimized for the task associated with Long Short-Term Memory (LSTM), Bidirectional LSTM, Gated Recurrent Unit (GRU), and Bidirectional Gated Recurrent Unit (Bi-GRU). Experimentation achieved the highest accuracy of 90.17 for Bi-GRU, applying learned word class features along with embedding with GloVe. Moreover, this study addresses the issues related to multilingual environments, such as Roman words merged with English characters, generative spellings, and phonetic typing.
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Khalis Sofi, Aswan Supriyadi Sunge, Sasmitoh Rahmad Riady, and Antika Zahrotul Kamalia. "PERBANDINGAN ALGORITMA LINEAR REGRESSION, LSTM, DAN GRU DALAM MEMPREDIKSI HARGA SAHAM DENGAN MODEL TIME SERIES." SEMINASTIKA 3, no. 1 (2021): 39–46. http://dx.doi.org/10.47002/seminastika.v3i1.275.
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Penelitian ini bertujuan untuk memprediksi harga saham dengan membandingkan algoritma Linear Regression, Long Short-Term Memory (LSTM), dan Gated Recurrent Unit (GRU) dengan dataset publik kemudian menentukan performa terbaik dari ketiga algoritma tersebut. Dataset yang diuji bersumber dari Indonesia Stock Exchange (IDX), yaitu dataset harga saham KEJU berbentuk time series dari tanggal 15 November 2019 sampai dengan 08 Juni 2021. Parameter yang digunakan untuk pengukuran perbandingan adalah RMSE (Root Mean Square Error), MSE (Mean Square Error), dan MAE (Mean Absolute Error). Setelah dilakukan proses training dan testing, dihasilkan sebuah analisis bahwa dari hasil perbandingan algoritma yang digunakan, algoritma Gated Recurrent Unit (GRU) memiliki performance paling baik dibandingkan Linear Regression dan Long-Short Term Memory (LSTM) dalam hal memprediksi harga saham, dibuktikan dengan nilai RMSE, MSE, dan MAE dari uji coba GRU paling rendah, yaitu nilai RMSE 0.034, MSE 0.001, dan nilai MAE 0.024.
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Duan, Wenxian, Chuanxue Song, Silun Peng, Feng Xiao, Yulong Shao, and Shixin Song. "An Improved Gated Recurrent Unit Network Model for State-of-Charge Estimation of Lithium-Ion Battery." Energies 13, no. 23 (2020): 6366. http://dx.doi.org/10.3390/en13236366.
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An accurate state-of-charge (SOC) can not only provide a safe and reliable guarantee for the entirety of equipment but also extend the service life of the battery pack. Given that the chemical reaction inside the lithium-ion battery is a highly nonlinear dynamic system, obtaining an accurate SOC for the battery management system is very challenging. This paper proposed a gated recurrent unit recurrent neural network model with activation function layers (GRU-ATL) to estimate battery SOC. The model used deep learning technology to establish the nonlinear relationship between current, voltage, and temperature measurement signals and battery SOC. Then the online SOC estimation was carried out on different testing sets using the trained model. The experiments in this paper showed that the GRU-ATL network model could realize online SOC estimation under different working conditions without relying on an accurate battery model. Compared with the gated recurrent unit recurrent neural (GRU) network model and long short-term memory (LSTM) network model, the GRU-ATL network model had more stable and accurate SOC prediction performance. When the measurement data contained noise, the experimental results showed that the SOC prediction accuracy of GRU-ATL model was 0.1–0.4% higher than the GRU model and 0.3–0.7% higher than the LSTM model. The mean absolute error (MAE) of SOC predicted by the GRU-ATL model was stable in the range of 0.7–1.4%, and root mean square error (RMSE) was stable between 1.2–1.9%. The model still had high prediction accuracy and robustness, which could meet the SOC estimation in complex vehicle working conditions.
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Onyekpe, Uche, Vasile Palade, Stratis Kanarachos, and Stavros-Richard G. Christopoulos. "A Quaternion Gated Recurrent Unit Neural Network for Sensor Fusion." Information 12, no. 3 (2021): 117. http://dx.doi.org/10.3390/info12030117.
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Recurrent Neural Networks (RNNs) are known for their ability to learn relationships within temporal sequences. Gated Recurrent Unit (GRU) networks have found use in challenging time-dependent applications such as Natural Language Processing (NLP), financial analysis and sensor fusion due to their capability to cope with the vanishing gradient problem. GRUs are also known to be more computationally efficient than their variant, the Long Short-Term Memory neural network (LSTM), due to their less complex structure and as such, are more suitable for applications requiring more efficient management of computational resources. Many of such applications require a stronger mapping of their features to further enhance the prediction accuracy. A novel Quaternion Gated Recurrent Unit (QGRU) is proposed in this paper, which leverages the internal and external dependencies within the quaternion algebra to map correlations within and across multidimensional features. The QGRU can be used to efficiently capture the inter- and intra-dependencies within multidimensional features unlike the GRU, which only captures the dependencies within the sequence. Furthermore, the performance of the proposed method is evaluated on a sensor fusion problem involving navigation in Global Navigation Satellite System (GNSS) deprived environments as well as a human activity recognition problem. The results obtained show that the QGRU produces competitive results with almost 3.7 times fewer parameters compared to the GRU. The QGRU code is available at.
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Liyakathunisa, Abdullah Alsaeedi, Saima Jabeen, and Hoshang Kolivand. "Ambient assisted living framework for elderly care using Internet of medical things, smart sensors, and GRU deep learning techniques." Journal of Ambient Intelligence and Smart Environments 14, no. 1 (2022): 5–23. http://dx.doi.org/10.3233/ais-210162.
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Due to the increase in the global aging population and its associated age-related challenges, various cognitive, physical, and social problems can arise in older adults, such as reduced walking speed, mobility, falls, fatigue, difficulties in performing daily activities, memory-related and social isolation issues. In turn, there is a need for continuous supervision, intervention, assistance, and care for elderly people for active and healthy aging. This research proposes an ambient assisted living system with the Internet of Medical Things that leverages deep learning techniques to monitor and evaluate the elderly activities and vital signs for clinical decision support. The novelty of the proposed approach is that bidirectional Gated Recurrent Unit, and Gated Recurrent Unit deep learning techniques with mutual information-based feature selection technique is applied to select robust features to identify the target activities and abnormalities. Experiments were conducted on two datasets (the recorded Ambient Assisted Living data and MHealth benchmark data) with bidirectional Gated Recurrent Unit, and Gated Recurrent Unit deep learning techniques and compared with other state of art techniques. Different evaluation metrics were used to assess the performance, findings reveal that bidirectional Gated Recurrent Unit deep learning techniques outperform other state of art approaches with an accuracy of 98.14% for Ambient Assisted Living data, and 99.26% for MHealth data using the proposed approach.
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Noh, Jiseong, Hyun-Ji Park, Jong Soo Kim, and Seung-June Hwang. "Gated Recurrent Unit with Genetic Algorithm for Product Demand Forecasting in Supply Chain Management." Mathematics 8, no. 4 (2020): 565. http://dx.doi.org/10.3390/math8040565.
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Product demand forecasting plays a vital role in supply chain management since it is directly related to the profit of the company. According to companies’ concerns regarding product demand forecasting, many researchers have developed various forecasting models in order to improve accuracy. We propose a hybrid forecasting model called GA-GRU, which combines Genetic Algorithm (GA) with Gated Recurrent Unit (GRU). Because many hyperparameters of GRU affect its performance, we utilize GA that finds five kinds of hyperparameters of GRU including window size, number of neurons in the hidden state, batch size, epoch size, and initial learning rate. To validate the effectiveness of GA-GRU, this paper includes three experiments: comparing GA-GRU with other forecasting models, k-fold cross-validation, and sensitive analysis of the GA parameters. During each experiment, we use root mean square error and mean absolute error for calculating the accuracy of the forecasting models. The result shows that GA-GRU obtains better percent deviations than other forecasting models, suggesting setting the mutation factor of 0.015 and the crossover probability of 0.70. In short, we observe that GA-GRU can optimally set five types of hyperparameters and obtain the highest forecasting accuracy.
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Abid, Fazeel, Muhammad Alam, Faten S. Alamri, and Imran Siddique. "Multi-directional gated recurrent unit and convolutional neural network for load and energy forecasting: A novel hybridization." AIMS Mathematics 8, no. 9 (2023): 19993–20017. http://dx.doi.org/10.3934/math.20231019.
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<abstract> <p>Energy operations and schedules are significantly impacted by load and energy forecasting systems. An effective system is a requirement for a sustainable and equitable environment. Additionally, a trustworthy forecasting management system enhances the resilience of power systems by cutting power and load-forecast flaws. However, due to the numerous inherent nonlinear properties of huge and diverse data, the classical statistical methodology cannot appropriately learn this non-linearity in data. Energy systems can appropriately evaluate data and regulate energy consumption because of advanced techniques. In comparison to machine learning, deep learning techniques have lately been used to predict energy consumption as well as to learn long-term dependencies. In this work, a fusion of novel multi-directional gated recurrent unit (MD-GRU) with convolutional neural network (CNN) using global average pooling (GAP) as hybridization is being proposed for load and energy forecasting. The spatial and temporal aspects, along with the high dimensionality of the data, are addressed by employing the capabilities of MD-GRU and CNN integration. The obtained results are compared to baseline algorithms including CNN, Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (Bi-LSTM), Gated Recurrent Unit (GRU), and Bidirectional Gated Recurrent Unit (Bi-GRU). The experimental findings indicate that the proposed approach surpasses conventional approaches in terms of accuracy, Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RSME).</p> </abstract>
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Mateus, Balduíno César, Mateus Mendes, José Torres Farinha, Rui Assis, and António Marques Cardoso. "Comparing LSTM and GRU Models to Predict the Condition of a Pulp Paper Press." Energies 14, no. 21 (2021): 6958. http://dx.doi.org/10.3390/en14216958.
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The accuracy of a predictive system is critical for predictive maintenance and to support the right decisions at the right times. Statistical models, such as ARIMA and SARIMA, are unable to describe the stochastic nature of the data. Neural networks, such as long short-term memory (LSTM) and the gated recurrent unit (GRU), are good predictors for univariate and multivariate data. The present paper describes a case study where the performances of long short-term memory and gated recurrent units are compared, based on different hyperparameters. In general, gated recurrent units exhibit better performance, based on a case study on pulp paper presses. The final result demonstrates that, to maximize the equipment availability, gated recurrent units, as demonstrated in the paper, are the best options.
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Zhao, Xuehua, Hanfang Lv, Yizhao Wei, Shujin Lv, and Xueping Zhu. "Streamflow Forecasting via Two Types of Predictive Structure-Based Gated Recurrent Unit Models." Water 13, no. 1 (2021): 91. http://dx.doi.org/10.3390/w13010091.
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Data-intelligent methods designed for forecasting the streamflow of the Fenhe River are crucial for enhancing water resource management. Herein, the gated recurrent unit (GRU) is coupled with the optimization algorithm improved grey wolf optimizer (IGWO) to design a hybrid model (IGWO-GRU) to carry out streamflow forecasting. Two types of predictive structure-based models (sequential IGWO-GRU and monthly IGWO-GRU) are compared with other models, such as the single least-squares support vector machine (LSSVM) and single extreme learning machine (ELM) models. These models incorporate the historical streamflow series as inputs of the model to forecast the future streamflow with data from January 1956 to December 2016 at the Shangjingyou station and from January 1958 to December 2016 at the Fenhe reservoir station. The IGWO-GRU model exhibited a strong ability for mapping in streamflow series when the parameters were carefully tuned. The monthly predictive structure can effectively extract the instinctive hydrological information that is more easily learned by the predictive model than the traditional sequential predictive structure. The monthly IGWO-GRU model was found to be a better forecasting tool, with an average qualification rate of 91.66% in two stations. It also showed good performance in absolute error and peak flow forecasting.
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Liu, Mian, Zhiwu Wang, Pingping Jiang, and Guozheng Yan. "Temperature Compensation Method for Piezoresistive Pressure Sensors Based on Gated Recurrent Unit." Sensors 24, no. 16 (2024): 5394. http://dx.doi.org/10.3390/s24165394.
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Piezoresistive pressure sensors have broad applications but often face accuracy challenges due to temperature-induced drift. Traditional compensation methods based on discrete data, such as polynomial interpolation, support vector machine (SVM), and artificial neural network (ANN), overlook the thermal hysteresis, resulting in lower accuracy. Considering the sequence-dependent nature of temperature drift, we propose the RF-IWOA-GRU temperature compensation model. Random forest (RF) is used to interpolate missing values in continuous data. A combination of gated recurrent unit (GRU) networks and an improved whale optimization algorithm (IWOA) is employed for temperature compensation. This model leverages the memory capability of GRU and the optimization efficiency of the IWOA to enhance the accuracy and stability of the pressure sensors. To validate the compensation method, experiments were designed under continuous variations in temperature and actual pressure. The experimental results show that the compensation capability of the proposed RF-IWOA-GRU model significantly outperforms that of traditional methods. After compensation, the standard deviation of pressure decreased from 10.18 kPa to 1.14 kPa, and the mean absolute error and root mean squared error were reduced by 75.10% and 76.15%, respectively.
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Fujita, Tomohiro, Zhiwei Luo, Changqin Quan, Kohei Mori, and Sheng Cao. "Performance Evaluation of RNN with Hyperbolic Secant in Gate Structure through Application of Parkinson’s Disease Detection." Applied Sciences 11, no. 10 (2021): 4361. http://dx.doi.org/10.3390/app11104361.
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This paper studies a novel recurrent neural network (RNN) with hyperbolic secant (sech) in the gate for a specific medical application task of Parkinson’s disease (PD) detection. In detail, it focuses on the fact that patients with PD have motor speech disorders, by converting the voice data into black-and-white images of a recurrence plot (RP) at specific time intervals and constructing the detection model that combines RNN and convolutional neural network (CNN); the study evaluates the performance of the RNN with sech gate compared with long short-term memory (LSTM) and gated recurrent unit (GRU) with conventional gates. As a result, the proposed model obtained similar results to LSTM and GRU (an average accuracy of about 70%) with less hyperparameters, resulting in faster learning. In addition, in the framework of the RNN with sech in gate, the accuracy obtained by using tanh as the output activation function is higher than using the relu function. The proposed method will see more improvement by increasing the data in the future. More analysis on the input sound type, the RP image size, and the deep learning structures will be included in our future work for further improving the performance of PD detection from voice.
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Mohammed, Dhurgham Ali, and Kalyani A. Patel. "AN IMPROVED GRU BASED ON RECURRENT ATTENTION UNIT AND SELF- ATTENTION TECHNIQUE FOR TEXT SENTIMENT ANALYSIS." ICTACT Journal on Soft Computing 15, no. 4 (2025): 3737–45. https://doi.org/10.21917/ijsc.2025.0518.
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In text sentiment analysis, a crucial challenge is that conventional word vectors fail to capture lexical ambiguity. The Gated Recurrent Unit (GRU), an advanced variant of RNN, is extensively utilized in natural language processing tasks such as information filtering, sentiment analysis, machine translation, and speech recognition. GRU can retain sequential information, but it lacks the ability to focus on the most relevant features of a sequence. Therefore, this paper introduces a novel text sentiment analysis-based RNN approach, a Recurrent Attention Unit (RAU), which incorporates an attention gate directly within the traditional GRU cell. This addition enhances GRU’s capacity to retain long-term information and selectively concentrates on critical elements in sequential data. Furthermore, this study integrates an improved Self-Attention technique (SA) with RA-GRU known as SA+RA-GRU. The improved self-attention technique is executed to reallocate the weights of deep text sequences. While attention techniques have recently become a significant innovation in deep learning, their precise impact on sentiment analysis has yet to be fully evaluated. The experimental findings show that the proposed approach SA+RA-GRU attains an accuracy of 92.17%, and 82.38% on the IMDB, and MR datasets, and outperformed traditional approaches. Moreover, the SA+RA-GRU model demonstrates excellent generalization and robust performance.
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Li, Naixin, Xincheng Tian, Zehan Lu, and Lin Han. "Multicomponent load forecasting of integrated energy system based on deep learning under low-carbon background." International Journal of Low-Carbon Technologies 19 (2024): 1468–76. http://dx.doi.org/10.1093/ijlct/ctae085.
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Abstract In order to support the economic scheduling and optimal operation of integrated energy distribution system, a multiload forecasting method of integrated energy system based on deep learning is proposed. Firstly, Pearson coefficient is used to analyze the correlation between the three loads. Then, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) model is used to improve the hidden layer of recurrent neural network (RNN). GRU and LSTM adopt gate structure instead of hidden unit in traditional RNN structure, which can selectively remember important information, and then learn historical load parameter information efficiently, making the prediction result more accurate. Finally, the actual data of the integrated energy system is applied to verify the effectiveness of the algorithm. The experimental results show that the prediction accuracy of the LSTM-GRU model proposed in this article is more accurate, and the research results can provide a reference for the comprehensive load prediction of the integrated energy distribution system.
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Zhang, Qingyong, Lingfeng Zhou, Yixin Su, Huiwen Xia, and Bingrong Xu. "Gated Recurrent Unit Embedded with Dual Spatial Convolution for Long-Term Traffic Flow Prediction." ISPRS International Journal of Geo-Information 12, no. 9 (2023): 366. http://dx.doi.org/10.3390/ijgi12090366.
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Considering the spatial and temporal correlation of traffic flow data is essential to improve the accuracy of traffic flow prediction. This paper proposes a traffic flow prediction model named Dual Spatial Convolution Gated Recurrent Unit (DSC-GRU). In particular, the GRU is embedded with the DSC unit to enable the model to synchronously capture the spatiotemporal dependence. When considering spatial correlation, current prediction models consider only nearest-neighbor spatial features and ignore or simply overlay global spatial features. The DSC unit models the adjacent spatial dependence by the traditional static graph and the global spatial dependence through a novel dependency graph, which is generated by calculating the correlation between nodes based on the correlation coefficient. More than that, the DSC unit quantifies the different contributions of the adjacent and global spatial correlation with a modified gated mechanism. Experimental results based on two real-world datasets show that the DSC-GRU model can effectively capture the spatiotemporal dependence of traffic data. The prediction precision is better than the baseline and state-of-the-art models.
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Handhayani, Teny, Clara Tanudy, and Janson Hendryli. "Prediksi Harga Emas di Indonesia Menggunakan Gated Recurrent Unit." JURNAL FASILKOM 13, no. 3 (2023): 480–88. http://dx.doi.org/10.37859/jf.v13i3.6185.
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Prediction system for the price of gold in Indonesia using a machine learning algorithm, namely the Gated Recurrent Unit (GRU), with influencing variables being the closing price of PT. Aneka Tambang's stock and the closing price of the US dollar exchange rate. The main objective of developing this system is to provide accurate and reliable information about the gold price trends for the next 7 days to the general public, investors, and other relevant parties. The dataset used consists of historical data for the closing prices of gold, the closing prices of PT. Aneka Tambang's stock, and the closing prices of the US dollar exchange rate, obtained from Yahoo Finance's website from January 2018 to October 2023. The dataset was pre-processed by extracting the dates from the three data sources used. In the results of training the GRU model for prediction, the best results were achieved with hyperparameters of 70% training data, 30% testing data, a timestep of 20, 50 epochs, and a batch size of 16, with an R-Squared value of 0.97, an MAE of 300.17, and an RMSE of 17.33. With the development of this system, it is expected to provide guidance for the general public, investors, and related parties in making timely decisions regarding gold purchases and to enhance their understanding of gold price movements in Indonesia.
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Khairunisa, Nurul Khairunisa, and Putriaji Hendikawati. "Long Short-Term Memory and Gated Recurrent Unit Modeling for Stock Price Forecasting." Jurnal Matematika, Statistika dan Komputasi 21, no. 1 (2024): 321–33. http://dx.doi.org/10.20956/j.v21i1.35930.
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The rapid advancement of deep learning technology offers significant benefits, particularly for time series data forecasting. LSTM and GRU are two deep learning methods used for forecasting. This study aims to compare the LSTM and GRU methods in predicting the stock prices of PT Mayora Indah Tbk, listed on the Indonesia Stock Exchange (IDX), accessed through Yahoo Finance. The model architecture was developed using combinations of data splitting, learning rate, epoch count, and neuron count. The combinations used in this study include data splits of 70:30 and 80:20, with varying parameter combinations of learning rates at 0.01, 0.001, and 0.0001, epoch counts of 50, 100, and 150, and neuron counts of 50 and 100. The results indicate that the GRU method outperforms the LSTM method in prediction accuracy. The RMSE and MAPE tests show that the GRU method yields RMSE and MAPE of 34.4233 and 1.27%, respectively, compared to the LSTM method with RMSE and MAPE of 35.3775 and 1.28%. The best GRU architecture was achieved with a learning rate of 0.001, 50 neurons, and 150 epochs, while the best LSTM architecture was achieved with a learning rate of 0.001, 100 neurons, and 150 epochs. The optimal architecture for both methods was found with a data split of 70:30
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Chen, Xiaolei, Hao Chang, Baoning Cao, Yubing Lu, and Dongmei Lin. "Prediction of Continuous Blood Pressure Using Multiple Gated Recurrent Unit Embedded in SENet." Journal of Advanced Computational Intelligence and Intelligent Informatics 26, no. 2 (2022): 256–63. http://dx.doi.org/10.20965/jaciii.2022.p0256.
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In order to accurately predict blood pressure waveform from pulse waveform, a multiple gated recurrent unit (GRU) model embedded in squeeze-and-excitation network (SENet) is proposed for continuous blood pressure prediction. Firstly, the features of the pulse are extracted from multiple GRU channels. Then, the SENet module is embedded to learn the interdependence among the channels, so as to get the weight of each channel. Finally, the weights were added to each channel and the predicted continuous blood pressure values were obtained by integrating the two linear layers. The experimental results show that the embedded SENet can effectively enhance the predictive ability of multi-GRU structure and obtain good continuous blood pressure waveform. Compared with the LSTM and GRU model without SENet, the MSE errors of the proposed method are reduced by 29.3% and 25.0% respectively, the training time of the proposed method are decreased by 69.8% and 68.7%, the test time is reduced by 65.9% and 25.2% and it has the fewest model parameters.
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Wang, Zhongguo, and Bao Zhang. "Toxic Comment Classification Based on Bidirectional Gated Recurrent Unit and Convolutional Neural Network." ACM Transactions on Asian and Low-Resource Language Information Processing 21, no. 3 (2022): 1–12. http://dx.doi.org/10.1145/3488366.
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For English toxic comment classification, this paper presents the model that combines Bi-GRU and CNN optimized by global average pooling (BG-GCNN) based on the bidirectional gated recurrent unit (Bi-GRU) and global pooling optimized convolution neural network (CNN) . The model treats each type of toxic comment as a binary classification. First, Bi-GRU is used to extract the time-series features of the comment and then the dimensionality is reduced through global pooling optimized convolution neural network. Finally, the classification result is output by Sigmoid function. Comparative experiments show the BG-GCNN model has a better classification effect than Text-CNN, LSTM, Bi-GRU, and other models. The Macro-F1 value of the toxic comment dataset on the Kaggle competition platform is 0.62. The F1 values of the three toxic label classification results (toxic, obscene, and insult label) are 0.81, 0.84, and 0.74, respectively, which are the highest values in the comparative experiment.