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1
Han, Tian, Zhu Zhang, Mingyuan Ren, Changchun Dong, Xiaolin Jiang, and Quansheng Zhuang. "Speech Emotion Recognition Based on Deep Residual Shrinkage Network." Electronics 12, no. 11 (June 2, 2023): 2512. http://dx.doi.org/10.3390/electronics12112512.
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Speech emotion recognition (SER) technology is significant for human–computer interaction, and this paper studies the features and modeling of SER. Mel-spectrogram is introduced and utilized as the feature of speech, and the theory and extraction process of mel-spectrogram are presented in detail. A deep residual shrinkage network with bi-directional gated recurrent unit (DRSN-BiGRU) is proposed in this paper, which is composed of convolution network, residual shrinkage network, bi-directional recurrent unit, and fully-connected network. Through the self-attention mechanism, DRSN-BiGRU can automatically ignore noisy information and improve the ability to learn effective features. Network optimization, verification experiment is carried out in three emotional datasets (CASIA, IEMOCAP, and MELD), and the accuracy of DRSN-BiGRU are 86.03%, 86.07%, and 70.57%, respectively. The results are also analyzed and compared with DCNN-LSTM, CNN-BiLSTM, and DRN-BiGRU, which verified the superior performance of DRSN-BiGRU.
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Akalya, Devi C., Renuka D. Karthika, T. Harisudhan, V. K. Jeevanantham, J. Jhanani, and Varshini S. Kavi. "Text emotion recognition using fast text word embedding in bi-directional gated recurrent unit." i-manager's Journal on Information Technology 11, no. 4 (2022): 1. http://dx.doi.org/10.26634/jit.11.4.19119.
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Emotions are states of readiness in the mind that result from evaluations of one's own thinking or events. Although almost all of the important events in our lives are marked by emotions, the nature, causes, and effects of emotions are some of the least understood parts of the human experience. Emotion recognition is playing a promising role in the domains of human-computer interaction and artificial intelligence. A human's emotions can be detected using a variety of methods, including facial gestures, blood pressure, body movements, heart rate, and textual data. From an application standpoint, the ability to identify human emotions in text is becoming more and more crucial in computational linguistics. In this work, we present a classification methodology based on deep neural networks. The Bi-directional Gated Recurrent Unit (Bi-GRU) employed here demonstrates its effectiveness on the Multimodal Emotion Lines Dataset (MELD) when compared to Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM). For word encoding, a comparison of three pre-trained word embeddings namely Glove, Word2Vec, and fastText is made. The findings from the MELD corpus support the conclusion that fastText is the best word embedding for the proposed Bi-GRU model. The experiment utilized the "glove.6B.300d" vector space. It consists of two million word representations in 300 dimensions trained on Common Crawl with sub-word information (600 billion tokens). The accuracy scores of GloVe, Word2Vec, and fastText (300 dimensions each) are tabulated and studied in order to highlight the improved results with fastText on the MELD dataset tested. It is observed that the Bidirectional Gated Recurrent Unit (Bi-GRU) with fastText word embedding outperforms GloVe and Word2Vec with an accuracy of 79.7%.
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Zhang, Xue, Helmut Kuehnelt, and Wim De Roeck. "Traffic Noise Prediction Applying Multivariate Bi-Directional Recurrent Neural Network." Applied Sciences 11, no. 6 (March 18, 2021): 2714. http://dx.doi.org/10.3390/app11062714.
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With the drastically increasing traffic in the last decades, crucial environmental problems have been caused, such as greenhouse gas emission and traffic noise pollution. These problems have adversely affected our life quality and health conditions. In this paper, modelling of traffic noise employing deep learning is investigated. The goal is to identify the best machine-learning model for predicting traffic noise from real-life traffic data with multivariate traffic features as input. An extensive study on recurrent neural network (RNN) is performed in this work for modelling time series traffic data, which was collected through an experimental campaign at an inner city roundabout, including both video traffic data and audio data. The preprocessing of the data, namely how to generate the appropriate input and output for deep learning model, is detailed in this paper. A selection of different architectures of RNN, such as many-to-one, many-to-many, encoder–decoder architectures, was investigated. Moreover, gated recurrent unit (GRU) and long short-term memory (LSTM) were further discussed. The results revealed that a multivariate bi-directional GRU model with many-to-many architecture achieved the best performance with both high accuracy and computation efficiency. The trained model could be promising for a future smart city concept; with the proposed model, real-time traffic noise predictions can be potentially feasible using only traffic data collected by different sensors in the city, thanks to the generated big data by smart cities. The forecast of excessive noise exposure can help the regulation and policy makers to make early decisions, in order to mitigate the noise level.
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Appati, Justice Kwame, Ismail Wafaa Denwar, Ebenezer Owusu, and Michael Agbo Tettey Soli. "Construction of an Ensemble Scheme for Stock Price Prediction Using Deep Learning Techniques." International Journal of Intelligent Information Technologies 17, no. 2 (April 2021): 72–95. http://dx.doi.org/10.4018/ijiit.2021040104.
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This study proposes a deep learning approach for stock price prediction by bridging the long short-term memory with gated recurrent unit. In its evaluation, the mean absolute error and mean square error were used. The model proposed is an extension of the study of Hossain et al. established in 2018 with an MSE of 0.00098 as its lowest error. The current proposed model is a mix of the bidirectional LSTM and bidirectional GRU resulting in 0.00000008 MSE as the lowest error recorded. The LSTM model recorded 0.00000025 MSE, the GRU model recorded 0.00000077 MSE, and the LSTM + GRU model recorded 0.00000023 MSE. Other combinations of the existing models such as the bi-directional LSTM model recorded 0.00000019 MSE, bi-directional GRU recorded 0.00000011 MSE, bidirectional LSTM + GRU recorded 0.00000027 MSE, LSTM and bi-directional GRU recorded 0.00000020 MSE.
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Thakur, Narina, Sunil K. Singh, Akash Gupta, Kunal Jain, Rachna Jain, Dragan Peraković, Nadia Nedjah, and Marjan Kuchaki Rafsanjani. "A Novel CNN, Bidirectional Long-Short Term Memory, and Gated Recurrent Unit-Based Hybrid Approach for Human Activity Recognition." International Journal of Software Science and Computational Intelligence 14, no. 1 (January 1, 2022): 1–19. http://dx.doi.org/10.4018/ijssci.311445.
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Human activity recognition (HAR) is a crucial and challenging classification task for a range of applications from surveillance to assistance. Existing sensor-based HAR systems have limited training data availability and lack fast and accurate methods for robust and rapid activity recognition. In this paper, a novel hybrid HAR technique based on CNN, bi-directional long short-term memory, and gated recurrent units is proposed that can accurately and quickly recognize new human activities with a limited training set and high accuracy. The experiment was conducted on UCI Machine Learning Repository's MHEALTH dataset to analyze the effectiveness of the proposed method. The confusion matrix and accuracy score are utilized to gauge the performance of the presented model. Experiments indicate that the proposed hybrid approach for human activity recognition integrating CNN, bi-directional LSTM, and gated recurrent outperforms computing complexity and efficiency. The overall findings demonstrate that the proposed hybrid model performs exceptionally well, with enhanced accuracy of 94.68%.
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Gurumoorthy, Sasikumar, Aruna Kumari Kokku, Przemysław Falkowski-Gilski, and Parameshachari Bidare Divakarachari. "Effective Air Quality Prediction Using Reinforced Swarm Optimization and Bi-Directional Gated Recurrent Unit." Sustainability 15, no. 14 (July 24, 2023): 11454. http://dx.doi.org/10.3390/su151411454.
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In the present scenario, air quality prediction (AQP) is a complex task due to high variability, volatility, and dynamic nature in space and time of particulates and pollutants. Recently, several nations have had poor air quality due to the high emission of particulate matter (PM2.5) that affects human health conditions, especially in urban areas. In this research, a new optimization-based regression model was implemented for effective forecasting of air pollution. Firstly, the input data were acquired from a real-time Beijing PM2.5 dataset recorded from 1 January 2010 to 31 December 2014. Additionally, the newer real-time dataset was recorded from 2016 to 2022 for four Indian cities: Cochin, Hyderabad, Chennai, and Bangalore. Then, data normalization was accomplished using the Min-Max normalization technique, along with correlation analysis for selecting highly correlated variables (wind direction, temperature, dew point, wind speed, and historical PM2.5). Next, the important features from the highly correlated variables were selected by implementing an optimization algorithm named reinforced swarm optimization (RSO). Further, the selected optimal features were given to the bi-directional gated recurrent unit (Bi-GRU) model for effective AQP. The extensive numerical analysis shows that the proposed model obtained a mean absolute error (MAE) of 9.11 and 0.19 and a mean square error (MSE) of 2.82 and 0.26 on the Beijing PM2.5 dataset and a real-time dataset. On both datasets, the error rate of the proposed model was minimal compared to other regression models.
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Liu, Xinyu, Yongjun Wang, Xishuo Wang, Hui Xu, Chao Li, and Xiangjun Xin. "Bi-directional gated recurrent unit neural network based nonlinear equalizer for coherent optical communication system." Optics Express 29, no. 4 (February 9, 2021): 5923. http://dx.doi.org/10.1364/oe.416672.
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Endalie, Demeke, Getamesay Haile, and Wondmagegn Taye. "Bi-directional long short term memory-gated recurrent unit model for Amharic next word prediction." PLOS ONE 17, no. 8 (August 18, 2022): e0273156. http://dx.doi.org/10.1371/journal.pone.0273156.
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The next word prediction is useful for the users and helps them to write more accurately and quickly. Next word prediction is vital for the Amharic Language since different characters can be written by pressing the same consonants along with different vowels, combinations of vowels, and special keys. As a result, we present a Bi-directional Long Short Term-Gated Recurrent Unit (BLST-GRU) network model for the prediction of the next word for the Amharic Language. We evaluate the proposed network model with 63,300 Amharic sentence and produces 78.6% accuracy. In addition, we have compared the proposed model with state-of-the-art models such as LSTM, GRU, and BLSTM. The experimental result shows, that the proposed network model produces a promising result.
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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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Seabe, Phumudzo Lloyd, Claude Rodrigue Bambe Moutsinga, and Edson Pindza. "Forecasting Cryptocurrency Prices Using LSTM, GRU, and Bi-Directional LSTM: A Deep Learning Approach." Fractal and Fractional 7, no. 2 (February 18, 2023): 203. http://dx.doi.org/10.3390/fractalfract7020203.
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Highly accurate cryptocurrency price predictions are of paramount interest to investors and researchers. However, owing to the nonlinearity of the cryptocurrency market, it is difficult to assess the distinct nature of time-series data, resulting in challenges in generating appropriate price predictions. Numerous studies have been conducted on cryptocurrency price prediction using different Deep Learning (DL) based algorithms. This study proposes three types of Recurrent Neural Networks (RNNs): namely, Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Bi-Directional LSTM (Bi-LSTM) for exchange rate predictions of three major cryptocurrencies in the world, as measured by their market capitalization—Bitcoin (BTC), Ethereum (ETH), and Litecoin (LTC). The experimental results on the three major cryptocurrencies using both Root Mean Squared Error (RMSE) and the Mean Absolute Percentage Error (MAPE) show that the Bi-LSTM performed better in prediction than LSTM and GRU. Therefore, it can be considered the best algorithm. Bi-LSTM presented the most accurate prediction compared to GRU and LSTM, with MAPE values of 0.036, 0.041, and 0.124 for BTC, LTC, and ETH, respectively. The paper suggests that the prediction models presented in it are accurate in predicting cryptocurrency prices and can be beneficial for investors and traders. Additionally, future research should focus on exploring other factors that may influence cryptocurrency prices, such as social media and trading volumes.
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Zheng, Zhijie, Liang Feng, Xuan Wang, Rui Liu, Xian Wang, and Yi Sun. "Multi-energy load forecasting model based on bi-directional gated recurrent unit multi-task neural network." E3S Web of Conferences 256 (2021): 02032. http://dx.doi.org/10.1051/e3sconf/202125602032.
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The complex coupling, coordination and complementarity of different energy in the integrated energy system puts forward higher requirements for the technology of multi-energy load forecasting. To this end, this paper proposes a novel multi-energy load forecasting model based on bi-directional gated recurrent unit (BiGRU) multi-task neural network. Firstly, through the correlation analysis, an effective multi-energy load input data set is constructed. Secondly, the input data set is utilized to train the BiGRU and master the evolution laws of multi-energy loads. Then, multi-task learning (MTL) is used to share the information learned by BiGRU from perspectives of different load forecasting tasks, so as to fully dig the coupling relations among various energy loads. Finally, different types of load forecasting results can be obtained. Simulation results show that BiGRU can simultaneously consider the known data of the past and the future, and it can learn more characteristic information effectively. At the same time, the proposed model utilizes MTL to carry out parallel learning and information sharing for forecasting tasks of various energy loads, which can dig the complex coupling relations among different types of loads more deeply, thus improving the forecasting accuracy of multi-energy loads.
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Khan, Shakir, Ashraf Kamal, Mohd Fazil, Mohammed Ali Alshara, Vineet Kumar Sejwal, Reemiah Muneer Alotaibi, Abdul Rauf Baig, and Salihah Alqahtani. "HCovBi-Caps: Hate Speech Detection Using Convolutional and Bi-Directional Gated Recurrent Unit With Capsule Network." IEEE Access 10 (2022): 7881–94. http://dx.doi.org/10.1109/access.2022.3143799.
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Fu, Yuexin, Zhuhua Hu, Yaochi Zhao, and Mengxing Huang. "A Long-Term Water Quality Prediction Method Based on the Temporal Convolutional Network in Smart Mariculture." Water 13, no. 20 (October 16, 2021): 2907. http://dx.doi.org/10.3390/w13202907.
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In smart mariculture, traditional methods are not only difficult to adapt to the complex, dynamic and changeable environment in open waters, but also have many problems, such as poor accuracy, high time complexity and poor long-term prediction. To solve these deficiencies, a new water quality prediction method based on TCN (temporal convolutional network) is proposed to predict dissolved oxygen, water temperature, and pH. The TCN prediction network can extract time series features and in-depth data features by introducing dilated causal convolution, and has a good effect of long-term prediction. At the same time, it is predicted that the network can process time series data in parallel, which greatly improves the time throughput of the model. Firstly, we arrange the 23,000 sets of water quality data collected in the cages according to time. Secondly, we use the Pearson correlation coefficient method to analyze the correlation information between water quality parameters. Finally, a long-term prediction model of water quality parameters based on a time domain convolutional network is constructed by using prior information and pre-processed water quality data. Experimental results show that long-term prediction method based on TCN has higher accuracy and less time complexity, compared with RNN (recurrent neural network), SRU (simple recurrent unit), BI-SRU (bi-directional simple recurrent unit), GRU (gated recurrent unit) and LSTM (long short-term memory). The prediction accuracy can reach up to 91.91%. The time costs of training model and prediction are reduced by an average of 64.92% and 7.24%, respectively.
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A. S, Sujeesha, and Rajeev Rajan. "Transformer-based Automatic Music Mood Classification Using Multi-modal Framework." Journal of Computer Science and Technology 23, no. 1 (April 3, 2023): e02. http://dx.doi.org/10.24215/16666038.23.e02.
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The mood is a psychological state of feeling that is related to internal emotions and affect, which is how emotions are expressed outwardly. According to studies, music affects our moods, and we are also inclined to choose a theme based on our current moods. Audio-based techniques can achieve promising results, but lyrics also give relevant information about the moods of a song which may not be present in the audio part. So a multi-modal with both textual features and acoustic features can provide enhanced accuracy. Sequential networks such as long short-term memory networks (LSTM) and gated recurrent unit networks (GRU) are widely used in the most state-of-the-art natural language processing (NLP) models. A transformer model uses self-attention to compute representations of its inputs and outputs, unlike recurrent unit networks (RNNs) that use sequences and transformers that can parallelize over input positions during training. In this work, we proposed a multi-modal music mood classification system based on transformers and compared the system's performance using a bi-directional GRU (Bi-GRU)-based system with and without attention. The performance is also analyzed for other state-of-the-art approaches. The proposed transformer-based model acquired higher accuracy than the Bi-GRU-based multi-modal system withsingle-layer attention by providing a maximum accuracy of 77.94\%.
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Wang, Jujie, Yinan Liao, Zhenzhen Zhuang, and Dongming Gao. "An Optimal Weighted Combined Model Coupled with Feature Reconstruction and Deep Learning for Multivariate Stock Index Forecasting." Mathematics 9, no. 21 (October 20, 2021): 2640. http://dx.doi.org/10.3390/math9212640.
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Stock index prediction plays an important role in the creation of better investment strategies. However, prediction can be difficult due to the random fluctuation of financial time series. In pursuit of improved stock index prediction, a hybrid prediction model is proposed in this paper, which contains two-step data pretreatment, double prediction models, and smart optimization. In the data pretreatment stage, in order to carry more information about the prediction target, multidimensional explanatory variables are selected by the Granger causality test, and to eliminate data redundancy, feature extraction is inserted with the help of principal component analysis; both of these can provide a higher-quality dataset. Bi-directional long short-term memory and bi-directional gated recurrent unit network, as the concurrent prediction models, can improve not only the precision, but also the robustness of results. In the last stage, the proposed model integrates the weight optimization of the cuckoo search of the two prediction results to take advantage of both. For the model performance test, four main global stock indices are used. The experimental results show that our model performs better than other benchmark models, which indicates the potential of the proposed model for wide application.
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Chingamtotattil, Rahul, and Rajamma Gopikakumar. "Neural machine translation for Sanskrit to Malayalam using morphology and evolutionary word sense disambiguation." Indonesian Journal of Electrical Engineering and Computer Science 28, no. 3 (October 7, 2022): 1709. http://dx.doi.org/10.11591/ijeecs.v28.i3.pp1709-1719.
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Neural machine translation (NMT) is a fast-evolving MT paradigm and showed good results, particularly in large training data circumstances, for several language pairs. In this paper, we have utilized Sanskrit to Malayalam language pair neural machines translation. The attention-based mechanism for the development of the machine translation system was particularly exploited. Word sense disambiguation (WSD) is a phenomenon for disambiguating the text to let the machine infer the proper definition of the particular word. Sequential deep learning approaches such as a recurrent neural network (RNN), a gated recurrent unit (GRU), a long short term memory (LSTM), and a bi-directional LSTM (BLSTM) were used to analyze the tagged data. By adding morphological elements and evolutionary word sense disambiguation, the suggested common character-word embedding-based NMT model gives a BLEU score of 38.58 which was higher than the others.
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Li, Han, Zhenxiong Liu, Jixiang Niu, Zhongguo Yang, and Sikandar Ali. "Trend-Aware Data Imputation Based on Generative Adversarial Network for Time Series." International Journal of Information Technologies and Systems Approach 16, no. 3 (June 27, 2023): 1–17. http://dx.doi.org/10.4018/ijitsa.325212.
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To solve the problems of generative adversarial network (GAN)-based imputation method for time series, which are ignoring the implied trends in data and using multi-stage training that may lead to high training complexity, this article proposes a trend-aware data imputation method based on GAN (TrendGAN). It implements an end-to-end training using de-noising auto-encoder (DAE). It also uses bidirectional gated recurrent unit (Bi-GRU) in the generator model to consider the bi-directional characteristics and supplement the features lost by de-noising auto-encoder and improves the discriminator's ability using Bi-GRU and hint vector. The authors conducted experiments on four real datasets. The results showed that all components introduced into the method contribute to enhancing the imputation accuracy, and the MSE values of TrendGAN are much lower than those of baseline methods when dealing with time series with random and continuous missing patterns. That is, TrendGAN is suitable for data imputation in complex scenarios with two missing patterns coexist, such as electric power and transportation.
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Ullah, Hayat, and Arslan Munir. "Human Activity Recognition Using Cascaded Dual Attention CNN and Bi-Directional GRU Framework." Journal of Imaging 9, no. 7 (June 26, 2023): 130. http://dx.doi.org/10.3390/jimaging9070130.
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Vision-based human activity recognition (HAR) has emerged as one of the essential research areas in video analytics. Over the last decade, numerous advanced deep learning algorithms have been introduced to recognize complex human actions from video streams. These deep learning algorithms have shown impressive performance for the video analytics task. However, these newly introduced methods either exclusively focus on model performance or the effectiveness of these models in terms of computational efficiency, resulting in a biased trade-off between robustness and computational efficiency in their proposed methods to deal with challenging HAR problem. To enhance both the accuracy and computational efficiency, this paper presents a computationally efficient yet generic spatial–temporal cascaded framework that exploits the deep discriminative spatial and temporal features for HAR. For efficient representation of human actions, we propose an efficient dual attentional convolutional neural network (DA-CNN) architecture that leverages a unified channel–spatial attention mechanism to extract human-centric salient features in video frames. The dual channel–spatial attention layers together with the convolutional layers learn to be more selective in the spatial receptive fields having objects within the feature maps. The extracted discriminative salient features are then forwarded to a stacked bi-directional gated recurrent unit (Bi-GRU) for long-term temporal modeling and recognition of human actions using both forward and backward pass gradient learning. Extensive experiments are conducted on three publicly available human action datasets, where the obtained results verify the effectiveness of our proposed framework (DA-CNN+Bi-GRU) over the state-of-the-art methods in terms of model accuracy and inference runtime across each dataset. Experimental results show that the DA-CNN+Bi-GRU framework attains an improvement in execution time up to 167× in terms of frames per second as compared to most of the contemporary action-recognition methods.
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He, S., H. Jing, and H. Xue. "SPECTRAL-SPATIAL MULTISCALE RESIDUAL NETWORK FOR HYPERSPECTRAL IMAGE CLASSIFICATION." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2022 (May 30, 2022): 389–95. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2022-389-2022.
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Abstract. In recent years, deep neural networks (DNN) are commonly adopted for hyperspectral image (HSI) classification. As the most representative supervised DNN model, convolutional neural networks (CNNs) have outperformed most algorithms. But the main problem of CNN-based methods lies in the over-smoothing phenomenon. Meanwhile, mainstream methods usually require a large number of samples and a large amount of computation. A multi-task learning spectral-spatial multiscale residual network (SSMRN) is proposed to learn features of objects effectively. In the implementation of the SSMRN, a multiscale residual convolutional neural network (MRCNN) is proposed as spatial feature extractors and a band grouping-based bi-directional gated recurrent unit (Bi-GRU) is utilized as spectral feature extractors. To evaluate the effectiveness of the SSMRN, extensive experiments are conducted on public benchmark data sets. The proposed method can retain the detailed boundary of different objects better and yield a competitive performance compared with two state-of-the-art methods especially when the training samples are inadequate.
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Xie, Kangmin, Jichun Liu, and Youbo Liu. "A Power System Timing Data Recovery Method Based on Improved VMD and Attention Mechanism Bi-Directional CNN-GRU." Electronics 12, no. 7 (March 28, 2023): 1590. http://dx.doi.org/10.3390/electronics12071590.
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The temporal data of the power system are expanding with the growth of the power system and the proliferation of automated equipment. However, data loss may arise during the acquisition, measurement, transmission, and storage of temporal data. To address the insufficiency of temporal data in the power system, this study proposes a sequence-to-sequence (Seq2Seq) architecture to restore power system temporal data. This architecture comprises a radial convolutional neural unit (CNN) network and a gated recurrent unit (GRU) network. Specifically, to account for the periodicity and volatility of temporal data, VMD is employed to decompose the time series data output into components of different frequencies. CNN is utilized to extract the spatial characteristics of temporal data. At the same time, Seq2Seq is employed to reconstruct each component based on introducing a feature timing and multi-model combination triple attention mechanism. The feature attention mechanism calculates the contribution rate of each feature quantity and independently mines the correlation between the time series data output and each feature value. The temporal attention mechanism autonomously extracts historical–critical moment information. A multi-model combination attention mechanism is introduced, and the missing data repair value is obtained after modeling the combination of data on both sides of the missing data. Recovery experiments are conducted based on actual data, and the method’s effectiveness is verified by comparison with other methods.
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Rupapara, Vaibhav, Furqan Rustam, Aashir Amaar, Patrick Bernard Washington, Ernesto Lee, and Imran Ashraf. "Deepfake tweets classification using stacked Bi-LSTM and words embedding." PeerJ Computer Science 7 (October 21, 2021): e745. http://dx.doi.org/10.7717/peerj-cs.745.
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The spread of altered media in the form of fake videos, audios, and images, has been largely increased over the past few years. Advanced digital manipulation tools and techniques make it easier to generate fake content and post it on social media. In addition, tweets with deep fake content make their way to social platforms. The polarity of such tweets is significant to determine the sentiment of people about deep fakes. This paper presents a deep learning model to predict the polarity of deep fake tweets. For this purpose, a stacked bi-directional long short-term memory (SBi-LSTM) network is proposed to classify the sentiment of deep fake tweets. Several well-known machine learning classifiers are investigated as well such as support vector machine, logistic regression, Gaussian Naive Bayes, extra tree classifier, and AdaBoost classifier. These classifiers are utilized with term frequency-inverse document frequency and a bag of words feature extraction approaches. Besides, the performance of deep learning models is analyzed including long short-term memory network, gated recurrent unit, bi-direction LSTM, and convolutional neural network+LSTM. Experimental results indicate that the proposed SBi-LSTM outperforms both machine and deep learning models and achieves an accuracy of 0.92.
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Wang, Yanting, Dingkun Zheng, and Rong Jia. "Fault Diagnosis Method for MMC-HVDC Based on Bi-GRU Neural Network." Energies 15, no. 3 (January 28, 2022): 994. http://dx.doi.org/10.3390/en15030994.
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The Modular Multilevel Converter-High Voltage Direct Current (MMC-HVDC) system is recognized worldwide as a highly efficient strategy for transporting renewable energy across regions. As most of the MMC-HVDC system electronics are weak against overcurrent, protections of the MMC-HVDC system are the major focus of research. Because of the insufficiencies of the conventioned fault diagnosis method of MMC-HVDC system, such as hand-designed fault thresholds and complex data pre-processing, this paper proposes a new method for fault detection and location based on Bidirectional Gated Recurrent Unit (Bi-GRU). The proposed method has obvious advantages of feature extraction on the bi-directional structure, and it simplifies the pre-processing of fault data. The simplified pre-processing avoids the loss of valid information in the data and helps to extract detailed fault characteristics, thus improving the accuracy of the method. Extensive simulation experiments show that the proposed method meets the speed requirement of MMC-HVDC protections (2 ms) and the accuracy rate reaches 99.9994%. In addition, the method is not affected by noise and has a high potential for practical applications.
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Amit Pimpalkar and Jeberson Retna Raj. "A Bi-Directional GRU Architecture for the Self-Attention Mechanism: An Adaptable, Multi-Layered Approach with Blend of Word Embedding." International Journal of Engineering and Technology Innovation 13, no. 3 (July 4, 2023): 251–64. http://dx.doi.org/10.46604/ijeti.2023.11510.
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Sentiment analysis (SA) has become an essential component of natural language processing (NLP) with numerous practical applications to understanding “what other people think”. Various techniques have been developed to tackle SA using deep learning (DL); however, current research lacks comprehensive strategies incorporating multiple-word embeddings. This study proposes a self-attention mechanism that leverages DL and involves the contextual integration of word embedding with a time-dispersed bidirectional gated recurrent unit (Bi-GRU). This work employs word embedding approaches GloVe, word2vec, and fastText to achieve better predictive capabilities. By integrating these techniques, the study aims to improve the classifier’s capability to precisely analyze and categorize sentiments in textual data from the domain of movies. The investigation seeks to enhance the classifier’s performance in NLP tasks by addressing the challenges of underfitting and overfitting in DL. To evaluate the model’s effectiveness, an openly available IMDb dataset was utilized, achieving a remarkable testing accuracy of 99.70%.
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Zeng, Yajing, Siyu Yang, Xiongkai Yu, Wenting Lin, Wei Wang, Jijun Tong, and Shudong Xia. "A multimodal parallel method for left ventricular dysfunction identification based on phonocardiogram and electrocardiogram signals synchronous analysis." Mathematical Biosciences and Engineering 19, no. 9 (2022): 9612–35. http://dx.doi.org/10.3934/mbe.2022447.
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<abstract> <p>Heart failure (HF) is widely acknowledged as the terminal stage of cardiac disease and represents a global clinical and public health problem. Left ventricular ejection fraction (LVEF) measured by echocardiography is an important indicator of HF diagnosis and treatment. Early identification of LVEF reduction and early treatment is of great significance to improve LVEF and the prognosis of HF. This research aims to introduce a new method for left ventricular dysfunction (LVD) identification based on phonocardiogram (ECG) and electrocardiogram (PCG) signals synchronous analysis. In the present study, we established a database called Synchronized ECG and PCG Database for Patients with Left Ventricular Dysfunction (SEP-LVDb) consisting of 1046 synchronous ECG and PCG recordings from patients with reduced (n = 107) and normal (n = 699) LVEF. 173 and 873 recordings were available from the reduced and normal LVEF group, respectively. Then, we proposed a parallel multimodal method for LVD identification based on synchronous analysis of PCG and ECG signals. Two-layer bidirectional gate recurrent unit (Bi-GRU) was used to extract features in the time domain, and the data were classified using residual network 18 (ResNet-18). This research confirmed that fused ECG and PCG signals yielded better performance than ECG or PCG signals alone, with an accuracy of 93.27%, precision of 93.34%, recall of 93.27%, and F1-score of 93.27%. Verification of the model's performance with an independent dataset achieved an accuracy of 80.00%, precision of 79.38%, recall of 80.00% and F1-score of 78.67%. The Bi-GRU model outperformed Bi-directional long short-term memory (Bi-LSTM) and recurrent neural network (RNN) models with a best selection frame length of 3.2 s. The Saliency Maps showed that SEP-LVDPN could effectively learn features from the data.</p> </abstract>
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Wang, Qian, Qingguo Yao, Yuli Li, Yansong Zhang, and Cuicui Xu. "PLAP: CSI-Based Passive Localization with Amplitude and Phase Information Using CNN and BGRU." Mobile Information Systems 2023 (May 3, 2023): 1–15. http://dx.doi.org/10.1155/2023/1684490.
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With the increasing popularization and development of WiFi devices, nowadays WiFi-based indoor localization has become a hot topic. Traditional Wi-Fi-based localization technologies which utilize received signal strength indication suffer from indoor multi-path effects and result in localization performance degradation. Therefore, choosing the appropriate characteristic of the WiFi signal is crucial for indoor localization. To improve the localization accuracy, we propose PLAP, a passive localization method using amplitude and phase of channel state information (CSI). Specifically, Hampel filter is used to process the amplitude signals and linear transformation is employed for calibrating phases. To extract representative features from calibrated amplitude and phase signals, we developed a deep learning framework which combines a convolutional neural network (CNN) and a bi-directional Gated recurrent unit (BGRU) to estimate the location of an objective. The experimental results show that the proposed PLAP outperforms other baselines with real-world evaluation.
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Xie, Zaimi, Zhenhua Li, Chunmei Mo, and Ji Wang. "A PCA–EEMD–CNN–Attention–GRU–Encoder–Decoder Accurate Prediction Model for Key Parameters of Seawater Quality in Zhanjiang Bay." Materials 15, no. 15 (July 27, 2022): 5200. http://dx.doi.org/10.3390/ma15155200.
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In order to effectively solve the problem of low accuracy of seawater water quality prediction, an optimized water quality parameter prediction model is constructed in this paper. The model first screened the key factors of water quality data with the principal component analysis (PCA) algorithm, then realized the de-noising of the key factors of water quality data with an ensemble empirical mode decomposition (EEMD) algorithm, and the data were input into the two-dimensional convolutional neural network (2D-CNN) module to extract features, which were used for training and learning by attention, gated recurrent unit, and an encoder–decoder (attention–GRU–encoder–decoder, attention–GED) integrated module. The trained prediction model was used to predict the content of key parameters of water quality. In this paper, the water quality data of six typical online monitoring stations from 2017 to 2021 were used to verify the proposed model. The experimental results show that, based on short-term series prediction, the root mean square error (RMSE), mean absolute percentage error (MAPE), and decision coefficient (R2) were 0.246, 0.307, and 97.80%, respectively. Based on the long-term series prediction, RMSE, MAPE, and R2 were 0.878, 0.594, and 92.23%, respectively, which were all better than the prediction model based on an enhanced clustering algorithm and adam with a radial basis function neural network (ECA–Adam–RBFNN), a prediction model based on a softplus extreme learning machine method with partial least squares and particle swarm optimization (PSO–SELM–PLS), and a wavelet transform-depth Bi–S–SRU (Bi-directional Stacked Simple Recurrent Unit) prediction model. The PCA–EEMD–CNN–attention–GED prediction model not only has high prediction accuracy but can also provide a decision-making basis for the water quality control and management of aquaculture in the waters around Zhanjiang Bay.
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Zhang, Bohan, Katsutoshi Hirayama, Hongxiang Ren, Delong Wang, and Haijiang Li. "Ship Anomalous Behavior Detection Using Clustering and Deep Recurrent Neural Network." Journal of Marine Science and Engineering 11, no. 4 (March 31, 2023): 763. http://dx.doi.org/10.3390/jmse11040763.
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In this study, we propose a real-time ship anomaly detection method driven by Automatic Identification System (AIS) data. The method uses ship trajectory clustering classes as a normal model and a deep learning algorithm as an anomaly detection tool. The method is divided into three main steps: (1) quality maintenance of the original AIS data, (2) extraction of normal ship trajectory clusters using Density-Based Spatial Clustering of Applications with Noise (DBSCAN), in which a segmented improved Dynamic Time Warping (DTW) algorithm is used to measure the degree of trajectory similarity, (3) the clustering results are used as a normative model to train a Bi-directional Gated Recurrent Unit (BiGRU) recurrent neural network, which is used as a trajectory predictor to achieve real-time ship anomaly detection. Experiments were conducted using real AIS data from the port of Tianjin, China. The experimental results are manifold. Firstly, the data pre-processing process effectively improves the quality of raw AIS data. Secondly, the ship trajectory clustering model can accurately classify the traffic flow of different modes in the sea area. Moreover, the trajectory prediction result of the BiGRU model has the smallest error with the actual ship trajectory and has a better trajectory prediction performance compared with the Long Short-Term Memory Network model (LSTM) and Gated Recurrent Unit (GRU). In the final anomaly detection experiment, the detection accuracy and timeliness of the BiGRU model are also higher than LSTM and GRU. Therefore, the proposed method can achieve effective and timely detection of ship anomalous behaviors in terms of position, heading and speed during ship navigation, which provides insight to enhance the intelligence of marine traffic supervision and improve the safety of marine navigation.
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Yadav, Harshwardhan, Param Shah, Neel Gandhi, Tarjni Vyas, Anuja Nair, Shivani Desai, Lata Gohil, et al. "CNN and Bidirectional GRU-Based Heartbeat Sound Classification Architecture for Elderly People." Mathematics 11, no. 6 (March 10, 2023): 1365. http://dx.doi.org/10.3390/math11061365.
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Cardiovascular diseases (CVDs) are a significant cause of death worldwide. CVDs can be prevented by diagnosing heartbeat sounds and other conventional techniques early to reduce the harmful effects caused by CVDs. However, it is still challenging to segment, extract features, and predict heartbeat sounds in elderly people. The inception of deep learning (DL) algorithms has helped detect various types of heartbeat sounds at an early stage. Motivated by this, we proposed an intelligent architecture categorizing heartbeat into normal and murmurs for elderly people. We have used a standard heartbeat dataset with heartbeat class labels, i.e., normal and murmur. Furthermore, it is augmented and preprocessed by normalization and standardization to significantly reduce computational power and time. The proposed convolutional neural network and bi-directional gated recurrent unit (CNN + BiGRU) attention-based architecture for the classification of heartbeat sound achieves an accuracy of 90% compared to the baseline approaches. Hence, the proposed novel CNN + BiGRU attention-based architecture is superior to other DL models for heartbeat sound classification.
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So, Dayeong, Jinyeong Oh, Insu Jeon, Jihoon Moon, Miyoung Lee, and Seungmin Rho. "BiGTA-Net: A Hybrid Deep Learning-Based Electrical Energy Forecasting Model for Building Energy Management Systems." Systems 11, no. 9 (September 2, 2023): 456. http://dx.doi.org/10.3390/systems11090456.
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The growth of urban areas and the management of energy resources highlight the need for precise short-term load forecasting (STLF) in energy management systems to improve economic gains and reduce peak energy usage. Traditional deep learning models for STLF present challenges in addressing these demands efficiently due to their limitations in modeling complex temporal dependencies and processing large amounts of data. This study presents a groundbreaking hybrid deep learning model, BiGTA-net, which integrates a bi-directional gated recurrent unit (Bi-GRU), a temporal convolutional network (TCN), and an attention mechanism. Designed explicitly for day-ahead 24-point multistep-ahead building electricity consumption forecasting, BiGTA-net undergoes rigorous testing against diverse neural networks and activation functions. Its performance is marked by the lowest mean absolute percentage error (MAPE) of 5.37 and a root mean squared error (RMSE) of 171.3 on an educational building dataset. Furthermore, it exhibits flexibility and competitive accuracy on the Appliances Energy Prediction (AEP) dataset. Compared to traditional deep learning models, BiGTA-net reports a remarkable average improvement of approximately 36.9% in MAPE. This advancement emphasizes the model’s significant contribution to energy management and load forecasting, accentuating the efficacy of the proposed hybrid approach in power system optimizations and smart city energy enhancements.
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Zhou, Yong, Lingyu Wang, and Junhao Qian. "Application of Combined Models Based on Empirical Mode Decomposition, Deep Learning, and Autoregressive Integrated Moving Average Model for Short-Term Heating Load Predictions." Sustainability 14, no. 12 (June 15, 2022): 7349. http://dx.doi.org/10.3390/su14127349.
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Short-term building energy consumption prediction is of great significance for the optimized operation of building energy management systems and energy conservation. Due to the high-dimensional nonlinear characteristics of building heat loads, traditional single machine-learning models cannot extract the features well. Therefore, in this paper, a combined model based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), four deep learning (DL), and the autoregressive integrated moving average (ARIMA) models is proposed. The DL models include a convolution neural network, long- and short-term memory (LSTM), bi-directional LSTM (bi-LSTM), and the gated recurrent unit. The CEEMDAN decomposed the heating load into different components to extract the different features, while the DL and ARIMA models were used for the prediction of heating load features with high and low complexity, respectively. The single-DL models and the CEEMDAN-DL combinations were also implemented for comparison purposes. The results show that the combined models achieved much higher accuracy compared to the single-DL models and the CEEMDAN-DL combinations. Compared to the single-DL models, the average coefficient of determination (R2), root mean square error (RMSE), and coefficient of variation of the RMSE (CV-RMSE) were improved by 2.91%, 47.93%, and 47.92%, respectively. Furthermore, CEEMDAN-bi-LSTM-ARIMA performed the best of all the combined models, achieving values of R2 = 0.983, RMSE = 70.25 kWh, and CV-RMSE = 1.47%. This study provides a new guide for developing combined models for building energy consumption prediction.
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Wang, Qianyang, Yuan Liu, Qimeng Yue, Yuexin Zheng, Xiaolei Yao, and Jingshan Yu. "Impact of Input Filtering and Architecture Selection Strategies on GRU Runoff Forecasting: A Case Study in the Wei River Basin, Shaanxi, China." Water 12, no. 12 (December 16, 2020): 3532. http://dx.doi.org/10.3390/w12123532.
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A gated recurrent unit (GRU) network, which is a kind of artificial neural network (ANN), has been increasingly applied to runoff forecasting. However, knowledge about the impact of different input data filtering strategies and the implications of different architectures on the GRU runoff forecasting model’s performance is still insufficient. This study has selected the daily rainfall and runoff data from 2007 to 2014 in the Wei River basin in Shaanxi, China, and assessed six different scenarios to explore the patterns of that impact. In the scenarios, four manually-selected rainfall or runoff data combinations and principal component analysis (PCA) denoised input have been considered along with single directional and bi-directional GRU network architectures. The performance has been evaluated from the aspect of robustness to 48 various hypermeter combinations, also, optimized accuracy in one-day-ahead (T + 1) and two-day-ahead (T + 2) forecasting for the overall forecasting process and the flood peak forecasts. The results suggest that the rainfall data can enhance the robustness of the model, especially in T + 2 forecasting. Additionally, it slightly introduces noise and affects the optimized prediction accuracy in T + 1 forecasting, but significantly improves the accuracy in T + 2 forecasting. Though with relevance (R = 0.409~0.763, Grey correlation grade >0.99), the runoff data at the adjacent tributary has an adverse effect on the robustness, but can enhance the accuracy of the flood peak forecasts with a short lead time. The models with PCA denoised input has an equivalent, even better performance on the robustness and accuracy compared with the models with the well manually filtered data; though slightly reduces the time-step robustness, the bi-directional architecture can enhance the prediction accuracy. All the scenarios provide acceptable forecasting results (NSE of 0.927~0.951 for T + 1 forecasting and 0.745~0.836 for T + 2 forecasting) when the hyperparameters have already been optimized. Based on the results, recommendations have been provided for the construction of the GRU runoff forecasting model.
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Zhang, Pengjv, and Yuanyao Lu. "Research on Anomaly Detection of Surveillance Video Based on Branch-Fusion Net and CSAM." Sensors 23, no. 3 (January 26, 2023): 1385. http://dx.doi.org/10.3390/s23031385.
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As the monitor probes are used more and more widely these days, the task of detecting abnormal behaviors in surveillance videos has gained widespread attention. The generalization ability and parameter overhead of the model affect how accurate the detection result is. To deal with the poor generalization ability and high parameter overhead of the model in existing anomaly detection methods, we propose a three-dimensional multi-branch convolutional fusion network, named “Branch-Fusion Net”. The network is designed with a multi-branch structure not only to significantly reduce parameter overhead but also to improve the generalization ability by understanding the input feature map from different perspectives. To ignore useless features during the model training, we propose a simple yet effective Channel Spatial Attention Module (CSAM), which sequentially focuses attention on key channels and spatial feature regions to suppress useless features and enhance important features. We combine the Branch-Fusion Net and the CSAM as a local feature extraction network and use the Bi-Directional Gated Recurrent Unit (Bi-GRU) to extract global feature information. The experiments are validated on a self-built Crimes-mini dataset, and the accuracy of anomaly detection in surveillance videos reaches 93.55% on the test set. The result shows that the model proposed in the paper significantly improves the accuracy of anomaly detection in surveillance videos with low parameter overhead.
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Alam, Muhammad S., AKM B. Hossain, and Farhan B. Mohamed. "Performance Evaluation of Recurrent Neural Networks Applied to Indoor Camera Localization." International Journal of Emerging Technology and Advanced Engineering 12, no. 8 (August 2, 2022): 116–24. http://dx.doi.org/10.46338/ijetae0822_15.
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Researchers in robotics and computer vision are experimenting with the image-based localization of indoor cameras. Implementation of indoor camera localization problems using a Convolutional neural network (CNN) or Recurrent neural network (RNN) is more challenging from a large image dataset because of the internal structure of CNN or RNN. We can choose a preferable CNN or RNN variant based on the problem type and size of the dataset. CNN is the most flexible method for implementing indoor localization problems. Despite CNN's suitability for hyper-parameter selection, it requires a lot of training images to achieve high accuracy. In addition, overfitting leads to a decrease in accuracy. Introduce RNN, which accurately keeps input images in internal memory to solve these problems. Longshort-term memory (LSTM), Bi-directional LSTM (BiLSTM), and Gated recurrent unit (GRU) are three variants of RNN. We may choose the most appropriate RNN variation based on the problem type and dataset. In this study, we can recommend which variant is effective for training more speedily and which variant produces more accurate results. Vanishing gradient issues also affect RNNs, making it difficult to learn more data. Overcome the gradient vanishing problem by utilizing LSTM. The BiLSTM is an advanced version of the LSTM and is capable of higher performance than the LSTM. A more advanced RNN variant is GRU which is computationally more efficient than an LSTM. In this study, we explore a variety of recurring units for localizing indoor cameras. Our focus is on more powerful recurrent units like LSTM, BiLSTM, and GRU. Using the Microsoft 7-Scenes and InteriorNet datasets, we evaluate the performance of LSTM, BiLSTM, and GRU. Our experiment has shown that the BiLSTM is more efficient in accuracy than the LSTM and GRU. We also observed that the GRU is faster than LSTM and BiLSTM
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Tang, Xuliang, Heng Wan, Weiwen Wang, Mengxu Gu, Linfeng Wang, and Linfeng Gan. "Lithium-Ion Battery Remaining Useful Life Prediction Based on Hybrid Model." Sustainability 15, no. 7 (April 6, 2023): 6261. http://dx.doi.org/10.3390/su15076261.
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Accurate prediction of the remaining useful life (RUL) is a key function for ensuring the safety and stability of lithium-ion batteries. To solve the capacity regeneration and model adaptability under different working conditions, a hybrid RUL prediction model based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and a bi-directional gated recurrent unit (BiGRU) is proposed. CEEMDAN is used to divide the capacity into intrinsic mode functions (IMFs) to reduce the impact of capacity regeneration. In addition, an improved grey wolf optimizer (IGOW) is proposed to maintain the reliability of the BiGRU network. The diversity of the initial population in the GWO algorithm was improved using chaotic tent mapping. An improved control factor and dynamic population weight are adopted to accelerate the convergence speed of the algorithm. Finally, capacity and RUL prediction experiments are conducted to verify the battery prediction performance under different training data and working conditions. The results indicate that the proposed method can achieve an MAE of less than 4% with only 30% of the training set, which is verified using the CALCE and NASA battery data.
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Tariq, Muhammad Usman, Shuhaida Binti Ismail, Muhammad Babar, and Ashir Ahmad. "Harnessing the power of AI: Advanced deep learning models optimization for accurate SARS-CoV-2 forecasting." PLOS ONE 18, no. 7 (July 20, 2023): e0287755. http://dx.doi.org/10.1371/journal.pone.0287755.
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The pandemic has significantly affected many countries including the USA, UK, Asia, the Middle East and Africa region, and many other countries. Similarly, it has substantially affected Malaysia, making it crucial to develop efficient and precise forecasting tools for guiding public health policies and approaches. Our study is based on advanced deep-learning models to predict the SARS-CoV-2 cases. We evaluate the performance of Long Short-Term Memory (LSTM), Bi-directional LSTM, Convolutional Neural Networks (CNN), CNN-LSTM, Multilayer Perceptron, Gated Recurrent Unit (GRU), and Recurrent Neural Networks (RNN). We trained these models and assessed them using a detailed dataset of confirmed cases, demographic data, and pertinent socio-economic factors. Our research aims to determine the most reliable and accurate model for forecasting SARS-CoV-2 cases in the region. We were able to test and optimize deep learning models to predict cases, with each model displaying diverse levels of accuracy and precision. A comprehensive evaluation of the models’ performance discloses the most appropriate architecture for Malaysia’s specific situation. This study supports ongoing efforts to combat the pandemic by offering valuable insights into the application of sophisticated deep-learning models for precise and timely SARS-CoV-2 case predictions. The findings hold considerable implications for public health decision-making, empowering authorities to create targeted and data-driven interventions to limit the virus’s spread and minimize its effects on Malaysia’s population.
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Guo, Tiantian, Jianzhuo Yan, Jianhui Chen, and Yongchuan Yu. "Overflow Capacity Prediction of Pumping Station Based on Data Drive." Water 15, no. 13 (June 28, 2023): 2380. http://dx.doi.org/10.3390/w15132380.
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In recent years, the information requirements of pumping stations have become higher and higher. The prediction of overflow capacity can provide important reference for flood carrying capacity, water resource scheduling and water safety. In order to improve the accuracy, stability and generalization ability of the model, a BiGRU–ARIMA data-driven method based on self-attention mechanism is proposed to predict the flow capacity of the pump station. Bidirectional gated recurrent unit (BiGRU), a variant of cyclic neural network (RNN), can not only deal with nonlinear components well, but also deal with the problem of insufficient dependence over long distances and has a simple structure. Autoregressive integrated moving average (ARIMA) has the advantage of being sensitive to linear components. Firstly, the characteristics of the pre-processed pump station data are selected and screened through Pearson correlation coefficient and a self-attention mechanism. Then, a bi-directional gated recurrent unit (BiGRU) is used to process the nonlinear components of the data, and a dropout layer is added to avoid overfitting phenomena. We extract the linear features of the obtained error terms using the ARIMA model and use them as correction items to correct the prediction results of the BiGRU model. Finally, we obtain the prediction results of the overflow and water level. The variation characteristics of overdischarge are analyzed by the relation of flow and water level. In this paper, the actual production data of a Grade 9 pumping station of Miyun Reservoir is taken as an example to verify the validity of the model. Model performance is evaluated according to mean absolute error (MAE), mean absolute percentage error (MAPE) and linear regression correlation coefficient (R2). The experimental results show that, compared with the single ARIMAX, BiGRU model and BP neural network, the SA–BiGRU–ARIMA hybrid prediction model has a better prediction effect than other data-driven models.
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He, Ping, Huaying Qi, Shiyi Wang, and Jiayue Cang. "Cross-Modal Sentiment Analysis of Text and Video Based on Bi-GRU Cyclic Network and Correlation Enhancement." Applied Sciences 13, no. 13 (June 25, 2023): 7489. http://dx.doi.org/10.3390/app13137489.
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Cross-modal sentiment analysis is an emerging research area in natural language processing. The core task of cross-modal fusion lies in cross-modal relationship extraction and joint feature learning. The existing research methods of cross-modal sentiment analysis focus on static text, video, audio, and other modality data but ignore the fact that different modality data are often unaligned in practical applications. There is a long-term time dependence among unaligned data sequences, and it is difficult to explore the interaction between different modalities. The paper proposes a sentiment analysis model (UA-BFET) based on feature enhancement technology in unaligned data scenarios, which can perform sentiment analysis on unaligned text and video modality data in social media. Firstly, the model adds a cyclic memory enhancement network across time steps. Then, the obtained cross-modal fusion features with interaction are applied to the unimodal feature extraction process of the next time step in the Bi-directional Gated Recurrent Unit (Bi-GRU) so that the progressively enhanced unimodal features and cross-modal fusion features continuously complement each other. Secondly, the extracted unimodal text and video features taken jointly from the enhanced cross-modal fusion features are subjected to canonical correlation analysis (CCA) and input into the fully connected layer and Softmax function for sentiment analysis. Through experiments executed on unaligned public datasets MOSI and MOSEI, the UA-BFET model has achieved or even exceeded the sentiment analysis effect of text, video, and audio modality fusion and has outstanding advantages in solving cross-modal sentiment analysis in unaligned data scenarios.
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Kareem, Kola Yusuff, Yeonjeong Seong, Kyungtak Kim, and Younghun Jung. "A Case Study of Tidal Analysis Using Theory-Based Artificial Intelligence Techniques for Disaster Management in Taehwa River, South Korea." Water 14, no. 14 (July 9, 2022): 2172. http://dx.doi.org/10.3390/w14142172.
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Monitoring tidal dynamics is imperative to disaster management because it requires a high level of precision to avert possible dangers. Good knowledge of the physical drivers of tides is vital to achieving such a precision. The Taehwa River in Ulsan City, Korea experiences tidal currents in the estuary that drains into the East Sea. The contribution of wind to tide prediction is evaluated by comparing tidal predictions using harmonic analysis and three deep learning models. Harmonic analysis is conducted on hourly water level data from 2010–2021 using the commercial pytides toolbox to generate constituents and predict tidal elevations. Three deep learning models of long short-term memory (LSTM), gated recurrent unit (GRU), and bi-directional lstm (BiLSTM) are fitted to the water level and wind speed to evaluate wind and no-wind scenarios. Results show that Taehwa tides are categorized as semidiurnal tides based on a computed form ratio of 0.2714 in a 24-h tidal cycle. The highest tidal range of 0.60 m is recorded on full moon spring tide indicating the significant lunar pull. Wind effect improved tidal prediction NSE of optimal LSTM model from 0.67 to 0.90. Knowledge of contributing effect of wind will inform flood protection measures to enhance disaster preparedness.
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Wang, Xiaomin, Haoriqin Wang, Guocheng Zhao, Zhichao Liu, and Huarui Wu. "ALBERT over Match-LSTM Network for Intelligent Questions Classification in Chinese." Agronomy 11, no. 8 (July 30, 2021): 1530. http://dx.doi.org/10.3390/agronomy11081530.
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This paper introduces a series of experiments with an ALBERT over match-LSTM network on the top of pre-trained word vectors, for accurate classification of intelligent question answering and thus the guarantee of precise information service. To improve the performance of data classification, a short text classification method based on an ALBERT and match-LSTM model was proposed to overcome the limitations of the classification process, such as few vocabularies, sparse features, large amount of data, lots of noise and poor normalization. In the model, Jieba word segmentation tools and agricultural dictionary were selected to text segmentation, GloVe algorithm was then adopted to expand the text characteristic and weighted word vector according to the text of key vector, bi-directional gated recurrent unit was applied to catch the context feature information and multi-convolutional neural networks were finally established to gain local multidimensional characteristics of text. Batch normalization, Dropout, Global Average Pooling and Global Max Pooling were utilized to solve overfitting problem. The results showed that the model could classify questions accurately, with a precision of 96.8%. Compared with other classification models, such as multi-SVM model and CNN model, ALBERT+match-LSTM had obvious advantages in classification performance in intelligent Agri-tech information service.
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Zhang, Jingren, Fang’ai Liu, Weizhi Xu, and Hui Yu. "Feature Fusion Text Classification Model Combining CNN and BiGRU with Multi-Attention Mechanism." Future Internet 11, no. 11 (November 12, 2019): 237. http://dx.doi.org/10.3390/fi11110237.
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Convolutional neural networks (CNN) and long short-term memory (LSTM) have gained wide recognition in the field of natural language processing. However, due to the pre- and post-dependence of natural language structure, relying solely on CNN to implement text categorization will ignore the contextual meaning of words and bidirectional long short-term memory (BiLSTM). The feature fusion model is divided into a multiple attention (MATT) CNN model and a bi-directional gated recurrent unit (BiGRU) model. The CNN model inputs the word vector (word vector attention, part of speech attention, position attention) that has been labeled by the attention mechanism into our multi-attention mechanism CNN model. Obtaining the influence intensity of the target keyword on the sentiment polarity of the sentence, and forming the first dimension of the sentiment classification, the BiGRU model replaces the original BiLSTM and extracts the global semantic features of the sentence level to form the second dimension of sentiment classification. Then, using PCA to reduce the dimension of the two-dimensional fusion vector, we finally obtain a classification result combining two dimensions of keywords and sentences. The experimental results show that the proposed MATT-CNN+BiGRU fusion model has 5.94% and 11.01% higher classification accuracy on the MRD and SemEval2016 datasets, respectively, than the mainstream CNN+BiLSTM method.
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Jeon, Sanghun, Ahmed Elsharkawy, and Mun Sang Kim. "Lipreading Architecture Based on Multiple Convolutional Neural Networks for Sentence-Level Visual Speech Recognition." Sensors 22, no. 1 (December 23, 2021): 72. http://dx.doi.org/10.3390/s22010072.
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In visual speech recognition (VSR), speech is transcribed using only visual information to interpret tongue and teeth movements. Recently, deep learning has shown outstanding performance in VSR, with accuracy exceeding that of lipreaders on benchmark datasets. However, several problems still exist when using VSR systems. A major challenge is the distinction of words with similar pronunciation, called homophones; these lead to word ambiguity. Another technical limitation of traditional VSR systems is that visual information does not provide sufficient data for learning words such as “a”, “an”, “eight”, and “bin” because their lengths are shorter than 0.02 s. This report proposes a novel lipreading architecture that combines three different convolutional neural networks (CNNs; a 3D CNN, a densely connected 3D CNN, and a multi-layer feature fusion 3D CNN), which are followed by a two-layer bi-directional gated recurrent unit. The entire network was trained using connectionist temporal classification. The results of the standard automatic speech recognition evaluation metrics show that the proposed architecture reduced the character and word error rates of the baseline model by 5.681% and 11.282%, respectively, for the unseen-speaker dataset. Our proposed architecture exhibits improved performance even when visual ambiguity arises, thereby increasing VSR reliability for practical applications.
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Ha, Manh-Hung, The-Anh Pham, Dao Thi Thanh, and Van Luan Tran. "Attention correlated appearance and motion feature followed temporal learning for activity recognition." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 2 (April 1, 2023): 1510. http://dx.doi.org/10.11591/ijece.v13i2.pp1510-1521.
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<span lang="EN-US">Recent advances in deep neural networks have been successfully demonstrated with fairly good accuracy for multi-class activity identification. However, existing methods have limitations in achieving complex spatial-temporal dependencies. In this work, we design two stream fusion attention (2SFA) connected to a temporal bidirectional gated recurrent unit (GRU) one-layer model and classified by prediction voting classifier (PVC) to recognize the action in a video. Particularly in the proposed deep neural network (DNN), we present 2SFA for capturing appearance information from red green blue (RGB) and motion from optical flow, where both streams are correlated by proposed fusion attention (FA) as the input of a temporal network. On the other hand, the temporal network with a bi-directional temporal layer using a GRU single layer is preferred for temporal understanding because it yields practical merits against six topologies of temporal networks in the UCF101 dataset. Meanwhile, the new proposed classifier scheme called PVC employs multiple nearest class mean (NCM) and the SoftMax function to yield multiple features outputted from temporal networks, and then votes their properties for high-performance classifications. The experiments achieve the best average accuracy of 70.8% in HMDB51 and 91.9%, the second best in UCF101 in terms of 2DConvNet for action recognition.</span>
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Zeng, Shi, and Dechang Pi. "Milling Surface Roughness Prediction Based on Physics-Informed Machine Learning." Sensors 23, no. 10 (May 22, 2023): 4969. http://dx.doi.org/10.3390/s23104969.
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Surface roughness is a key indicator of the quality of mechanical products, which can precisely portray the fatigue strength, wear resistance, surface hardness and other properties of the products. The convergence of current machine-learning-based surface roughness prediction methods to local minima may lead to poor model generalization or results that violate existing physical laws. Therefore, this paper combined physical knowledge with deep learning to propose a physics-informed deep learning method (PIDL) for milling surface roughness predictions under the constraints of physical laws. This method introduced physical knowledge in the input phase and training phase of deep learning. Data augmentation was performed on the limited experimental data by constructing surface roughness mechanism models with tolerable accuracy prior to training. In the training, a physically guided loss function was constructed to guide the training process of the model with physical knowledge. Considering the excellent feature extraction capability of convolutional neural networks (CNNs) and gated recurrent units (GRUs) in the spatial and temporal scales, a CNN–GRU model was adopted as the main model for milling surface roughness predictions. Meanwhile, a bi-directional gated recurrent unit and a multi-headed self-attentive mechanism were introduced to enhance data correlation. In this paper, surface roughness prediction experiments were conducted on the open-source datasets S45C and GAMHE 5.0. In comparison with the results of state-of-the-art methods, the proposed model has the highest prediction accuracy on both datasets, and the mean absolute percentage error on the test set was reduced by 3.029% on average compared to the best comparison method. Physical-model-guided machine learning prediction methods may be a future pathway for machine learning evolution.
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Liang, Jianqin, Daichao Li, Yiting Lin, Sheng Wu, and Zongcai Huang. "Named Entity Recognition of Chinese Crop Diseases and Pests Based on RoBERTa-wwm with Adversarial Training." Agronomy 13, no. 3 (March 22, 2023): 941. http://dx.doi.org/10.3390/agronomy13030941.
Full textAbstract:
This paper proposes a novel model for named entity recognition of Chinese crop diseases and pests. The model is intended to solve the problems of uneven entity distribution, incomplete recognition of complex terms, and unclear entity boundaries. First, a robustly optimized BERT pre-training approach-whole word masking (RoBERTa-wwm) model is used to extract diseases and pests’ text semantics, acquiring dynamic word vectors to solve the problem of incomplete word recognition. Adversarial training is then introduced to address unclear boundaries of diseases and pest entities and to improve the generalization ability of models in an effective manner. The context features are obtained by the bi-directional gated recurrent unit (BiGRU) neural network. Finally, the optimal tag sequence is obtained by conditional random fields (CRF) decoding. A focal loss function is introduced to optimize conditional random fields (CRF) and thus solve the problem of unbalanced label classification in the sequence. The experimental results show that the model’s precision, recall, and F1 values on the crop diseases and pests corpus reached 89.23%, 90.90%, and 90.04%, respectively, demonstrating effectiveness at improving the accuracy of named entity recognition for Chinese crop diseases and pests. The named entity recognition model proposed in this study can provide a high-quality technical basis for downstream tasks such as crop diseases and pests knowledge graphs and question-answering systems.
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Zhang, Yujie, Lei Zhang, Duo Sun, Kai Jin, and Yu Gu. "Short-Term Wind Power Forecasting Based on VMD and a Hybrid SSA-TCN-BiGRU Network." Applied Sciences 13, no. 17 (August 31, 2023): 9888. http://dx.doi.org/10.3390/app13179888.
Full textAbstract:
Wind power generation is a renewable energy source, and its power output is influenced by multiple factors such as wind speed, direction, meteorological conditions, and the characteristics of wind turbines. Therefore, accurately predicting wind power is crucial for the grid operation and maintenance management of wind power plants. This paper proposes a hybrid model to improve the accuracy of wind power prediction. Accurate wind power forecasting is critical for the safe operation of power systems. To improve the accuracy of wind power prediction, this paper proposes a hybrid model incorporating variational modal decomposition (VMD), a Sparrow Search Algorithm (SSA), and a temporal-convolutional-network-based bi-directional gated recurrent unit (TCN-BiGRU). The model first uses VMD to break down the raw power data into several modal components, and then it builds an SSA-TCN-BIGRU model for each component for prediction, and finally, it accumulates all the predicted components to obtain the wind power prediction results. The proposed short-term wind power prediction model was validated using measured data from a wind farm in China. The proposed VMD-SSA-TCN-BiGRU forecasting framework is compared with benchmark models to verify its practicability and reliability. Compared with the TCN-BiGRU, the symmetric mean absolute percentage error, the mean absolute error, and the root mean square error of the VMD-SSA-TCN-BiGRU model reduced by 34.36%, 49.14%, and 55.94%.
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Zheng, Chunjun, Chunli Wang, and Ning Jia. "An Ensemble Model for Multi-Level Speech Emotion Recognition." Applied Sciences 10, no. 1 (December 26, 2019): 205. http://dx.doi.org/10.3390/app10010205.
Full textAbstract:
Speech emotion recognition is a challenging and widely examined research topic in the field of speech processing. The accuracy of existing models in speech emotion recognition tasks is not high, and the generalization ability is not strong. Since the feature set and model design of effective speech directly affect the accuracy of speech emotion recognition, research on features and models is important. Because emotional expression is often correlated with the global features, local features, and model design of speech, it is often difficult to find a universal solution for effective speech emotion recognition. Based on this, the main research purpose of this paper is to generate general emotion features in speech signals from different angles, and use the ensemble learning model to perform emotion recognition tasks. It is divided into the following aspects: (1) Three expert roles of speech emotion recognition are designed. Expert 1 focuses on three-dimensional feature extraction of local signals; expert 2 focuses on extraction of comprehensive information in local data; and expert 3 emphasizes global features: acoustic feature descriptors (low-level descriptors (LLDs)), high-level statistics functionals (HSFs), and local features and their timing relationships. A single-/multiple-level deep learning model that meets expert characteristics is designed for each expert, including convolutional neural network (CNN), bi-directional long short-term memory (BLSTM), and gated recurrent unit (GRU). Convolutional recurrent neural network (CRNN), based on a combination of an attention mechanism, is used for internal training of experts. (2) By designing an ensemble learning model, each expert can play to its own advantages and evaluate speech emotions from different focuses. (3) Through experiments, the performance of various experts and ensemble learning models in emotion recognition is compared in the Interactive Emotional Dyadic Motion Capture (IEMOCAP) corpus and the validity of the proposed model is verified.
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Lu, Yiwei, Ruopeng Yang, Xuping Jiang, Dan Zhou, Changsheng Yin, and Zizhuo Li. "MRE: A Military Relation Extraction Model Based on BiGRU and Multi-Head Attention." Symmetry 13, no. 9 (September 19, 2021): 1742. http://dx.doi.org/10.3390/sym13091742.
Full textAbstract:
A great deal of operational information exists in the form of text. Therefore, extracting operational information from unstructured military text is of great significance for assisting command decision making and operations. Military relation extraction is one of the main tasks of military information extraction, which aims at identifying the relation between two named entities from unstructured military texts. However, the traditional methods of extracting military relations cannot easily resolve problems such as inadequate manual features and inaccurate Chinese word segmentation in military fields, failing to make full use of symmetrical entity relations in military texts. With our approach, based on the pre-trained language model, we present a Chinese military relation extraction method, which combines the bi-directional gate recurrent unit (BiGRU) and multi-head attention mechanism (MHATT). More specifically, the conceptual foundation of our method lies in constructing an embedding layer and combining word embedding with position embedding, based on the pre-trained language model; the output vectors of BiGRU neural networks are symmetrically spliced to learn the semantic features of context, and they fuse the multi-head attention mechanism to improve the ability of expressing semantic information. On the military text corpus that we have built, we conduct extensive experiments. We demonstrate the superiority of our method over the traditional non-attention model, attention model, and improved attention model, and the comprehensive evaluation value F1-score of the model is improved by about 4%.
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Pathan, Refat Khan, Mohammad Amaz Uddin, Ananda Mohan Paul, Md Imtiaz Uddin, Zuhal Y. Hamd, Hanan Aljuaid, and Mayeen Uddin Khandaker. "Monkeypox genome mutation analysis using a timeseries model based on long short-term memory." PLOS ONE 18, no. 8 (August 23, 2023): e0290045. http://dx.doi.org/10.1371/journal.pone.0290045.
Full textAbstract:
Monkeypox is a double-stranded DNA virus with an envelope and is a member of the Poxviridae family’s Orthopoxvirus genus. This virus can transmit from human to human through direct contact with respiratory secretions, infected animals and humans, or contaminated objects and causing mutations in the human body. In May 2022, several monkeypox affected cases were found in many countries. Because of its transmitting characteristics, on July 23, 2022, a nationwide public health emergency was proclaimed by WHO due to the monkeypox virus. This study analyzed the gene mutation rate that is collected from the most recent NCBI monkeypox dataset. The collected data is prepared to independently identify the nucleotide and codon mutation. Additionally, depending on the size and availability of the gene dataset, the computed mutation rate is split into three categories: Canada, Germany, and the rest of the world. In this study, the genome mutation rate of the monkeypox virus is predicted using a deep learning-based Long Short-Term Memory (LSTM) model and compared with Gated Recurrent Unit (GRU) model. The LSTM model shows “Root Mean Square Error” (RMSE) values of 0.09 and 0.08 for testing and training, respectively. Using this time series analysis method, the prospective mutation rate of the 50th patient has been predicted. Note that this is a new report on the monkeypox gene mutation. It is found that the nucleotide mutation rates are decreasing, and the balance between bi-directional rates are maintained.
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ASIAGWU, Harriet, UGHERUGHE, Joseph Ediri, and EZEABASILI, N. Vincent. "DISAGGREGATED ANALYSIS OF PUBLIC EXPENDITURE AND ECONOMIC DEVELOPMENT ON THE NIGERIAN ECONOMY." International Journal of Management & Entrepreneurship Research 5, no. 1 (January 23, 2023): 41–56. http://dx.doi.org/10.51594/ijmer.v5i1.435.
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This study empirically investigated public expenditure and economic development of Nigeria. To achieve this objective, relevant data used spanning from 1981-2021 were sourced from Central Bank of Nigeria (CBN) Statistical Bulletin for the period under review. Descriptive statistics, Augmented Dickey Fuller (ADF) Unit root test, Granger causality and Ordinary Least Square (OLS) regression were the analytical tools for this study. Real Gross Domestic Product (RGDP) was used as the dependent variable while capital expenditure on administration, capital expenditure on economic services, capital expenditure on Social and Community Services, capital expenditure on Transfers, recurrent expenditure on administration, recurrent expenditure on economic services, recurrent expenditure on Social and Community Services, and recurrent expenditure on Transfers as the independent variables. Based on the analysis, the F-statistic of the regression output stood at 56.23992, this implies that the regression plane is statistically significant. Also, the Prob.(F-Statistic) 0.000000 is less than the 0.05 level of significance implies that there is a statistical significant relationship between the variables. R2 = 0.933599 implies that about 93.36% of the total variation in the model specified was accounted for by the independent variables. RGDP and RES are platykurtic, CSCS, RA, and RSCS are mesokurtic, and CA, CES, CSCS, CT, and RT are leptokurtic, according to the descriptive analysis, which also showed that all the variables were normally distributed; All of the variables were stationary and significant at their respective values. RGDP granger caused CA, CES, CSCS, CT, RES, RSCS, and RT, so it is unidirectional causality, however RGDP granger cause RA while RA granger cause RGDP, therefore there is bi-directional causality between the two. There is the existence of a long-run relationship between the variables as the result the Johansen co-integration test indicates six co-integration equation. Therefore, public expenditure has significant impact on economic development of Nigeria. In conclusion, public expenditure (capital and recurrent) is an important determinants of economic growth and development in Nigeria. The study recommended that Government spending if properly managed will raise the nation’s production capacity and employment, which in turn increase economic growth in Nigeria, also government should increase its expenditure on rural development, roads, water and electrification in order to accelerate the level of productivity, increase income and raise the standard of living of poor citizens in Nigeria
Keywords: Real Gross Domestic Product, Administration, Economic Services, Total Social and Community Services and Total Transfers expenditures.
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Shi, Chenbo, Yanhong Cheng, Chun Zhang, Jin Yuan, Yuxin Wang, Xin Jiang, and Changsheng Zhu. "Wavelet Scattering Convolution Network-Based Detection Algorithm on Nondestructive Microcrack Electrical Signals of Eggs." Agriculture 13, no. 3 (March 22, 2023): 730. http://dx.doi.org/10.3390/agriculture13030730.
Full textAbstract:
The detection of poultry egg microcracks based on electrical characteristic models is a new and effective method. However, due to the disorder, mutation, nonlinear, time discontinuity, and other factors of the current data, detection algorithms such as support-vector machines (SVM) and random forest (RF) under traditional statistical characteristics cannot identify subtle defects. The detection system voltage is set to 1500 V in the existing method, and higher voltages may cause damage to the hatched eggs; therefore, how to reduce the voltage is also a focus of research. In this paper, to address the problem of the low signal-to-noise ratio of microcracks in current signals, a wavelet scattering transform capable of extracting translation-invariant and small deformation-stable features is proposed to extract multi-scale high-frequency feature vectors. In view of the time series and low feature scale of current signals, various convolutional networks, such as a one-dimensional convolutional neural network (1DCNN), long short-term memory (LSTM), bi-directional long short-term memory (Bi-LSTM), and gated recurrent unit (GRU) are adopted. The detection algorithm of the wavelet scattering convolutional network is implemented for electrical sensing signals. The experimental results show that compared with previous works, the accuracy, precision, recall, F1-score, and Matthews correlation coefficient of the proposed wavelet scattering convolutional network on microcrack datasets smaller than 3 μm at a voltage of 1000 V are 99.4393%, 99.2523%, 99.6226%, 99.4357%, and 98.8819%, respectively, with an average increase of 2.0561%. In addition, the promotability and validity of the proposed detection algorithm were verified on a class-imbalanced dataset and a duck egg dataset. Based on the good results of the above experiments, further experiments were conducted with different voltages. The new feature extraction and detection method reduces the sensing voltage from 1500 V to 500 V, which allows for achieving higher detection accuracy with a lower signal-to-noise ratio, significantly reducing the risk of high voltage damage to hatching eggs and meeting the requirements for crack detection.