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Добірка наукової літератури з теми "BI-DIRECTIONAL GRATED RECURRENT UNIT"

Автор: Grafiati

Опубліковано: 11 вересня 2023

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Статті в журналах з теми "BI-DIRECTIONAL GRATED RECURRENT UNIT"

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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Більше джерел

Дисертації з теми "BI-DIRECTIONAL GRATED RECURRENT UNIT"

SACHDEVA, NITIN. "CYBERBULLYING DETECTION ON SOCIAL MEDIA USING DEEP LEARNING MODELS." Thesis, DELHI TECHNOLOGICAL UNIVERSITY, 2021. http://dspace.dtu.ac.in:8080/jspui/handle/repository/18914.

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Application of deep learning models for cyberbullying detection in social media is an upcoming area for both researchers and practitioners for finding, exploring and analysing the extensibility of human-based expressions. Automated cyberbullying detection is typically a classification problem in natural language processing where the intent is to classify each abusive or offensive comment or post or message or image as either bullying or non-bullying. It needs high-level semantic analysis as well. Most of the earlier attempts on cyberbullying detection rely on manual feature extraction methods. Such methods are not only time-consuming and cumbersome, but often fail to correctly capture the meaning of the sentence. This fosters the need to build an intelligent analytic paradigm for detecting cyberbullying in social media data to lower down its hazard with minimal human intervention. Motivated by it, this research utilizes deep learning models for cyberbullying detection in social media as they trivialize the need of explicit feature extraction and are highly skilful, fast and more efficient in retrieval of essential features and patterns by themselves. In our research, we have applied deep learning for cyberbullying detection on textual and non-textual social media content. With high volume and variety of user-generated content on complex social media platforms, the challenges to detect cyberbullying in real-time have amplified. The influx of content makes it challenging to timely regulate online expression. Moreover, the anonymity and context-independence of expressions in online posts can be ambiguous or misleading. Nowadays, cyberbullying, through varied content modalities is also very common. At the same time, cultural diversities, unconventional use of typographical resources and easy availability of native-language keyboards augment to the variety and volume of user- generated content compounding the linguistic challenges in detecting online bullying posts. In an effort to deal with this antagonistic online delinquency referred to as cyberbullying, this research computationally analysed the content, modality and language-use in social media using deep learning models. This research has shown that the use of embeddings with deep learning architectures show better representation learning capabilities and simplify the feature selection process with enhanced classification accuracy as compared to baseline machine learning methods. The goal of the research is to automatically detect cyberbullying on textual, multimodal and mash-up social media content using deep learning models. In our research, we build models for these using deep architectures including capsule network, convolution neural network, multi-layer perceptron, self-attention mechanism, bi-directional gated recurrent unit, long short-term memory & bi-directional long short-term memory using embeddings such as GloVe, fastText and ELMo on social media like Askfm.in, Formspring.me, MySpace, Twitter, YouTube, Instagram and Facebook. The results show superlative performance as compared to SOTA as well.

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Частини книг з теми "BI-DIRECTIONAL GRATED RECURRENT UNIT"

Jha, Kanchan, Sriparna Saha, and Matloob Khushi. "Protein-Protein Interactions Prediction Based on Bi-directional Gated Recurrent Unit and Multimodal Representation." In Communications in Computer and Information Science, 164–71. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-63823-8_20.

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Тези доповідей конференцій з теми "BI-DIRECTIONAL GRATED RECURRENT UNIT"

Khan, Saqib Ali, Syed Muhammad Daniyal Khalid, Muhammad Ali Shahzad, and Faisal Shafait. "Table Structure Extraction with Bi-Directional Gated Recurrent Unit Networks." In 2019 International Conference on Document Analysis and Recognition (ICDAR). IEEE, 2019. http://dx.doi.org/10.1109/icdar.2019.00220.

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Kumar R, Jeen Retna, Berakhah F. Stanley, and Joel Devadass D. J. Daniel. "Effective Facial Emotion Recognition Using Bi-wavelet Bi-directional Gated Recurrent Unit Neural Network." In 2023 International Conference on Recent Advances in Electrical, Electronics, Ubiquitous Communication, and Computational Intelligence (RAEEUCCI). IEEE, 2023. http://dx.doi.org/10.1109/raeeucci57140.2023.10134292.

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Su, Bo-Hao, Chun-Min Chang, Yun-Shao Lin, and Chi-Chun Lee. "Improving Speech Emotion Recognition Using Graph Attentive Bi-Directional Gated Recurrent Unit Network." In Interspeech 2020. ISCA: ISCA, 2020. http://dx.doi.org/10.21437/interspeech.2020-1733.

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Jabreel, Mohammed, and Antonio Moreno. "Target-dependent Sentiment Analysis of Tweets using a Bi-directional Gated Recurrent Unit." In 13th International Conference on Web Information Systems and Technologies. SCITEPRESS - Science and Technology Publications, 2017. http://dx.doi.org/10.5220/0006299900800087.

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Wickramaratne, Sajila D., and MD Shaad Mahmud. "Bi-Directional Gated Recurrent Unit Based Ensemble Model for the Early Detection of Sepsis." In 2020 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) in conjunction with the 43rd Annual Conference of the Canadian Medical and Biological Engineering Society. IEEE, 2020. http://dx.doi.org/10.1109/embc44109.2020.9175223.

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Aim-Nang, Sawetsit, Pusadee Seresangtakul, and Pongsathon Janyoi. "Isarn Dialect Word Segmentation using Bi-directional Gated Recurrent Unit with transfer learning approach." In 2022 26th International Computer Science and Engineering Conference (ICSEC). IEEE, 2022. http://dx.doi.org/10.1109/icsec56337.2022.10049346.

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Wang, Shunjiang, Dianyang Li, Gang Liu, Zhaowei Ling, and Duo Wang. "Short-term PV Power Prediction Based on Bi-directional Gated Recurrent Unit Network and Adaptive Chirp Mode Decomposition." In 2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE). IEEE, 2023. http://dx.doi.org/10.1109/nnice58320.2023.10105683.

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