Relevant bibliographies by topics / BI-DIRECTIONAL GRATED RECURRENT UNIT

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'BI-DIRECTIONAL GRATED RECURRENT UNIT'

Author: Grafiati
Published: 11 September 2023

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'BI-DIRECTIONAL GRATED RECURRENT UNIT.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "BI-DIRECTIONAL GRATED RECURRENT UNIT"

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 (2023): 2512. http://dx.doi.org/10.3390/electronics12112512.

Full text
Abstract:
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 aut
APA, Harvard, Vancouver, ISO, and other styles
2

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.

Full text
Abstract:
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
APA, Harvard, Vancouver, ISO, and other styles
3

Zhang, Xue, Helmut Kuehnelt, and Wim De Roeck. "Traffic Noise Prediction Applying Multivariate Bi-Directional Recurrent Neural Network." Applied Sciences 11, no. 6 (2021): 2714. http://dx.doi.org/10.3390/app11062714.

Full text
Abstract:
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 traf
APA, Harvard, Vancouver, ISO, and other styles
4

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 (2021): 72–95. http://dx.doi.org/10.4018/ijiit.2021040104.

Full text
Abstract:
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
APA, Harvard, Vancouver, ISO, and other styles
5

Thakur, Narina, Sunil K. Singh, Akash Gupta, et al. "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 (2022): 1–19. http://dx.doi.org/10.4018/ijssci.311445.

Full text
Abstract:
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 Reposit
APA, Harvard, Vancouver, ISO, and other styles
6

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 (2023): 11454. http://dx.doi.org/10.3390/su151411454.

Full text
Abstract:
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. Add
APA, Harvard, Vancouver, ISO, and other styles
7

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 (2021): 5923. http://dx.doi.org/10.1364/oe.416672.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

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 (2022): e0273156. http://dx.doi.org/10.1371/journal.pone.0273156.

Full text
Abstract:
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 pr
APA, Harvard, Vancouver, ISO, and other styles
9

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.

Full text
Abstract:
<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 consump
APA, Harvard, Vancouver, ISO, and other styles
10

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 (2023): 203. http://dx.doi.org/10.3390/fractalfract7020203.

Full text
Abstract:
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)
APA, Harvard, Vancouver, ISO, and other styles

Dissertations / Theses on the topic "BI-DIRECTIONAL GRATED RECURRENT UNIT"

1

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.

Full text
Abstract:
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 me
APA, Harvard, Vancouver, ISO, and other styles

Book chapters on the topic "BI-DIRECTIONAL GRATED RECURRENT UNIT"

1

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. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-63823-8_20.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "BI-DIRECTIONAL GRATED RECURRENT UNIT"

1

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

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, 2020. http://dx.doi.org/10.21437/interspeech.2020-1733.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography