Relevant bibliographies by topics / Bidirectional GRU

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

Academic literature on the topic 'Bidirectional GRU'

Author: Grafiati
Published: 7 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "Bidirectional GRU"

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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 (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
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Alamsyah, Nur, Titan Parama Yoga, Budiman, Imannudin Akbar, Acep Hendra, and Alif Januantara Prima. "A Bidirectional GRU Approach with Hyperparameter Optimization for Sentiment Classification in Game Reviews." NUANSA INFORMATIKA 19, no. 2 (2025): 88–95. https://doi.org/10.25134/ilkom.v19i2.399.

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Sentiment analysis plays a vital role in understanding user perspectives, especially in domains such as game reviews where user feedback influences product perception and engagement. This study presents a comparative approach using Gated Recurrent Unit (GRU), hyperparameter-tuned GRU, and Bidirectional GRU models to classify sentiments in a dataset of game reviews. The experiment begins with standard preprocessing and tokenization steps, followed by vectorization and supervised training. Hyperparameter optimization is conducted using Keras Tuner to identify the most effective configuration of
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Phua, Yeong Tsann, Sujata Navaratnam, Chon-Moy Kang, and Wai-Seong Che. "Sequence-to-sequence neural machine translation for English-Malay." IAES International Journal of Artificial Intelligence (IJ-AI) 11, no. 2 (2022): 658. http://dx.doi.org/10.11591/ijai.v11.i2.pp658-665.

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Machine translation aims to translate text from a specific language into another language using computer software. In this work, we performed neural machine translation with attention implementation on English-Malay parallel corpus. We attempt to improve the model performance by rectified linear unit (ReLU) attention alignment. Different sequence-to-sequence models were trained. These models include long-short term memory (LSTM), gated recurrent unit (GRU), bidirectional LSTM (Bi-LSTM) and bidirectional GRU (Bi-GRU). In the experiment, both bidirectional models, Bi-LSTM and Bi-GRU yield a conv
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Yeong-Tsann, Phua, Navaratnam Sujata, Kang Chon-Moy, and Chew Wai-Seong. "Sequence-to-sequence neural machine translation for English-Malay." International Journal of Artificial Intelligence (IJ-AI) 11, no. 2 (2022): 658–65. https://doi.org/10.11591/ijai.v11.i2.pp658-665.

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Machine translation aims to translate text from a specific language into another language using computer software. In this work, we performed neural machine translation with attention implementation on English-Malay parallel corpus. We attempt to improve the model performance by rectified linear unit (ReLU) attention alignment. Different sequence-to-sequence models were trained. These models include long-short term memory (LSTM), gated recurrent unit (GRU), bidirectional LSTM (Bi-LSTM) and bidirectional GRU (Bi-GRU). In the experiment, both bidirectional models, Bi-LSTM and Bi-GRU yield a conv
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Alsyaibani, Omar Muhammad Altoumi, Ema Utami, Suwanto Raharjo, and Anggit Dwi Hartanto. "Stacked LSTM-GRU Model for Traffic Anomalies Detection." Telematika 15, no. 2 (2022): 81–91. http://dx.doi.org/10.35671/telematika.v15i2.1855.

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This study aims to improve the accuracy of the intrusion detection system model. It focused on LSTM and GRU methods proposed by several previous studies. The bidirectional layer was also tested to see if it improves model performance. Dataset used in the study was CIC IDS 2017. The dataset was divided into 3 parts, for training, validation, and testing purposes. Validation data was used to evaluate model performance in every training iteration. It helped to make the model would not overfit the training data. Furthermore, Dropout layer and L2 regularization were also added to the model architec
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Asrawi, Hannan, Ema Utami, and Ainul Yaqin. "LSTM and Bidirectional GRU Comparison for Text Classification." sinkron 8, no. 4 (2023): 2264–74. http://dx.doi.org/10.33395/sinkron.v8i4.12899.

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Although the phrases machine learning and AI are frequently used interchangeably and are frequently discussed together, they do not have the same meanings. While all artificial intelligence (AI) is machine learning, not all AI is machine learning, which is a key distinction. In the beginning, machine learning and natural language processing (NLP) are related since machine learning is frequently employed as a tool for NLP tasks. The advantage of NLP is that it can perform analysis, and examine a lot of data, including comments on social media accounts and hundreds of online customer evaluations
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Zhou, Yuchen. "Music Generation Based on Bidirectional GRU Model." Highlights in Science, Engineering and Technology 85 (March 13, 2024): 684–90. http://dx.doi.org/10.54097/t2szjs78.

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Lately, substantial advancements in the realm of deep learning have given rise to new approaches for autonomously generating music. This study has devised a generative framework intended to produce musical melodies. This framework capitalizes on bidirectional gated recurrent units (GRU) as its foundational architecture. To impart knowledge to the model, a collection of classical piano compositions in MIDI format has been employed as the training dataset. One implements a stacked architecture of bidirectional GRU layers to capture long-term musical patterns. The addition of dropout regularizati
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Nurhopipah, Ade, Jali Suhaman, and Anan Widianto. "Exploring Pre-Trained Model and Language Model for Translating Image to Bahasa." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 17, no. 4 (2023): 347. http://dx.doi.org/10.22146/ijccs.76389.

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In the last decade, there have been significant developments in Image Caption Generation research to translate images into English descriptions. This task has also been conducted to produce texts in non-English, including Bahasa. However, the references in this study are still limited, so exploration opportunities are open widely. This paper presents comparative research by examining several state-of-the-art Deep Learning algorithms to extract images and generate their descriptions in Bahasa. We extracted images using three pre-trained models, namely InceptionV3, Xception, and EfficientNetV2S.
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Wu, Jun, Xinli Zheng, Jiangpeng Wang, Junwei Wu, and Ji Wang. "AB-GRU: An attention-based bidirectional GRU model for multimodal sentiment fusion and analysis." Mathematical Biosciences and Engineering 20, no. 10 (2023): 18523–44. http://dx.doi.org/10.3934/mbe.2023822.

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<abstract><p>Multimodal sentiment analysis is an important area of artificial intelligence. It integrates multiple modalities such as text, audio, video and image into a compact multimodal representation and obtains sentiment information from them. In this paper, we improve two modules, i.e., feature extraction and feature fusion, to enhance multimodal sentiment analysis and finally propose an attention-based two-layer bidirectional GRU (AB-GRU, gated recurrent unit) multimodal sentiment analysis method. For the feature extraction module, we use a two-layer bidirectional GRU networ
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Lu, Yumeng,. "Protein Secondary Structure Prediction Using Convolutional Bidirectional GRU." Journal of Mathematics Research 16, no. 4 (2024): 11. http://dx.doi.org/10.5539/jmr.v16n4p11.

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In this paper, a protein secondary structure prediction method based on convolutional bidirectional GRU Model (CBi-GRU model) is adopted, which combines the advantages of sliding window in extracting local features of data. The use of CNN and Bi-GRU in the construction of the model improves the feature expression and data utilization, and improves the performance of the model. Protein data from FoxChase Institute were used, and high quality, complete and representative CullPDB dataset, CB513, CASP10 and CASP11 datasets were selected to train, test and validate the model. The results show that
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Dissertations / Theses on the topic "Bidirectional GRU"

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Le, Johnny. "A Bidirectional Two-Hop Relay Network Using GNU Radio and USRP." Thesis, University of North Texas, 2011. https://digital.library.unt.edu/ark:/67531/metadc84237/.

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A bidirectional two-hop relay network with decode-and-forward strategy is implemented using GNU Radio (software) and several USRPs (hardware) on Ubuntu (operating system). The relay communication system is comprised of three nodes; Base Station A, Base Station B, and Relay Station (the intermediate node). During the first time slot, Base Station A and Base Station B will each transmit data, e.g., a JPEG file, to Relay Station using DBPSK modulation and FDMA. For the final time slot, Relay Station will perform a bitwise XOR of the data, and transmit the XORed data to Base Station A and Base Sta
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Javid, Gelareh. "Contribution à l’estimation de charge et à la gestion optimisée d’une batterie Lithium-ion : application au véhicule électrique." Thesis, Mulhouse, 2021. https://www.learning-center.uha.fr/.

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L'estimation de l'état de charge (SOC) est un point crucial pour la sécurité des performances et la durée de vie des batteries lithium-ion (Li-ion) utilisées pour alimenter les VE.Dans cette thèse, la précision de l'estimation de l'état de charge est étudiée à l'aide d'algorithmes de réseaux neuronaux récurrents profonds (DRNN). Pour ce faire, pour une cellule d’une batterie Li-ion, trois nouvelles méthodes sont proposées : une mémoire bidirectionnelle à long et court terme (BiLSTM), une mémoire robuste à long et court terme (RoLSTM) et une technique d'unités récurrentes à grille (GRU).En util
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Wang, Wei-Jhih, and 王暐智. "Compression and Rendering of Multi-Spectral Bidirectional Texture Functions Using GPU-Based Tensor Approximation." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/3527nz.

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碩士<br>元智大學<br>資訊工程學系<br>105<br>Multi-Spectral Bidirectional Texture Functions (MSBTFs) are designed for the accurate color reproduction of complex materials in virtual scenes with arbitrary illumination. However, rendering MSBTFs at interactive rates is challenging since the amount of datasets is huge. This thesis applies a GPU-based tensor approximation framework for compressing MSBTFs and discusses some practical details about compressing and rendering MSBTFs. We also present a heuristic method that decides suitable compression parameters for better offline performance and a novel technique
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Book chapters on the topic "Bidirectional GRU"

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Gridach, Mourad, and Hatem Haddad. "Arabic Named Entity Recognition: A Bidirectional GRU-CRF Approach." In Computational Linguistics and Intelligent Text Processing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77113-7_21.

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Gong, Zhongxin, Qingbin Tong, Feiyu Lu, et al. "Life Prediction of Rolling Bearing Based on Bidirectional GRU." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0553-9_17.

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Cheng, Lin, Yongjian Ren, Kun Zhang, and Yuliang Shi. "Medical Treatment Migration Prediction in Healthcare via Attention-Based Bidirectional GRU." In Web and Big Data. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-26072-9_2.

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Li, Haoran, Daiwei Li, Haiqing Zhang, Xi Yu, Dan Tang, and Lei He. "DXVNet Multimodal Classification Recognition Network Based on Dissymmetric Bidirectional D-GRU." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3951-0_28.

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Tereshchenko, Oleksandr, and Nataliia Komleva. "Vulnerability Detection of Smart Contracts Based on Bidirectional GRU and Attention Mechanism." In Information and Communication Technologies in Education, Research, and Industrial Applications. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-48325-7_21.

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Flores, Anibal, Hugo Tito-Chura, and Victor Yana-Mamani. "Wind Speed Time Series Imputation with a Bidirectional Gated Recurrent Unit (GRU) Model." In Lecture Notes in Networks and Systems. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-89880-9_34.

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Remadna, Ikram, Sadek Labib Terrissa, Ryad Zemouri, Soheyb Ayad, and Noureddine Zerhouni. "Unsupervised Feature Reduction Techniques with Bidirectional GRU Neural Network for Aircraft Engine RUL Estimation." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-36674-2_50.

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Stamate, Daniel, Pradyumna Davuloori, Doina Logofatu, Evelyne Mercure, Caspar Addyman, and Mark Tomlinson. "Ensembles of Bidirectional LSTM and GRU Neural Nets for Predicting Mother-Infant Synchrony in Videos." In Engineering Applications of Neural Networks. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-62495-7_25.

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Cheng, Ming, and Yu Wang. "DFDGRU-DTI: Drug-Target Interaction Prediction Based on Random Walk Embeddings and Bidirectional GRU Neural Network." In Lecture Notes in Computer Science. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-95-0030-7_11.

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Liang, Ye, and Chonghui Guo. "Chinese Medicinal Materials Price Index Trend Prediction Using GA-XGBoost Feature Selection and Bidirectional GRU Deep Learning." In Communications in Computer and Information Science. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-8318-6_6.

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Conference papers on the topic "Bidirectional GRU"

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Zhang, Xiaole, Jingchen Zuo, and Xiecheng Shao. "Efficient Neural Decoder: Mixture-Regularized Bidirectional GRU with Attention." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10651174.

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Yao, Siyu, and Jianlong Bi. "Sentiment Analysis Based on the BERT and Bidirectional GRU Model." In 2024 IEEE 7th International Conference on Information Systems and Computer Aided Education (ICISCAE). IEEE, 2024. https://doi.org/10.1109/iciscae62304.2024.10761342.

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Parvez, Rasel, Nitta Nando Roy, Dewan Aminul Islam, Ayon Mazumder, Atik Asif Khan Akash, and Md Hasan Imam Bijoy. "Efficient Bangla Tense Classification Using GRU, LSTM, and Bidirectional Approaches." In 2025 International Conference on Electrical, Computer and Communication Engineering (ECCE). IEEE, 2025. https://doi.org/10.1109/ecce64574.2025.11012984.

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Dong, Zhiming, Gang Wu, Jie Li, Bowen Liu, Wenping Zhang, and Jie Li. "Self-Attentive Bidirectional GRU Time-Series for Electricity Load Forecasting." In 2025 IEEE 5th International Conference on Electronic Technology, Communication and Information (ICETCI). IEEE, 2025. https://doi.org/10.1109/icetci64844.2025.11084038.

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Muhammad, Auwal Sagir, Rufai Yusuf Zakari, Abdullahi Baba Ari, Cheng Wang, and Longbiao Chen. "Explainable Traffic Accident Severity Prediction with Attention-Enhanced Bidirectional GRU-LSTM." In 2024 IEEE Smart World Congress (SWC). IEEE, 2024. https://doi.org/10.1109/swc62898.2024.00174.

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Hussein, Layth, Prashant Johri, A. Anusha Priya, R. Ramya, J. Karpagam, and A. Devendran. "Crop Yield Forecasting Using Bidirectional Gated Recurrent Unit (Bi-GRU) Networks." In 2025 International Conference on Automation and Computation (AUTOCOM). IEEE, 2025. https://doi.org/10.1109/autocom64127.2025.10957465.

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Yang, Peiliang, He Li, Xingjian Han, Yanqing Sun, Zhenwei Zhou, and Manshuang She. "Fault Prediction Algorithm of IGBT Devices Based on Bidirectional GRU Network Model." In 2024 5th International Conference on Electronic Communication and Artificial Intelligence (ICECAI). IEEE, 2024. http://dx.doi.org/10.1109/icecai62591.2024.10675302.

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Gao, Zhoutian, and Ting Wang. "A study of automatic classification of news headlines by bidirectional GRU models." In Fourth International Conference on Computer Vision and Pattern Analysis (ICCPA 2024), edited by Ji Zhao and Yonghui Yang. SPIE, 2024. http://dx.doi.org/10.1117/12.3037938.

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Kalpanadevi, D., S. Nandhini Devi, Silas Stephen D, Sunita Jadhav, P. M. D. Ali Khan, and Muralidharan J. "Temperature Variation Modelling in Mushroom Growing Hall with IAN-Bidirectional GRU Model." In 2024 4th International Conference on Sustainable Expert Systems (ICSES). IEEE, 2024. https://doi.org/10.1109/icses63445.2024.10763227.

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Ninama, Hitesh, and Jagdish Raikwal. "Hybrid Deep Learning for Automatic Gauge Reading with CNN-Bidirectional GRU-LSTM." In 2025 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI). IEEE, 2025. https://doi.org/10.1109/iatmsi64286.2025.10985101.

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