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Journal articles on the topic 'Bigroup'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Dr., M. Mary Jansi Rani, and Kiruthika S. "INTUITIONISTIC ANTIFUZZY SUB-BIGROUP." International Journal of Computational Research and Development 3, no. 1 (2018): 64–67. https://doi.org/10.5281/zenodo.1196606.

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In this paper we made an attempt to study the algebraic nature of intuitionistic antifuzzy sub-bigroup and intuitionistic bi-lower level subset of the antifuzzy sub-bigroup of the bigroup and discussed some of its properties with theorems.

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K. P., Sheena, and K. Uma Devi. "Homomorphism on bipolar-valued fuzzy sub-bigroup of a bigroup for secured data transmission over WSN." Automatika 64, no. 4 (2023): 956–63. http://dx.doi.org/10.1080/00051144.2023.2226945.

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G. Nirmala, G. Nirmala, and S. Priyadarshini S. Priyadarshini. "P-Fuzzy Sub- Bigroup and its Properties." International Journal of Scientific Research 2, no. 9 (2012): 257–58. http://dx.doi.org/10.15373/22778179/sep2013/85.

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Muthuraj, R., M. Rajinikannan, and M. S. Muthuraman. "A Study on Anti Fuzzy Sub-Bigroup." International Journal of Computer Applications 2, no. 1 (2010): 31–35. http://dx.doi.org/10.5120/614-865.

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Prabu, T. Justin, and K. Arju nan. "Translations of Q-Intuitionistic Fuzzy Subbigroup of a Bigroup." International Journal of Mathematics Trends and Technology 64, no. 1 (2018): 1–5. http://dx.doi.org/10.14445/22315373/ijmtt-v64p501.

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Shao, Lushan, Bo Xu, Wen Ma, Jingyu Wang, Yanting Liu, and Lijun Qian. "Flame retardant application of a hypophosphite/cyclotetrasiloxane bigroup compound on polycarbonate." Journal of Applied Polymer Science 137, no. 14 (2019): 48699. http://dx.doi.org/10.1002/app.48699.

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Qiu, Yong, Lijun Qian, Haisheng Feng, Shanglin Jin, and Jianwei Hao. "Toughening Effect and Flame-Retardant Behaviors of Phosphaphenanthrene/Phenylsiloxane Bigroup Macromolecules in Epoxy Thermoset." Macromolecules 51, no. 23 (2018): 9992–10002. http://dx.doi.org/10.1021/acs.macromol.8b02090.

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Azhar, Aziz Sangoor. "Bigraph in GraphTheory." Journal of Progressive Research in Mathematics 15, no. 1 (2019): 2585–93. https://doi.org/10.5281/zenodo.3974094.

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In this paper we study bigraph in graph theory and discussed properties bigraph of some type graph, we study odd complete graph and even complete graph has bigraph such that when partition graph into two part 𝐺1 , 𝐺2 , if even complete graph such 𝐺1 is odd complete graph after partition and 𝐺2 is not complete graph, either if odd complete graph such 𝐺1 is even complete graph after partition and 𝐺2 is not complete graph, we study regular graph for me bigraph too we get after partition 𝐺1 either odd complete graph or even complete graph, will we discuss the status every bigraph is disconnected g

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N., Duraimanickam, and Deepica N. "ALGEBRAIC STRUCTURE OF UNION OF FUZZY SUBGROUPS AND FUZZY SUB-BIGROUPS." International Journal of Current Research and Modern Education, Special Issue (August 16, 2017): 95–99. https://doi.org/10.5281/zenodo.844069.

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Sita, Rani. "Lyrebird Optimization for Bidirectional Gated Recurrent Unit-Based Risk Prediction in Supply Chain Management." Journal of Advancement in Software Engineering and Testing 8, no. 1 (2024): 13–21. https://doi.org/10.5281/zenodo.13985908.

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<em>The interconnectivity and globalization of today's world have made supply chains more complex than ever. Production, delivery, and profitability are increasingly vulnerable to unforeseen disruptions. This study introduces a novel approach to proactive risk prediction in supply chains using deep learning techniques. Inspired by the Lyrebird Optimization Algorithm (LOA), our proposed method employs a Bidirectional Gated Recurrent Unit (BiGRU). BiGRUs excel at processing sequential data, such as supply chain metrics, due to their remarkable ability to capture temporal relationships within the

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Mekruksavanich, Sakorn, Wikanda Phaphan, Narit Hnoohom, and Anuchit Jitpattanakul. "Recognition of sports and daily activities through deep learning and convolutional block attention." PeerJ Computer Science 10 (May 31, 2024): e2100. http://dx.doi.org/10.7717/peerj-cs.2100.

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Portable devices like accelerometers and physiological trackers capture movement and biometric data relevant to sports. This study uses data from wearable sensors to investigate deep learning techniques for recognizing human behaviors associated with sports and fitness. The proposed CNN-BiGRU-CBAM model, a unique hybrid architecture, combines convolutional neural networks (CNNs), bidirectional gated recurrent unit networks (BiGRUs), and convolutional block attention modules (CBAMs) for accurate activity recognition. CNN layers extract spatial patterns, BiGRU captures temporal context, and CBAM

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Chang, Yumiao, Jianwen Ma, Long Sun, Zeqiu Ma, and Yue Zhou. "Vessel Traffic Flow Prediction in Port Waterways Based on POA-CNN-BiGRU Model." Journal of Marine Science and Engineering 12, no. 11 (2024): 2091. http://dx.doi.org/10.3390/jmse12112091.

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Vessel traffic flow forecasting in port waterways is critical to improving safety and efficiency of port navigation. Aiming at the stage characteristics of vessel traffic in port waterways in time sequence, which leads to complexity of data in the prediction process and difficulty in adjusting the model parameters, a convolutional neural network (CNN) based on the optimization of the pelican algorithm (POA) and the combination of bi-directional gated recurrent units (BiGRUs) is proposed as a prediction model, and the POA algorithm is used to search for optimized hyper-parameters, and then the

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Palyutkin, V. G. "Lie-Kac bigroups." Ukrainian Mathematical Journal 52, no. 5 (2000): 754–64. http://dx.doi.org/10.1007/bf02487287.

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Yu, Feng, Changzhou Zhang, and Jihan Li. "Research on Industrial Process Fault Diagnosis Method Based on DMCA-BiGRUN." Mathematics 13, no. 15 (2025): 2331. https://doi.org/10.3390/math13152331.

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With the rising automation and complexity level of industrial systems, the efficiency and accuracy of fault diagnosis have become a critical challenge. The convolutional neural network (CNN) has shown some success in the fault diagnosis field. However, typical convolutional kernels are commonly fixed-sized, which makes it difficult to capture multi-scale features simultaneously. Additionally, the use of numerous fixed-size convolutional filters often results in redundant parameters. During the feature extraction process, the CNN often struggles to take inter-channel dependencies and spatial lo

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Li, Changli, Xiaoyu Chen та Yi Shi. "An Event Recognition Method for a Φ-OTDR System Based on CNN-BiGRU Network Model with Attention". Photonics 12, № 4 (2025): 313. https://doi.org/10.3390/photonics12040313.

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The phase-sensitive optical time domain reflectometry (Φ-OTDR) technique offers a method for distributed acoustic sensing (DAS) systems to detect external acoustic fluctuations and mechanical vibrations. By accurately identifying vibration events, DAS systems provide a non-invasive solution for security monitoring. However, limitations in temporal signal analysis and the lack of spatial features significantly impact classification accuracy in event recognition. To address these challenges, this paper proposes a network model for vibration-event recognition that integrates convolutional neural

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Selvarajah, Jarashanth, and Ruwan Nawarathna. "Identifying Tweets with Personal Medication Intake Mentions using Attentive Character and Localized Context Representations." JUCS - Journal of Universal Computer Science 28, no. 12 (2022): 1312–29. http://dx.doi.org/10.3897/jucs.84130.

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Individuals with health anomalies often share their experiences on social media sites, such as Twitter, which yields an abundance of data on a global scale. Nowadays, social media data constitutes a leading source to build drug monitoring and surveillance systems. However, a proper assessment of such data requires discarding mentions which do not express drug-related personal health experiences. We automate this process by introducing a novel deep learning model. The model includes character-level and word-level embeddings, embedding-level attention, convolu- tional neural networks (CNN), bidi

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Selvarajah, Jarashanth, and Ruwan Nawarathna. "Identifying Tweets with Personal Medication Intake Mentions using Attentive Character and Localized Context Representations." JUCS - Journal of Universal Computer Science 28, no. (12) (2022): 1312–29. https://doi.org/10.3897/jucs.84130.

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Individuals with health anomalies often share their experiences on social media sites, such as Twitter, which yields an abundance of data on a global scale. Nowadays, social media data constitutes a leading source to build drug monitoring and surveillance systems. However, a proper assessment of such data requires discarding mentions which do not express drug-related personal health experiences. We automate this process by introducing a novel deep learning model. The model includes character-level and word-level embeddings, embedding-level attention, convolu- tional neural networks (CNN), bidi

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Fang, Yetong. "Application of a grey wolf optimization-enhanced convolutional neural network and bidirectional gated recurrent unit model for credit scoring prediction." PLOS One 20, no. 5 (2025): e0322225. https://doi.org/10.1371/journal.pone.0322225.

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With the digital transformation of the financial industry, credit score prediction, as a key component of risk management, faces increasingly complex challenges. Traditional credit scoring methods often have difficulty in fully capturing the characteristics of large-scale, high-dimensional financial data, resulting in limited prediction performance. To address these issues, this paper proposes a credit score prediction model that combines CNNs and BiGRUs, and uses the GWO algorithm for hyperparameter tuning. CNN performs well in feature extraction and can effectively capture patterns in custom

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19

Li, Bin, Yaping Lu, Xuguang Meng, and Peijie Li. "Joint Control Strategy of Wind Storage System Based on Temporal Pattern Attention and Bidirectional Gated Recurrent Unit." Applied Sciences 15, no. 5 (2025): 2654. https://doi.org/10.3390/app15052654.

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Increasing wind power penetration will profoundly impact a power system’s operating mechanism. It is necessary to study a control strategy so that wind farms can use energy storage to improve their controllability to the level of traditional units. Therefore, this paper proposes a control strategy for wind storage systems based on temporal pattern attention (TPA) and bidirectional gated recurrent units (BiGRUs). The control strategy uses BiGRU to extract the time series information between the energy storage output, the actual output of the wind farm, and the energy storage state, which improv

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Chen, Tao, Caixia Yang, Yao Xiao, Chaoying Yan, and Chonlatee Photong. "A more robust CNN-BiGRU-TPA model for wind turbine blade icing prediction." Edelweiss Applied Science and Technology 9, no. 6 (2025): 2035–54. https://doi.org/10.55214/25768484.v9i6.8312.

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Wind turbine blade icing poses a significant challenge to the reliability and efficiency of wind power generation, especially in cold and harsh climates. Accurately detecting icing conditions is essential for maintaining optimal turbine performance and preventing potential mechanical failures. However, conventional detection methods often face limitations when processing complex multivariate time-series data collected from Supervisory Control and Data Acquisition (SCADA) systems. In this study, we propose a novel hybrid deep learning model, CNN-BiGRU-TPA, which integrates Convolutional Neural

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21

Gupta*, C. K., and A. N. Krasilnikov. "A JUST NON-FINITELY BASED VARIETY OF BIGROUPS." Communications in Algebra 29, no. 9 (2001): 4011–46. http://dx.doi.org/10.1081/agb-100105987.

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22

Mekruksavanich, Sakorn, and Anuchit Jitpattanakul. "Deep Convolutional Neural Network with RNNs for Complex Activity Recognition Using Wrist-Worn Wearable Sensor Data." Electronics 10, no. 14 (2021): 1685. http://dx.doi.org/10.3390/electronics10141685.

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Sensor-based human activity recognition (S-HAR) has become an important and high-impact topic of research within human-centered computing. In the last decade, successful applications of S-HAR have been presented through fruitful academic research and industrial applications, including for healthcare monitoring, smart home controlling, and daily sport tracking. However, the growing requirements of many current applications for recognizing complex human activities (CHA) have begun to attract the attention of the HAR research field when compared with simple human activities (SHA). S-HAR has shown

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Dong, Anming, Jiahao Zhang, Wendong Xu, Jia Jia, Shanshan Yun, and Jiguo Yu. "Wi-FiAG: Fine-Grained Abnormal Gait Recognition via CNN-BiGRU with Attention Mechanism from Wi-Fi CSI." Mathematics 13, no. 8 (2025): 1227. https://doi.org/10.3390/math13081227.

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Abnormal gait recognition, which aims to detect and identify deviations from normal walking patterns indicative of various health conditions or impairments, holds promising applications in healthcare and many other related fields. Currently, Wi-Fi-based abnormal gait recognition methods in the literature mainly distinguish the normal and abnormal gaits, which belongs to coarse-grained classification. In this work, we explore fine-grained gait rectification methods for distinguishing multiple classes of abnormal gaits. Specifically, we propose a deep learning-based framework for multi-class abn

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24

Jurkštienė, Vilma, Anatolijus Kondrotas, and Egidijus Kėvelaitis. "Immunostimulatory properties of bigroot geranium (Geranium macrorrhizum L.) extract." Medicina 43, no. 1 (2007): 60. http://dx.doi.org/10.3390/medicina43010008.

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The aim of the study was to investigate the immunostimulatory properties of bigroot geranium. Material and methods. Possible nonspecific characteristics of bigroot geranium were evaluated by the total leukocyte count in the peripheral blood, and qualitative changes of blood were assessed using Shilling’s formula by evaluating changes in lymphocyte counts. In addition, we also studied changes in the counts of Tcell precursors in the thymus and B lymphocytes in the spleen. Ethanol extract of the leaves of bigroot geranium was produced at the Department of Food Technology, Kaunas University of Te

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Mekruksavanich, Sakorn, Narit Hnoohom, and Anuchit Jitpattanakul. "A Hybrid Deep Residual Network for Efficient Transitional Activity Recognition Based on Wearable Sensors." Applied Sciences 12, no. 10 (2022): 4988. http://dx.doi.org/10.3390/app12104988.

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Numerous learning-based techniques for effective human behavior identification have emerged in recent years. These techniques focus only on fundamental human activities, excluding transitional activities due to their infrequent occurrence and short period. Nevertheless, postural transitions play a critical role in implementing a system for recognizing human activity and cannot be ignored. This study aims to present a hybrid deep residual model for transitional activity recognition utilizing signal data from wearable sensors. The developed model enhances the ResNet model with hybrid Squeeze-and

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Xiao, Zhiguo, Xinyao Cao, Huihui Hao, Siwen Liang, Junli Liu, and Dongni Li. "A Spatio-Temporal Joint Diagnosis Framework for Bearing Faults via Graph Convolution and Attention-Enhanced Bidirectional Gated Networks." Sensors 25, no. 13 (2025): 3908. https://doi.org/10.3390/s25133908.

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In recent years, Academia and industry have conducted extensive and in-depth research on bearing-fault-diagnosis technology. However, the current modeling of time–space coupling characteristics in rolling bearing fault diagnosis remains inadequate, and the integration of multi-modal correlations requires further improvement. To address these challenges, this paper proposes a joint diagnosis framework integrating graph convolutional networks (GCNs) with attention-enhanced bidirectional gated recurrent units (BiGRUs). The proposed framework first constructs an improved K-nearest neighbor-based s

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27

Sundararajan, P., and R. Muthuraj. "Anti MFuzzy SubBigroup and its Bi Lower Level MSub Bigroups." International Journal of Computer Applications 26, no. 8 (2011): 1–4. http://dx.doi.org/10.5120/3121-4280.

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28

Lu, Wanjie, Chun Shi, Hua Fu, and Yaosong Xu. "A Power Transformer Fault Diagnosis Method Based on Improved Sand Cat Swarm Optimization Algorithm and Bidirectional Gated Recurrent Unit." Electronics 12, no. 3 (2023): 672. http://dx.doi.org/10.3390/electronics12030672.

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The bidirectional gated recurrent unit (BiGRU) method based on dissolved gas analysis (DGA) has been studied in the field of power transformer fault diagnosis. However, there are still some shortcomings such as the fuzzy boundaries of DGA data, and the BiGRU parameters are difficult to determine. Therefore, this paper proposes a power transformer fault diagnosis method based on landmark isometric mapping (L-Isomap) and Improved Sand Cat Swarm Optimization (ISCSO) to optimize the BiGRU (ISCSO-BiGRU). Firstly, L-Isomap is used to extract features from DGA feature quantities. In addition, ISCSO i

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Horak, Michael J., and Loyd M. Wax. "Control of Bigroot Morningglory (Ipomoea pandurata)." Weed Technology 6, no. 4 (1992): 824–27. http://dx.doi.org/10.1017/s0890037x00036332.

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Herbicide efficacy was examined in the greenhouse on bigroot morningglory seedlings and on established plants in the field. Dicamba (0.14 kg ae ha-1) and 2,4-D (0.28 kg ae ha-1) alone or combined, glyphosate (2.24 kg ai ha-1), fluroxypyr (0.28 kg ae ha-1), and triclopyr (0.42 kg ae ha-1) controlled more than 90% of bigroot morningglory seedlings. Fluroxypyr (0.56 kg ha-1), triclopyr (0.84 kg ha-1), and 2,4-D (0.56 kg ha-1) alone or combined with dicamba (0.28 kg ha-1) reduced established stands in the field at least 50% within the season of application, whereas clopyralid (0.56 kg ha-1) did no

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30

Guo, Derui, and Yufei Xie. "Research on Network Intrusion Detection Model Based on Hybrid Sampling and Deep Learning." Sensors 25, no. 5 (2025): 1578. https://doi.org/10.3390/s25051578.

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This study proposes an enhanced network intrusion detection model, 1D-TCN-ResNet-BiGRU-Multi-Head Attention (TRBMA), aimed at addressing the issues of incomplete learning of temporal features and low accuracy in the classification of malicious traffic found in existing models. The TRBMA model utilizes Temporal Convolutional Networks (TCNs) to improve the ResNet18 architecture and incorporates Bidirectional Gated Recurrent Units (BiGRUs) and Multi-Head Self-Attention mechanisms to enhance the comprehensive learning of temporal features. Additionally, the ResNet network is adapted into a one-dim

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31

Duan, Yuanshuai, Yuanxin Liu, Yi Wang, Shangsheng Ren, and Yibo Wang. "Improved BIGRU Model and Its Application in Stock Price Forecasting." Electronics 12, no. 12 (2023): 2718. http://dx.doi.org/10.3390/electronics12122718.

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In order to obtain better prediction results, this paper combines improved complete ensemble EMD (ICEEMDAN) and the whale algorithm of multi-objective optimization (MOWOA) to improve the bidirectional gated recurrent unit (BIGRU), which makes full use of original complex stock price time series data and improves the hyperparameters of the BIGRU network. To address the problem that BIGRU cannot make full use of the stationary data, the original sequence data are processed using the ICEEMDAN decomposition algorithm to derive the non-stationary and stationary parts of the data and modeled with th

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Putri Oktavia, Nabiilah, Lutfi Hakim, Dian Candra Rini Novitasari, Ahmad Hanif Asyhar, and Fajar Setiawan. "Prediksi Tinggi Gelombang dan Kecepatan Angin di Pantai Menggunakan Metode BiGRU." Fountain of Informatics Journal 10, no. 1 (2025): 1–10. https://doi.org/10.21111/fij.v10i1.13018.

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Abstrak Indonesia terletak di antara Samudera Pasifik dan Samudera Hindia yang membuat Indonesia menjadi pusat jalur perdagangan internasional. Pada lokasi desa Karangduwur yang berlokasi di Jawa Tengah memiliki potensi ekonomi maritim yang kuat tetapi juga memiliki risiko cuaca yang besar juga. Oleh karena itu tujuan dari penelitian ini yaitu untuk memprediksi tinggi gelombang dan kecepatan angin. Â Â Metode prediksi yang digunakan pada penelitian kali ini adalah BiGRU (Bidirectional Gated Recurrent Unit) karena BiGRU memiliki hasil prediksi yang baik dibanding metode deep learning yang lain.

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Riyadi, Willy, Jasmir, and Xaverius Sika. "Comparison Airport Traffic Prediction Performance Using BiGRU and CNN-BiGRU Models." Jurnal Online Informatika 10, no. 1 (2025): 12–21. https://doi.org/10.15575/join.v10i1.1362.

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COVID-19 pandemic has significantly disrupted the aviation industry, highlighting the critical need for accurate airport traffic predictions. This study compares the performance of BiGRU and CNN-BiGRU models to enhance airport traffic forecasting accuracy models from March to December 2020. Data preprocessing was performed using Python's Pandas library. This involved filtering, scaling using min-max normalization, and splitting the data into 80:20 training-testing split using Python's Pandas library. Various optimization techniques—RMSProp, Adam, Nadam, Adamax, AdamW, and Lion—were applied, al

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Wang, Luping, and Shanze Wang. "The Application of BiGRU-MSTA Based on Multi-Scale Temporal Attention Mechanism in Predicting the Remaining Life of Lithium-Ion Batteries." Batteries 11, no. 6 (2025): 223. https://doi.org/10.3390/batteries11060223.

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Lithium-ion batteries are an indispensable component of numerous contemporary applications, such as electric vehicles and renewable energy systems. However, accurately predicting their remaining service life is a significant challenge due to the complexity of degradation patterns and time series data. To tackle these challenges, this study introduces a novel Multi-Scale Time Attention (MSTA) mechanism designed to enhance the modeling of both short-term fluctuations and long-term degradation trends in battery performance. This mechanism is integrated with the Bidirectional Gated Recurrent Unit

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35

Masethe, Hlaudi Daniel, Mosima Anna Masethe, Sunday O. Ojo, Pius A. Owolawi, and Fausto Giunchiglia. "Hybrid Transformer-Based Large Language Models for Word Sense Disambiguation in the Low-Resource Sesotho sa Leboa Language." Applied Sciences 15, no. 7 (2025): 3608. https://doi.org/10.3390/app15073608.

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This study addresses a lexical ambiguity issue in Sesotho sa Leboa that arises from terms with various meanings, often known as homonyms or polysemous words. When compared to, for instance, European languages, this lexical ambiguity in Sesotho sa Leboa causes computational semantic problems in NLP when trying to identify the lexicon of a language. In other words, it is challenging to determine the proper lexical category and sense of words due to this ambiguity problem. In order to address the issue of polysemy in the Sesotho sa Leboa language, this study set out to create a word sense discrim

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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.

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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 aut

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Horak, Michael J., and Loyd M. Wax. "Growth and Development of Bigroot Morningglory (Ipomoea pandurata)." Weed Technology 5, no. 4 (1991): 805–10. http://dx.doi.org/10.1017/s0890037x00033893.

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Growth and development of bigroot morningglory was observed and quantified. Emergence occurred 75 ± 5 growing degree days (GDD) after seeding. Flower and seed production began 630 ± 20 GDD after emergence and continued until the first frost killed the shoots. Seedlings needed approximately 460 GDD of growth to become perennial. In the second year of growth, plants emerged in early May and flowered within 425 ± 50 GDD. Shoot dry weight accumulation in first-year plants was 3.5 g for the first 600 GDD after which a fifteenfold increase in dry weight occurred. Root growth followed the same patter

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Liu, Die, Yihao Bao, Yingying He, and Likai Zhang. "A Data Loss Recovery Technique Using EMD-BiGRU Algorithm for Structural Health Monitoring." Applied Sciences 11, no. 21 (2021): 10072. http://dx.doi.org/10.3390/app112110072.

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Missing data caused by sensor faults is a common problem in structural health monitoring systems. Due to negative effects, many methods that adopt measured data to infer missing data have been proposed to tackle this problem in previous studies. However, capturing complex correlations from measured data remains a significant challenge. In this study, empirical mode decomposition (EMD) combined with a bidirectional gated recurrent unit (BiGRU) is proposed for the recovery of the measured data. The proposed EMD-BiGRU converts the missing data task as predicted task of time sequence. The core of

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Cheng, Zibin, Zongtao Duan, Nana Bu, et al. "ERNIE-based Named Entity Recognition Method for Traffic Accident Cases." Journal of Physics: Conference Series 2589, no. 1 (2023): 012020. http://dx.doi.org/10.1088/1742-6596/2589/1/012020.

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Abstract Traffic accident case named entity recognition, which helps mine key information in traffic accident texts, plays a vital role in downstream tasks such as the construction of knowledge graphs in road traffic and intelligent policing. In this paper, we construct a named entity recognition model based on the EDE (Entity Data Enhancement)-ERNIE-Bidirectional Gated Recurrent Unit Network (BiGRU)-Conditional Random Field (CRF) to address the current situation of low traffic accident case data and poor recognition of long-text entities. First, the amount of accident case data is enhanced us

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Malhotra, Ruchika, and Priya Singh. "DHG-BiGRU: Dual-attention based hierarchical gated BiGRU for software defect prediction." Information and Software Technology 179 (March 2025): 107646. https://doi.org/10.1016/j.infsof.2024.107646.

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Zhang, Biao, Mingqi Jia, Jiazhong Xu, Wanzhao Zhao, and Liwei Deng. "Network Security Situation Prediction Model Based on EMD and ELPSO Optimized BiGRU Neural Network." Computational Intelligence and Neuroscience 2022 (June 21, 2022): 1–17. http://dx.doi.org/10.1155/2022/6031129.

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In order to improve the accuracy of network security situation prediction and the convergence speed of prediction algorithm, this paper proposes a combined prediction model (EMD-ELPSO-BiGRU) based on empirical mode decomposition (EMD) and improved particle swarm optimization (ELPSO) to optimize BiGRU neural network. Firstly, the network security situation data sequence is decomposed into a series of intrinsic mode function by EMD. Then, a particle swarm optimization algorithm (ELPSO) based on cooperative update of evolutionary state judgment and learning strategy is proposed to optimize the hy

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Fan, Runyu, Lizhe Wang, Jining Yan, Weijing Song, Yingqian Zhu, and Xiaodao Chen. "Deep Learning-Based Named Entity Recognition and Knowledge Graph Construction for Geological Hazards." ISPRS International Journal of Geo-Information 9, no. 1 (2019): 15. http://dx.doi.org/10.3390/ijgi9010015.

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Constructing a knowledge graph of geological hazards literature can facilitate the reuse of geological hazards literature and provide a reference for geological hazard governance. Named entity recognition (NER), as a core technology for constructing a geological hazard knowledge graph, has to face the challenges that named entities in geological hazard literature are diverse in form, ambiguous in semantics, and uncertain in context. This can introduce difficulties in designing practical features during the NER classification. To address the above problem, this paper proposes a deep learning-ba

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Shih, Dong-Her, Feng-I. Chung, Ting-Wei Wu, Bo-Hao Wang, and Ming-Hung Shih. "Advanced Trans-BiGRU-QA Fusion Model for Atmospheric Mercury Prediction." Mathematics 12, no. 22 (2024): 3547. http://dx.doi.org/10.3390/math12223547.

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With the deepening of the Industrial Revolution and the rapid development of the chemical industry, the large-scale emissions of corrosive dust and gases from numerous factories have become a significant source of air pollution. Mercury in the atmosphere, identified by the United Nations Environment Programme (UNEP) as one of the globally concerning air pollutants, has been proven to pose a threat to the human environment with potential carcinogenic risks. Therefore, accurately predicting atmospheric mercury concentration is of critical importance. This study proposes a novel advanced model—th

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Tanjung, Andjani Ayu Cahaya, Dewi Retno Sari Saputro, and Nughthoh Arfawi Kurdhi. "IMPLEMENTATION OF THE BIDIRECTIONAL GATED RECURRENT UNIT ALGORITHM ON CONSUMER PRICE INDEX DATA IN INDONESIA." BAREKENG: Jurnal Ilmu Matematika dan Terapan 18, no. 1 (2024): 0095–104. http://dx.doi.org/10.30598/barekengvol18iss1pp0095-0104.

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The Consumer Price Index (CPI) is the main index in measuring the inflation rate. Changes in the CPI from time to time reflect inflation and deflation, namely the higher the CPI value, the higher the inflation rate. This study aims to apply Birectional Gated Recurrent Unit (BiGRU) model to the CPI data in Indonesia. BiGRU comprises two GRU layers so it captures sequences that are ignored by the GRU. The research data is in the form of CPI data in Indonesia from January 2006 to December 2022 sourced from the website of the Central Bureau of Statistics totaling 204 data. The data is divided into

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Wang, Yudong, Guibin Pang, Tianyu Wang, et al. "Future Reference Evapotranspiration Trends in Shandong Province, China: Based on SAO-CNN-BiGRU-Attention and CMIP6." Agriculture 14, no. 9 (2024): 1556. http://dx.doi.org/10.3390/agriculture14091556.

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One of the primary factors in the hydrological cycle is reference evapotranspiration (ET0). The prediction of ET0 is crucial to manage irrigation water in agriculture under climate change; however, little research has been conducted on the trends of ET0 changes in Shandong Province. In this study, to estimate ET0 in the entire Shandong Province, 245 sites were chosen, and the monthly ET0 values during 1901–2020 were computed using the Hargreaves–Samani formula. A deep learning model, termed SAO-CNN-BiGRU-Attention, was utilized to forecast the monthly ET0 during 2021–2100, and the predictions

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Hu, Guangwu, Maoqi Sun, and Chaoqin Zhang. "A High-Accuracy Advanced Persistent Threat Detection Model: Integrating Convolutional Neural Networks with Kepler-Optimized Bidirectional Gated Recurrent Units." Electronics 14, no. 9 (2025): 1772. https://doi.org/10.3390/electronics14091772.

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Advanced Persistent Threat (APT) refers to a highly targeted, sophisticated, and prolonged form of cyberattack, typically directed at specific organizations or individuals. The primary objective of such attacks is the theft of sensitive information or the disruption of critical operations. APT attacks are characterized by their stealth and complexity, often resulting in significant economic losses. Furthermore, these attacks may lead to intelligence breaches, operational interruptions, and even jeopardize national security and political stability. Given the covert nature and extended durations

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Wang, Guanqun, Haibo Teng, Lei Qiao, Hongtao Yu, You Cui, and Kun Xiao. "Well Logging Reconstruction Based on a Temporal Convolutional Network and Bidirectional Gated Recurrent Unit Network with Attention Mechanism Optimized by Improved Sand Cat Swarm Optimization." Energies 17, no. 11 (2024): 2710. http://dx.doi.org/10.3390/en17112710.

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Geophysical logging plays a very important role in reservoir evaluation. In the actual production process, some logging data are often missing due to well wall collapse and instrument failure. Therefore, this paper proposes a logging reconstruction method based on improved sand cat swarm optimization (ISCSO) and a temporal convolutional network (TCN) and bidirectional gated recurrent unit network with attention mechanism (BiGRU-AM). The ISCSO-TCN-BiGRU-AM can process both past and future states efficiently, thereby extracting valuable deterioration information from logging data. Firstly, the s

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Liu, Yong, Cheng Liu, Xianguo Tuo, and Xiang He. "Application of BITCN-BIGRU Neural Network Based on ICPO Optimization in Pit Deformation Prediction." Buildings 15, no. 11 (2025): 1956. https://doi.org/10.3390/buildings15111956.

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Predicting pit deformation to prevent safety accidents is the primary objective of pit deformation forecasting. A reliable predictive model enhances the ability to accurately monitor future deformation trends in pits. To enhance the prediction of pit deformation and improve accuracy and precision, an Improved Crown Porcupine Optimization Algorithm (ICPO) based on a Bidirectional Time Convolution Network–Bidirectional Gated Recirculation Unit (BITCN-BIGRU) is developed. This model is utilized to forecast the future deformation trends of the pit. Utilizing site data from a metro station pit proj

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Horak, Michael J., and Loyd M. Wax. "Germination and Seedling Development of Bigroot Morningglory (Ipomoea pandurata)." Weed Science 39, no. 3 (1991): 390–96. http://dx.doi.org/10.1017/s0043174500073112.

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Studies were undertaken to determine effects of scarification, temperature, stratification, pH, and osmotic potential on seed germination; to determine the effect of planting depth on emergence; to describe the emergence sequence; and to quantify seedling development of bigroot morningglory. Mechanical and chemical scarification caused increased germination but cool, moist stratification did not break dormancy. Optimum germination occurred at 20 and 25 C and with alternating temperatures of 20/10 and 30/20 C. The optimum pH range for germination was between 6 and 8.5. Increasing the solution o

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He, Lin, Shengnan Wang, and Xinran Cao. "Multi-Feature Fusion Method for Chinese Shipping Companies Credit Named Entity Recognition." Applied Sciences 13, no. 9 (2023): 5787. http://dx.doi.org/10.3390/app13095787.

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Shipping Enterprise Credit Named Entity Recognition (NER) aims to recognize shipping enterprise credit entities from unstructured shipping enterprise credit texts. Aiming at the problem of low entity recognition rate caused by complex and diverse entities and nesting phenomenon in the field of shipping enterprise credit, a deep learning method based on multi-feature fusion is proposed to improve the recognition effect of shipping enterprise credit entities. In this study, the shipping enterprise credit dataset is manually labeled using the BIO labeling model, combining the pre-trained model Bi

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