Relevant bibliographies by topics / Bi- Directional Long Short-Term Memory

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  2. Dissertations / Theses
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Academic literature on the topic 'Bi- Directional Long Short-Term Memory'

Author: Grafiati
Published: 2 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Bi- Directional Long Short-Term Memory"

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Majid, Muhammad Althaf, Prilyandari Dina Saputri, and Soehardjoepri Soehardjoepri. "Stock Market Index Prediction using Bi-directional Long Short-Term Memory." Journal of Applied Informatics and Computing 8, no. 1 (2024): 55–61. http://dx.doi.org/10.30871/jaic.v8i1.7195.

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The IHSG (Indonesia Stock Exchange Composite Index) is a stock price index in the Indonesia Stock Exchange (BEI) that serves as an indicator reflecting the performance of company stocks through stock price movements. Therefore, IHSG becomes a reference for investors in making investment decisions. Advanced stock exchanges generally have a strong influence on other stock exchanges. Several studies have proven the influence of one global index on another. Global index is a term that refers to each country's index to represent the movement of its country's stock performance. Forecasting IHSG can
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Xie, Xingang, Min Huang, Yue Liu, and Qi An. "Intelligent Tool-Wear Prediction Based on Informer Encoder and Bi-Directional Long Short-Term Memory." Machines 11, no. 1 (2023): 94. http://dx.doi.org/10.3390/machines11010094.

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Herein, to accurately predict tool wear, we proposed a new deep learning network—that is, the IE-Bi-LSTM—based on an informer encoder and bi-directional long short-term memory. The IE-Bi-LSTM uses the encoder part of the informer model to capture connections globally and to extract long feature sequences with rich information from multichannel sensors. In contrast to methods using CNN and RNN, this model could achieve remote feature extraction and the parallel computation of long-sequence-dependent features. The informer encoder adopts the attention distillation layer to increase computational
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Qian, Tiantian, ke Wang, Fei Shi, Gang Li, and Lizhong Xu. "Bus Load Decomposition Method Based on Deep Learning." E3S Web of Conferences 118 (2019): 01053. http://dx.doi.org/10.1051/e3sconf/201911801053.

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The current research work is mainly based on the decomposition of the total load of the family house into the electrical level load, and less research on the bus load of the high voltage level. To solve this problem, in this paper, a bus load composition decomposition algorithm based on Bi-directional Long Short-Term Memory (Bi-LSTM) is proposed. The experimental results show that this method can effectively identify the bus load with unknown components. Compared with the traditional recurrent neural network and long-term and short-term memory network, the proposed algorithm has better identif
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Zhang, Chenjun, Fuqian Zhang, Fuyang Gou, and Wensi Cao. "Study on Short-Term Electricity Load Forecasting Based on the Modified Simplex Approach Sparrow Search Algorithm Mixed with a Bidirectional Long- and Short-Term Memory Network." Processes 12, no. 9 (2024): 1796. http://dx.doi.org/10.3390/pr12091796.

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In order to balance power supply and demand, which is crucial for the safe and effective functioning of power systems, short-term power load forecasting is a crucial component of power system planning and operation. This paper aims to address the issue of low prediction accuracy resulting from power load volatility and nonlinearity. It suggests optimizing the number of hidden layer nodes, number of iterations, and learning rate of bi-directional long- and short-term memory networks using the improved sparrow search algorithm, and predicting the actual load data using the load prediction model.
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Banna, Md Hasan Al, Tapotosh Ghosh, Md Jaber Al Nahian, et al. "Attention-Based Bi-Directional Long-Short Term Memory Network for Earthquake Prediction." IEEE Access 9 (2021): 56589–603. http://dx.doi.org/10.1109/access.2021.3071400.

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Yang, Dewei, Shaobo Zhong, Xin Mei, Xinlan Ye, Fei Niu, and Weiqi Zhong. "A Comparative Study of Several Popular Models for Near-Land Surface Air Temperature Estimation." Remote Sensing 15, no. 4 (2023): 1136. http://dx.doi.org/10.3390/rs15041136.

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Near-land surface air temperature (NLSAT) is an important meteorological and climatic parameter widely used in climate change, urban heat island and environmental science, in addition to being an important input parameter for various earth system simulation models. However, the spatial distribution and the limited number of ground-based meteorological stations make it difficult to obtain a large range of high-precision NLSAT values. This paper constructs neural network, long short-term memory, bi-directional long short-term memory, support vector machine, random forest, and Gaussian process re
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Raut, Supriya. "Analysis & Stock Price Prediction and Forecasting Using Different LSTM Models." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem30115.

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The objective of this research is to develop a Deep Learning model to forecast the stock price, by using the variant of Long Short-Term Memory. This model predicts the close price of the stock for the future selected date, choosing as inputs the following data: open, high, low, adj close and close prices. This model shows a comparative analysis between three different LSTM networks: Long Short-Term Memory (LSTM), Stacked Long Short-Term Memory (Stacked LSTM), and Stacked Bi-directional Long Short-Term Memory (Stacked Bidirectional LSTM) concluding which one is the best and implementing the mod
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Tatavarthy, Murthy Venkata Surya Narayana, and Naga Lakshmi Vadlamani. "A course review analysis using bidirectional long short-term memory model." International Journal of Advances in Applied Sciences 14, no. 2 (2025): 580. https://doi.org/10.11591/ijaas.v14.i2.pp580-589.

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In recent years, sentiment analysis and online review analysis have gained popularity as critical components in the growth and development of educational courses. An innovative method has been created to increase the quality of learning experiences by rapidly collecting relevant data from course comments. This technique leverages bidirectional encoder representation from transformers (BERT) for word vector training. When combined with a learning mechanism, the recommended BERT accurately predicts the sentiment of online course reviews. Additionally, a dual-channel model based on Bi-directional
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Mai, Lijun, Dongyang Li, and Zehua Zhao. "Text sentiment analysis and classification based on bidirectional long and short term memory networks." Applied and Computational Engineering 77, no. 1 (2024): 183–89. http://dx.doi.org/10.54254/2755-2721/77/20240689.

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In this study, we explored the method of text sentiment analysis and classification using bi-directional long and short-term memory networks, which brings new ideas to the field of sentiment classification. In the text pre-processing stage, we processed chat phrases, non-alphanumeric characters, deactivated words, numbers, special characters, blanks, and addresses to convert the raw text data into numerical data that can be processed by the model. Subsequently, word frequency statistics were conducted for texts of different mood types to analyse the frequency of occurrence of different words i
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Ghosh, Lidia, Sriparna Saha, and Amit Konar. "Bi-directional Long Short-Term Memory model to analyze psychological effects on gamers." Applied Soft Computing 95 (October 2020): 106573. http://dx.doi.org/10.1016/j.asoc.2020.106573.

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Dissertations / Theses on the topic "Bi- Directional Long Short-Term Memory"

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Gustafsson, Anton, and Julian Sjödal. "Energy Predictions of Multiple Buildings using Bi-directional Long short-term Memory." Thesis, Högskolan i Halmstad, Akademin för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-43552.

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The process of energy consumption and monitoring of a buildingis time-consuming. Therefore, an feasible approach for using trans-fer learning is presented to decrease the necessary time to extract re-quired large dataset. The technique applies a bidirectional long shortterm memory recurrent neural network using sequence to sequenceprediction. The idea involves a training phase that extracts informa-tion and patterns of a building that is presented with a reasonablysized dataset. The validation phase uses a dataset that is not sufficientin size. This dataset was acquired through a related paper
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Annaç, Efsun [Verfasser], and Thomas [Akademischer Betreuer] Geyer. "Bi-directional relationship between attention and long-term context memory / Efsun Annaç ; Betreuer: Thomas Geyer." München : Universitätsbibliothek der Ludwig-Maximilians-Universität, 2018. http://d-nb.info/1189067005/34.

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Fancellu, Federico. "Computational models for multilingual negation scope detection." Thesis, University of Edinburgh, 2018. http://hdl.handle.net/1842/33038.

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Negation is a common property of languages, in that there are few languages, if any, that lack means to revert the truth-value of a statement. A challenge to cross-lingual studies of negation lies in the fact that languages encode and use it in different ways. Although this variation has been extensively researched in linguistics, little has been done in automated language processing. In particular, we lack computational models of processing negation that can be generalized across language. We even lack knowledge of what the development of such models would require. These models however exist
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Zhang, Jiahui. "Bi-Objective Dispatch of Multi-Energy Virtual Power Plant: Deep-Learning based Prediction and Particle Swarm Optimization." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019.

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This paper addresses the coordinative operation problem of multi-energy virtual power plant (ME-VPP) in the context of energy internet. A bi-objective dispatch model is established to optimize the performance of ME-VPP on both economic cost(EC) and power quality (PQ).Various realistic factors are considered, which include environmental governance, transmission ratings, output limits, etc. Long short-term memory (LSTM), a deep learning method, is applied to the promotion of the accuracy of wind prediction. An improved multi-objective particle swarm optimization (MOPSO) is utilized as the solvin
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ZENG, WEI-XIANG, and 曾威翔. "Daily Activity Recognition for Elderly Living Alone Using Bi-directional Long Short-Term Memory Network." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/tx6d28.

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碩士<br>國立臺北科技大學<br>電機工程系<br>107<br>Accurately monitoring daily living status of the elderly is a key factor to make long-term care successful. This study aims to implement an activity recognition system by using Bi-directional Long Short-Term Memory Network (BiLSTM). Firstly, we convert the sequences of sensor events occurred in the smart home into the text strings. Those text strings are then classified by use of BiLSTM to recognize the activities of elderly living in a smart home. Notably, the recognition accuracy is further improved with extra data. 　　The Aruba database collected by the CASA
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Roy, Bipraneel. "A deep learning approach for intrusion detection in Internet of Things using bi-directional long short-term memory recurrent neural network." Thesis, 2018. http://hdl.handle.net/1959.7/uws:51774.

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Internet-of-Things connects every ‘thing’ with the Internet and allows these ‘things’ to communicate with each other. IoT comprises of innumerous interconnected devices of diverse complexities and trends. This fundamental nature of IoT structure intensifies the amount of attack targets which might affect the sustainable growth of IoT. Thus, security issues become a crucial factor to be addressed. A novel deep learning approach have been proposed in this thesis, for performing real-time detections of security threats in IoT systems using the Bi-directional Long Short-Term Memory Recurrent Neura
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Book chapters on the topic "Bi- Directional Long Short-Term Memory"

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Wang, Jingyuan, Fei Hu, and Li Li. "Deep Bi-directional Long Short-Term Memory Model for Short-Term Traffic Flow Prediction." In Neural Information Processing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70139-4_31.

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Ramana Kumar, D., and S. Krishna Mohan Rao. "A Sentiment Analysis of Twitter Data Using Bi-Directional Long Short Term Memory." In Learning and Analytics in Intelligent Systems. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30271-9_16.

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Yin, Bin, Xiaolong Li, Lilan Liu, and Fang Wu. "Research on Fault Diagnosis Algorithm Based on Bi-directional Long Short-Term Memory." In Lecture Notes in Electrical Engineering. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6318-2_34.

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Jayashree, M., and N. Anitha. "Deep learning-based Bi-directional long short-term memory networks for prediction analysis of long-term tenal ailment." In Data Science & Exploration in Artificial Intelligence. CRC Press, 2025. https://doi.org/10.1201/9781003589273-79.

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Amyas, Oguru Matthew, and MD Nur Alam. "Sentiment Analysis of Tweets Using Bi-Directional Long Short-Term Memory and Word Embeddings." In Proceedings of 3rd International Conference on Smart Computing and Cyber Security. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-0573-3_6.

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Nakkeeran, M., and V. Anantha Narayanan. "Anomaly Detection in SCADA Industrial Control Systems Using Bi-Directional Long Short-Term Memory." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3481-2_33.

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Samantaray, Suprakash, and Abhinav Kumar. "Bi-directional Long Short-Term Memory Network for Fake News Detection from Social Media." In Smart Innovation, Systems and Technologies. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9873-6_42.

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Nguyen, Ngoc Khuong, Anh-Cuong Le, and Hong Thai Pham. "Deep Bi-directional Long Short-Term Memory Neural Networks for Sentiment Analysis of Social Data." In Lecture Notes in Computer Science. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-49046-5_22.

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Das, Ajit, Abhijit Baruah, and Sudipta Roy. "Bi-directional Long Short-Term Memory with Gated Recurrent Unit Approach for Next Word Prediction in Bodo Language." In Communications in Computer and Information Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-47224-4_20.

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Ranjan, Ashish, Varun Nagesh Jolly Behera, and Motahar Reza. "Using a Bi-Directional Long Short-Term Memory Model with Attention Mechanism Trained on MIDI Data for Generating Unique Music." In Artificial Intelligence for Data Science in Theory and Practice. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-92245-0_10.

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Conference papers on the topic "Bi- Directional Long Short-Term Memory"

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Kamma, Vidya, Saef Wbaid, Sudhakar K, Nisha A, and T. Saravanan. "Toxic Comment Detection based on Improved Bi Directional Long Short Term Memory." In 2025 International Conference on Intelligent Systems and Computational Networks (ICISCN). IEEE, 2025. https://doi.org/10.1109/iciscn64258.2025.10934215.

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M, Sowmya, Mohammed Al-Farouni, Naresh Kumar Reddy Panga, R. Indira, and N. Naga Saranya. "Stock Price Prediction using Bi-directional Long-Short Term Memory based Deep Neural Network." In 2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems (IACIS). IEEE, 2024. http://dx.doi.org/10.1109/iacis61494.2024.10721651.

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Rupavath, Rana Veer Samara Sihman Brahmatej, Zaid Alsalami, Shaik Rafikiran, B. Sheeba, and Er Tatiraju V. Rajani Kanth. "Shuffle Attention Mechanism Based Bi-Directional Long Short-Term Memory for Detecting Neurodegenerative Disorders." In 2024 4th International Conference on Mobile Networks and Wireless Communications (ICMNWC). IEEE, 2024. https://doi.org/10.1109/icmnwc63764.2024.10872280.

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Raghuvanshi, Akash, Ramakant Katiyar, and Nilesh Chandra. "Bi-Directional Long Short Term Memory with Dynamic Time Wrapping for Time Series Classifications." In 2025 3rd International Conference on Disruptive Technologies (ICDT). IEEE, 2025. https://doi.org/10.1109/icdt63985.2025.10986640.

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Shetty, Sahana, and Surjeet Dalal. "Expression of Concern for: Bi-Directional Long Short-Term Memory Neural Networks for Music Composition." In 2022 Fourth International Conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT). IEEE, 2022. http://dx.doi.org/10.1109/icerect56837.2022.10703602.

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Geetha, B., Boggarapu Sushmitha, P. Ilanchezhian, Haider Alabdeli, and R. Ahila. "A Bi-directional Gated Recurrent Unit and Long Short-Term Memory based Fake Profile Identification System." In 2024 First International Conference on Software, Systems and Information Technology (SSITCON). IEEE, 2024. https://doi.org/10.1109/ssitcon62437.2024.10796272.

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Jiang, Meng, Xin Yang, Peng Jiang, Fengbo Ma, and Shaojun Zhou. "Student Behavior Detection on Campus using Ghost Crayfish Optimization Algorithm and Bi Directional Long Short-Term Memory." In 2024 International Conference on Distributed Systems, Computer Networks and Cybersecurity (ICDSCNC). IEEE, 2024. https://doi.org/10.1109/icdscnc62492.2024.10939745.

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Sen, Souptik, Muralidhar Kurni, Ramesh Krishnamaneni, and Ashwin Murthy. "Improved Bi-directional Long Short-Term Memory for Heart Disease Diagnosis using Statistical and Entropy Feature Set." In 2024 9th International Conference on Communication and Electronics Systems (ICCES). IEEE, 2024. https://doi.org/10.1109/icces63552.2024.10860019.

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Bansal, Ankit, and Ruchi Bansal. "Initial Population Based Crayfish Optimization Algorithm with Bi-directional Long Short-Term Memory of Customer Churn Prediction." In 2024 4th International Conference on Mobile Networks and Wireless Communications (ICMNWC). IEEE, 2024. https://doi.org/10.1109/icmnwc63764.2024.10872039.

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Song, Lingling. "Fault Diagnosis and Localization of Power Cables Using Bi-Directional Long Short Term Memory with Adam Optimizer." In 2024 4th International Conference on Mobile Networks and Wireless Communications (ICMNWC). IEEE, 2024. https://doi.org/10.1109/icmnwc63764.2024.10872012.

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Reports on the topic "Bi- Directional Long Short-Term Memory"

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Maupin, Julie, and Dr Michael Mamoun. DTPH56-06-T-0004 Plastic Pipe Failure, Risk, and Threat Analysis. Pipeline Research Council International, Inc. (PRCI), 2006. http://dx.doi.org/10.55274/r0012119.

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Reports, publications, papers, and databases were reviewed to better define risks and threats to plastic gas distribution piping. Failure modes were described for plastic PE piping with the most significant being slow crack growth (SCG). Short-term mechanical tests such as tensile, quick burst, melt index, and density tests did not show a correlation with a material's susceptibility to SCG failure. The bend-back test was able to visually identify 1971 low-ductile inner wall materials. PENT test failure times were reported for materials manufactured during the period1972-1985. The PENT test did
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