Log in

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

To see the other types of publications on this topic, follow the link: Bi- Directional Long Short-Term Memory.

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

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Bi- Directional Long Short-Term Memory.'

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.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

2

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

3

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

4

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

5

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

7

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

8

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

9

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

10

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

11

Zhang, Kangkang, Tong Liu, Shengjing Song, et al. "Separating overlapping bat calls with a bi‐directional long short‐term memory network." Integrative Zoology 17, no. 5 (2022): 741–51. https://doi.org/10.5281/zenodo.13472712.

Full text

Abstract:

(Uploaded by Plazi for the Bat Literature Project) Acquiring clear acoustic signals is critical for the analysis of animal vocalizations. Bioacoustics studies commonly face the problem of overlapping signals, which can impede the structural identification of vocal units, but there is currently no satisfactory solution. This study presents a bi-directional long short-term memory network to separate overlapping echolocation-communication calls of 6 different bat species and reconstruct waveforms. The separation quality was evaluated using 7 temporal-spectrum parameters. All the echolocation puls

APA, Harvard, Vancouver, ISO, and other styles

12

Zhang, Kangkang, Tong Liu, Shengjing Song, et al. "Separating overlapping bat calls with a bi‐directional long short‐term memory network." Integrative Zoology 17, no. 5 (2022): 741–51. https://doi.org/10.5281/zenodo.13472712.

Full text

Abstract:

(Uploaded by Plazi for the Bat Literature Project) Acquiring clear acoustic signals is critical for the analysis of animal vocalizations. Bioacoustics studies commonly face the problem of overlapping signals, which can impede the structural identification of vocal units, but there is currently no satisfactory solution. This study presents a bi-directional long short-term memory network to separate overlapping echolocation-communication calls of 6 different bat species and reconstruct waveforms. The separation quality was evaluated using 7 temporal-spectrum parameters. All the echolocation puls

APA, Harvard, Vancouver, ISO, and other styles

13

Zhang, Kangkang, Tong Liu, Shengjing Song, et al. "Separating overlapping bat calls with a bi‐directional long short‐term memory network." Integrative Zoology 17, no. 5 (2022): 741–51. https://doi.org/10.5281/zenodo.13472712.

Full text

Abstract:

(Uploaded by Plazi for the Bat Literature Project) Acquiring clear acoustic signals is critical for the analysis of animal vocalizations. Bioacoustics studies commonly face the problem of overlapping signals, which can impede the structural identification of vocal units, but there is currently no satisfactory solution. This study presents a bi-directional long short-term memory network to separate overlapping echolocation-communication calls of 6 different bat species and reconstruct waveforms. The separation quality was evaluated using 7 temporal-spectrum parameters. All the echolocation puls

APA, Harvard, Vancouver, ISO, and other styles

14

Zhang, Kangkang, Tong Liu, Shengjing Song, et al. "Separating overlapping bat calls with a bi‐directional long short‐term memory network." Integrative Zoology 17, no. 5 (2022): 741–51. https://doi.org/10.5281/zenodo.13472712.

Full text

Abstract:

(Uploaded by Plazi for the Bat Literature Project) Acquiring clear acoustic signals is critical for the analysis of animal vocalizations. Bioacoustics studies commonly face the problem of overlapping signals, which can impede the structural identification of vocal units, but there is currently no satisfactory solution. This study presents a bi-directional long short-term memory network to separate overlapping echolocation-communication calls of 6 different bat species and reconstruct waveforms. The separation quality was evaluated using 7 temporal-spectrum parameters. All the echolocation puls

APA, Harvard, Vancouver, ISO, and other styles

15

Zhang, Kangkang, Tong Liu, Shengjing Song, et al. "Separating overlapping bat calls with a bi‐directional long short‐term memory network." Integrative Zoology 17, no. 5 (2022): 741–51. https://doi.org/10.5281/zenodo.13472712.

Full text

Abstract:

(Uploaded by Plazi for the Bat Literature Project) Acquiring clear acoustic signals is critical for the analysis of animal vocalizations. Bioacoustics studies commonly face the problem of overlapping signals, which can impede the structural identification of vocal units, but there is currently no satisfactory solution. This study presents a bi-directional long short-term memory network to separate overlapping echolocation-communication calls of 6 different bat species and reconstruct waveforms. The separation quality was evaluated using 7 temporal-spectrum parameters. All the echolocation puls

APA, Harvard, Vancouver, ISO, and other styles

16

Nilesh Kumar Patel, VanamaYaswanth, Ajay Kumar, Neelesh Kumar Jain,. "Leveraging Bi-Directional LSTM for Robust Lyrics Generation in Telugu: Methodology and Improvements." Tuijin Jishu/Journal of Propulsion Technology 44, no. 3 (2023): 1908–13. http://dx.doi.org/10.52783/tjjpt.v44.i3.618.

Full text

Abstract:

The paper aims to analyze the various steps involved in creating semi-automated lyrics generators for Indian languages such as Telugu. Our study also examined the effects of bi-directional LSTM (long short-term memory) on different genres. After trying out several methods, we found that bi-directional LSTM works well with all formats. We improved our model with the help of 50,000 parameters during the training period and this is one of the largest crops for the Telugu language.

APA, Harvard, Vancouver, ISO, and other styles

17

Ding, Xianghua, Jingnan Wang, Yiqi Liu, and Uk Jung. "Multivariate Time Series Anomaly Detection Using Working Memory Connections in Bi-Directional Long Short-Term Memory Autoencoder Network." Applied Sciences 15, no. 5 (2025): 2861. https://doi.org/10.3390/app15052861.

Full text

Abstract:

“Normal” events are characterized as data patterns or behaviors that align with expected operational conditions, while “anomalies” are defined as deviations from these patterns, potentially signaling faults, errors, or unexpected system behaviors. The timely and accurate detection of anomalies plays a critical role in domains such as industrial manufacturing, financial transactions, and other related domains. In the context of Industry 4.0, the proliferation of sensors has resulted in a massive influx of time series data, making the anomaly detection of such multivariate time series data a pop

APA, Harvard, Vancouver, ISO, and other styles

18

Zhu, Chunxiang, Zhiwei He, Zhengyi Bao, Changcheng Sun, and Mingyu Gao. "Prognosis of Lithium-Ion Batteries’ Remaining Useful Life Based on a Sequence-to-Sequence Model with Variational Mode Decomposition." Energies 16, no. 2 (2023): 803. http://dx.doi.org/10.3390/en16020803.

Full text

Abstract:

The time-varying, dynamic, nonlinear, and other characteristics of lithium-ion batteries, as well as the capacity regeneration phenomenon, leads to the low accuracy of the traditional deep learning models in predicting the remaining useful life of lithium-ion batteries. This paper established a sequence-to-sequence model for remaining useful life prediction by combining the variational modal decomposition with bi-directional long short-term memory and Bayesian hyperparametric optimization. First, variational modal decomposition is used for noise reduction processing to maximize the retention o

APA, Harvard, Vancouver, ISO, and other styles

19

Wang, Shiying, and Xinyu Yao. "The performance analysis of stock predication based on recurrent neural network." Applied and Computational Engineering 6, no. 1 (2023): 1276–82. http://dx.doi.org/10.54254/2755-2721/6/20230696.

Full text

Abstract:

The stock exchange is unpredictable, and the stock price seems unpredictable. However, with the continuous development of the deep learning model's ability to deal with massive data, forecasting stock prices has become feasible and has reference value for investors. Many factors affect the stock price, and it is a great challenge to define these factors' influence on the price clearly. This paper selects multi-features stock price data sets of different companies. Because of the superiority of recurrent neural networks in dealing with time series problems, this paper compares and analyzes the

APA, Harvard, Vancouver, ISO, and other styles

20

Koklu, Murat, Ilkay Cinar, and Yavuz Selim Taspinar. "CNN-based bi-directional and directional long-short term memory network for determination of face mask." Biomedical Signal Processing and Control 71 (January 2022): 103216. http://dx.doi.org/10.1016/j.bspc.2021.103216.

Full text

APA, Harvard, Vancouver, ISO, and other styles

21

Sharma, Richa, Sudha Morwal, and Basant Agarwal. "Entity-Extraction Using Hybrid Deep-Learning Approach for Hindi text." International Journal of Cognitive Informatics and Natural Intelligence 15, no. 3 (2021): 1–11. http://dx.doi.org/10.4018/ijcini.20210701.oa1.

Full text

Abstract:

This article presents a neural network-based approach to develop named entity recognition for Hindi text. In this paper, the authors propose a deep learning architecture based on convolutional neural network (CNN) and bi-directional long short-term memory (Bi-LSTM) neural network. Skip-gram approach of word2vec model is used in the proposed model to generate word vectors. In this research work, several deep learning models have been developed and evaluated as baseline systems such as recurrent neural network (RNN), long short-term memory (LSTM), Bi-LSTM. Furthermore, these baseline systems are

APA, Harvard, Vancouver, ISO, and other styles

22

Wang, Yuhao, Qibai Chen, Meng Ding, and Jiangyun Li. "High Precision Dimensional Measurement with Convolutional Neural Network and Bi-Directional Long Short-Term Memory (LSTM)." Sensors 19, no. 23 (2019): 5302. http://dx.doi.org/10.3390/s19235302.

Full text

Abstract:

In modern industries, high precision dimensional measurement plays a pivotal role in product inspection and sub-pixel edge detection is the core algorithm. Traditional interpolation and moment methods have achieved some success. However, those methods still have shortcomings. For example, the accuracy is still insufficient with the resolution limitation of the image sensor. Moreover, prediction results can be affected by image noise. With the recent success of deep learning technology, we propose a sub-pixel edge detection method based on convolution neural network (CNN) and bi-directional lon

APA, Harvard, Vancouver, ISO, and other styles

23

Ji, Jiahao. "Research on Stock Price Prediction and Quantitative Stock Picking Strategy Based on Deep Learning." Transactions on Computer Science and Intelligent Systems Research 3 (April 10, 2024): 19–26. http://dx.doi.org/10.62051/v47p3p43.

Full text

Abstract:

With the continuous development of the domestic stock market and the continuous improvement of the financial system system, and at the same time, the domestic stock market gradually rises in the financial system, based on the prediction research of the domestic stock market will become more and more important. In order to solve the problems of low precision and poor accuracy of short-term stock price prediction, this paper selects the bi-directional long- and short-term memory network of attention mechanism (WOA-BiLSTM-Attenion) model under the whale optimization algorithm for stock price pred

APA, Harvard, Vancouver, ISO, and other styles

24

Zhong, Rui, Diyang Xiao, Shi Dong, and Min Hu. "Spatial attention model‐modulated bi‐directional long short‐term memory for unsupervised video summarisation." Electronics Letters 57, no. 6 (2021): 252–54. http://dx.doi.org/10.1049/ell2.12111.

Full text

APA, Harvard, Vancouver, ISO, and other styles

25

Siddalingappa, Rashmi, and Kanagaraj Sekar. "Bi-directional long short term memory using recurrent neural network for biological entity recognition." IAES International Journal of Artificial Intelligence (IJ-AI) 11, no. 1 (2022): 89. http://dx.doi.org/10.11591/ijai.v11.i1.pp89-101.

Full text

Abstract:

<p>Biomedical named entity recognition (NER) aims at identifying medical entities from unstructured data. A quintessential task in the supervision of biological databases is handling biomedical terms such as cancer type, DeoxyriboNucleic and RiboNucleic Acid, gene and protein name, and others. However, due to the massive size of online medical repositories, data processing becomes a challenge for a gazetteer without proper annotation. The traditional NER systems depend on feature engineering that is tedious and time-consuming. The research study presents a new model for Bio-NER using rec

APA, Harvard, Vancouver, ISO, and other styles

26

Shao, Dangguo, Na Zheng, Zhaoqiang Yang, et al. "Domain-Specific Chinese Word Segmentation Based on Bi-Directional Long-Short Term Memory Model." IEEE Access 7 (2019): 12993–3002. http://dx.doi.org/10.1109/access.2019.2892836.

Full text

APA, Harvard, Vancouver, ISO, and other styles

27

Das, Suchandan, Ashish Paramane, Soumya Chatterjee, and Ungarala Mohan Rao. "Sensing Incipient Faults in Power Transformers Using Bi-Directional Long Short-Term Memory Network." IEEE Sensors Letters 7, no. 1 (2023): 1–4. http://dx.doi.org/10.1109/lsens.2022.3233135.

Full text

APA, Harvard, Vancouver, ISO, and other styles

28

Saad, Muhammad, Nauman Munir, and Chul-Hwan Kim. "Pattern Recognition Based Auto-Reclosing Scheme Using Bi-Directional Long Short-Term Memory Network." IEEE Access 10 (2022): 119734–44. http://dx.doi.org/10.1109/access.2022.3221818.

Full text

APA, Harvard, Vancouver, ISO, and other styles

29

Rashmi, Siddalingappa, and Sekar Kanagaraj. "Bi-directional long short term memory using recurrent neural network for biological entity recognition." International Journal of Artificial Intelligence (IJ-AI) 11, no. 1 (2022): 89–101. https://doi.org/10.11591/ijai.v11.i1.pp89-101.

Full text

Abstract:

Biomedical named entity recognition (NER) aims at identifying medical entities from unstructured data. A quintessential task in the supervision of biological databases is handling biomedical terms such as cancer type, DeoxyriboNucleic and RiboNucleic Acid, gene and protein name, and others. However, due to the massive size of online medical repositories, data processing becomes a challenge for a gazetteer without proper annotation. The traditional NER systems depend on feature engineering that is tedious and time-consuming. The research study presents a new model for Bio-NER using recurrent ne

APA, Harvard, Vancouver, ISO, and other styles

30

S. Patil, Sandip, Bhavsar R.P., and Pawar B.V. "BERT AND INDOWORDNET COLLABORATIVE EMBEDDING FOR ENHANCED MARATHI WORD SENSE DISAMBIGUATION." ICTACT Journal on Soft Computing 13, no. 2 (2023): 2842–49. https://doi.org/10.21917/ijsc.2023.0403.

Full text

Abstract:

Ambiguity in word meanings is a long-standing challenge in processing natural language. Word sense disambiguation (WSD) deals with this challenge. Prior neural language models make use of recurrent neural network and architecture with long short-term memory. These models process the words in sequence, are slower and not truly bi-directional, so they are not able to capture and represent the contextual meanings of the words, hence they are not competent in contextual semantic representation for WSD. Recent, Bi-Directional Encoder Representation from Transformers (BERT) is long short-term memory

APA, Harvard, Vancouver, ISO, and other styles

31

Sudhanshu Tyagi, Neeraj Dwivedi, Sachin Kumar, Sudeep Tanwar,. "Signal Identification in Non-Orthogonal Multiple Access Wireless Systems Using Bi-Directional Long Short-Term Memory Network." Tuijin Jishu/Journal of Propulsion Technology 44, no. 3 (2023): 2313–27. http://dx.doi.org/10.52783/tjjpt.v44.i3.697.

Full text

Abstract:

This study's goal is to provide an early analysis of deep learning (DL) for signal identification in wireless systems that use non-orthogonal multiple access (NOMA). The successive interference cancellation (SIC) approach is frequently used at the receiver in NOMA systems when several users are decoded successively. Without explicitly calculating channels, a DL-based NOMA receiver can decode messages for several users at once. To estimate the multiuser uplink channel (CE) and recognize the initial broadcast signal in this study, it is recommended that a deep neural network with bi-directional

APA, Harvard, Vancouver, ISO, and other styles

32

Huang, Gang, Lin Gong, Yuhan Zhang, Zhongmei Wang, and Songlin Yuan. "The Remaining Life Prediction of Rails Based on Convolutional Bi-Directional Long and Short-Term Memory Neural Network with Residual Self-Attention Mechanism." Applied Sciences 14, no. 9 (2024): 3781. http://dx.doi.org/10.3390/app14093781.

Full text

Abstract:

In the railway industry, the rail is the basic load-bearing structure of railway tracks. The prediction of the remaining useful life (RUL) for rails is important to avoid unexpected system failures and reduce the cost of maintaining the system. However, the existing detection of rail flaws is difficult, the rail deterioration mechanisms are diverse, and the traditional data-driven methods have insufficient feature extraction. This causes low prediction accuracy. With objectives set in relation to the problems outlined above, a rail RUL prediction approach based on a convolutional bidirectional

APA, Harvard, Vancouver, ISO, and other styles

33

A, Chandrasekar, and Yamuna K. "Using Deep Learning an Effiecient Bot Attack Detection Methods." South Asian Journal of Engineering and Technology 15, no. 2 (2025): 123–28. https://doi.org/10.26524/sajet.2025.15.13.

Full text

Abstract:

Deep Learning (DL) is an effective way to detect botnet attacks. However, the amount of network traffic data and the required memory space are usually large. Therefore, it is almost impossible to use the DL method on memory-restricted IoT devices. In this paper, we reduce the size of the IoT network traffic data feature using the Long Short-Term Short-Term Memory Autoencoder (LAE) codec section. In order to classify network traffic samples correctly, we analyze long-term variables related to low-dimensional feature produced by LAE using Bi-directional Long Short-Term Memory (BLSTM). Comprehens

APA, Harvard, Vancouver, ISO, and other styles

34

Yu, Kun. "Adaptive Bi-Directional LSTM Short-Term Load Forecasting with Improved Attention Mechanisms." Energies 17, no. 15 (2024): 3709. http://dx.doi.org/10.3390/en17153709.

Full text

Abstract:

Special load customers such as electric vehicles are emerging in modern power systems. They lead to a higher penetration of special load patterns, raising difficulty for short-term load forecasting (STLF). We propose a hierarchical STLF framework to improve load forecasting accuracy. An improved adaptive K-means clustering algorithm is designed for load pattern recognition and avoiding local sub-optimal clustering centroids. We also design bi-directional long-short-term memory neural networks with an attention mechanism to filter important load information and perform load forecasting for each

APA, Harvard, Vancouver, ISO, and other styles

35

Bi, Mingwen, Qingchuan Zhang, Min Zuo, Zelong Xu, and Qingyu Jin. "Bi-directional Long Short-Term Memory Model with Semantic Positional Attention for the Question Answering System." ACM Transactions on Asian and Low-Resource Language Information Processing 20, no. 5 (2021): 1–13. http://dx.doi.org/10.1145/3439800.

Full text

Abstract:

The intelligent question answering system aims to provide quick and concise feedback on the questions of users. Although the performance of phrase-level and numerous attention models have been improved, the sentence components and position information are not emphasized enough. This article combines Ci-Lin and word2vec to divide all of the words in the question-answer pairs into groups according to the semantics and select one kernel word in each group. The remaining words are common words and realize the semantic mapping mechanism between kernel words and common words. With this Chinese seman

APA, Harvard, Vancouver, ISO, and other styles

36

Mangal, Dharmendra, and Hemant Makwana. "Extracting geo-references from social media text using bi-long short term memory networks." Indonesian Journal of Electrical Engineering and Computer Science 35, no. 2 (2024): 1263. http://dx.doi.org/10.11591/ijeecs.v35.i2.pp1263-1270.

Full text

Abstract:

<p>The social media data provides great source of information about global and local events, with millions of users. More precisely, the fact that brief messages are practical and are highly popular. Many recent studies have been motivated to estimate the location of the events identified by tracking posts in social media text messages. It might be difficult to extract location data and estimate the location of an event while maintaining a sufficient level of situation awareness, particularly in disaster situations like fires or traffic accidents. In this presented work we proposed an ap

APA, Harvard, Vancouver, ISO, and other styles

37

Dharmendra, Mangal Hemant Makwana. "Extracting geo-references from social media text using bi-long short term memory networks." Indonesian Journal of Electrical Engineering and Computer Science 35, no. 2 (2024): 1263–70. https://doi.org/10.11591/ijeecs.v35.i2.pp1263-1270.

Full text

Abstract:

The social media data provides great source of information about global and local events, with millions of users. More precisely, the fact that brief messages are practical and are highly popular. Many recent studies have been motivated to estimate the location of the events identified by tracking posts in social media text messages. It might be difficult to extract location data and estimate the location of an event while maintaining a sufficient level of situation awareness, particularly in disaster situations like fires or traffic accidents. In this presented work we proposed an approach to

APA, Harvard, Vancouver, ISO, and other styles

38

Ali Khan, Mehmood, Iftikhar Ahmed Khan, Sajid Shah, Mohammed EL-Affendi, and Waqas Jadoon. "Short-term wind power forecasting through stacked and bi directional LSTM techniques." PeerJ Computer Science 10 (March 29, 2024): e1949. http://dx.doi.org/10.7717/peerj-cs.1949.

Full text

Abstract:

Background Computational intelligence (CI) based prediction models increase the efficient and effective utilization of resources for wind prediction. However, the traditional recurrent neural networks (RNN) are difficult to train on data having long-term temporal dependencies, thus susceptible to an inherent problem of vanishing gradient. This work proposed a method based on an advanced version of RNN known as long short-term memory (LSTM) architecture, which updates recurrent weights to overcome the vanishing gradient problem. This, in turn, improves training performance. Methods The RNN mode

APA, Harvard, Vancouver, ISO, and other styles

39

Ulu, Yasemin. "Forecasting Stock Prices via Deep Learning During COVID-19: A Case Study from an Emerging Economy." European Journal of Theoretical and Applied Sciences 2, no. 1 (2024): 497–503. http://dx.doi.org/10.59324/ejtas.2024.2(1).42.

Full text

Abstract:

In this study we apply a Deep Learning Technique to predict stock prices for the 30 stocks that compose the BIST30, Turkish Stock Market Index before and after the onset of Covid-19 crises. Specifically, we utilize the Bi-Directional Long-Short Term Memory (BiLSTM) model which is a variation of the Long-Short-Term Memory (LSTM) model to predict stock prices for the BIST30 stocks. We compare the performance of the model to other commonly used machine learning models like decision tree, bagging, random forest, adaptive boosting (Adaboost), gradient boosting, and eXtreme gradient boosting (XGBoos

APA, Harvard, Vancouver, ISO, and other styles

40

Yasemin, Ulu. "Forecasting Stock Prices via Deep Learning During COVID-19: A Case Study from an Emerging Economy." European Journal of Theoretical and Applied Sciences 2, no. 1 (2024): 497–503. https://doi.org/10.59324/ejtas.2024.2(1).42.

Full text

Abstract:

In this study we apply a Deep Learning Technique to predict stock prices for the 30 stocks that compose the BIST30, Turkish Stock Market Index before and after the onset of Covid-19 crises. Specifically, we utilize the Bi-Directional Long-Short Term Memory (BiLSTM) model which is a variation of the Long-Short-Term Memory (LSTM) model to predict stock prices for the BIST30 stocks. We compare the performance of the model to other commonly used machine learning models like decision tree, bagging, random forest, adaptive boosting (Adaboost), gradient boosting, and eXtreme gradient boosting (XGBoos

APA, Harvard, Vancouver, ISO, and other styles

41

Wang, Shouxiang, Xuan Wang, Shaomin Wang, and Dan Wang. "Bi-directional long short-term memory method based on attention mechanism and rolling update for short-term load forecasting." International Journal of Electrical Power & Energy Systems 109 (July 2019): 470–79. http://dx.doi.org/10.1016/j.ijepes.2019.02.022.

Full text

APA, Harvard, Vancouver, ISO, and other styles

42

Wollacott, Andrew M., Chonghua Xue, Qiuyuan Qin, et al. "Quantifying the nativeness of antibody sequences using long short-term memory networks." Protein Engineering, Design and Selection 32, no. 7 (2019): 347–54. http://dx.doi.org/10.1093/protein/gzz031.

Full text

Abstract:

Abstract Antibodies often undergo substantial engineering en route to the generation of a therapeutic candidate with good developability properties. Characterization of antibody libraries has shown that retaining native-like sequence improves the overall quality of the library. Motivated by recent advances in deep learning, we developed a bi-directional long short-term memory (LSTM) network model to make use of the large amount of available antibody sequence information, and use this model to quantify the nativeness of antibody sequences. The model scores sequences for their similarity to natu

APA, Harvard, Vancouver, ISO, and other styles

43

Lin, Ziyao, Zhangfang Hu, and Kuilin Zhu. "Speech emotion recognition based on dynamic convolutional neural network." Journal of Computing and Electronic Information Management 10, no. 1 (2023): 72–77. http://dx.doi.org/10.54097/jceim.v10i1.5756.

Full text

Abstract:

In speech emotion recognition, the use of deep learning algorithms that extract and classify features of audio emotion samples usually requires the use of a large amount of resources, which makes the system more complex. This paper proposes a speech emotion recognition system based on dynamic convolutional neural network combined with bi-directional long and short-term memory network. On the one hand, the dynamic convolutional kernel allows the neural network to extract global dynamic emotion information, which can improve the performance while ensuring the computational power of the model, an

APA, Harvard, Vancouver, ISO, and other styles

44

HaoXiang, Chang. "Discussion of a method for analysing adolescent depression based on BiLSTM and Attentional Mechanisms." Applied and Computational Engineering 42, no. 1 (2024): 248–53. http://dx.doi.org/10.54254/2755-2721/42/20230785.

Full text

Abstract:

The incidence of depression in adolescents has been commonplace, but the number of people diagnosed with depression in hospitals is not large, so it is important to analyze people's textual content in normal times to determine whether they are in a depressive mood, and then intervene in a timely manner. The article firstly introduces the research results of previous scholars in sentiment analysis, and then briefly introduces the LSTM(Long Short-Term Memory) model, explains the operation formula of the model, and points out that BiLSTM(Bi-directional Long Short-Term Memory) has greater predicti

APA, Harvard, Vancouver, ISO, and other styles

45

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

46

Wang, Zhifeng, Jinwei Fan, Yi Dai, et al. "Intelligent Detection Method of Atrial Fibrillation by CEPNCC-BiLSTM Based on Long-Term Photoplethysmography Data." Sensors 24, no. 16 (2024): 5243. http://dx.doi.org/10.3390/s24165243.

Full text

Abstract:

Atrial fibrillation (AF) is the most prevalent arrhythmia characterized by intermittent and asymptomatic episodes. However, traditional detection methods often fail to capture the sporadic and intricate nature of AF, resulting in an increased risk of false-positive diagnoses. To address these challenges, this study proposes an intelligent AF detection and diagnosis method that integrates Complementary Ensemble Empirical Mode Decomposition, Power-Normalized Cepstral Coefficients, Bi-directional Long Short-term Memory (CEPNCC-BiLSTM), and photoelectric volumetric pulse wave technology to enhance

APA, Harvard, Vancouver, ISO, and other styles

47

Ahmed, Abrar, Safdar Ali, Ali Raza, et al. "Novel deep neural network architecture fusion to simultaneously predict short-term and long-term energy consumption." PLOS ONE 20, no. 1 (2025): e0315668. https://doi.org/10.1371/journal.pone.0315668.

Full text

Abstract:

Energy is integral to the socio-economic development of every country. This development leads to a rapid increase in the demand for energy consumption. However, due to the constraints and costs associated with energy generation resources, it has become crucial for both energy generation companies and consumers to predict energy consumption well in advance. Forecasting energy needs through accurate predictions enables companies and customers to make informed decisions, enhancing the efficiency of both energy generation and consumption. In this context, energy generation companies and consumers

APA, Harvard, Vancouver, ISO, and other styles

48

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

49

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

50

Han, Chenyu, and Xiaoyu Fu. "Challenge and Opportunity: Deep Learning-Based Stock Price Prediction by Using Bi-Directional LSTM Model." Frontiers in Business, Economics and Management 8, no. 2 (2023): 51–54. http://dx.doi.org/10.54097/fbem.v8i2.6616.

Full text

Abstract:

Stock price prediction is a challenging and important task in finance, with many potential applications in investment, risk management, and portfolio optimization. In this paper, we propose a bi-directional long short-term memory (Bi-LSTM) model for predicting the future price of a stock based on its historical prices. The Bi-LSTM model is a variant of the popular LSTM model that is capable of processing input sequences in both forward and backward directions, allowing it to capture both short- and long-term dependencies in the data. We apply the Bi-LSTM model to historical stock price data fo

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!