Journal articles: 'LSTM bidirectionnel'

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Bae, Jangseong, and Changki Lee. "Korean Semantic Role Labeling using Stacked Bidirectional LSTM-CRFs." Journal of KIISE 44, no. 1 (January 15, 2017): 36–43. http://dx.doi.org/10.5626/jok.2017.44.1.36.

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Ashebir, Desalegn, and Prabhakar Gantela. "Named Entity Recognition for Sheko Language Using Bidirectional LSTM." Indian Journal of Science and Technology 15, no. 23 (June 21, 2022): 1124–32. http://dx.doi.org/10.17485/ijst/v15i23.642.

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Yu, Hongyeon, and Youngjoong Ko. "Expansion of Word Representation for Named Entity Recognition Based on Bidirectional LSTM CRFs." Journal of KIISE 44, no. 3 (March 15, 2017): 306–13. http://dx.doi.org/10.5626/jok.2017.44.3.306.

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Oh, Yeongtaek, Mintae Kim, and Wooju Kim. "Korean Movie-review Sentiment Analysis Using Parallel Stacked Bidirectional LSTM Model." Journal of KIISE 46, no. 1 (January 31, 2019): 45–49. http://dx.doi.org/10.5626/jok.2019.46.1.45.

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Karyadi, Yadi. "Prediksi Kualitas Udara Dengan Metoda LSTM, Bidirectional LSTM, dan GRU." JATISI (Jurnal Teknik Informatika dan Sistem Informasi) 9, no. 1 (March 17, 2022): 671–84. http://dx.doi.org/10.35957/jatisi.v9i1.1588.

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Kualitas udara menjadi salah satu masalah utama di kota besar. Salah satu cara pengendalian kualitas udara adalah dengan cara memprediksi beberapa parameter utama dengan menggunakan algoritma deep learning. Penelitian ini menggunakan metoda deep learning yang merupakan bagian dari Recurrent Neural network yaitu Long Short Term Memory, Bidirectional Long Short Term Memory, dan Gated Recurrent Unit yang diterapkan pada permasalahan memprediksi data time series kualitas udara dengan parameter suhu, kelembaban, particular matter PM10, dan Indeks Standar Pencemar Udara (ISPU). Dari hasil pengujian 3 jenis model prediksi terhadap 4 variabel berdasarkan kreteria penilain menggunakan RMSE dari data testing dan dibandingkan dengan standard deviasi, maka model LSTM dan LSTM Bidirectional telah menunjukan hasil yang bagus untuk permasalahan data yang bersifat time series kualitas udara, Sedangkan model Gated Recurrent Unit (GRU) menampilkan hasil yang kurang bagus.

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Ismail, Mohammad Hafiz, and Tajul Rosli Razak. "Predicting the Kijang Emas Bullion Price using LSTM Networks." Journal of Entrepreneurship and Business 8, no. 2 (December 31, 2020): 11–18. http://dx.doi.org/10.17687/jeb.0802.02.

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This study investigates the potential of Deep Learning techniques, specifically LSTM networks, in forecasting Kijang Emas future value over a long period. Six LSTM models comprising of Simple LSTM, Bidirectional LSTM, and Stacked LSTM architecture were built and trained against a 15-year historical price data for Kijang Emas. The models’ performance was then measured against ARIMA (5,1,0) as a baseline reference and evaluated against the RAE, MSE and RMSE metric. The results revealed that LSTM networks models performed well in forecasting Kijang Emas price based on the test dataset where the average RMSE was between 49.9 to 50.3 while the Bidirectional LSTM was found to exhibit better performance as compared to the other LSTM models.

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Ismail, Mohammad Hafiz, and Tajul Rosli Razak. "Predicting the Kijang Emas Bullion Price using LSTM Networks." Journal of Entrepreneurship and Business 8, no. 2 (June 1, 2022): 11–18. http://dx.doi.org/10.17687/jeb.v8i2.849.

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This study investigates the potential of Deep Learning techniques, specifically LSTM networks, in forecasting Kijang Emas future value over a long period. Six LSTM models comprising of Simple LSTM, Bidirectional LSTM, and Stacked LSTM architecture were built and trained against a 15-year historical price data for Kijang Emas. The models’ performance was then measured against ARIMA (5,1,0) as a baseline reference and evaluated against the RAE, MSE and RMSE metric. The results revealed that LSTM networks models performed well in forecasting Kijang Emas price based on the test dataset where the average RMSE was between 49.9 to 50.3 while the Bidirectional LSTM was found to exhibit better performance as compared to the other LSTM models.

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Appati, Justice Kwame, Ismail Wafaa Denwar, Ebenezer Owusu, and Michael Agbo Tettey Soli. "Construction of an Ensemble Scheme for Stock Price Prediction Using Deep Learning Techniques." International Journal of Intelligent Information Technologies 17, no. 2 (April 2021): 72–95. http://dx.doi.org/10.4018/ijiit.2021040104.

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This study proposes a deep learning approach for stock price prediction by bridging the long short-term memory with gated recurrent unit. In its evaluation, the mean absolute error and mean square error were used. The model proposed is an extension of the study of Hossain et al. established in 2018 with an MSE of 0.00098 as its lowest error. The current proposed model is a mix of the bidirectional LSTM and bidirectional GRU resulting in 0.00000008 MSE as the lowest error recorded. The LSTM model recorded 0.00000025 MSE, the GRU model recorded 0.00000077 MSE, and the LSTM + GRU model recorded 0.00000023 MSE. Other combinations of the existing models such as the bi-directional LSTM model recorded 0.00000019 MSE, bi-directional GRU recorded 0.00000011 MSE, bidirectional LSTM + GRU recorded 0.00000027 MSE, LSTM and bi-directional GRU recorded 0.00000020 MSE.

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Kim, Mintae, Yeongtaek Oh, and Wooju Kim. "Sentence Similarity Prediction based on Siamese CNN-Bidirectional LSTM with Self-attention." Journal of KIISE 46, no. 3 (March 31, 2019): 241–45. http://dx.doi.org/10.5626/jok.2019.46.3.241.

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Jiang, Longquan, Xuan Sun, Francesco Mercaldo, and Antonella Santone. "DECAB-LSTM: Deep Contextualized Attentional Bidirectional LSTM for cancer hallmark classification." Knowledge-Based Systems 210 (December 2020): 106486. http://dx.doi.org/10.1016/j.knosys.2020.106486.

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Phua, Yeong Tsann, Sujata Navaratnam, Chon-Moy Kang, and Wai-Seong Che. "Sequence-to-sequence neural machine translation for English-Malay." IAES International Journal of Artificial Intelligence (IJ-AI) 11, no. 2 (June 1, 2022): 658. http://dx.doi.org/10.11591/ijai.v11.i2.pp658-665.

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Machine translation aims to translate text from a specific language into another language using computer software. In this work, we performed neural machine translation with attention implementation on English-Malay parallel corpus. We attempt to improve the model performance by rectified linear unit (ReLU) attention alignment. Different sequence-to-sequence models were trained. These models include long-short term memory (LSTM), gated recurrent unit (GRU), bidirectional LSTM (Bi-LSTM) and bidirectional GRU (Bi-GRU). In the experiment, both bidirectional models, Bi-LSTM and Bi-GRU yield a converge of below 30 epochs. Our study shows that the ReLU attention alignment improves the bilingual evaluation understudy (BLEU) translation score between score 0.26 and 1.12 across all the models as compare to the original Tanh models.

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Wang, Shuming, Bing Yang, Huimin Chen, Weihua Fang, and Tiantang Yu. "LSTM-Based Deformation Prediction Model of the Embankment Dam of the Danjiangkou Hydropower Station." Water 14, no. 16 (August 9, 2022): 2464. http://dx.doi.org/10.3390/w14162464.

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The Danjiangkou hydropower station is a water source project for the middle line of the South-to-North Water Transfer Project in China. The dam is composed of riverbed concrete dam and earth rock dam on both banks, with a total length of 3442 m. Once the dam is wrecked, it will yield disastrous consequences. Therefore, it is very important to evaluate the dam safety behavior in time. Based on the long-term and short-term memory (LSTM) network, the deformation prediction models of the embankment dam of the Danjiangkou hydropower station are constructed. The models contain two LSTM layers, adopt the rectified linear unit function as the activation function and determine the super parameters of the models with Bayesian optimization algorithm. According to the settlement monitoring data of LD12ZT01 measuring point (dam crest 0 + 648) on the left bank of the embankment dam of the Danjiangkou hydropower station from July 2013 to March 2022, the LSTM and bidirectional LSTM models are constructed. In total, 80% of the monitoring data are taken as the training set data and 20% of the monitoring data are taken as the test set data. The mean absolute error, root mean square error and mean square error for the test set are 0.42978, 0.56456 and 0.31873 for partial least squares regression (PLSR), 0.35264, 0.47561 and 0.22621 for LSTM and 0.34418, 0.45400 and 0.20612 for bidirectional LSTM, respectively. The results show that the bidirectional LSTM model can obtain better deformation prediction value than the LSTM model and the PLSR. Then, the bidirectional LSTM model is used to predict the settlement value of LD16YT01 measuring point (dam crest 0 + 658) on the right bank, and the mean absolute error, root mean square error and mean square error for the test set are 0.5425, 0.66971 and 0.4520, respectively. This shows the bidirectional LSTM model can effectively predict the settlement value of the embankment dam of the Danjiangkou hydropower station.

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Chen, Kelvin, Ronsen Purba, and Arwin Halim. "Stock Price Prediction Using XCEEMDAN-Bidirectional LSTM -Spline." Indonesian Journal of Artificial Intelligence and Data Mining 5, no. 1 (May 14, 2022): 1. http://dx.doi.org/10.24014/ijaidm.v5i1.14424.

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Bidirectional Long Short Term Memory (Bidirectional LSTM) is a machine learning technique with the ability to capture data context by traversing backward data to forward data and vice versa. However, the characteristics of stock data with large fluctuations, high dimensions and non-linearity become a challenge in obtaining high stock price prediction accuracy values. The purpose of this study is to provide a solution to the problem of stock data characteristics with large fluctuations, high dimensions and non-linearity by combining the Complete Ensemble Empirical Mode Decomposition With Adaptive Noise method for exogenous features (XCEEMDAN), Bidirectional Long Short Term Memory (LSTM), and Splines. The predicted data will go through normalization and preprocessing using XCEEMDAN then the XCEEMDAN decomposition results are divided into high and low frequency signals. The bidirectional LSTM handles high frequency signals and the Spline model handles low frequency signals. The test is carried out by comparing the proposed XCEEMDAN-Bidirectional LSTM-Spline model with the XCEEMDAN-LSTM-Spline model using the same parameters and changing the noise seed randomly 50 times. The test results show that the proposed model has the smallest RMSE average value of0.787213833 while model which is compared only has the smallest RMSE average value of 0.807393567.

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Pal, Subarno, Soumadip Ghosh, and Amitava Nag. "Sentiment Analysis in the Light of LSTM Recurrent Neural Networks." International Journal of Synthetic Emotions 9, no. 1 (January 2018): 33–39. http://dx.doi.org/10.4018/ijse.2018010103.

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Long short-term memory (LSTM) is a special type of recurrent neural network (RNN) architecture that was designed over simple RNNs for modeling temporal sequences and their long-range dependencies more accurately. In this article, the authors work with different types of LSTM architectures for sentiment analysis of movie reviews. It has been showed that LSTM RNNs are more effective than deep neural networks and conventional RNNs for sentiment analysis. Here, the authors explore different architectures associated with LSTM models to study their relative performance on sentiment analysis. A simple LSTM is first constructed and its performance is studied. On subsequent stages, the LSTM layer is stacked one upon another which shows an increase in accuracy. Later the LSTM layers were made bidirectional to convey data both forward and backward in the network. The authors hereby show that a layered deep LSTM with bidirectional connections has better performance in terms of accuracy compared to the simpler versions of LSTM used here.

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Choudhary, Anshika, and Anuja Arora. "Continuous Attention Mechanism Embedded (CAME) Bi-Directional Long Short-Term Memory Model for Fake News Detection." International Journal of Ambient Computing and Intelligence 13, no. 1 (January 1, 2022): 1–24. http://dx.doi.org/10.4018/ijaci.309407.

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The credible analysis of news on social media due to the fact of spreading unnecessary restlessness and reluctance in the community is a need. Numerous individual or social media marketing entities radiate inauthentic news through online social media. Henceforth, delineating these activities on social media and the apparent identification of delusive content is a challenging task. This work projected a continuous attention-driven memory-based deep learning model to predict the credibility of an article. To exhibit the importance of continuous attention, research work is presented in accretive exaggeration mode. Initially, long short-term memory (LSTM)-based deep learning model has been applied, which is extended by merging the concept of bidirectional LSTM for fake news identification. This research work proposed a continuous attention mechanism embedded (CAME)-bidirectional LSTM model for predicting the nature of news. Result shows the proposed CAME model outperforms the performance as compared to LSTM and the bidirectional LSTM model.

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Nam, Young-Jin, and Ha-Hyun Jo. "Prediction of Weekly Load using Stacked Bidirectional LSTM and Stacked Unidirectional LSTM." Journal of Korean Institute of Information Technology 18, no. 9 (September 30, 2020): 9–17. http://dx.doi.org/10.14801/jkiit.2020.18.9.9.

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Nurdin, A., and N. U. Maulidevi. "5W1H Information Extraction with CNN-Bidirectional LSTM." Journal of Physics: Conference Series 978 (March 2018): 012078. http://dx.doi.org/10.1088/1742-6596/978/1/012078.

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Bin, Yi, Yang Yang, Fumin Shen, Ning Xie, Heng Tao Shen, and Xuelong Li. "Describing Video With Attention-Based Bidirectional LSTM." IEEE Transactions on Cybernetics 49, no. 7 (July 2019): 2631–41. http://dx.doi.org/10.1109/tcyb.2018.2831447.

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Chiu, Jason P. C., and Eric Nichols. "Named Entity Recognition with Bidirectional LSTM-CNNs." Transactions of the Association for Computational Linguistics 4 (December 2016): 357–70. http://dx.doi.org/10.1162/tacl_a_00104.

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Named entity recognition is a challenging task that has traditionally required large amounts of knowledge in the form of feature engineering and lexicons to achieve high performance. In this paper, we present a novel neural network architecture that automatically detects word- and character-level features using a hybrid bidirectional LSTM and CNN architecture, eliminating the need for most feature engineering. We also propose a novel method of encoding partial lexicon matches in neural networks and compare it to existing approaches. Extensive evaluation shows that, given only tokenized text and publicly available word embeddings, our system is competitive on the CoNLL-2003 dataset and surpasses the previously reported state of the art performance on the OntoNotes 5.0 dataset by 2.13 F1 points. By using two lexicons constructed from publicly-available sources, we establish new state of the art performance with an F1 score of 91.62 on CoNLL-2003 and 86.28 on OntoNotes, surpassing systems that employ heavy feature engineering, proprietary lexicons, and rich entity linking information.

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Malki, Zohair, Elsayed Atlam, Guesh Dagnew, Ahmad Reda Alzighaibi, Elmarhomy Ghada, and Ibrahim Gad. "Bidirectional Residual LSTM-based Human Activity Recognition." Computer and Information Science 13, no. 3 (June 8, 2020): 40. http://dx.doi.org/10.5539/cis.v13n3p40.

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The Residual Long Short Term Memory (LSTM) deep learning approach is attracting attension of many researchers due to its efficiency when trained on high dimensional datasets. Nowadays, Human Activity Recognition (HAR) has come with enormous challenges that have to be addressed. In addressing such a problem, one can think of developing an application that can help the elderly people as an assistant when it works in collaboration with other timely technologies such as wearable devices with the help of IoT. Many research works are using a standard dataset in evaluating their proposed method in this regard. The dataset comes with its own challenge such as imbalanced classes. In this work, we propose to apply different machine learning techniques to address the specified problems and the method is validated on a standard dataset. To validate the proposed method, we evaluated using different standard metrics such as classification accuracy, precision, recall, f1-score, and Receiver Operating Characteristic (ROC) curve. The proposed method achieves an Area Under Curve (AUC) of 100%, 97.66% of accuracy, 91.59% precision,  93.75% of recall and 92.66% of F1-score respectively.

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Chahkandi, Vahid, Mohammad Javad Fadaeieslam, and Farzin Yaghmaee. "Improvement of image description using bidirectional LSTM." International Journal of Multimedia Information Retrieval 7, no. 3 (July 19, 2018): 147–55. http://dx.doi.org/10.1007/s13735-018-0158-y.

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Yonglan, Li, and He Wenjia. "English-Chinese Machine Translation Model Based on Bidirectional Neural Network with Attention Mechanism." Journal of Sensors 2022 (March 17, 2022): 1–11. http://dx.doi.org/10.1155/2022/5199248.

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In recent years, with the development of deep learning, machine translation using neural network has gradually become the mainstream method in industry and academia. The existing Chinese-English machine translation models generally adopt the deep neural network architecture based on attention mechanism. However, it is still a challenging problem to model short and long sequences simultaneously. Therefore, a bidirectional LSTM model integrating attention mechanism is proposed. Firstly, by using the word vector as the input data of the translation model, the linguistic symbols used in the translation process are mathematized. Secondly, two attention mechanisms are designed: local attention mechanism and global attention mechanism. The local attention mechanism is mainly used to learn which words or phrases in the input sequence are more important for modeling, while the global attention mechanism is used to learn which layer of expression vector in the input sequence is more critical. Bidirectional LSTM can better fuse the feature information in the input sequence, while bidirectional LSTM with attention mechanism can simultaneously model short and long sequences. The experimental results show that compared with many existing translation models, the bidirectional LSTM model with attention mechanism can effectively improve the quality of machine translation.

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Matsumoto, Kazuyuki, Seiji Tsuchiya, Takumi Kojima, Hiroya Kondo, Minoru Yoshida, and Kenji Kita. "Classification of Smartphone Application Reviews Using Small Corpus Based on Bidirectional LSTM Transformer." International Journal of Machine Learning and Computing 10, no. 1 (January 2020): 148–57. http://dx.doi.org/10.18178/ijmlc.2020.10.1.912.

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Zahara, Soffa, and Sugianto. "Peramalan Data Indeks Harga Konsumen Berbasis Time Series Multivariate Menggunakan Deep Learning." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 5, no. 1 (February 13, 2021): 24–30. http://dx.doi.org/10.29207/resti.v5i1.2562.

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Multivariate Time Series based forecasting is a type of forecasting that has more than one criterion changes from time to time that it can forecast based on historical patterns of data sequences. The Consumer Price Index (CPI) issued regularly every month by the Statistics Indonesia calculated based on data observations. This study is a development of previous research that only used on type of algorithm to predict CPI value resulting poor of accuracy due to lack of architecture variations testing. This study developed a CPI forecasting model with a new approach about using several types of deep learning algorithms, namely LSTM, Bidirectional LSTM, and Multilayer Perceptron with architectural variations of the number of neurons and epochs. Furthermore, this study adapt ADDIE model of Research and Development method. Based on the results, the best accuracy is obtained from the LSTM Bidirectional with 10 neurons and 2000 epoch resulting 3,519 of RMSE value. Meanwhile, based on the average RMSE value for the whole test, LSTM gets the smallest average of RMSE followed Bidirectional LSTM and Multilayer Perceptron with the RMSE value 4,334, 5,630, 6,304 respectively.

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Yadav, Omprakash, Rachael Dsouza, Rhea Dsouza, and Janice Jose. "Soccer Action video Classification using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (June 30, 2022): 1060–63. http://dx.doi.org/10.22214/ijraset.2022.43929.

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Abstract: This paper proposes a deep learning approach for the classification of different soccer actions like Goal, Yellow Card and Soccer Juggling from an input soccer video. The approach used for the same included a Hybrid model which consisted of VGG16 CNN model and Bidirectional Long short-term memory (Bi-LSTM) a Recurrent Neural Network (RNN) model. Our approach involved manually annotating approximately 400 soccer clips from 3 action classes for training. Using the VGG16 model to extract the features from the frames of these clips and then training the bi-LSTM on the features obtained. Bi-LSTM being useful in predicting input sequence problems like videos. Keywords: Soccer Videos, Convolution Neural Networks (CNNs), Recurrent Neural Network (RNN), Bidirectional Long shortterm memory (Bi-LSTM)

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Afzaal, Hassan, Aitazaz A. Farooque, Farhat Abbas, Bishnu Acharya, and Travis Esau. "Computation of Evapotranspiration with Artificial Intelligence for Precision Water Resource Management." Applied Sciences 10, no. 5 (February 29, 2020): 1621. http://dx.doi.org/10.3390/app10051621.

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Accurate estimation of reference evapotranspiration (ETo) provides useful information for water resource management and sustainable agriculture. This study estimates ETo with recurrent neural networks (RNNs), namely long short-term memory (LSTM) and bidirectional LSTM. Four representative meteorological sites (North Cape, Summerside, Harrington, and Saint Peters) were selected across Prince Edward Island (PEI), Canada to form a PEI dataset from mean values of the four sites’ climatic variables for capturing climatic variability from all parts of the province. Based on subset regression analysis, the highest contributing climatic variables, namely maximum air temperature and relative humidity, were selected as input variables for RNNs’ training (2011–2015) and testing (2016–2017) runs. The results suggested that the LSTM and bidirectional LSTM are suitable methods to accurately (R2 > 0.90) estimate ETo for all sites except Harrington. Testing period (2016–2017) root mean square errors were recorded in range of 0.38–0.58 mm/day for all sites. No major differences were observed in accuracy of LSTM and bidirectional LSTM. Another objective of this study was to highlight the potential gap between ETO and rainfall for assessing agriculture sustainability in Prince Edward Island. Analyses of the data highlighted that the cumulative ETo surpassed the cumulative rainfall potentially affecting yield of major crops in the island. Therefore, agriculture sustainability requires viable options such as supplemental irrigation to replenish the crop water requirements as and when needed.

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Wu, Yiqi, Mei Liu, Zhaoyuan Peng, Meiqi Liu, Miao Wang, and Yingqi Peng. "Recognising Cattle Behaviour with Deep Residual Bidirectional LSTM Model Using a Wearable Movement Monitoring Collar." Agriculture 12, no. 8 (August 17, 2022): 1237. http://dx.doi.org/10.3390/agriculture12081237.

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Cattle behaviour is a significant indicator of cattle welfare. With the advancements in electronic equipment, monitoring and classifying multiple cattle behaviour patterns is becoming increasingly important in precision livestock management. The aim of this study was to detect important cattle physiological states using a neural network model and wearable electronic sensors. A novel long short-term memory (LSTM) recurrent neural network model that uses two-way information was developed to accurately classify cattle behaviour and compared with baseline LSTM. Deep residual bidirectional LSTM and baseline LSTM were used to classify six behavioural patterns of cows with window sizes of 64, 128 and 256 (6.4 s, 12.8 s and 25.6 s, respectively). The results showed that when using deep residual bidirectional LSTM with window size 128, four classification performance indicators, namely, accuracy, precision, recall, and F1-score, achieved the best results of 94.9%, 95.1%, 94.9%, and 94.9%, respectively. The results showed that the deep residual bidirectional LSTM model can be used to classify time-series data collected from twelve cows using inertial measurement unit collars. Six aim cattle behaviour patterns can be classified with high accuracy. This method can be used to quickly detect whether a cow is suffering from bovine dermatomycosis. Furthermore, this method can be used to implement automated and precise cattle behaviour classification techniques for precision livestock farming.

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Hsu, Fu-Shun, Shang-Ran Huang, Chien-Wen Huang, Chao-Jung Huang, Yuan-Ren Cheng, Chun-Chieh Chen, Jack Hsiao, et al. "Benchmarking of eight recurrent neural network variants for breath phase and adventitious sound detection on a self-developed open-access lung sound database—HF_Lung_V1." PLOS ONE 16, no. 7 (July 1, 2021): e0254134. http://dx.doi.org/10.1371/journal.pone.0254134.

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A reliable, remote, and continuous real-time respiratory sound monitor with automated respiratory sound analysis ability is urgently required in many clinical scenarios—such as in monitoring disease progression of coronavirus disease 2019—to replace conventional auscultation with a handheld stethoscope. However, a robust computerized respiratory sound analysis algorithm for breath phase detection and adventitious sound detection at the recording level has not yet been validated in practical applications. In this study, we developed a lung sound database (HF_Lung_V1) comprising 9,765 audio files of lung sounds (duration of 15 s each), 34,095 inhalation labels, 18,349 exhalation labels, 13,883 continuous adventitious sound (CAS) labels (comprising 8,457 wheeze labels, 686 stridor labels, and 4,740 rhonchus labels), and 15,606 discontinuous adventitious sound labels (all crackles). We conducted benchmark tests using long short-term memory (LSTM), gated recurrent unit (GRU), bidirectional LSTM (BiLSTM), bidirectional GRU (BiGRU), convolutional neural network (CNN)-LSTM, CNN-GRU, CNN-BiLSTM, and CNN-BiGRU models for breath phase detection and adventitious sound detection. We also conducted a performance comparison between the LSTM-based and GRU-based models, between unidirectional and bidirectional models, and between models with and without a CNN. The results revealed that these models exhibited adequate performance in lung sound analysis. The GRU-based models outperformed, in terms of F1 scores and areas under the receiver operating characteristic curves, the LSTM-based models in most of the defined tasks. Furthermore, all bidirectional models outperformed their unidirectional counterparts. Finally, the addition of a CNN improved the accuracy of lung sound analysis, especially in the CAS detection tasks.

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Abduljabbar, Rusul L., Hussein Dia, and Pei-Wei Tsai. "Unidirectional and Bidirectional LSTM Models for Short-Term Traffic Prediction." Journal of Advanced Transportation 2021 (March 26, 2021): 1–16. http://dx.doi.org/10.1155/2021/5589075.

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This paper presents the development and evaluation of short-term traffic prediction models using unidirectional and bidirectional deep learning long short-term memory (LSTM) neural networks. The unidirectional LSTM (Uni-LSTM) model provides high performance through its ability to recognize longer sequences of traffic time series data. In this work, Uni-LSTM is extended to bidirectional LSTM (BiLSTM) networks which train the input data twice through forward and backward directions. The paper presents a comparative evaluation of the two models for short-term speed and traffic flow prediction using a common dataset of field observations collected from multiple freeways in Australia. The results showed BiLSTM performed better for variable prediction horizons for both speed and flow. Stacked and mixed Uni-LSTM and BiLSTM models were also investigated for 15-minute prediction horizons resulting in improved accuracy when using 4-layer BiLSTM networks. The optimized 4-layer BiLSTM model was then calibrated and validated for multiple prediction horizons using data from three different freeways. The validation results showed a high degree of prediction accuracy exceeding 90% for speeds up to 60-minute prediction horizons. For flow, the model achieved accuracies above 90% for 5- and 10-minute prediction horizons and more than 80% accuracy for 15- and 30-minute prediction horizons. These findings extend the set of AI models available for road operators and provide them with confidence in applying robust models that have been tested and evaluated on different freeways in Australia.

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Kim, Seon-Wu, and Sung-Pil Choi. "Research on Joint Models for Korean Word Spacing and POS (Part-Of-Speech) Tagging based on Bidirectional LSTM-CRF." Journal of KIISE 45, no. 8 (August 31, 2018): 792–800. http://dx.doi.org/10.5626/jok.2018.45.8.792.

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Syed, Zafi Sherhan, Muhammad Zaigham Abbas Shah Syed, Muhammad Shehram Shah Syed, and Aunsa Shah. "Sequential Modeling for the Recognition of Activities in Logistics." Sukkur IBA Journal of Emerging Technologies 4, no. 1 (June 10, 2021): 12–21. http://dx.doi.org/10.30537/sjet.v4i1.848.

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Activity recognition is an important task in cyber physical system research and has been the focus of researchers worldwide. This paper presents a method for activity recognition in logistic operations using data from accelerometer and gyroscope sensors. A Long Short Term Memory (LSTM) recurrent neural network, bidirectional LSTM and a Convolutional LSTM (ConvLSTM) are used to classify between six activities being performed in the logistics operations being carried out. Comparing the performance of the LSTMs to the Conv-LSTM network, the designed Bi-LSTM RNN outperforms the other networks considered

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Zheng, Tianwei, Mei Wang, Yuan Guo, and Zheng Wang. "The Bidirectional Information Fusion Using an Improved LSTM Model." Mobile Information Systems 2021 (April 20, 2021): 1–15. http://dx.doi.org/10.1155/2021/5595898.

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The information fusion technology is of great significance in intelligent systems. At present, the modern coal-fired power plant has the fully functional sensor network. However, many data that are important for the operation of a power plant, such as the coal quality, cannot be directly obtained. Therefore, the information fusion technology needs to be introduced to obtain the implied information of the power plant. As a practical application, the soft measurement of coal quality is taken as the research object. This paper proposes an improved LSTM model combined with the bidirectional deep fusion, alertness mechanism, and parameter self-learning (DFAS-LSTM) to realize online soft computing for the coal quality analyses of industries and elements. First, a latent structure model is established to preprocess the noisy and redundant sensor network data. Second, an alertness mechanism is proposed and the self-learning method of the activation function parameters is used for the data feature extraction. Third, a deeply bidirectional fusion layer is added to the long short-term memory neural network model to solve the problem of the insufficient accuracy and the weak generalization. Using the historical data of the sensor network, the DFAS-LSTM model is established. Then, the online data of the sensor network is input to the DFAS-LSTM model to implement the online coal quality analyses. Experiment shows that the accuracy of the coal quality analyses is increased by 1%–2.42% compared to the traditionally bidirectional LSTM.

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Kundu, Ripan Kumar, Akhlaqur Rahman, and Shuva Paul. "A Study on Sensor System Latency in VR Motion Sickness." Journal of Sensor and Actuator Networks 10, no. 3 (August 6, 2021): 53. http://dx.doi.org/10.3390/jsan10030053.

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One of the most frequent technical factors affecting Virtual Reality (VR) performance and causing motion sickness is system latency. In this paper, we adopted predictive algorithms (i.e., Dead Reckoning, Kalman Filtering, and Deep Learning algorithms) to reduce the system latency. Cubic, quadratic, and linear functions are used to predict and curve fitting for the Dead Reckoning and Kalman Filtering algorithms. We propose a time series-based LSTM (long short-term memory), Bidirectional LSTM, and Convolutional LSTM to predict the head and body motion and reduce the motion to photon latency in VR devices. The error between the predicted data and the actual data is compared for statistical methods and deep learning techniques. The Kalman Filtering method is suitable for predicting since it is quicker to predict; however, the error is relatively high. However, the error property is good for the Dead Reckoning algorithm, even though the curve fitting is not satisfactory compared to Kalman Filtering. To overcome this poor performance, we adopted deep-learning-based LSTM for prediction. The LSTM showed improved performance when compared to the Dead Reckoning and Kalman Filtering algorithm. The simulation results suggest that the deep learning techniques outperformed the statistical methods in terms of error comparison. Overall, Convolutional LSTM outperformed the other deep learning techniques (much better than LSTM and Bidirectional LSTM) in terms of error.

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Zhao, Chuanchuan, Jinguo You, Xinxian Wen, and Xiaowu Li. "Deep Bi-LSTM Networks for Sequential Recommendation." Entropy 22, no. 8 (August 7, 2020): 870. http://dx.doi.org/10.3390/e22080870.

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Recent years have seen a surge in approaches that combine deep learning and recommendation systems to capture user preference or item interaction evolution over time. However, the most related work only consider the sequential similarity between the items and neglects the item content feature information and the impact difference of interacted items on the next items. This paper introduces the deep bidirectional long short-term memory (LSTM) and self-attention mechanism into the sequential recommender while fusing the information of item sequences and contents. Specifically, we deal with the issues in a three-pronged attack: the improved item embedding, weight update, and the deep bidirectional LSTM preference learning. First, the user-item sequences are embedded into a low-dimensional item vector space representation via Item2vec, and the class label vectors are concatenated for each embedded item vector. Second, the embedded item vectors learn different impact weights of each item to achieve item awareness via self-attention mechanism; the embedded item vectors and corresponding weights are then fed into the bidirectional LSTM model to learn the user preference vectors. Finally, the top similar items in the preference vector space are evaluated to generate the recommendation list for users. By conducting comprehensive experiments, we demonstrate that our model outperforms the traditional recommendation algorithms on Recall@20 and Mean Reciprocal Rank (MRR@20).

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Song, Xuyan, Chen Chen, Baojiang Cui, and Junsong Fu. "Malicious JavaScript Detection Based on Bidirectional LSTM Model." Applied Sciences 10, no. 10 (May 16, 2020): 3440. http://dx.doi.org/10.3390/app10103440.

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JavaScript has been widely used on the Internet because of its powerful features, and almost all the websites use it to provide dynamic functions. However, these dynamic natures also carry potential risks. The authors of the malicious scripts started using JavaScript to launch various attacks, such as Cross-Site Scripting (XSS), Cross-site Request Forgery (CSRF), and drive-by download attack. Traditional malicious script detection relies on expert knowledge, but even for experts, this is an error-prone task. To solve this problem, many learning-based methods for malicious JavaScript detection are being explored. In this paper, we propose a novel deep learning-based method for malicious JavaScript detection. In order to extract semantic information from JavaScript programs, we construct the Program Dependency Graph (PDG) and generate semantic slices, which preserve rich semantic information and are easy to transform into vectors. Then, a malicious JavaScript detection model based on the Bidirectional Long Short-Term Memory (BLSTM) neural network is proposed. Experimental results show that, in comparison with the other five methods, our model achieved the best performance, with an accuracy of 97.71% and an F1-score of 98.29%.

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Ran, Ziyong, Desheng Zheng, Yanling Lai, and Lulu Tian. "Applying Stack Bidirectional LSTM Model to Intrusion Detection." Computers, Materials & Continua 65, no. 1 (2020): 309–20. http://dx.doi.org/10.32604/cmc.2020.010102.

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Kadari, Rekia, Yu Zhang, Weinan Zhang, and Ting Liu. "CCG supertagging via Bidirectional LSTM-CRF neural architecture." Neurocomputing 283 (March 2018): 31–37. http://dx.doi.org/10.1016/j.neucom.2017.12.050.

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Elsheikh, Ahmed, Soumaya Yacout, and Mohamed-Salah Ouali. "Bidirectional handshaking LSTM for remaining useful life prediction." Neurocomputing 323 (January 2019): 148–56. http://dx.doi.org/10.1016/j.neucom.2018.09.076.

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Ogawa, Takahiro, Yuma Sasaka, Keisuke Maeda, and Miki Haseyama. "Favorite Video Classification Based on Multimodal Bidirectional LSTM." IEEE Access 6 (2018): 61401–9. http://dx.doi.org/10.1109/access.2018.2876710.

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Pan, Qiao, Shiyu Wang, and Junhao Zhang. "Prediction of Alzheimer’s Disease Based on Bidirectional LSTM." Journal of Physics: Conference Series 1187, no. 5 (April 2019): 052030. http://dx.doi.org/10.1088/1742-6596/1187/5/052030.

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Wang, Ye, Xinxiang Zhang, Mi Lu, Han Wang, and Yoonsuck Choe. "Attention augmentation with multi-residual in bidirectional LSTM." Neurocomputing 385 (April 2020): 340–47. http://dx.doi.org/10.1016/j.neucom.2019.10.068.

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Tong, Weitian, Lixin Li, Xiaolu Zhou, Andrew Hamilton, and Kai Zhang. "Deep learning PM2.5 concentrations with bidirectional LSTM RNN." Air Quality, Atmosphere & Health 12, no. 4 (January 7, 2019): 411–23. http://dx.doi.org/10.1007/s11869-018-0647-4.

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Kulevome, Delanyo Kwame Bensah, Hong Wang, and Xuegang Wang. "A BIDIRECTIONAL LSTM-BASED PROGNOSTICATION OF ELECTROLYTIC CAPACITOR." Progress In Electromagnetics Research C 109 (2021): 139–52. http://dx.doi.org/10.2528/pierc20120201.

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Cheng, Lin, Yuliang Shi, Kun Zhang, Xinjun Wang, and Zhiyong Chen. "GGATB-LSTM: Grouping and Global Attention-based Time-aware Bidirectional LSTM Medical Treatment Behavior Prediction." ACM Transactions on Knowledge Discovery from Data 15, no. 3 (May 2021): 1–16. http://dx.doi.org/10.1145/3441454.

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In China, with the continuous development of national health insurance policies, more and more people have joined the health insurance. How to accurately predict patients future medical treatment behavior becomes a hotspot issue. The biggest challenge in this issue is how to improve the prediction performance by modeling health insurance data with high-dimensional time characteristics. At present, most of the research is to solve this issue by using Recurrent Neural Networks (RNNs) to construct an overall prediction model for the medical visit sequences. However, RNNs can not effectively solve the long-term dependence, and RNNs ignores the importance of time interval of the medical visit sequence. Additionally, the global model may lose some important content to different groups. In order to solve these problems, we propose a Grouping and Global Attention based Time-aware Bidirectional Long Short-Term Memory (GGATB-LSTM) model to achieve medical treatment behavior prediction. The model first constructs a heterogeneous information network based on health insurance data, and uses a tensor CANDECOMP/PARAFAC decomposition method to achieve similarity grouping. In terms of group prediction, a global attention and time factor are introduced to extend the bidirectional LSTM. Finally, the proposed model is evaluated by using real dataset, and conclude that GGATB-LSTM is better than other methods.

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Yu, Xiangqian, Xiaoming Li, Jianhua Chen, Zhiguo Wang, and Shiyuan Zhang. "Research on Nonintrusive Load Decomposition of Enterprises Based on Bidirectional LSTM." E3S Web of Conferences 233 (2021): 01070. http://dx.doi.org/10.1051/e3sconf/202123301070.

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To detect the operating condition of equipment and understand the environmental management situation of enterprises in real-time, this paper studies the non-intrusive load decomposition of enterprises based on bidirectional LSTM. In this paper, we first obtain the load characteristic parameters of different equipment in different states, and then obtain the electrical power measured from the main power meter of decontamination equipment in TOP-5 through the softmax layer of bidirectional LSTM, and then change the softmax layer to decompose the load data from the main power meter.

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Wang, Yuchao, Hui Wang, Dexin Zou, and Huixuan Fu. "Ship Roll Prediction Algorithm Based on Bi-LSTM-TPA Combined Model." Journal of Marine Science and Engineering 9, no. 4 (April 6, 2021): 387. http://dx.doi.org/10.3390/jmse9040387.

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When ships sail on the sea, the changes of ship motion attitude presents the characteristics of nonlinearity and high randomness. Aiming at the problem of low accuracy of ship roll angle prediction by traditional prediction algorithms and single neural network model, a ship roll angle prediction method based on bidirectional long short-term memory network (Bi-LSTM) and temporal pattern attention mechanism (TPA) combined deep learning model is proposed. Bidirectional long short-term memory network extracts time features from the forward and reverse of the ship roll angle time series, and temporal pattern attention mechanism extracts the time patterns from the deep features of a bidirectional long short-term memory network output state that are beneficial to ship roll angle prediction, ignore other features that contribute less to the prediction. The experimental results of real ship data show that the proposed Bi-LSTM-TPA combined model has a significant reduction in MAPE, MAE, and MSE compared with the LSTM model and the SVM model, which verifies the effectiveness of the proposed algorithm.

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Jeong, Yewon, and Jong-Hyeok Lee. "Extending Word Representations with Predicted Affix Features for Bidirectional LSTM-CRF-based Korean Named Entity Recognition." KIISE Transactions on Computing Practices 26, no. 9 (September 30, 2020): 408–13. http://dx.doi.org/10.5626/ktcp.2020.26.9.408.

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Liu, Chen. "Prediction and Analysis of Artwork Price Based on Deep Neural Network." Scientific Programming 2022 (March 10, 2022): 1–10. http://dx.doi.org/10.1155/2022/7133910.

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The use of deep learning methods to solve problems in the field of artwork prices has attracted widespread attention, especially the superiority of long short-term memory network (LSTM) in dealing with time series problems. However, the potential for deep learning in the prediction of artwork price has not been fully explored. This paper proposes a deep prediction network structure that considers the correlation between time series data and the combination of two-way LSTM as well as one-way LSTM networks to predict the price of artworks. This paper proposes a deep-level two-way and one-way LSTM to predict the price of artworks in the art market. Taking into account the potential reverse dependence of the time series, the bidirectional LSTM layer is used to obtain bidirectional time correlation from historical data. This research uses a matrix to represent the artwork price data and fully considers the spatial correlation characteristics of the artwork price. Simultaneously, this paper uses the two-way LSTM network to correlate the potential contextual information of the historical data of the artwork price stream and fully perform feature learning. This study applies the two-way LSTM network layer to the building blocks of the deep architecture to measure the inverse dependence of the price fluctuation data. The comparison with other prediction models shows that the LSTM neural network fused with one-way and two-way proposed in this paper is superior to other neural networks for predicting price of artworks in terms of prediction accuracy.

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Lee, Chien-Hsing, Phuong Nguyen Thanh, Chao-Tsung Yeh, and Ming-Yuan Cho. "Three-Phase Load Prediction-Based Hybrid Convolution Neural Network Combined Bidirectional Long Short-Term Memory in Solar Power Plant." International Transactions on Electrical Energy Systems 2022 (September 16, 2022): 1–15. http://dx.doi.org/10.1155/2022/2870668.

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The economic renewable energy generations have been rapidly developed because of the sharp reduction in the costs of solar panels. It is imperative to forecast the three-phase load power for more effective energy planning and optimization in a smart solar microgrid installed on a building in the Linyuan District, Taiwan. To alleviate this problem, this article proposes a convolution neural network bidirectional long short-term memory (CNN-Bi-LSTM) to accurately predict the short-term three-phase load power in building the energy management system in the smart solar microgrid with the collected data from advanced metering infrastructure (AMI), which have not been investigated before. The three-phase load-predicting methodology is developed using weather parameters and different collected data from AMI. The project evaluates the performance of the CNN-Bi-LSTM model by utilizing hyper-parameter optimization to attain the optimum parameters. The prediction models are trained based on hourly historical input features, selected based on the Pearson correlation coefficient. The performances’ optimal structure CNN-Bi-LSTM are validated and compared with the bidirectional LSTM (Bi-LSTM), LSTM, the Gated Recurrent Unit (GRU), and the recurrent neural network (RNN) models. The obtained optimized structure of CNN-Bi-LSTM demonstrates the effectiveness of the proposed models in the short-term prediction of three-phase load power in a smart solar microgrid for building with a maximum enhancement of 68.36% and 8.81% average MSE, and 30.26% and 36.36% average MAE during the testing and validating operations.

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Teng, Zhiyang, and Yue Zhang. "Head-Lexicalized Bidirectional Tree LSTMs." Transactions of the Association for Computational Linguistics 5 (December 2017): 163–77. http://dx.doi.org/10.1162/tacl_a_00053.

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Sequential LSTMs have been extended to model tree structures, giving competitive results for a number of tasks. Existing methods model constituent trees by bottom-up combinations of constituent nodes, making direct use of input word information only for leaf nodes. This is different from sequential LSTMs, which contain references to input words for each node. In this paper, we propose a method for automatic head-lexicalization for tree-structure LSTMs, propagating head words from leaf nodes to every constituent node. In addition, enabled by head lexicalization, we build a tree LSTM in the top-down direction, which corresponds to bidirectional sequential LSTMs in structure. Experiments show that both extensions give better representations of tree structures. Our final model gives the best results on the Stanford Sentiment Treebank and highly competitive results on the TREC question type classification task.

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Journal articles on the topic 'LSTM bidirectionnel'

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