Готові списки джерел за темами / LSTM bidirectionnel

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Добірка наукової літератури з теми "LSTM bidirectionnel"

Автор: Grafiati
Опубліковано: 28 вересня 2022

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Статті в журналах з теми "LSTM bidirectionnel"

1

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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2

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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3

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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4

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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5

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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Анотація:
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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6

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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Анотація:
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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7

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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Анотація:
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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8

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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9

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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10

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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Дисертації з теми "LSTM bidirectionnel"

1

Tang, Hao. "Bidirectional LSTM-CNNs-CRF Models for POS Tagging." Thesis, Uppsala universitet, Institutionen för lingvistik och filologi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-362823.

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In order to achieve state-of-the-art performance for part-of-speech(POS) tagging, the traditional systems require a significant amount of hand-crafted features and data pre-processing. In this thesis, we present a discriminative word embedding, character embedding and byte pair encoding (BPE) hybrid neural network architecture to implement a true end-to-end system without feature engineering and data pre-processing. The neural network architecture is a combination of bidirectional LSTM, CNNs, and CRF, which can achieve a state-of-the-art performance for a wide range of sequence labeling tasks. We evaluate our model on Universal Dependencies (UD) dataset for English, Spanish, and German POS tagging. It outperforms other models with 95.1%, 98.15%, and 93.43% accuracy on testing datasets respectively. Moreover, the largest improvements of our model appear on out-of-vocabulary corpora for Spanish and German. According to statistical significance testing, the improvements of English on testing and out-of-vocabulary corpora are not statistically significant. However, the improvements of the other more morphological languages are statistically significant on their corresponding corpora.
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2

Javid, Gelareh. "Contribution à l’estimation de charge et à la gestion optimisée d’une batterie Lithium-ion : application au véhicule électrique." Thesis, Mulhouse, 2021. https://www.learning-center.uha.fr/.

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L'estimation de l'état de charge (SOC) est un point crucial pour la sécurité des performances et la durée de vie des batteries lithium-ion (Li-ion) utilisées pour alimenter les VE.Dans cette thèse, la précision de l'estimation de l'état de charge est étudiée à l'aide d'algorithmes de réseaux neuronaux récurrents profonds (DRNN). Pour ce faire, pour une cellule d’une batterie Li-ion, trois nouvelles méthodes sont proposées : une mémoire bidirectionnelle à long et court terme (BiLSTM), une mémoire robuste à long et court terme (RoLSTM) et une technique d'unités récurrentes à grille (GRU).En utilisant ces techniques, on ne dépend pas de modèles précis de la batterie et on peut éviter les méthodes mathématiques complexes, en particulier dans un bloc de batterie. En outre, ces modèles sont capables d'estimer précisément le SOC à des températures variables. En outre, contrairement au réseau de neurones récursif traditionnel dont le contenu est réécrit à chaque fois, ces réseaux peuvent décider de préserver la mémoire actuelle grâce aux passerelles proposées. Dans ce cas, il peut facilement transférer l'information sur de longs chemins pour recevoir et maintenir des dépendances à long terme.La comparaison des résultats indique que le réseau BiLSTM a de meilleures performances que les deux autres méthodes. De plus, le modèle BiLSTM peut travailler avec des séquences plus longues provenant de deux directions, le passé et le futur, sans problème de disparition du gradient. Cette caractéristique permet de sélectionner une longueur de séquence équivalente à une période de décharge dans un cycle de conduite, et d'obtenir une plus grande précision dans l'estimation. En outre, ce modèle s'est bien comporté face à une valeur initiale incorrecte du SOC.Enfin, une nouvelle méthode BiLSTM a été introduite pour estimer le SOC d'un pack de batteries dans un EV. Le logiciel IPG Carmaker a été utilisé pour collecter les données et tester le modèle en simulation. Les résultats ont montré que l'algorithme proposé peut fournir une bonne estimation du SOC sans utilisation de filtre dans le système de gestion de la batterie (BMS)
The State Of Charge (SOC) estimation is a significant issue for safe performance and the lifespan of Lithium-ion (Li-ion) batteries, which is used to power the Electric Vehicles (EVs). In this thesis, the accuracy of SOC estimation is investigated using Deep Recurrent Neural Network (DRNN) algorithms. To do this, for a one cell Li-ion battery, three new SOC estimator based on different DRNN algorithms are proposed: a Bidirectional LSTM (BiLSTM) method, Robust Long-Short Term Memory (RoLSTM) algorithm, and a Gated Recurrent Units (GRUs) technique. Using these, one is not dependent on precise battery models and can avoid complicated mathematical methods especially in a battery pack. In addition, these models are able to precisely estimate the SOC at varying temperature. Also, unlike the traditional recursive neural network where content is re-written at each time, these networks can decide on preserving the current memory through the proposed gateways. In such case, it can easily transfer the information over long paths to receive and maintain long-term dependencies. Comparing the results indicates the BiLSTM network has a better performance than the other two. Moreover, the BiLSTM model can work with longer sequences from two direction, the past and the future, without gradient vanishing problem. This feature helps to select a sequence length as much as a discharge period in one drive cycle, and to have more accuracy in the estimation. Also, this model well behaved against the incorrect initial value of SOC. Finally, a new BiLSTM method introduced to estimate the SOC of a pack of batteries in an Ev. IPG Carmaker software was used to collect data and test the model in the simulation. The results showed that the suggested algorithm can provide a good SOC estimation without using any filter in the Battery Management System (BMS)
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3

Pavai, Arumugam Thendramil. "SENSOR-BASED HUMAN ACTIVITY RECOGNITION USING BIDIRECTIONAL LSTM FOR CLOSELY RELATED ACTIVITIES." CSUSB ScholarWorks, 2018. https://scholarworks.lib.csusb.edu/etd/776.

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Анотація:
Recognizing human activities using deep learning methods has significance in many fields such as sports, motion tracking, surveillance, healthcare and robotics. Inertial sensors comprising of accelerometers and gyroscopes are commonly used for sensor based HAR. In this study, a Bidirectional Long Short-Term Memory (BLSTM) approach is explored for human activity recognition and classification for closely related activities on a body worn inertial sensor data that is provided by the UTD-MHAD dataset. The BLSTM model of this study could achieve an overall accuracy of 98.05% for 15 different activities and 90.87% for 27 different activities performed by 8 persons with 4 trials per activity per person. A comparison of this BLSTM model is made with the Unidirectional LSTM model. It is observed that there is a significant improvement in the accuracy for recognition of all 27 activities in the case of BLSTM than LSTM.
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4

Coelho, Jorge Andre de Carvalho, and 卡橋安. "Music Structural Segmentation from Audio Signals using CNN Bidirectional LSTM." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/m2j6q8.

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Анотація:
碩士
國立清華大學
資訊系統與應用研究所
107
In this paper, we investigate the problems of segmenting a piece of music into its structural components from its audio signals. We devise a deep learning neural network architecture called CNN Bidirectional LSTM model which combines convolutional neural networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) to perform music boundary detection. The music audio input to the model is first converted into one spectrogram and two SSMs that can be classified by the deep neural network. We also propose the use of Chroma Energy Normalized Statistics on this task. We show the resulting improvements over previous work with respect to precision and recall. We verified improvement of 11.2\% and 6.58\% F1-score at $ m0.5$ seconds and $ m3$ seconds tolerance, respectively.
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5

Chen, Brian, and 陳柏穎. "AUC oriented Bidirectional LSTM-CRF Models to Identify Algorithms Described in an Abstract." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/p3grat.

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Анотація:
碩士
國立臺灣大學
資訊工程學研究所
105
In this thesis, we attempt to identify algorithms mentioned in the paper abstract. We further want to discriminate the algorithm proposed in this paper from algorithms only mentioned or compared, since we are more interested in the former. We model this task as a sequential labeled task and propose to use a state-of-the-art deep learning model LSTM-CRF as our solution. However, the data or labels are generally imbalanced since not all the sentence in the abstract is describing its algorithm. That is, the ratio between different labels is skewed. As a result, it is not suitable to use traditional LSTM-CRF model since it only optimizes accuracy. Instead, it is more reasonable to optimize AUC in imbalanced data because it can deal with skewed labels and perform better in predicting rare labels. Our experiment shows that the proposed AUC-optimized LSTM-CRF outperforms the traditional LSTM-CRF. We also show the ranking of algorithms used currently, and find the trend of different algorithms used in recent years. Moreover, we are able to discover some new algorithms that do not exist in our training data.
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6

Zhou, Quan. "Bidirectional long short-term memory network for proto-object representation." Thesis, 2018. https://hdl.handle.net/2144/31682.

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Анотація:
Researchers have developed many visual saliency models in order to advance the technology in computer vision. Neural networks, Convolution Neural Networks (CNNs) in particular, have successfully differentiate objects in images through feature extraction. Meanwhile, Cummings et al. has proposed a proto-object image saliency (POIS) model that shows perceptual objects or shapes can be modelled through the bottom-up saliency algorithm. Inspired from their work, this research is aimed to explore the imbedding features in the proto-object representations and utilizing artificial neural networks (ANN) to capture and predict the saliency output of POIS. A combination of CNN and a bi-directional long short-term memory (BLSTM) neural network is proposed for this saliency model as a machine learning alternative to the border ownership and grouping mechanism in POIS. As ANNs become more efficient in performing visual saliency tasks, the result of this work would extend their application in computer vision through successful implementation for proto-object based saliency.
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7

Kao, Shiuan-Kai, and 高炫凱. "Improving Automatic Behavior Rating System of Couple Therapy using Multi-granular Word Fusion Approach with bidirectional LSTM Architecture." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/y6t94r.

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Анотація:
碩士
國立清華大學
電機工程學系所
106
In psychology field research, experts generally design a standard experimental procedure, e.g., consultation, show or talk, to observe the mental state of human. They expect to trigger reactions of internal emotion by stimulating external behavior. However, when analyzing whole interaction process, different lengths of fragments of interaction including different strength of emotional information, and experts make more complete and suitable decision. Our work inspired by the conception and apply it on automatic behavior rating system of couple therapy database, to improve the accuracy of scoring interaction process of psychotherapy. This program recruit seriously and chronically distressed married couples, and let them make a problem-solving communication for specific topic, recording the audio, video and text of process, experts analyze the extent of behavior of couples interaction process to evaluate treatment effects by these information. This paper use Bidirectional Long Short Term Memory structure to extract multi- granular and high-level features for lexical modality, also combine Doc2Vec into document level with feature selection to integrate different temporal level of behavioral features, and finally join audio modality to train binary classifier with machine learning algorithm. For the performance of six behavioral codes, husband and wife's average accuracy of behavior achieve 79.3% and 82.4% separately, this enhance 5.3% and 7.4% average accuracy compared to 74% and 75% of previous paper[1]. Our experiments and results present the merit of use of Bidirectional Long Short Term Memory can learn time series information effectively, the computation of different level granularity of intensity of behavior improving the algorithm on couple therapy rating system.
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Частини книг з теми "LSTM bidirectionnel"

1

Bakalos, Nikolaos, Athanasios Voulodimos, Nikolaos Doulamis, Anastasios Doulamis, Kassiani Papasotiriou, and Matthaios Bimpas. "Fusing RGB and Thermal Imagery with Channel State Information for Abnormal Activity Detection Using Multimodal Bidirectional LSTM." In Cyber-Physical Security for Critical Infrastructures Protection, 77–86. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-69781-5_6.

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AbstractIn this paper, we present a multimodal deep model for detection of abnormal activity, based on bidirectional Long Short-Term Memory neural networks (LSTM). The proposed model exploits three different input modalities: RGB imagery, thermographic imagery and Channel State Information from Wi-Fi signal reflectance to estimate human intrusion and suspicious activity. The fused multimodal information is used as input in a Bidirectional LSTM, which has the benefit of being able to capture temporal interdependencies in both past and future time instances, a significant aspect in the discussed unusual activity detection scenario. We also present a Bayesian optimization framework that fine-tunes the Bidirectional LSTM parameters in an optimal manner. The proposed framework is evaluated on real-world data from a critical water infrastructure protection and monitoring scenario and the results indicate a superior performance compared to other unimodal and multimodal approaches and classification models.
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2

Ali, Hazrat, Feroz Karim, Junaid Javed Qureshi, Adnan Omer Abuassba, and Mohammad Farhad Bulbul. "Seizure Prediction Using Bidirectional LSTM." In Communications in Computer and Information Science, 349–56. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-1922-2_25.

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3

Aljbali, Sarah, and Kaushik Roy. "Anomaly Detection Using Bidirectional LSTM." In Advances in Intelligent Systems and Computing, 612–19. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-55180-3_45.

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4

Attardi, Giuseppe, and Daniele Sartiano. "Bidirectional LSTM Models for DGA Classification." In Communications in Computer and Information Science, 687–94. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-5826-5_54.

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5

Bsir, Bassem, and Mounir Zrigui. "Bidirectional LSTM for Author Gender Identification." In Computational Collective Intelligence, 393–402. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-98443-8_36.

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6

Fu, Hailin, Jianguo Li, Jiemin Chen, Yong Tang, and Jia Zhu. "Sequence-Based Recommendation with Bidirectional LSTM Network." In Advances in Multimedia Information Processing – PCM 2018, 428–38. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00764-5_39.

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7

Ahmed, Saad Bin, Saeeda Naz, Muhammad Imran Razzak, Rubiyah Yusof, and Thomas M. Breuel. "Balinese Character Recognition Using Bidirectional LSTM Classifier." In Lecture Notes in Electrical Engineering, 201–11. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-32213-1_18.

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8

Zhao, Xue, Chao Wang, Zhifan Yang, Ying Zhang, and Xiaojie Yuan. "Online News Emotion Prediction with Bidirectional LSTM." In Web-Age Information Management, 238–50. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-39958-4_19.

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9

Chen, Wenwu, Su Yang, Xu An Wang, Wei Zhang, and Jindan Zhang. "Network Malicious Behavior Detection Using Bidirectional LSTM." In Advances in Intelligent Systems and Computing, 627–35. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93659-8_57.

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10

Pang, Ning, Weidong Xiao, and Xiang Zhao. "Chinese Text Classification via Bidirectional Lattice LSTM." In Knowledge Science, Engineering and Management, 250–62. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-55393-7_23.

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Тези доповідей конференцій з теми "LSTM bidirectionnel"

1

Kumar, Sachin, Soumen Chakrabarti, and Shourya Roy. "Earth Mover's Distance Pooling over Siamese LSTMs for Automatic Short Answer Grading." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/284.

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Анотація:
Automatic short answer grading (ASAG) can reduce tedium for instructors, but is complicated by free-form student inputs. An important ASAG task is to assign ordinal scores to student answers, given some “model” or ideal answers. Here we introduce a novel framework for ASAG by cascading three neural building blocks: Siamese bidirectional LSTMs applied to a model and a student answer, a novel pooling layer based on earth-mover distance (EMD) across all hidden states from both LSTMs, and a flexible final regression layer to output scores. On standard ASAG data sets, our system shows substantial reduction in grade estimation error compared to competitive baselines. We demonstrate that EMD pooling results in substantial accuracy gains, and that a support vector ordinal regression (SVOR) output layer helps outperform softmax. Our system also outperforms recent attention mechanisms on LSTM states.
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2

Mohapatra, Nilamadhaba, Namrata Sarraf, and Swapna sarit Sahu. "Ensemble Model for Chunking." In 2nd International Conference on Blockchain and Internet of Things (BIoT 2021). AIRCC Publishing Corporation, 2021. http://dx.doi.org/10.5121/csit.2021.110811.

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Transformer Models have taken over most of the Natural language Inference tasks. In recent times they have proved to beat several benchmarks. Chunking means splitting the sentences into tokens and then grouping them in a meaningful way. Chunking is a task that has gradually moved from POS tag-based statistical models to neural nets using Language models such as LSTM, Bidirectional LSTMs, attention models, etc. Deep neural net Models are deployed indirectly for classifying tokens as different tags defined under Named Recognition Tasks. Later these tags are used in conjunction with pointer frameworks for the final chunking task. In our paper, we propose an Ensemble Model using a fine-tuned Transformer Model and a recurrent neural network model together to predict tags and chunk substructures of a sentence. We analyzed the shortcomings of the transformer models in predicting different tags and then trained the BILSTM+CNN accordingly to compensate for the same.
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3

Sibal, Ritika, Ding Zhang, Julie Rocho-Levine, K. Alex Shorter, and Kira Barton. "Bidirectional LSTM Recurrent Neural Network Plus Hidden Markov Model for Wearable Sensor Based Dynamic State Estimation." In ASME 2019 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/dscc2019-9198.

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Abstract Publisher’s Note: This paper was selected for publication in ASME Letters in Dynamic Systems and Control. https://www.asmedigitalcollection.asme.org/lettersdynsys/article/doi/10.1115/1.4046685/1081846/Bidirectional-LSTM-Recurrent-Neural-Network-Plus
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Tavakoli, Neda. "Modeling Genome Data Using Bidirectional LSTM." In 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC). IEEE, 2019. http://dx.doi.org/10.1109/compsac.2019.10204.

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Zayats, Vicky, Mari Ostendorf, and Hannaneh Hajishirzi. "Disfluency Detection Using a Bidirectional LSTM." In Interspeech 2016. ISCA, 2016. http://dx.doi.org/10.21437/interspeech.2016-1247.

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Rakshith, J., Sharath Savasere, Arvind Ramachandran, Akhila P, and Shashidhar G. Koolagudi. "Word Sense Disambiguation using Bidirectional LSTM." In 2019 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER). IEEE, 2019. http://dx.doi.org/10.1109/discover47552.2019.9008031.

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Cheng, Gaofeng, Lu Huang, Jiasong Sun, and Yonghong Yan. "Bidirectional LSTM with Extended Input Context." In 2018 11th International Symposium on Chinese Spoken Language Processing (ISCSLP). IEEE, 2018. http://dx.doi.org/10.1109/iscslp.2018.8706711.

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Pang, Dong, and Xinyi Le. "Indoor Localization Using Bidirectional LSTM Networks." In 2021 13th International Conference on Advanced Computational Intelligence (ICACI). IEEE, 2021. http://dx.doi.org/10.1109/icaci52617.2021.9435876.

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Pratiwi, Monica, Adhi Dharma Wibawa, and Mauridhi Hery Purnomo. "EEG-based Happy and Sad Emotions Classification using LSTM and Bidirectional LSTM." In 2021 3rd International Conference on Electronics Representation and Algorithm (ICERA). IEEE, 2021. http://dx.doi.org/10.1109/icera53111.2021.9538698.

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Graves, Alex, Navdeep Jaitly, and Abdel-rahman Mohamed. "Hybrid speech recognition with Deep Bidirectional LSTM." In 2013 IEEE Workshop on Automatic Speech Recognition & Understanding (ASRU). IEEE, 2013. http://dx.doi.org/10.1109/asru.2013.6707742.

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