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Journal articles on the topic 'Seq2seq'

Author: Grafiati
Published: 4 June 2021
Last updated: 26 July 2025

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1

Aydın, Özlem, and Hüsein Kantarcı. "Türkçe Anahtar Sözcük Çıkarımında LSTM ve BERT Tabanlı Modellerin Karşılaştırılması." Bilgisayar Bilimleri ve Mühendisliği Dergisi 17, no. 1 (2024): 9–18. http://dx.doi.org/10.54525/bbmd.1454220.

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Günümüzde internet ortamında metne dayalı veri çok hızlı bir şekilde artış göstermektedir ve bu büyük veri içinden istenilen bilgiyi barındıran doğru içeriklere ulaşabilmek önemli bir ihtiyaçtır. İçeriklere ait anahtar sözcüklerin bilinmesi bu ihtiyacı karşılamada olumlu bir etki sağlayabilmektedir. Bu çalışmada, doğal dil işleme ve derin öğrenme modelleri ile Türkçe metinleri temsil eden anahtar sözcüklerin belirlenmesi amaçlanmıştır. Veri kümesi olarak Türkçe Etiketli Metin Derlemi ve Metin Özetleme-Anahtar Kelime Çıkarma Veri Kümesi birlikte kullanılmıştır. Derin öğrenme modeli olarak çalış
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Sak, Semih, and Mustafa Alper Akkaş. "6G'de Nesnelerin İnterneti Teknolojisinin Medikal Alandaki Gelişmeleri." Bilgisayar Bilimleri ve Mühendisliği Dergisi 17, no. 1 (2024): 1–8. http://dx.doi.org/10.54525/bbmd.1454186.

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Günümüzde internet ortamında metne dayalı veri çok hızlı bir şekilde artış göstermektedir ve bu büyük veri içinden istenilen bilgiyi barındıran doğru içeriklere ulaşabilmek önemli bir ihtiyaçtır. İçeriklere ait anahtar sözcüklerin bilinmesi bu ihtiyacı karşılamada olumlu bir etki sağlayabilmektedir. Bu çalışmada, doğal dil işleme ve derin öğrenme modelleri ile Türkçe metinleri temsil eden anahtar sözcüklerin belirlenmesi amaçlanmıştır. Veri kümesi olarak Türkçe Etiketli Metin Derlemi ve Metin Özetleme-Anahtar Kelime Çıkarma Veri Kümesi birlikte kullanılmıştır. Derin öğrenme modeli olarak çalış
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Jin, Weihua, Shijie Zhang, Bo Sun, Pengli Jin, and Zhidong Li. "An Analytical Investigation of Anomaly Detection Methods Based on Sequence to Sequence Model in Satellite Power Subsystem." Sensors 22, no. 5 (2022): 1819. http://dx.doi.org/10.3390/s22051819.

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The satellite power subsystem is responsible for all power supply in a satellite, and is an important component of it. The system’s performance has a direct impact on the operations of other systems as well as the satellite’s lifespan. Sequence to sequence (seq2seq) learning has recently advanced, gaining even more power in evaluating complicated and large-scale data. The potential of the seq2seq model in detecting anomalies in the satellite power subsystem is investigated in this work. A seq2seq-based scheme is given, with a thorough comparison of different neural-network cell types and level
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Palasundram, Kulothunkan, Nurfadhlina Mohd Sharef, Khairul Azhar Kasmiran, and Azreen Azman. "SEQ2SEQ++: A Multitasking-Based Seq2seq Model to Generate Meaningful and Relevant Answers." IEEE Access 9 (2021): 164949–75. http://dx.doi.org/10.1109/access.2021.3133495.

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Bo, Tao, Weiyi Li, and Yue Liu. "A Technical Review of Sequence-to-Sequence Models." Academic Journal of Natural Science 2, no. 2 (2025): 1–9. https://doi.org/10.70393/616a6e73.323834.

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Seq2Seq models and their variants have become a mainstay of modern natural language processing and sequence modelling tasks. Just Information about Seq2Seq models. In this paper, we provide a comprehensive overview of the evolution of Seq2Seq architecture from early-stage RNN based approaches to recent Transformer based methods. The paper extensively covers additional important methods such as attention mechanisms, bidirectional encoders, pointer-generator networks, as well as optimization methods such as beam search, scheduled sampling and reinforcement learning. It also discusses the challen
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Palasundram, Kulothunkan, Nurfadhlina Mohd Sharef, Nurul Amelina Nasharuddin, Khairul Azhar Kasmiran, and Azreen Azman. "Sequence to Sequence Model Performance for Education Chatbot." International Journal of Emerging Technologies in Learning (iJET) 14, no. 24 (2019): 56. http://dx.doi.org/10.3991/ijet.v14i24.12187.

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Chatbot for education has great potential to complement human educators and education administrators. For example, it can be around the clock tutor to answer and clarify any questions from students who may have missed class. A chatbot can be implemented either by ruled based or artificial intel-ligence based. However, unlike the ruled-based chatbots, artificial intelli-gence based chatbots can learn and become smarter overtime and is more scalable and has become the popular choice for chatbot researchers recently. Recurrent Neural Network based Sequence-to-sequence (Seq2Seq) model is one of th
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Zhou, Lijian, Lijun Wang, Zhiang Zhao, Yuwei Liu, and Xiwu Liu. "A Seq2Seq Model Improved by Transcendental Learning and Imaged Sequence Samples for Porosity Prediction." Mathematics 11, no. 1 (2022): 39. http://dx.doi.org/10.3390/math11010039.

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Since the accurate prediction of porosity is one of the critical factors for estimating oil and gas reservoirs, a novel porosity prediction method based on Imaged Sequence Samples (ISS) and a Sequence to Sequence (Seq2Seq) model fused by Transcendental Learning (TL) is proposed using well-logging data. Firstly, to investigate the correlation between logging features and porosity, the original logging features are normalized and selected by computing their correlation with porosity to obtain the point samples. Secondly, to better represent the depositional relations with depths, an ISS set is e
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Geng, Xiaoran, Yue Ma, Wennian Cai, et al. "Evaluation of models for multi-step forecasting of hand, foot and mouth disease using multi-input multi-output: A case study of Chengdu, China." PLOS Neglected Tropical Diseases 17, no. 9 (2023): e0011587. http://dx.doi.org/10.1371/journal.pntd.0011587.

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Background Hand, foot and mouth disease (HFMD) is a public health concern that threatens the health of children. Accurately forecasting of HFMD cases multiple days ahead and early detection of peaks in the number of cases followed by timely response are essential for HFMD prevention and control. However, many studies mainly predict future one-day incidence, which reduces the flexibility of prevention and control. Methods We collected the daily number of HFMD cases among children aged 0–14 years in Chengdu from 2011 to 2017, as well as meteorological and air pollutant data for the same period.
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Nishtar, Zuhaib, and Jamil Afzal. "Seq2Seq-Based-Day-Ahead Scheduling for SCUC in Islanded Power Systems with Limited Intermittent Generation." Journal of Engineering, Science and Technological Trends 1, no. 1 (2024): 43–50. http://dx.doi.org/10.48112/jestt.v1i1.683.

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Due to their dependence on intermittent renewable energy sources, island power systems, which are generally located in remote places or on islands, offer particular issues for day-ahead scheduling. Using the capabilities of neural networks, we offer a Seq2Seq-based technique for day-ahead scheduling, which increases the precision and flexibility of unit commitment choices. The attention mechanisms in the Seq2Seq model are trained with historical data that includes projections for intermittent generation, demand, and unit commitment choices. The model is tested for its capacity to incorporate d
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Zhang, Gang. "A study of Grammar Analysis in English Teaching With Deep Learning Algorithm." International Journal of Emerging Technologies in Learning (iJET) 15, no. 18 (2020): 20. http://dx.doi.org/10.3991/ijet.v15i18.15425.

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In English teaching, grammar is a very important part. Based on the seq2seq model, a grammar analysis method combining the attention mechanism, word embedding and CNN seq2seq was designed using the deep learning algorithm, then the algorithm training was completed on NUCLE, and it was tested on CoNIL-2014. The experimental results showed that of seq2seq+attention improved 33.43% compared to the basic seq2seq; in the comparison between the method proposed in this study and CAMB, the P value of the former was 59.33% larger than that of CAMB, the R value was 8.9% larger, and the value of was 42.9
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Guo, Yinuo, Tao Ge, and Furu Wei. "Fact-Aware Sentence Split and Rephrase with Permutation Invariant Training." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (2020): 7855–62. http://dx.doi.org/10.1609/aaai.v34i05.6291.

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Sentence Split and Rephrase aims to break down a complex sentence into several simple sentences with its meaning preserved. Previous studies tend to address the issue by seq2seq learning from parallel sentence pairs, which takes a complex sentence as input and sequentially generates a series of simple sentences. However, the conventional seq2seq learning has two limitations for this task: (1) it does not take into account the facts stated in the long sentence; As a result, the generated simple sentences may miss or inaccurately state the facts in the original sentence. (2) The order variance o
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Orduna-Cabrera, Fernando, Alejandro Rios-Ochoa, Federico Frank, et al. "Short-Term Forecasting Arabica Coffee Cherry Yields by Seq2Seq over LSTM for Smallholder Farmers." Sustainability 17, no. 9 (2025): 3888. https://doi.org/10.3390/su17093888.

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Coffee production is a vital source of income for smallholder farmers in Mexico’s Chiapas, Oaxaca, Puebla, and Veracruz regions. However, climate change, fluctuating yields, and the lack of decision-support tools pose challenges to the implementation of sustainable agricultural practices. The SABERES project aims to address these challenges through a Seq2Seq-LSTM model for predicting coffee yields in the short term, using datasets from Mexican national institutions, including the Agricultural Census (SIAP) and environmental data from the National Water Commission (CONAGUA). The model has demon
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Loh, Zheung Yik, Wan Mohd Nasir Wan Kadir, and Noraini Ibrahim. "A Comparative Evaluation of Transformers in Seq2Seq Code Mutation: Non-Pre-trained Vs. Pre-trained Variants." Journal of Advanced Research Design 123, no. 1 (2024): 45–65. https://doi.org/10.37934/ard.123.1.4565.

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Mutation testing (MT) is a gold standard way to assess the efficacy of software test suites. However, the accuracy of mutation score is affected by the presence of trivial mutants which can be “killed” by even the simplest and most basic test suites. Since the existence of trivial mutants is due to the fixed set of mutation operators that constraints the complexity of code mutations, state-of-the-art recurrent neural network (RNN) model is used for sequence-to-sequence (seq2seq) code mutation without relying on mutation operators. However, the quality of the produced mutants is affected by the
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Jeon, Wang-Su, and Sang-Yong Rhee. "Tool Wear Monitoring System Using Seq2Seq." Machines 12, no. 3 (2024): 169. http://dx.doi.org/10.3390/machines12030169.

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The advancement of smart factories has brought about small quantity batch production. In multi-variety production, both materials and processing methods change constantly, resulting in irregular changes in the progression of tool wear, which is often affected by processing methods. This leads to changes in the timing of tool replacement, and failure to correctly determine this timing may result in substantial damage and financial loss. In this study, we sought to address the issue of incorrect timing for tool replacement by using a Seq2Seq model to predict tool wear. We also trained LSTM and G
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Byambadorj, Zolzaya, Ryota Nishimura, Altangerel Ayush, and Norihide Kitaoka. "Normalization of Transliterated Mongolian Words Using Seq2Seq Model with Limited Data." ACM Transactions on Asian and Low-Resource Language Information Processing 20, no. 6 (2021): 1–19. http://dx.doi.org/10.1145/3464361.

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The huge increase in social media use in recent years has resulted in new forms of social interaction, changing our daily lives. Due to increasing contact between people from different cultures as a result of globalization, there has also been an increase in the use of the Latin alphabet, and as a result a large amount of transliterated text is being used on social media. In this study, we propose a variety of character level sequence-to-sequence (seq2seq) models for normalizing noisy, transliterated text written in Latin script into Mongolian Cyrillic script, for scenarios in which there is a
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Xiong, Gu, Krzysztof Przystupa, Yao Teng, et al. "Online Measurement Error Detection for the ElectronicTransformer in a Smart Grid." Energies 14, no. 12 (2021): 3551. http://dx.doi.org/10.3390/en14123551.

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With the development of smart power grids, electronic transformers have been widely used to monitor the online status of power grids. However, electronic transformers have the drawback of poor long-term stability, leading to a requirement for frequent measurement. Aiming to monitor the online status frequently and conveniently, we proposed an attention mechanism-optimized Seq2Seq network to predict the error state of transformers, which combines an attention mechanism, Seq2Seq network, and bidirectional long short-term memory networks to mine the sequential information from online monitoring d
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Li, Bo, Dingyao Yu, Wei Ye, Jinglei Zhang, and Shikun Zhang. "Sequence Generation with Label Augmentation for Relation Extraction." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 11 (2023): 13043–50. http://dx.doi.org/10.1609/aaai.v37i11.26532.

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Sequence generation demonstrates promising performance in recent information extraction efforts, by incorporating large-scale pre-trained Seq2Seq models. This paper investigates the merits of employing sequence generation in relation extraction, finding that with relation names or synonyms as generation targets, their textual semantics and the correlation (in terms of word sequence pattern) among them affect model performance. We then propose Relation Extraction with Label Augmentation (RELA), a Seq2Seq model with automatic label augmentation for RE. By saying label augmentation, we mean prod
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Lai, Yihan. "Enhancing Linguistic Bridges: Seq2seq Models and the Future of Machine Translation." Highlights in Science, Engineering and Technology 111 (August 19, 2024): 410–14. https://doi.org/10.54097/pf2xsr76.

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Machine translation has evolved significantly since the introduction of rule-based and statistical methods, leading to groundbreaking advances with the advent of neural networks. These neural networks, particularly sequence-to-sequence (seq2seq) models, have revolutionized the field by enabling more fluent and contextually accurate translations. As digital interactions increase globally, the demand for efficient and precise translation tools has never been more pressing, especially for language pairs that pose substantial linguistic challenges due to their structural differences. This study de
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Gong, Gangjun, Xiaonan An, Nawaraj Kumar Mahato, Shuyan Sun, Si Chen, and Yafeng Wen. "Research on Short-Term Load Prediction Based on Seq2seq Model." Energies 12, no. 16 (2019): 3199. http://dx.doi.org/10.3390/en12163199.

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Electricity load prediction is the primary basis on which power-related departments to make logical and effective generation plans and scientific scheduling plans for the most effective power utilization. The perpetual evolution of deep learning has recommended advanced and innovative concepts for short-term load prediction. Taking into consideration the time and nonlinear characteristics of power system load data and further considering the impact of historical and future information on the current state, this paper proposes a Seq2seq short-term load prediction model based on a long short-ter
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You, Lan, Siyu Xiao, Qingxi Peng, et al. "ST-Seq2Seq: A Spatio-Temporal Feature-Optimized Seq2Seq Model for Short-Term Vessel Trajectory Prediction." IEEE Access 8 (2020): 218565–74. http://dx.doi.org/10.1109/access.2020.3041762.

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Kim, Hyun Soo, Jun Hyeok Kang, Ho Won Moon, and Jae Gil Lee. "Anomalous Trajectory Detection Based on Seq2Seq Auto-Encoder." Journal of Korean Society for Geospatial Information Science 28, no. 1 (2020): 35–40. http://dx.doi.org/10.7319/kogsis.2020.28.1.035.

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Du, Muyuan, Zhimeng Zhang, and Chunning Ji. "Prediction for Coastal Wind Speed Based on Improved Variational Mode Decomposition and Recurrent Neural Network." Energies 18, no. 3 (2025): 542. https://doi.org/10.3390/en18030542.

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Accurate and comprehensive wind speed forecasting is crucial for improving efficiency in offshore wind power operation systems in coastal regions. However, raw wind speed data often suffer from noise and missing values, which can undermine the prediction performance. This study proposes a systematic framework, termed VMD-RUN-Seq2Seq-Attention, for noise reduction, outlier detection, and wind speed prediction by integrating Variational Mode Decomposition (VMD), the Runge–Kutta optimization algorithm (RUN), and a Sequence-to-Sequence model with an Attention mechanism (Seq2Seq-Attention). Using w
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Singh, Aditya, Tessy Mariam Thomas, Nitin Tandon, and Jinlong (Torres) Li. "1003 Dissecting Speech Planning and Articulation Circuits Using Seq2Seq Models." Neurosurgery 71, Supplement_1 (2025): 129. https://doi.org/10.1227/neu.0000000000003360_1003.

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INTRODUCTION: Understanding the cortical mechanisms underlying speech articulation is crucial for developing efficient, tractable speech brain-computer interface (BCI) devices. This study investigates pre-articulatory and articulatory kinematics during single-word production, utilizing sequence-to-sequence (Seq2Seq) models to elucidate the spatiotemporal signatures of articulatory trajectories. METHODS: Intracranial recordings were obtained from patients implanted with depth electrodes in the subcentral and pre-central gyri during single-word speech production. A Seq2Seq model was employed to
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S., Keerthana, and Venkatesan R. "Abstractive Text Summarization using Seq2seq Model." International Journal of Computer Applications 176, no. 33 (2020): 24–26. http://dx.doi.org/10.5120/ijca2020920401.

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Colombo, Pierre, Emile Chapuis, Matteo Manica, Emmanuel Vignon, Giovanna Varni, and Chloe Clavel. "Guiding Attention in Sequence-to-Sequence Models for Dialogue Act Prediction." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (2020): 7594–601. http://dx.doi.org/10.1609/aaai.v34i05.6259.

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The task of predicting dialog acts (DA) based on conversational dialog is a key component in the development of conversational agents. Accurately predicting DAs requires a precise modeling of both the conversation and the global tag dependencies. We leverage seq2seq approaches widely adopted in Neural Machine Translation (NMT) to improve the modelling of tag sequentiality. Seq2seq models are known to learn complex global dependencies while currently proposed approaches using linear conditional random fields (CRF) only model local tag dependencies. In this work, we introduce a seq2seq model tai
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Wang, Guoju, Rongjie Zhu, Xiang Gong, et al. "A New Hybrid Deep Sequence Model for Decomposing, Interpreting, and Predicting Sulfur Dioxide Decline in Coastal Cities of Northern China." Sustainability 17, no. 6 (2025): 2546. https://doi.org/10.3390/su17062546.

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The recent success of emission reduction policies in China has significantly lowered sulfur dioxide (SO2) levels. However, accurately forecasting these concentrations remains challenging due to their inherent non-stationary tendency. This study introduces an innovative hybrid deep learning model, RF-VMD-Seq2Seq, combining the Random Forest (RF) algorithm, Variational Mode Decomposition (VMD), and the Sequence-to-Sequence (Seq2Seq) framework to improve SO2 concentration forecasting in five coastal cities of northern China. Our results show that the predicted SO2 concentrations closely align wit
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Yang, Qun, and Dejian Shen. "Learning Damage Representations with Sequence-to-Sequence Models." Sensors 22, no. 2 (2022): 452. http://dx.doi.org/10.3390/s22020452.

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Natural hazards have caused damages to structures and economic losses worldwide. Post-hazard responses require accurate and fast damage detection and assessment. In many studies, the development of data-driven damage detection within the research community of structural health monitoring has emerged due to the advances in deep learning models. Most data-driven models for damage detection focus on classifying different damage states and hence damage states cannot be effectively quantified. To address such a deficiency in data-driven damage detection, we propose a sequence-to-sequence (Seq2Seq)
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Sulisetyo Puji Widodo and Adila Alfa Krisnadhi. "Enhancing Table-to-Text Generation with Numerical Reasoning Using Graph2Seq Models." International Journal of Innovation in Enterprise System 8, no. 2 (2024): 11–21. https://doi.org/10.25124/ijies.v8i02.236.

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Interpreting data in tables into narratives is necessary because tables cannot explain their own data.Additionally, there is a need to produce more analytic narratives from the results of numericalreasoning on data from tables. The sequence-to-sequence (Seq2Seq) encoder-decoder structure is themost widely used in table-to-text generation (T2XG). However, Seq2Seq requires the linearization oftables, which can omit structural information and create hallucination problems. Alternatively, thegraph-to-sequence (Graph2Seq) encoder-decoder structure utilizes a graph encoder to better captureimportant
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Nishtar, Zuhaib, NA Li, Abdul Razzaque Soomro, and Jamil Afzal. "Optimizing Distributed Energy Resources in Microgrid SCUC through Seq2Seq Scheduling Algorithms." Mehran University Research Journal of Engineering and Technology 43, no. 4 (2024): 100. http://dx.doi.org/10.22581/muet1982.309.

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In order to optimize Distributed Energy Resources (DERs) inside a microgrid's Security-Constrained Unit Commitment framework, this study investigates the use of Seq2Seq (Sequence-to-Sequence) scheduling methods (SCUC). There is a growing consensus that microgrids are an important part of the future of the electric grid because of the advantages they provide in terms of reliability, renewable energy integration, and overall efficiency. In the context of complicated SCUC issues, efficient scheduling and optimization of DERs are essential for realizing their full potential. Seq2Seq models, a kind
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Vinokurov, Igor Victorovich. "Recovering text sequences using deep learning models." Program Systems: Theory and Applications 15, no. 3 (2024): 75–110. http://dx.doi.org/10.25209/2079-3316-2024-15-3-75-110.

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В статье приведены результаты формирования, обучения и оценки качества работы моделей с архитектурами Encoder-Decoder и Sequence-To-Sequence (Seq2Seq) для решения задачи дополнения неполных текстов. Задачи такого типа достаточно часто возникают при восстановлении содержимого документов по их некачественным изображениям. Проведённые в работе исследования ориентированы на решение практической задачи формирования электронных копий отсканированных документов ППК «Роскадастр», распознавание которых стандартными средствами затруднительно или невозможно. Формирование и исследование моделей осуществля
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Nishtar, Zuhaib, NA Li, Muhammad Zahid, Abdul Razzaque Soomro, and Jamil Afzal. "Optimizing Distributed Energy Resources in Microgrid SCUC through Seq2Seq Scheduling Algorithms." Mehran University Research Journal of Engineering and Technology 43, no. 4 (2024): 100. http://dx.doi.org/10.22581/muet1982.3098.

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In order to optimize Distributed Energy Resources (DERs) inside a microgrid's Security-Constrained Unit Commitment framework, this study investigates the use of Seq2Seq (Sequence-to-Sequence) scheduling methods (SCUC). There is a growing consensus that microgrids are an important part of the future of the electric grid because of the advantages they provide in terms of reliability, renewable energy integration, and overall efficiency. In the context of complicated SCUC issues, efficient scheduling and optimization of DERs are essential for realizing their full potential. Seq2Seq models, a kind
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Bae, Yong Seok, Sungwon Lee, and Janghyuk Moon. "Developing an Innovative Seq2Seq Model to Predict the Remaining Useful Life of Low-Charged Battery Performance Using High-Speed Degradation Data." Batteries 10, no. 11 (2024): 389. http://dx.doi.org/10.3390/batteries10110389.

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This study introduces a novel Sequence-to-Sequence (Seq2Seq) deep learning model for predicting lithium-ion batteries’ remaining useful life. We address the challenge of extrapolating battery performance from high-rate to low-rate charging conditions, a significant limitation in previous studies. Experiments were also conducted on commercial cells using charge rates from 1C to 3C. Comparative analysis of fully connected neural networks, convolutional neural networks, and long short-term memory networks revealed their limitations in extrapolating to untrained conditions. Our Seq2Seq model overc
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Dhanda, Namrata, and Kapil Kumar Gupta. "A Novel Approach to Text Summarization Using Machine Learning." Asian Journal of Research in Computer Science 17, no. 4 (2024): 95–104. http://dx.doi.org/10.9734/ajrcos/2024/v17i4432.

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Text summarization is a key strategy in the domains of information retrieval and natural language processing (NLP). Its objective is to reduce a lengthy written document into a clearer, more succinct summary of the information it contains. When a text document is too lengthy or intricate to analyse completely, as in news stories, academic papers, or legal documents, this approach is extremely helpful. The major challenge of text summarising is to take the most important and relevant information from the original text and convey it in an understandable and concise way. In this study, extractive
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Hùng, Dương Ngọc, Nguyễn Minh Tâm, Nguyễn Tùng Linh, Nguyễn Thanh Hoan та Nguyễn Thanh Duy. "ỨNG DỤNG SEQ2SEQ-LSTM TRONG MÔ HÌNH DỰ BÁO NGẮN HẠN PHỤ TẢI CHO LƯỚI ĐIỆN Ở TIỀN GIANG". TNU Journal of Science and Technology 228, № 14 (2023): 290–301. http://dx.doi.org/10.34238/tnu-jst.9060.

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Dự báo phụ tải ngắn hạn là rất quan trọng đối với các nhà cung cấp năng lượng để đáp ứng tải trọng của người tiêu dùng kết nối với lưới điện. Nghiên cứu này khám phá hiệu suất của các mô hình dự báo ngắn hạn nhu cầu phụ tải, bao gồm CNN-LSTM, Wavenet và Seq2Seq tích hợp long short-term memory (LSTM). Mô hình dự báo Seq2Seq-LSTM được thiết lập bằng cách kết hợp cấu trúc từ chuỗi đến chuỗi (Seq2Seq) với mô hình nơ-ron dài ngắn hạn để cải thiện độ chính xác dự báo. Nghiên cứu xác thực các mô hình bằng dữ liệu nhu cầu từ hệ thống điện Tiền Giang từ năm 2020 đến 2022, lấy vào cân nhắc nhu cầu lị
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Chen, Xingguo, Yang Li, Xiaoyan Xu, and Min Shao. "A Novel Interpretable Deep Learning Model for Ozone Prediction." Applied Sciences 13, no. 21 (2023): 11799. http://dx.doi.org/10.3390/app132111799.

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Due to the limited understanding of the physical and chemical processes involved in ozone formation, as well as the large uncertainties surrounding its precursors, commonly used methods often result in biased predictions. Deep learning, as a powerful tool for fitting data, offers an alternative approach. However, most deep learning-based ozone-prediction models only take into account temporality and have limited capacity. Existing spatiotemporal deep learning models generally suffer from model complexity and inadequate spatiality learning. Thus, we propose a novel spatiotemporal model, namely
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Jia, Xingbin, Xiang Gong, Xiaohuan Liu, et al. "Deep Sequence Learning for Prediction of Daily NO2 Concentration in Coastal Cities of Northern China." Atmosphere 14, no. 3 (2023): 467. http://dx.doi.org/10.3390/atmos14030467.

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Nitrogen dioxide (NO2) is an important precursor of atmospheric aerosol. Forecasting urban NO2 concentration is vital for effective control of air pollution. This paper proposes a hybrid deep learning model for predicting daily average NO2 concentrations on the next day, based on atmospheric pollutants, meteorological data, and historical data during 2014 to 2020 in five coastal cities of Shandong peninsula, northern China. A random Forest (RF) algorithm was used to select input variables to reduce data dimensionality trained by the sequence to sequence (Seq2Seq) the model and describe how the
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Jin, Guozhe, and Zhezhou Yu. "A Hierarchical Sequence-to-Sequence Model for Korean POS Tagging." ACM Transactions on Asian and Low-Resource Language Information Processing 20, no. 2 (2021): 1–13. http://dx.doi.org/10.1145/3421762.

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Part-of-speech (POS) tagging is a fundamental task in natural language processing. Korean POS tagging consists of two subtasks: morphological analysis and POS tagging. In recent years, scholars have tended to use the seq2seq model to solve this problem. The full context of a sentence is considered in these seq2seq-based Korean POS tagging methods. However, Korean morphological analysis relies more on local contextual information, and in many cases, there exists one-to-one matching between morpheme surface form and base form. To make better use of these characteristics, we propose a hierarchica
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Kim, Yeongha, Chang-Reung Park, Jae-Pyoung Ahn, and Beakcheol Jang. "COVID-19 outbreak prediction using Seq2Seq + Attention and Word2Vec keyword time series data." PLOS ONE 18, no. 4 (2023): e0284298. http://dx.doi.org/10.1371/journal.pone.0284298.

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As of 2022, COVID-19, first reported in Wuhan, China, in November 2019, has become a worldwide epidemic, causing numerous infections and casualties and enormous social and economic damage. To mitigate its impact, various COVID-19 prediction studies have emerged, most of them using mathematical models and artificial intelligence for prediction. However, the problem with these models is that their prediction accuracy is considerably reduced when the duration of the COVID-19 outbreak is short. In this paper, we propose a new prediction method combining Word2Vec and the existing long short-term me
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Han, Xiaoming, Zhentao Dai, Mifeng Ren, Jing Cui, and Yunfeng Shi. "One-Time Prediction of Battery Capacity Fade Curve under Multiple Fast Charging Strategies." Batteries 10, no. 3 (2024): 74. http://dx.doi.org/10.3390/batteries10030074.

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Using different fast charging strategies for lithium-ion batteries can affect the degradation rate of the batteries. In this case, predicting the capacity fade curve can facilitate the application of new batteries. Considering the impact of fast charging strategies on battery aging, a battery capacity degradation trajectory prediction method based on the TM-Seq2Seq (Trend Matching—Sequence-to-Sequence) model is proposed. This method uses data from the first 100 cycles to predict the future capacity fade curve and EOL (end of life) in one-time. First, features are extracted from the discharge v
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Zhang, Yong, and Weidong Xiao. "Keyphrase Generation Based on Deep Seq2seq Model." IEEE Access 6 (2018): 46047–57. http://dx.doi.org/10.1109/access.2018.2865589.

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Torres, Johnny, Carmen Vaca, Luis Terán, and Cristina L. Abad. "Seq2Seq models for recommending short text conversations." Expert Systems with Applications 150 (July 2020): 113270. http://dx.doi.org/10.1016/j.eswa.2020.113270.

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Eka Setiawan, Karli, Gregorius N. Elwirehardja, and Bens Pardamean. "Indoor Climate Prediction Using Attention-Based Sequence-to-Sequence Neural Network." Civil Engineering Journal 9, no. 5 (2023): 1105–20. http://dx.doi.org/10.28991/cej-2023-09-05-06.

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The Solar Dryer Dome (SDD), a solar-powered agronomic facility for drying, retaining, and processing comestible commodities, needs smart systems for optimizing its energy consumption. Therefore, indoor condition variables such as temperature and relative humidity need to be forecasted so that actuators can be scheduled, as the largest energy usage originates from actuator activities such as heaters for increasing indoor temperature and dehumidifiers for maintaining optimal indoor humidity. To build such forecasting systems, prediction models based on deep learning for sequence-to-sequence case
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Zhang, Hanqin. "Application of LSTM-Based Seq2Seq Models in Natural Language to SQL Conversion in Financial Domain." Science, Technology and Social Development Proceedings Series 2 (November 10, 2024): 110–16. http://dx.doi.org/10.70088/n3mbj650.

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As a crucial branch of artificial intelligence, Natural Language Processing (NLP) enables computers to understand, interpret, and generate human language, significantly enhancing the efficiency of information retrieval and search. Given the growing demand for data processing in the financial sector, this paper proposes and implements a Seq2Seq model based on the LSTM algorithm to convert natural language queries into SQL statements (NL2SQL) for application in finance. The model demonstrates stable and significant performance improvements over 10 training epochs, with accuracy increasing from 0
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Han, Yerim, and Woohyun Kim. "Development and Validation of Building Control Algorithm Energy Management." Buildings 11, no. 3 (2021): 131. http://dx.doi.org/10.3390/buildings11030131.

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In this paper, a building control algorithm is proposed to reduce the electricity consumption of a building with a variable refrigerant flow (VRF) system. The algorithm uses sequence-to-sequence long short-term memory (seq2seq LSTM) to set target electricity consumption, and uses a VRF air conditioner system to reduce electricity consumption. After setting target electricity consumption, the algorithm is applied as a method of updating target electricity consumption. In addition, we propose two methods to increase the performance of the seq2seq LSTM model. First, among the feature selection me
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Cheng, Minhao, Jinfeng Yi, Pin-Yu Chen, Huan Zhang, and Cho-Jui Hsieh. "Seq2Sick: Evaluating the Robustness of Sequence-to-Sequence Models with Adversarial Examples." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 3601–8. http://dx.doi.org/10.1609/aaai.v34i04.5767.

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Crafting adversarial examples has become an important technique to evaluate the robustness of deep neural networks (DNNs). However, most existing works focus on attacking the image classification problem since its input space is continuous and output space is finite. In this paper, we study the much more challenging problem of crafting adversarial examples for sequence-to-sequence (seq2seq) models, whose inputs are discrete text strings and outputs have an almost infinite number of possibilities. To address the challenges caused by the discrete input space, we propose a projected gradient meth
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Bravo-Candel, Daniel, Jésica López-Hernández, José Antonio García-Díaz, Fernando Molina-Molina, and Francisco García-Sánchez. "Automatic Correction of Real-Word Errors in Spanish Clinical Texts." Sensors 21, no. 9 (2021): 2893. http://dx.doi.org/10.3390/s21092893.

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Real-word errors are characterized by being actual terms in the dictionary. By providing context, real-word errors are detected. Traditional methods to detect and correct such errors are mostly based on counting the frequency of short word sequences in a corpus. Then, the probability of a word being a real-word error is computed. On the other hand, state-of-the-art approaches make use of deep learning models to learn context by extracting semantic features from text. In this work, a deep learning model were implemented for correcting real-word errors in clinical text. Specifically, a Seq2seq N
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Huang, Jianfeng, Yuefeng Liu, Yue Chen, and Chen Jia. "Dynamic Recommendation of POI Sequence Responding to Historical Trajectory." ISPRS International Journal of Geo-Information 8, no. 10 (2019): 433. http://dx.doi.org/10.3390/ijgi8100433.

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Point-of-Interest (POI) recommendation is attracting the increasing attention of researchers because of the rapid development of Location-based Social Networks (LBSNs) in recent years. Differing from other recommenders, who only recommend the next POI, this research focuses on the successive POI sequence recommendation. A novel POI sequence recommendation framework, named Dynamic Recommendation of POI Sequence (DRPS), is proposed, which models the POI sequence recommendation as a Sequence-to-Sequence (Seq2Seq) learning task, that is, the input sequence is a historical trajectory, and the outpu
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Wang, Lei, Jun Hu, Rundong Jiang, and Zengping Chen. "A Deep Long-Term Joint Temporal–Spectral Network for Spectrum Prediction." Sensors 24, no. 5 (2024): 1498. http://dx.doi.org/10.3390/s24051498.

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Spectrum prediction is a promising technique to release spectrum resources and plays an essential role in cognitive radio networks and spectrum situation generating. Traditional algorithms normally focus on one-dimensional or predict spectrum values in a slot-by-slot manner and thus cannot fully perceive the spectrum states in complex environments and lack timeliness. In this paper, a deep learning-based prediction method with a simple structure is developed for temporal–spectral and multi-slot spectrum prediction simultaneously. Specifically, we first analyze and construct spectrum data suita
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Zhou, Jiayi, Jiaming Ji, Josef Dai, and Yaodong Yang. "Sequence to Sequence Reward Modeling: Improving RLHF by Language Feedback." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 26 (2025): 27765–73. https://doi.org/10.1609/aaai.v39i26.34992.

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Aligning the behavior of Large language models (LLMs) with human intentions and values remains a critical challenge. Reinforcement learning from human feedback (RLHF) aligns LLMs by training a reward model (RM) on human preferences and fine-tuning the LLMs to maximize RM feedback. Despite its effectiveness and popularity, RLHF is prone to biased local optimization. It means RM fails to provide feedback that accurately aligns with human preference, causing LLMs to explore unexpected generalizations, and failing to achieve alignment objectives. To mitigate this issue, we propose a novel sequence
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Zhang, Yong, Dan Li, Yuheng Wang, Yang Fang, and Weidong Xiao. "Abstract Text Summarization with a Convolutional Seq2seq Model." Applied Sciences 9, no. 8 (2019): 1665. http://dx.doi.org/10.3390/app9081665.

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Abstract text summarization aims to offer a highly condensed and valuable information that expresses the main ideas of the text. Most previous researches focus on extractive models. In this work, we put forward a new generative model based on convolutional seq2seq architecture. A hierarchical CNN framework is much more efficient than the conventional RNN seq2seq models. We also equip our model with a copying mechanism to deal with the rare or unseen words. Additionally, we incorporate a hierarchical attention mechanism to model the keywords and key sentences simultaneously. Finally we verify o
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