Results 1 to 10 of about 10,182 (170)
BiLSTM-SSVM: Training the BiLSTM with a Structured Hinge Loss for Named-Entity Recognition [PDF]
IEEE Transactions on Big Data, 2022 Building on the achievements of the BiLSTM-CRF in named-entity recognition (NER), this paper introduces the BiLSTM-SSVM, an equivalent neural model where training is performed using a structured hinge loss. The typical loss functions used for evaluating NER are entity-level variants of the F1 score such as the CoNLL and MUC losses.
Hanieh Poostchi, Massimo Piccardi
openaire +1 more source
Ship Motion-Based Prediction of Damage Locations Using Bidirectional Long Short-Term Memory
한국해양공학회지, 2022 The initial response to a marine accident can play a key role to minimize the accident. Therefore, various decision support systems have been developed using sensors, simulations, and active response equipment. In this study, we developed an algorithm to
Hye-young Son +5 more
doaj +1 more source
Evaluation of PSO-BiLSTM method for stock price forecasting using stock price time series data (Case study: Iran Stock Exchange and OTC stock) [PDF]
راهبرد مدیریت مالی, 2022 In recent years, with the increase in the penetration rate of the capital market, more people have invested in the stock market. Predicting the stock prices accurately with the least error can reduce investment risk and increase investment return. Due to
Jalil Vaziri Kordestani +3 more
doaj +1 more source
Short-Term Aggregated Residential Load Forecasting using BiLSTM and CNN-BiLSTM
2022 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT), 2022 Higher penetration of renewable and smart home technologies at the residential level challenges grid stability as utility-customer interactions add complexity to power system operations. In response, short-term residential load forecasting has become an increasing area of focus.
Bharat Bohara +3 more
openaire +2 more sources
Digital Transportation and Safety, 2023 Accurate and real-time passenger flow prediction of rail transit is an important part of intelligent transportation systems (ITS). According to previous studies, it is found that the prediction effect of a single model is not good for datasets with large
Qianru Qi, Rongjun Cheng, Hongxia Ge
doaj +1 more source
Journal of Intelligent Systems, 2023 This study proposes an emotional analysis method of consumer comment text based on Bidirectional Encoder Representations from Transformers (BERT) and hierarchical attention.
Chang Wanjun, Zhu Mingdong
doaj +1 more source
Connection Science, 2022 Manual evaluation could be time-consuming, unreliable and unreproducible in Chinese-English interpretation. Therefore, it is necessary to develop an automatic scoring system.
Xinguang Li +4 more
doaj +1 more source
Deep learning para la clasificación de usos de suelo agrícola con Sentinel-2
Revista de Teledetección, 2020 En el campo de la teledetección se ha producido recientemente un incremento del uso de técnicas de aprendizaje profundo (deep learning). Estos algoritmos se utilizan con éxito principalmente en la estimación de parámetros y en la clasificación de ...
M. Campos-Taberner +3 more
doaj +1 more source
Multi-mode deep auto-encoder recommendation model for fusion of text information
Xi'an Gongcheng Daxue xuebao, 2021 A new recommendation model is proposed to solve the problems of data sparsity and deep semantic information learning when using score information as auxiliary recommendation.
Jinguang CHEN, Xinyi XU, Ganglong FAN
doaj +1 more source
Research on Phishing URL Detection Technology Based on CNN-BiLSTM [PDF]
Zhengzhou Daxue xuebao. Gongxue ban, 2021 In order to solve the increasingly serious problem of phishing, a phishing URL detection method based on convolution neural network (CNN) and bi-directional long short termmemory (BiLSTM) was proposed.This method first classified the URL based on the ...
BU Youjun +4 more
doaj +1 more source