Suchergebnisse - ConvLSTM encoder-decoder model

An Optimized Model With Encoder-Decoder ConvLSTM for Global Ionospheric Forecasting von Wang, Cheng, Xue, Kaiyu, Shi, Chuang

“… This study introduces two optimized models based on the ConvLSTM cell with an encoder-decoder structure to enhance forecasting performance …”
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Prediction of maize growth stages based on deep learning von Yue, Yang, Li, Jin-Hai, Fan, Li-Feng, Zhang, Li-Li, Zhao, Peng-Fei, Zhou, Qiao, Wang, Nan, Wang, Zhong-Yi, Huang, Lan, Dong, Xue-Hui

“… •ConvLSTM encoder-decoder model can forecast daily weather factors correctly.•Hybrid model and data-driven model can predict maize growth stages …”
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PDED-ConvLSTM: Pyramid Dilated Deeper Encoder–Decoder Convolutional LSTM for Arctic Sea Ice Concentration Prediction von Zhang, Deyu, Wang, Changying, Huang, Baoxiang, Ren, Jing, Zhao, Junli, Hou, Guojia

“… To address these challenges, we propose an innovative encoder–decoder pyramid dilated convolutional long short-term memory network (DED-ConvLSTM …”
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ED‐ConvLSTM: A Novel Global Ionospheric Total Electron Content Medium‐Term Forecast Model von Xia, Guozhen, Zhang, Fubin, Wang, Cheng, Zhou, Chen

“… In this paper, we proposed an innovative encoder‐decoder structure with a convolution long short‐term memory (ED‐ConvLSTM …”
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EDDA-ConvLSTM: Encoder-Decoder Dual Attention ConvLSTM for Moroccan Coastal Sea Surface Temperature Prediction von Zahrae El Azhary, Fatima, Minaoui, Khalid

“… This study presents an advanced encoder-decoder dual attention convolutional long short-term memory (ConvLSTM …”
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Advancing spatiotemporal forecasts of CO2 plume migration using deep learning networks with transfer learning and interpretation analysis von Fan, Ming, Wang, Hongsheng, Zhang, Jing, Hosseini, Seyyed A., Lu, Dan

“… In this work, we propose two deep learning models, Auto-Encoder (AE)-LSTM and Encoder-Decoder (ED …”
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Forecasting Stock Market Using Machine Learning Approach Encoder-Decoder ConvLSTM von Iqbal, Khurum, Hassan, Ali, Hassan, Syed Shah Mir Ul, Iqbal, Shuaib, Aslam, Faheem, Mughal, Khurrum S

“… The goal of this study is to create a hybrid Deep Learning model (Encoder-Decoder ConvLSTM …”
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Self-Attention (SA)-ConvLSTM Encoder–Decoder Structure-Based Video Prediction for Dynamic Motion Estimation von Kim, Jeongdae, Choo, Hyunseung, Jeong, Jongpil

“… However, ConvLSTM has limitations in capturing long-term temporal dependencies. To solve this problem, this study proposes an encoder …”
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Spatiotemporal Prediction of Ionospheric Total Electron Content Based on ED-ConvLSTM von Li, Liangchao, Liu, Haijun, Le, Huijun, Yuan, Jing, Shan, Weifeng, Han, Ying, Yuan, Guoming, Cui, Chunjie, Wang, Junling

“… Our ED-ConvLSTM model is built based on the encoder-decoder architecture, which includes two modules …”
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A multi-embedding neural model for incident video retrieval von Chiang, Ting-Hui, Tseng, Yi-Chun, Tseng, Yu-Chee

“… We propose an encoder-decoder ConvLSTM model that explores multiple embeddings of a video to facilitate comparison of similarity between a pair of videos …”
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Deep unsupervised multi-modal fusion network for detecting driver distraction von Zhang, Yuxin, Chen, Yiqiang, Gao, Chenlong

“… It is an end-to-end model composing of three main modules: multi-modal representation learning, multi-scale feature fusion and unsupervised driver distraction detection …”
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Predicting 360° Video Saliency: A ConvLSTM Encoder-Decoder Network With Spatio-Temporal Consistency von Wan, Zhaolin, Qin, Han, Xiong, Ruiqin, Li, Zhiyang, Fan, Xiaopeng, Zhao, Debin

“… In this study, we propose a novel spatio-temporal consistency generative network for 360° VSP. A dual-stream encoder-decoder architecture is adopted to process the forward and backward frame sequences of 360 …”
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Optimizing hyperparameters of Data-driven simulation-assisted-Physics learned AI (DPAI) model to reduce compounding error von Gantala, Thulsiram, Balasubramaniam, Krishnan

“… DPAI model has layers of encoder–decoder structure with modified convolutional long short-term memory (ConvLSTM …”
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An advanced spatio-temporal convolutional recurrent neural network for storm surge predictions von Adeli, Ehsan, Sun, Luning, Wang, Jianxun, Taflanidis, Alexandros A.

“… In this research paper, we study the capability of artificial neural network models to emulate storm surge based on the storm track/size/intensity history, leveraging a database of synthetic storm simulations …”
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ResMIBCU-Net: an encoder–decoder network with residual blocks, modified inverted residual block, and bi-directional ConvLSTM for impacted tooth segmentation in panoramic X-ray images von Imak, Andaç, Çelebi, Adalet, Polat, Onur, Türkoğlu, Muammer, Şengür, Abdulkadir

“… Objective Impacted tooth is a common problem that can occur at any age, causing tooth decay, root resorption, and pain in the later stages. In recent years, …”
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Effective Multi-Step PM2.5 and PM10 Air Quality Forecasting Using Bidirectional ConvLSTM Encoder-Decoder With STA Mechanism von Lakshmi, S., Krishnamoorthy, A.

“… Address air quality prediction as an issue of predicting spatiotemporal sequences, since individual models have difficulties …”
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GPS Trajectory Completion Using End-to-End Bidirectional Convolutional Recurrent Encoder-Decoder Architecture with Attention Mechanism von Nawaz, Asif, Huang, Zhiqiu, Wang, Senzhang, Akbar, Azeem, AlSalman, Hussain, Gumaei, Abdu

“… GPS datasets in the big data regime provide rich contextual information that enable efficient implementation of advanced features such as navigation, tracking, …”
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Detection of Forest Burned Area Using a Spatiotemporal Algorithm Based on Spectral Index Time Series Data von Li, Yunhao, Liu, Jinxiu, Maeda, Eduardo Eiji, Li, Xuecao, Pellikka, Petri, Heiskanen, Janne

“… (ConvLSTM) units with a multiscale encoder-decoder architecture to capture both spatial and temporal dependencies inherent in satellite image time series …”
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Energy Demand Load Forecasting for Electric Vehicle Charging Stations Network based on ConvLSTM and BiConvLSTM Architectures von Mohammad, Faisal, Kang, Dong-Ki, Ahmed, Mohamed A., Kim, Young-Chon

“… The electrification of transport has proved to be a breakthrough to uplift the sustainable and eco-friendly platform in the global sector in which electric …”
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New encoder–decoder convolutional LSTM neural network architectures for next-day global ionosphere maps forecast von de Paulo, M. C. M., Marques, H. A., Feitosa, R. Q., Ferreira, M. P.

“… ) of the days before the prediction period. We proposed modifications to the encoder–decoder convolutional long short-term memory (ED-ConvLSTM …”
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