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A Spatiotemporal Deep Learning-Based Multisource Data Analytics Framework for Basketball Game [PDF]
IEEE AccessData analytics has been an important business demand for basketball game. Conventionally, it was implemented with use of statistical approaches, yet neglecting the perception of spatiotemporal characteristics data.
Han Lin, Muren Bao, Chenran Kang
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Spatiotemporal Observer Design for Predictive Learning of High-Dimensional Data [PDF]
arXivAlthough deep learning-based methods have shown great success in spatiotemporal predictive learning, the framework of those models is designed mainly by intuition. How to make spatiotemporal forecasting with theoretical guarantees is still a challenging issue.
Li, Han-Xiong, Liang, Tongyi
arxiv +2 more sources
Spatiotemporal identification of druggable binding sites using deep learning [PDF]
Communications Biology, 2020 AbstractIdentification of novel protein binding sites expands druggable genome and opens new opportunities for drug discovery. Generally, presence or absence of a binding site depends on the three-dimensional conformation of a protein, making binding site identification resemble the object detection problem in computer vision.
Igor Kozlovskii, Petr Popov
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WIFI LOG-BASED STUDENT BEHAVIOR ANALYSIS AND VISUALIZATION SYSTEM [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2022 Student behavior research can improve learning efficiency, provide decision evidences for infrastructure management. Existing campus-scale behavioral analysis work have not taken into account the students characteristics and spatiotemporal pattern ...
F. Chen, C. Jing, H. Zhang, X. Lv
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Spatiotemporal imputation of MAIAC AOD using deep learning with downscaling [PDF]
Remote Sensing of Environment, 2020 Aerosols have adverse health effects and play a significant role in the climate as well. The Multiangle Implementation of Atmospheric Correction (MAIAC) provides Aerosol Optical Depth (AOD) at high temporal (daily) and spatial (1 km) resolution, making it particularly useful to infer and characterize spatiotemporal variability of aerosols at a fine ...
Carrie V. Breton +9 more
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Partial Convolutional LSTM for Spatiotemporal Prediction of Incomplete Data
IEEE Access, 2020 Advanced data analysis techniques facilitate data-driven spatiotemporal prediction in various fields. However, in real-world data, missing values are inevitable, which causes the data incomplete and makes predictions more challenging.
Hyesook Son, Yun Jang
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SA-JSTN: Self-Attention Joint Spatiotemporal Network for Temperature Forecasting
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 The rapid development of remote sensing technology has brought abundant data support for deep learning based temperature forecasting research. However, recently proposed methods usually focus on the temporal relationship among temperature observation ...
Lukui Shi +4 more
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Deep Learning for Spatiotemporal Modeling of Urbanization
, 2021 Accepted by NeurIPS 2021 MLPH (Machine Learning in Public Health) Workshop; Best Paper Awarded by NeurIPS 2021 MLPH (Machine Learning in Public Health ...
Li, Tang, Gao, Jing, Peng, Xi
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Automatic Ultrasound Vessel Segmentation with Deep Spatiotemporal Context Learning [PDF]
, 2021 Accurate, real-time segmentation of vessel structures in ultrasound image sequences can aid in the measurement of lumen diameters and assessment of vascular diseases. This, however, remains a challenging task, particularly for extremely small vessels that are difficult to visualize.
Baichuan Jiang +4 more
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MSF-NET: Foreground Objects Detection With Fusion of Motion and Semantic Features
IEEE Access, 2023 Visual surveillance requires robust detection of foreground objects under challenging environments of abrupt lighting variation, stationary foreground objects, dynamic background objects, and severe weather conditions.
Jae-Yeul Kim, Jong-Eun Ha
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