Dynamic graph convolutional networks with Temporal representation learning for traffic flow prediction by Aihua Zhang

“…Abstract In the realm of traffic prediction, emerging are methodologies founded on graph convolutional networks. …”
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Leveraging airport CCTV footage through video understanding techniques for visibility prediction by Zeonlung Pun, Xinyu Tian, Shan Gao

“…In this study, we explore various video understanding models for visibility prediction, achieving promising results and pioneering the use of video understanding techniques in this domain. …”
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AI-Driven Drought Monitoring: Advanced Machine Learning Techniques for Early Prediction by Vij Priya, Tiwari Ankita

“…This research leverages state-of-the-art machine learning techniques and an extensive multi-source dataset—including satellite imagery, meteorological data, soil characteristics, and historical drought records—to develop an AI-driven framework for drought monitoring and early prediction. The study employs Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to capture complex spatial and temporal patterns, enabling more accurate and timely drought forecasting compared to traditional approaches. …”
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Predicting traffic flow with federated learning and graph neural with asynchronous computations network by Muhammad Yaqub, Shahzad Ahmad, Malik Abdul Manan, Muhammad Salman Pathan, Lan He

“…Our framework incorporates the principles of asynchronous graph convolutional networks with federated learning to enhance the accuracy and efficiency of real-time traffic flow prediction. The FLAGCN model employs a spatial-temporal graph convolution technique to asynchronously address spatio-temporal dependencies within traffic data effectively. …”
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Inpatient Length of Stay and Mortality Prediction Utilizing Clinical Time Series Data by Junde Chen, Mason Li, Miles Milosevich, Tiffany Le, Andrew Bahsoun, Yuxin Wen

“…By optimizing the loss function to address class imbalance and overfitting, our model ensures robust and accurate predictions. Experimental results demonstrate that the proposed model outperforms state-of-the-art methods, validating its effectiveness and feasibility in inpatient length of stay and mortality prediction.…”
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Integrating spatiotemperporal features into fault prediction using a multi-dimensional method by Chun-Yi Lin, Yu-Chuan Tseng, Wu-Sung Yao

“…The short-time Fourier transform is used to convert spatiotemporal data into the frequency domain for classification, and high-order features are extracted through convolutional networks. The model considers three spatial dimensions and three vibration measurement sources to form a nine-dimensional data structure, and a fault prediction algorithm based on these dimensions is established. …”
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Comparing XAI techniques for interpreting short-term burglary predictions at micro-places by Robin Khalfa, Naomi Theinert, Wim Hardyns

“…While previous research predominantly relies on SHAP to interpret spatiotemporal crime predictions, this is the first study to systematically evaluate SHAP alongside other XAI techniques, offering both global and local model interpretability within the context of crime prediction. …”
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An urban road traffic flow prediction method based on multi-information fusion by Xiao Wu, Hua Huang, Tong Zhou, Yudan Tian, Shisen Wang, Jingting Wang

“…Experiments on real datasets show that the MIFPN model improves by an average of 11.2% over the baseline model in long term predictions up to 60 min ago.…”
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AI-powered spatiotemporal imputation and prediction of chlorophyll-a concentration in coastal ecosystems by Fan Zhang, Hiusuet Kung, Fa Zhang, Can Yang, Jianping Gan

“…We developed an advanced AI-powered spatiotemporal imputation and prediction (STIMP) model for predicting Chl_a in coastal ocean. …”
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A Framework for User Traffic Prediction and Resource Allocation in 5G Networks by Ioannis Konstantoulas, Iliana Loi, Dimosthenis Tsimas, Kyriakos Sgarbas, Apostolos Gkamas, Christos Bouras

“…The models show high accuracy in the tasks performed, especially in the user traffic prediction task, where the models show an accuracy of over <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>99</mn><mo>%</mo></mrow></semantics></math></inline-formula>. …”
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Methods of security situation prediction for industrial internet fused attention mechanism and BSRU by Xiangdong HU, Zhengguo TIAN

“…The security situation prediction plays an important role in balanced and reliable work for industrial internet.In the face of massive, high-dimensional and time-series data generated in the industrial production process, traditional prediction models are difficult to accurately and efficiently predict the network security situation.Therefore, the methods of security situation prediction for industrial internet fused attention mechanism and bi-directional simple recurrent unit (BSRU) were proposed to meet the real-time and accuracy requirements of industrial production.Each security element was analyzed and processed, so that it could reflect the current network state and facilitate the calculation of the situation value.One-dimensional convolutional network was used to extract the spatial dimension features between each security element and preserve the temporal correlation between features.The BSRU network was used to extract the time dimension features between the data information and reduced the loss of historical information.Meanwhile, with the powerful parallel capability of SRU network, the training time of model was reduced.Attention mechanism was introduced to optimize the correlation weight of BSRU hidden state to highlight strong correlation factors, reduced the influence of weak correlation factors, and realized the prediction of industrial internet security situation combining attention mechanism and BSRU.The comparative experimental results show that the model reduces the training time and training error by 13.1% and 28.5% than the model using bidirectional long short-term memory network and bidirectional gated recurrent unit.Compared with the convolutional and BSRU network fusion model without attention mechanism, the prediction error is reduced by 28.8% despite the training time increased by 2%.The prediction effect under different prediction time is better than other models.Compared with other prediction network models, this model achieves the optimization of time performance and uses the attention mechanism to improve the prediction accuracy of the model under the premise of increasing a small amount of time cost.The proposed model can well fit the trend of network security situation, meanwhile, it has some advantages in multistep prediction.…”
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Predicting wheat yield using deep learning and multi-source environmental data by Muhammad Ashfaq, Imran Khan, Dilawar Shah, Shujaat Ali, Muhammad Tahir

“…The RNN and ANN models also demonstrated moderate predictive capabilities, with R2 values of 0.72 and 0.66, respectively. …”
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Research on shale TOC prediction method based on improved BP neural network by Chaorong Wu, Kaixing Huang, Zhengtao Sun, Yizhen Li, Yong Li, Yuexiang Hao, Zhengxing Sun, Ziqi Wang

“…Finally, combined with the seismic waveform-guided simulation inversion technology, the planar and spatial distribution of TOC in the study area was predicted. …”
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An End-to-End CRSwNP Prediction with Multichannel ResNet on Computed Tomography by Shixin Lai, Weipiao Kang, Yaowen Chen, Jisheng Zou, Siqi Wang, Xuan Zhang, Xiaolei Zhang, Yu Lin

“…Compared to the limited learning capacity of single-channel neural networks, our proposed multichannel feature adaptive fusion model captures multiscale spatial features, enhancing the model’s focus on crucial sinus information within the CT images to maximize detection accuracy. …”
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Exploring the potentialities and challenges of deep learning for simulation and prediction of urban sprawl features by Ange Gabriel Belinga, Stéphane Cedric Tékouabou Koumetio, Mohamed El Haziti

“…Through an examination of DL methodologies, we aim to highlight their effectiveness in capturing the complex spatial patterns and relationships associated with US. …”
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Prediction and Optimization of Civil Aviation Flight Delays Based on Machine Learning Algorithms by Qingwei Zhong, Yingxue Yu, Yiru Huang, Tianhang Zhang

“…This research aims to develop an innovative model that accurately predicts civil aviation flight delays and provides insights related to performance enhancement. …”
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Spatio-temporal risk prediction of leptospirosis: A machine-learning-based approach. by Rodrigue Govan, Romane Scherrer, Baptiste Fougeron, Christine Laporte-Magoni, Roman Thibeaux, Pierre Genthon, Philippe Fournier-Viger, Cyrille Goarant, Nazha Selmaoui-Folcher

“…<h4>Methodology/principal findings</h4>This study aims to establish a precise spatio-temporal risk map of leptospirosis at a national scale, using binarized incidence rates as the variable to predict. The spatial analysis was conducted at a finer resolution than the city level, while the temporal analysis was performed on a monthly basis from 2011 to 2022. …”
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Analysis and Prediction of Coverage and Channel Rank for UAV Networks in Rural Scenarios With Foliage by Donggu Lee, Ozgur Ozdemir, Ram Asokan, Ismail Guvenc

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Numerical Prediction of Fatigue Life for Landing Gear Considering the Shock Absorber Travel by Haihong Tang, Panglun Liu, Jianbin Ding, Jinsong Cheng, Yiyao Jiang, Bingyan Jiang

“…On the basis of the whole geometric model of a large passenger aircraft’s main landing gear (MLG), the quasi-static finite element model (FEM) of the whole MLG is established, and the high-cycle fatigue issue of the Main Fitting (MF) is studied by considering the variation in shock absorber travel (SAT). …”
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A Physics-Enhanced Network for Predicting Sequential Satellite Images of Typhoon Clouds by Jiawei Yuan, Liling Zhao, Runling Yu, Xiaoqin Lu, Min Xia, Yi Liu, Yuru Wang, Xinyue Wang

“…To further improve the fine structural details in the predicted typhoon cloud images, a concurrent spatial and channel squeeze-and-excitation attention mechanism is incorporated into both the encoder and decoder modules. …”
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