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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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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Spatial Analysis of the Effects of Women’s Socioeconomic Status on Divorce Rates in Turkey by Ayça Özekin, Burcu Süküti

“…In the study, the relationship between the socioeconomic status indicators of women in Turkey and the divorce rates were examined by estimating the Spatial Autocorrelation (SAR) and Spatial Error (SEM) Models for 26 sub-regions in the Turkish Statistical Regional Units Classification (NUTS) Level 2. …”
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Resting-state EEG network variability predicts individual working memory behavior by Chunli Chen, Shiyun Xu, Jixuan Zhou, Chanlin Yi, Liang Yu, Dezhong Yao, Yangsong Zhang, Fali Li, Peng Xu

“…Finally, using a multivariable predictive model based on these variability metrics, we effectively predicted individual WM performances. …”
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Spatially explicit metrics improve the evaluation of species distribution models facing sampling biases by Claudio A. Bracho-Estévanez, Salvador Arenas-Castro, Juan P. González-Varo, Pablo González-Moreno

“…Furthermore, most predictions rely only on non-spatial metrics such as the AUC and the TSS to evaluate model performance. …”
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Mt or not Mt: Temporal variation in detection probability in spatial capture-recapture and occupancy models by Sollmann, Rahel

“…Here, I investigate whether closed spatial capture-recapture (SCR) and single season occupancy models are robust to ignoring temporal variation in detection probability. …”
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Correlation-regression analysis of modelled chlorine residual's spatial variability in water supply network by Sanja Marčeta, Dušan Prodanović

“…From this point of view the water quality request for minimum chlorinated and safe potable water is understandable. In numerable modeling and experimental research the spatial diversity of chlorine residual was found to correlate with the daily consumption schedule, the water temperature, the initial dose of chlorine and organic matter content in the water. …”
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Novel extensions to the Fisher copula to model flood spatial dependence over North America by D. A. Alexandre, C. Chaudhuri, J. Gill-Fortin

“…We propose novel extensions to the Fisher copula to statistically model the spatial structure of observed historical flood record data across North America. …”
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A Computing Model of Selective Attention for Service Robot Based on Spatial Data Fusion by Huanzhao Chen, Guohui Tian

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Automated Generation of Urban Spatial Structures Based on Stable Diffusion and CoAtNet Models by Dian Yu, Bo Wan, Qiang Sheng

“…We simultaneously trained two models: one is a LoRA Model based on the Stable Diffusion architecture used for generating road networks similar to those of various city road spatial structures; the other is a CoAtNet Model (Convolution + Transformer) used as an evaluation model to predict the space-syntax parameters of road structures and calculate the Mean Absolute Percentage Error (MAPE) relative to real urban samples. …”
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An Iterative Pixel-Based Dimensional Voting Model for High Spatial-Resolution Image Classification by Muhamad Asyraf Mohd Pouzi, Haza Nuzly Abdull Hamed, Muhammad Razib Othman, Hishammuddin Asmuni, Mohd Adham Isa, Husni Ruslai

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A high resolution spatial modelling framework for landscape-level, strategic conservation planning by T. Foxley, P. Lintott, S. Stonehouse, J. Flannigan, E.L. Stone

“…The aim of this study was to develop a spatial modelling framework for protecting biodiversity in the planning process. …”
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Machine Learning for Spatiotemporal Prediction of River Siltation in Typical Reach in Jiangxi, China by Yong Fu, Jin Luo, Die Zhang, Lingjia Liu, Gan Luo, Xiaofang Zu

“…This study presents an initial, yet promising attempt to apply machine learning for spatially explicit siltation prediction in data-constrained river systems. …”
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