An Investigation on Prediction of Infrastructure Asset Defect with CNN and ViT Algorithms by Nam Lethanh, Tu Anh Trinh, Mir Tahmid Hossain

“…Convolutional Neural Networks (CNNs) have been demonstrated to be one of the most powerful methods for image recognition, being applied in many fields, including civil and structural health monitoring in infrastructure asset management. …”
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GNSS–VTEC prediction based on CNN–GRU neural network model during high solar activities by T. Y. Yang, J. Y. Lu, Y. Y. Yang, Y. H. Hao, M. Wang, J. Y. Li, G. C. Wei

“…Furthermore, the CNN–GRU model exhibits stable and excellent performance across different months and hour of the day, even during geomagnetic storms.…”
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Clinical Applicability and Cross-Dataset Validation of Machine Learning Models for Binary Glaucoma Detection by David Remyes, Daniel Nasef, Sarah Remyes, Joseph Tawfellos, Michael Sher, Demarcus Nasef, Milan Toma

“…This study evaluates the clinical applicability and robustness of three machine learning models for automated glaucoma detection: a convolutional neural network, a deep neural network, and an automated ensemble approach. …”
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Road Perception for Autonomous Driving: Pothole Detection in Complex Environments Based on Improved YOLOv8 by Siyuan Kong, Qiao Meng, Xin Li, Zhijie Wang, Xin Liu, Bingyu Li

“…This design significantly improves the robustness of the algorithm under different lighting and complex environmental conditions. …”
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Dynamic graph attention network based on multi-scale frequency domain features for motion imagery decoding in hemiplegic patients by Yinan Wang, Yinan Wang, Lizhou Gong, Yang Zhao, Yewei Yu, Hanxu Liu, Xiao Yang

“…Additionally, MFF-DANet integrates a graph attention convolutional network to capture spatial topological features across different electrode channels, utilizing electrode positions as prior knowledge to construct and update the graph adjacency matrix. …”
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A New Bearing Fault Diagnosis Method Based on Deep Transfer Network and Supervised Joint Matching by Chengyao Liu, Fei Dong, Kunpeng Ge, Yuanyuan Tian

“…Second, a deep transfer convolutional neural network is built by the way of fine-tuning, and the trained network is used to extract deep features from different domains. …”
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Time–frequency ensemble network for wind turbine mechanical fault diagnosis by Haiyu Guo, Xingzheng Guo, Xiaoguang Zhang, Fanfan Lu, Chuang Liang

“…In the frequency domain module, a mixhop graph convolutional network is used to extract the multi-scale frequency domain features of different neighbours, and a Multi Head Attention (MHA) mechanism is introduced to capture the intra-feature dependencies. …”
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Power Equipment Image Recognition Method Based on Feature Extraction and Deep Learning by Shuang Lin

“…We plan to introduce a lightweight convolutional structure combined with a graph neural network mechanism to strengthen global context modeling and device structural awareness. …”
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A VAN-Based Multi-Scale Cross-Attention Mechanism for Skin Lesion Segmentation Network by Shuang Liu, Zeng Zhuang, Yanfeng Zheng, Simon Kolmanic

“…With the rise of deep learning technology, the field of medical image segmentation has undergone rapid development. In recent years, convolutional neural networks (CNNs) have brought many achievements and become the consensus in medical image segmentation tasks. …”
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AIF: Infrared and Visible Image Fusion Based on Ascending–Descending Mechanism and Illumination Perception Subnetwork by Ying Liu, Xinyue Mi, Zhaofu Liu, Yu Yao

“…The AdC feature extractor adopts an ascending–descending feature extraction mechanism to organize convolutional layers and combines these convolutional layers with cross-modal interactive differential modules to achieve the effective extraction of hierarchical complementary and differential information. …”
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A Day-Ahead PV Power Forecasting Method Based on Irradiance Correction and Weather Mode Reliability Decision by Haonan Dai, Yumo Zhang, Fei Wang

“…Accurate day-ahead photovoltaics (PV) power forecasting results are significant for power grid operation. According to different weather modes, the existing research has established a classification forecast framework to improve the accuracy of day-ahead forecasts. …”
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A Robust Scheme of Vertebrae Segmentation for Medical Diagnosis by Faisal Rehman, Syed Irtiza Ali Shah, Naveed Riaz, Syed Omer Gilani

“…While remarkable success was achieved by deep convolutional neural networks (DCNNs) in medical image segmentation, it is still a difficult task for DCNNs to handle the medical image segmentation problems with various deformities and anatomical complexities. …”
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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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Classification of Structural and Functional Development Stage of Cardiomyocytes Using Machine Learning Techniques by V. R. Bondarev, K. O. Ivanko, N. G. Ivanushkina

“…The model is evaluated based on the confusion matrix and the heat maps of different convolutional layers are analyzed. Images from the classes with a large number of mutual errors are also considered. …”
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Dynamically Tunable Multidimensional Feature Focusing and Diffusion Networks for Water Surface Debris Detection by Chong Zhang, Jie Yue, Jianglong Fu

“…First, a Self-moving Point Convolutional Gating Network (SPCG-Net) was designed, which integrated an adaptive point-moving mechanism with a convolutional gating linear unit to enhance the flexibility and accuracy of feature extraction. …”
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GANFlow: A Hybrid Model for SAR Image Target Open-Set Recognition Based on GAN and the Flow-Based Module by Jikai Qin, Jiusheng Han, Zheng Liu, Lei Ran, Rong Xie, Tat-Soon Yeo

“…In this model, a classifiable convolution GAN is first designed to complete the training of the feature extraction module and classifier. …”
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MCADNet: A Multi-Scale Cross-Attention Network for Remote Sensing Image Dehazing by Tao Tao, Haoran Xu, Xin Guan, Hao Zhou

“…Although existing deep learning-based dehazing methods have made significant progress, it is still difficult to completely remove the uneven haze, which often leads to color or structural differences between the dehazed image and the original image. …”
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Robustness of atmospheric trace gas retrievals obtained from low-spectral-resolution Fourier transform infrared absorption spectra under variations of interferogram length by B. Langerock, M. De Mazière, F. Desmet, P. Heikkinen, R. Kivi, M. Kumar Sha, C. Vigouroux, M. Zhou, G. K. Darbha, M. Talib

“…Shortening an interferogram can be part of standard FTIR data processing and typically occurs with a convolution operation on the interferogram. Shortening will alter the leakage pattern in the associated spectrum, and we demonstrate that the removal of a relatively small number of points from the interferogram edges creates a beat pattern in the difference of the associated spectra obtained from the original and shortened interferograms. …”
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A modified deep neural network enables identification of foliage under complex background by Xiaolong Zhu, Junhao Zuo, Honge Ren

“…For the sake of enhancing the identification ability of current network and meeting the needs of the high accuracy of distinguishing similar small objects (foliage) in the complex scenes, this paper proposes a modified region-based fully convolutional network which adopts Inception V3 accompanying with residual connection as the main framework. …”
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On the performance of non‐profiled side channel attacks based on deep learning techniques by Ngoc‐Tuan Do, Van‐Phuc Hoang, Van Sang Doan, Cong‐Kha Pham

“…This paper proposes and evaluates the applications of different DL techniques including the Convolutional Neural Network and the multilayer perceptron models for non‐profiled attacks on the AES‐128 encryption implementation. …”
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