Deep Learning Algorithm for Keratoconus Detection from Tomographic Maps and Corneal Biomechanics: A Diagnostic Study by Wiyada Quanchareonsap, Ngamjit Kasetsuwan, Usanee Reinprayoon, Yonrawee Piyacomn, Thitima Wungcharoen, Monthira Jermjutitham

“…The sample size was divided into the Pentacam and combined Pentacam-Corvis groups. Different convolutional neural network approaches were used to enhance the KC and subclinical KC detection performance. …”
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A strategy for network multi-layer information fusion based on multimodel in user emotional polarity analysis by Ronghua Wang, Peng Zhuang

“…Then the emotional analysis results of different models were integrated to improve accuracy through a hierarchical information fusion strategy. …”
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6D Pose Estimation Algorithm Based on Improved YOLOv5 With Asymptotic Feature Pyramid Network and Attention Mechanism by Yan Zhang, Hanyu Ye

“…We enhance the original convolutional neural network model by introducing position-aware channel attention into the backbone network, increasing flexibility and precision in feature processing across different positions. …”
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RMVAD-YOLO: A Robust Multi-View Aircraft Detection Model for Imbalanced and Similar Classes by Keda Li, Xiangyue Zheng, Jingxin Bi, Gang Zhang, Yi Cui, Tao Lei

“…First, we propose a novel Robust Multi-Link Scale Interactive Feature Pyramid Network (RMSFPN), which robustly extracts features of the same aircraft category from multiple views while enhancing feature differentiation between different aircraft categories. Second, we propose the Shared Convolutional Dynamic Alignment Detection Head (SCDADH), which enhances task interaction and collaboration by sharing convolutions between the classification and localization branches while simultaneously reducing the number of parameters, enhancing the model’s ability to deal with multi-scale targets. …”
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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

“…Furthermore, a long short-term memory (LSTM) gate mechanism is employed to predict power equipment target features at different levels of image feature information, constructing an attention mechanism network based on the LSTM gating mechanism. …”
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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

“…Various Transformer-based networks have shown significant performance advantages over mainstream neural networks in different visual tasks, demonstrating the huge potential of Transformers in the field of image segmentation. …”
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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

“…It is more targeted and can effectively improve the fusion effect of visible images and infrared images under different lighting conditions. Ablation experiments demonstrate the effectiveness of the loss function. …”
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XCF-LSTMSATNet: A Classification Approach for EEG Signals Evoked by Dynamic Random Dot Stereograms by Tingting Zhang, Xu Yan, Xin Chen, Yi Mao

“…Stereovision is the visual perception of depth derived from the integration of two slightly different images from each eye, enabling understanding of the three-dimensional space. …”
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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

“…This proposed method was evaluated on two different datasets. The first one is 20 publically available 3D spine MRI dataset to perform disc segmentation and the second one is 173 computed tomography scans for thoracolumbar (thoracic and lumbar) vertebrae segmentation. …”
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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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Accurate, automated classification of radiographic knee osteoarthritis severity using a novel method of deep learning: Plug-in modules by Do Weon Lee, Dae Seok Song, Hyuk-Soo Han, Du Hyun Ro

“…The final deep learning model was designed through an ensemble of four different PIMs. Results The accuracy was the lowest for KL grade 1 (43%) and the highest for KL grade 4 (96%). …”
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Application of CycleGAN-based low-light image enhancement algorithm in foreign object detection on belt conveyors in underground mines by Anxin Zhao, Qiuhong Zheng, Liang Li

“…Second, a lighting enhancement module is designed to achieve global brightness equalization and reduce contrast differences between different regions. Finally, a dynamic effective self-attention aggregation module is designed to suppress the generation of noise and artifacts. …”
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Keywords, morpheme parsing and syntactic trees: features for text complexity assessment by Dmitry A. Morozov, Ivan A. Smal, Timur A. Garipov, Anna V. Glazkova

“…We conducted a comparison using four different machine learning algorithms and four annotated Russian-language text corpora. …”
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Diverse behavior clustering of students on campus with macroscopic attention by Wanghu Chen, Zongjuan Wu, Siqi Zeng, Hongle Guo, Jing Li

“…Since cognitive psychology believes that all behaviors can be regarded as attention to different objects, this paper proposes an analysis framework based on Macroscopic Attention (MA) to characterize the diverse behavior of individuals. …”
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4D trajectory lightweight prediction algorithm based on knowledge distillation technique by Weizhen Tang, Jie Dai, Zhousheng Huang, Boyang Hao, Weizheng Xie

“…The student network adopts a Temporal Convolutional Network–LSTM (TCN–LSTM) design, integrating dilated causal convolutions and two LSTM layers for efficient temporal modeling. …”
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DRGNet: Enhanced VVC Reconstructed Frames Using Dual-Path Residual Gating for High-Resolution Video by Zezhen Gai, Tanni Das, Kiho Choi

“…The proposed method is built upon a high-resolution dual-path residual gating system, which integrates deep features from different convolutional layers and introduces convolutional blocks equipped with gating mechanisms. …”
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