Geological Stratification Technology Based on Artificial Intelligence Algorithms and Its Application Effects by GAO Yuan, YAO Weihua, CAI Shaofeng, LI Liang, XUE Yuan, ZHAO Peipei, WANG Xiaoyang, WEI Wei

“…Moreover, there are significant differences in stratification among different geological researchers, and the stratification results are unstable. …”
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Electroencephalogram of Happy Emotional Cognition Based on Complex System of Music and Image Visual and Auditory by Lin Gan, Mu Zhang, Jiajia Jiang, Fajie Duan

“…Finally, the collected EEG signals were removed with the eye artifact and baseline drift, and the t-test was used to analyze the significant differences of different lead EEG data. Experimental data shows that, by adjusting the parameters of the convolutional neural network, the highest accuracy of the two-classification algorithm can reach 98.8%, and the average accuracy can reach 83.45%. …”
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Temporal–Spatial Partial Differential Equation Modeling of Land Cover Dynamics via Satellite Image Time Series and Sparse Regression by Ming Kang, Zheng Zhang, Zhitao Zhao, Keli Shi, Junfang Zhao, Ping Tang

“…Using MODIS and Planet satellite data, we demonstrate the effectiveness of the TS-PDE method in predicting the value of the normalized difference vegetation index (NDVI) and interpreting the physical significance of the derived equations. …”
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Overview of Deep Learning Algorithms and Optimizers for Brain Tumor Segmentation by Nisha Purohit, Chandi Prasad Bhatt

“…This review focuses on analyzing different deep learning architectures and explores their performance when optimized using different optimizers. …”
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Unsupervised Anomaly Detection for Volcanic Deformation in InSAR Imagery by Robert Popescu, Nantheera Anantrasirichai, Juliet Biggs

“…To tackle these issues, this paper explores the use of unsupervised deep learning on InSAR images for the purpose of identifying volcanic deformation as anomalies. We test three different state‐of‐the‐art architectures, one convolutional neural network Patch Distribution Modeling (PaDiM) and two generative models (GANomaly and Denoising diffusion probabilistic models (DDPM)). …”
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FruitNet: Lightweight CNN for High-Throughput Image-Based Fruit Yield Estimation by Yadav Kamlesh Kumar, Tandan Gajendra

“…Therefore, in order to ensure that the model is robust to different scenarios the model is trained on a robust dataset involving fruit of different variety, growth stage and under different environmental conditions. …”
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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

“…The models were trained and validated on retinal fundus images and tested on an independent dataset to assess their ability to generalize across different patient populations. Data preprocessing included resizing, normalization, and feature extraction to ensure consistency. …”
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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

“…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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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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TW-YOLO: An Innovative Blood Cell Detection Model Based on Multi-Scale Feature Fusion by Dingming Zhang, Yangcheng Bu, Qiaohong Chen, Shengbo Cai, Yichi Zhang

“…At the same time, utilizing the feature pyramid architecture of YOLO (You Only Look Once), we enhanced the fusion of features at different scales by incorporating the CBAM (Convolutional Block Attention Module) in the detection head and the EMA (Efficient Multi-Scale Attention) module in the neck, thereby improving the recognition ability of blood cells. …”
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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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