Robust low frequency seismic bandwidth extension with a U-net and synthetic training data by P. Zwartjes, J. Yoo

“…This work focuses on enhancing low frequency seismic data using a convolutional neural network trained on synthetic data. …”
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Machine Learning Monitoring Model for Fertilization and Irrigation to Support Sustainable Cassava Production: Systematic Literature Review by Ahmad Chusyairi, Yeni Herdiyeni, Heru Sukoco, Edi Santosa

“…Important new information on the application of UAV technology, multispectral imaging, thermal imaging, among the vegetation indices are the Soil-Adjusted Vegetation Index (SAVI), Leaf Color Index (LCI), Leaf Area Index (LAI), Normalized Difference Water Index (NDWI), Normalized Difference Red Edge Index (NDRE), and Green Normalized Difference Vegetation Index (GNDVI).…”
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Virtual Reality Video Image Classification Based on Texture Features by Guofang Qin, Guoliang Qin

“…As one of the most widely used methods in deep learning technology, convolutional neural networks have powerful feature extraction capabilities and nonlinear data fitting capabilities. …”
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A novel deep learning approach for predicting stone-free rates post-ESWL on uncontrasted CT by Ozgur Efiloglu, Muhammed Yildirim, Kadir Yildirim, Harun Bingol, Mustafa Kaan Akalin, Meftun Culpan, Bilal Alatas, Asif Yildirim

“…Results were obtained from seven different convolutional neural networks (CNNs) and two textural-based models in the study. …”
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Application analysis of computer vision and image recognition based on improved VGG16 network by Xuanzhang Zhu, Yafei Li

“…The proposed model achieves a recognition accuracy of 0.971 when recognizing images of different categories, significantly higher than other models. …”
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A Latent Multi-Scale Residual Transformer Approach for Cross-Modal Medical Image Synthesis by Xinmiao Zhu, Yang Li

“…This module consists of two layers of residual convolutional blocks and transformer blocks of different scales, where the transformer blocks assist the convolutional blocks in capturing contextual features, and lower-level blocks support higher-level blocks in learning high-dimensional global information. …”
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Non-stationary signal combined analysis based fault diagnosis method by Zhe CHEN, Yuqi HU, Shiqing TIAN, Huimin LU, Lizhong XU

“…Considering the complementarity between the deep learning,spectrum and time frequency analysis methods,a multi-stream framework was designed by combining the convolutional network,Fourier transform and wavelet package decomposition methods,with the aim to analyze the non-stationary signal.Accordingly,a none-stationary signal combined analysis based fault diagnosis method was proposed to extract features in difference aspects.The fault diagnosis experiments demonstrate that the combined analysis method can efficiently and stably depict the fault and significantly improve the performance of fault diagnosis.…”
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PBD-YOLO: Dual-Strategy Integration of Multi-Scale Feature Fusion and Weak Texture Enhancement for Lightweight Particleboard Surface Defect Detection by Haomeng Guo, Zheming Chai, Huize Dai, Lei Yan, Pengle Cheng, Jianhua Yang

“…In order to improve the ability of the algorithm to extract weak texture features, the SPDDEConv (Space to Depth and Difference Enhance Convolution) module was introduced in this study, which reduced the loss of information in the down-sampling process through space-to-depth transformation and enhanced the edge information of weak texture defects through difference convolution. …”
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Maize quality detection based on MConv-SwinT high-precision model. by Ning Zhang, Yuanqi Chen, Enxu Zhang, Ziyang Liu, Jie Yue

“…Concurrently, the extracted features undergo further processing through a specially designed convolutional block. The fused features, combined with those processed by the convolutional module, are fed into an attention layer. …”
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Detection of Substation Pollution in District Heating and Cooling Systems: A Comprehensive Comparative Analysis of Machine Learning and Artificial Neural Network Models by Emrah ASLAN, Yıldırım ÖZÜPAK

“…Within the scope of the analysis, machine learning algorithms such as K-Nearest Neighbors, XGBoost and AdaBoost are compared with the proposed Convolutional Neural Network (CNN) model. The machine learning algorithms and the Convolutional Neural Network model are trained to perform fault detection at different contamination levels. …”
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Comparison of Deep Learning-Based Auto-Segmentation Results on Daily Kilovoltage, Megavoltage, and Cone Beam CT Images in Image-Guided Radiotherapy by Zhixing Wang PhD, Chengyu Shi PhD, Carson Wong BS, Seyi M Oderinde PhD, William T Watkins PhD, Kun Qing PhD, Bo Liu PhD, Terence M Williams MD, PhD, An Liu PhD, Chunhui Han PhD

“…Introduction This study aims to evaluate auto-segmentation results using deep learning-based auto-segmentation models on different online CT imaging modalities in image-guided radiotherapy. …”
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Recognizing Special Art Pieces Through EEG: A Journey in Neuroaesthetics Classification by Maurizio Palmieri, Marco Avvenuti, Francesco Marcelloni, Alessio Vecchio

“…The first two methods exploit classical machine learning approaches based on various sets of features extracted using different techniques for EEG analysis. In particular, the first method analyzes features extracted from time and frequency domains using an ensemble classifier, while the second one analyzes the Phase Locking Values of different channels using a classifier based on K-Nearest Neighbors. …”
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Deep Learning-Based Carotid Plaque Ultrasound Image Detection and Classification Study by Hongzhen Zhang, Feng Zhao

“…Background: This study aimed to develop and evaluate the detection and classification performance of different deep learning models on carotid plaque ultrasound images to achieve efficient and precise ultrasound screening for carotid atherosclerotic plaques. …”
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Large-scale tobacco identification via a very-high-resolution unmanned aerial vehicle benchmark and a ConvFlow Transformer by Wei Han, Shaohao Chen, Shuanglin Xiao, Yunliang Chen, Huihui Zhao, Jining Yan, Xiaohan Zhang, Sheng Wang

“…Then, a dual-branch ConvFlow Transformer is proposed to address tobacco’s rich diversity and high inter-class similarity among different crops. A novel Convolutional Feature-enhanced Multi-Head Self-attention (CF-MHSA) with a location-free design in the ConvFlow Transformer is developed to replace the value matrix in the standard attention with the convolutional multi-scale features, which effectively achieves feature interaction and fusion from the convolutional and transformer branches. …”
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BCDnet: Parallel heterogeneous eight-class classification model of breast pathology. by Qingfang He, Guang Cheng, Huimin Ju

“…Two convolutional bases (VGG16 convolutional base and Resnet50 convolutional base) obtain breast tissue image features from different fields of view. …”
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Research on leaf identification of table grape varieties based on deep learning by PAN Bowen, LIN Meiling, JU Yanlun, SU Baofeng, SUN Lei, FAN Xiucai, ZHANG Ying, ZHANG Yonghui, LIU Chonghuai, JIANG Jianfu, FANG Yulin

“…The front images of different leaves were taken, and a dataset of 29 713 fresh grape leaves was constructed. …”
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Vocal performance evaluation of the intelligent note recognition method based on deep learning by Dongyun Chang

“…The accuracy of the model under different feature inputs is compared. The results indicate that different models show obvious differences in F-value, accuracy, precision, and recall. …”
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INFORMATION TECHNOLOGY FOR RECOGNITION OF ROAD SIGNS USING A NEURAL NETWORK by Elena Yashina, Roman Artiukh, Nikolai Рan, Andrei Zelensky

“…The process of interaction of the system with different data sources is represented by a diagram of precedents. …”
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Research on the Construction of Crossborder e-Commerce Logistics Service System Based on Machine Learning Algorithms by Jinbo Xu, Shibiao Mu

“…At the same time, it also compared the effect of the time series model, random forest model, GBDT, single Xgboost model, and the model used in this topic and analyzed the reasons for this difference and the application of each model.…”
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Multi-Step Peak Passenger Flow Prediction of Urban Rail Transit Based on Multi-Station Spatio-Temporal Feature Fusion Model by Jianan Sun, Xiaofei Ye, Xingchen Yan, Tao Wang, Jun Chen

“…A combination of a graph convolutional neural network and a Transformer is used. …”
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