Load recognition method based on convolutional neural network and attention mechanism by ZHAO Yitao, LI Zhao, LIU Xinglong, LUO Zhao, WANG Gang, SHEN Xin

“…Firstly, the power data of eight different household appliances are collected to establish a U-I trajectory curve database. …”
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Multiscale network alignment model based on convolution of homogeneous multilayer graphs by CUI Jiahao, JIANG Tao, XU Mengyao

“…In terms of node characteristics, the K-nearest neighbor algorithm was used to aggregate node neighborhood information to model the deep network structure, so as to enhance the data. In terms of graph convolution, the convolution process was guided by the construction of a homogeneity matrix according to the network homogeneity, and the social networks of different scales were processed based on the network community structure. …”
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Meta-path convolution based heterogeneous graph neural network algorithm by QIN Zhilong, DENG Kun, LIU Xingyan

“…In the multilayer graph convolution calculation, each node is usually represented as a single vector, which makes the high-order graph convolution layer unable to distinguish the information of different relationships and sequences, resulting in the loss of information in the transmission process. …”
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Using convolutional neural networks for image semantic segmentation and object detection by Shuangmei Li, Chengning Huang

“…Convolutional neural networks are widely used for feature extraction in the fields of object detection and image segmentation. …”
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Convolutional Residual-Attention: A Deep Learning Approach for Precipitation Nowcasting by Qing Yan, Fuxin Ji, Kaichao Miao, Qi Wu, Yi Xia, Teng Li

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Yoga pose recognition using dual structure convolutional neural network by Xiang Meng, Zhaobing Liu

“…With the development of deep learning technologies, automatic recognition of yoga postures has become popular. To recognize five different yoga postures, this article proposed a dual structure convolutional neural network with a feature fusion function, which consists of the convolutional neural network A (CNN A) and convolutional neural network B (CNN B). …”
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Identification of DNS covert channel based on improved convolutional neural network by Meng ZHANG, Haoliang SUN, Peng YANG

“…In order to effectively identify the multiple types of DNS covert channels,the implementation of different sorts of DNS covert channel software was studied,and a detection based on the improved convolutional neural network was proposed.The experimental results,grounded upon the campus network traffic,show that the detection can identify twenty-two kinds of data interaction modes of DNS covert channels and is able to identify the unknown DNS covert channel traffic.The proposed method outperforms the existing methods.…”
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Detection algorithm of electronic disguised voice based on convolutional neural network by Hongwei XU, Diqun YAN, Fan YANG, Rangding WANG, Chao JIN, Li XIANG

“…An electronic disguised voice detection algorithm based on the statistical features of MFCC and the convolution neural network was proposed.Firstly,the statistical features of MFCC were extracted and reconstructed as the input of convolution neural network.Considering the convolution kernel size,the number of convolution kernels and the pooling size,24 different network structures were evaluated in this work.Finally,the convolution neural network structure which could be effectively used for electronic disguised voice detection was determined.The experimental results show that the proposed algorithm can effectively detect the trace of electronic disguising.Meanwhile,the specific forgery operation of the electronic disguised voice can also be estimated.…”
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Research on sketch instruction recognition technology ased on convolutional neural network by ZHOU Yu, ZHANG Jingtao, LI Ligang, ZHANG Xiaoyan

“…L2 regularization constraint weight scale and Dropout randomly inactivated neurons complement each other, which can improve the accuracy of the model in recognizing different sketch instructions and improve the human-computer interaction experience.…”
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Modulation recognition method based on multiscale convolutional fusion coding networks by LI Guojun, ZHU Siyuan, ZHENG Jianzhong, WANG Jie, YE Changrong

“…Experimental results demonstrated the model's robust stability and generalization capability, showing minimal performance variation under different test batch sizes with fixed training batches. …”
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Crop classification with deep convolutional neural network based on crop feature by Mohamad Reza Gili, Davoud Ashourloo, Hosein Aghighi, Ali Akbar Matkan, Alireza SHakiba

“…Then the algorithm was implemented using these feature channels in the test area and the overall accuracy was upgraded to 86% and the kappa coefficient to 0.82 compared to which indicated a significant improvement in the results compared to the previous case.Conclusion:The deep convolutional neural network is very sensitive to the type of input channels for detecting agricultural crops and selecting the channels with suitable tempo-spectral characteristics for different types of crops, has a great impact on the accuracy of network training and can reduce the loss of training network and increase its efficiency in the classification of various crops.…”
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Deep Convolutional Neural Networks in Medical Image Analysis: A Review by Ibomoiye Domor Mienye, Theo G. Swart, George Obaido, Matt Jordan, Philip Ilono

“…Deep convolutional neural networks (CNNs) have revolutionized medical image analysis by enabling the automated learning of hierarchical features from complex medical imaging datasets. …”
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Image Recognition Based on Multiscale Pooling Deep Convolution Neural Networks by Haitao Sang, Li Xiang, Shifeng Chen, Bo Chen, Li Yan

“…To achieve this, through making full use of block information of different sizes and scales in the image, a multiscale pooling deep convolution neural network model is designed in this paper. …”
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Klasifikasi Penyakit Pneumonia menggunakan Metode Convolutional Neural Network (CNN) by irfan office

“…Research methods include literature reviews, data collection, pre-processing, modeling, training, testing, and evaluation using metrics such as accuracy, precision, recall, and F1 scores. Experiments with different epochs yield 99% accurate training data, 81% accurate validation data, and a lack of learning on models that influence accurability on validation. …”
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A point cloud segmentation network with hybrid convolution and differential channels by Xiaoyan Zhang, Yantao Bu

“…For this reason, we propose a 3D segmentation network based on hybrid convolution and differential channels. Specifically, we design a hybrid convolutional feature extraction (HCFE) module for processing 3D semantic information and spatial information independently, using different convolution kernels to obtain the subtle geometric structure differences between points. …”
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Dynamic Hypergraph Convolutional Networks for Hand Motion Gesture Sequence Recognition by Dong-Xing Jing, Kui Huang, Shi-Jian Liu, Zheng Zou, Chih-Yu Hsu

“…The T-Module, a multiscale temporal convolution module, aggregates features from multiple frames to capture gesture dynamics across different time scales. …”
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FOV Expansion of Bioinspired Multiband Polarimetric Imagers With Convolutional Neural Networks by Yongqiang Zhao, Miaomiao Wang, Guang Yang, Jonathan Cheung-Wai Chan

“…Motivated by the vision mechanism of some known aquatic insects, we construct a bioinspired multiband polarimetric imaging system using a camera array, which simultaneously captures multiple images of different spectral bands and polarimetric angles. But the disparity between the fixed positions of each component camera leads to the loss of information in the boundary region and a reduction in the field of view (FOV). …”
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Deep Communication: Exploring End-to-End Wireless with Convolutional Neural Network by Sumaira Mushtaq, Bahman R Alyaeia, Momna Sultan, Janshair Khan

“…We then evaluate the performance of our proposed method by extensive sets of simulations in different AWGN, Nakagami fading, and MIMO fading channel scenarios. …”
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Triple Graph Convolutional Network for Hyperspectral Image Feature Fusion and Classification by Maryam Imani, Daniele Cerra

“…These graphs are processed using graph convolutional networks, and their results are fused using a cross-attention mechanism. …”
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Runx1 mediates the development of the granular convoluted tubules in the submandibular glands. by Hitomi Ono Minagi, Safiye Esra Sarper, Hiroshi Kurosaka, Koh-Ichi Kuremoto, Ichiro Taniuchi, Takayoshi Sakai, Takashi Yamashiro

“…Moreover, the Runx genes have different temporospatial requirements depending on the biological situation. …”
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