Load aggregator adjustable capability forecasting based on graph convolution neural network by DONG Lingrui, WU Binyuan

“…An undirected graph is established, whose nodes are different clusters, edges are the response characteristics correlation among clusters, and node characteristic matrix is the response characteristics of each cluster. …”
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Spatio-temporal transformer and graph convolutional networks based traffic flow prediction by Jin Zhang, Yimin Yang, Xiaoheng Wu, Sen Li

“…In the spatial dimension, the model incorporates a spatial embedding module and a multi-graph convolutional module. The former is designed to learn traffic characteristics of different nodes, and the latter is used to extract spatial correlations effectively from multiple graphs. …”
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Learning super-resolution and pyramidal convolution residual network for vehicle re-identification by Mengxue Liu, Weidong Min, Qing Han, Hongyue Xiang, Meng Zhu

“…Then, multi levels of pyramidal convolution operations are designed to generate multi-scale features, which can capture information on different scales. …”
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Development and evaluation of machine learning models for premixed flame classification in different hydrogen-natural gas proportions using images and audio by Pedro Narvaez, Alejandro Lopez, Jousef E. Karam, Alejandro Restrepo, Andrés A. Amell

“…This study presents a novel approach for the automatic classification of flames in different hydrogen-natural gas mixtures using machine learning techniques. …”
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Novel similarity calculation method of multisource ontology based on graph convolution network by Liuqian SUN, Yuliang WEI, Bailing WANG

“…In the information age, the amount of data is growing exponentially.However, different data sources are heterogeneous, which makes it inconvenient to share and multiplex data.With the rapid development of semantic network, ontology mapping is an effective method to solve this problem.The core of ontology mapping is ontology similarity calculation.Therefore, a calculation method based on graph convolution network was proposed.Firstly, ontologiesare modeled as a heterogeneous graph network, then the graph convolution network was used to learn the text embedding rules, which made ontologies were definedin global unified representation.Lastly, multisource data fusion was completed.The experimental results show that the accuracy of the proposed method is higher than other methods, and the accuracy of multi-source data fusion was effectively improved.…”
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DDoS Attacks Detection With Deep Learning Approach Using Convolutional Neural Network by Rafiq Amalul Widodo, Mera Kartika Delimayanti, Asri Wulandari

“…The research employed a deep learning approach utilizing a Convolutional Neural Network (CNN) on a publicly available dataset. …”
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Voice activity detection in noisy conditions using tiny convolutional neural network by R. S. Vashkevich, E. S. Azarov

“…An extremely compact convolutional neural network is proposed. The model has only 385 trainable parameters. …”
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Applications of Convolutional Neural Network for Classification of Land Cover and Groundwater Potentiality Zones by Asnakew Mulualem Tegegne

“…In the field of groundwater engineering, a convolutional neural network (CNN) has become a great role to assess the spatial groundwater potentiality zones and land use/land cover changes based on remote sensing (RS) technology. …”
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Recognition of Industrial Spare Parts Using an Optimized Convolutional Neural Network Model by Chandralekha Mohan, Takfarinas Saber, Priyadharshini Jayadurga Nallathambi

“…In this article, a novel Deep Learning-based object recognition model based on a convolutional neural network architecture is proposed and constructed using stacked convolutional layers to extract and learn features of the spare parts efficiently with the goal of improving the effectiveness of the spare part image recognition process. …”
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SA-UMamba: Spatial attention convolutional neural networks for medical image segmentation. by Lei Liu, Zhao Huang, Shuai Wang, Jun Wang, Baosen Liu

“…Most recent medical image segmentation methods are based on a convolutional neural network (CNN) or Transformer model. …”
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Imbalanced Data Parameter Optimization of Convolutional Neural Networks Based on Analysis of Variance by Ruiao Zou, Nan Wang

“…This study primarily uses analysis of variance (ANOVA) to investigate the main and interaction effects of different parameters on imbalanced data, aiming to optimize convolutional neural network (CNN) parameters to improve minority class sample recognition. …”
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Classification of Neuropsychiatric Disorders via Brain-Region-Selected Graph Convolutional Network by Zhenzhe Qin, Yongbo Li, Xiaoying Song, Li Chai

“…Additionally, we also designed a comprehensive loss function, including a group-level consistency loss function for preserving the same brain regions in subjects of the same category, and an anti-consistency function for maximizing brain region preservation differences between subjects of different categories. …”
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An Observation and Analysis the role of Convolutional Neural Network towards Lung Cancer Prediction by Suranjana Mitra, Annwesha Banerjee Majumder, Tanusree Saha

“…Machine learning and AI-based models can identify and classify types of lung cancer quite accurately, which helps in the early-stage detection of lung cancer that can increase the survival rate. In this paper, Convolutional Neural Network is used to classify Adenocarcinoma, squamous cell carcinoma and normal case CT scan images from the Chest CT Scan Images Dataset using different combinations of hidden layers and parameters in CNN models. …”
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CST-Net: community-guided structural-temporal convolutional networks for popularity prediction by Xuxu Zheng, Peng Bao, Lin Qi, Chen Tian, Huawei Shen

“…We validate the effectiveness of the proposed CST-Net by applying it on two different types of population-scale datasets, i.e., a microblogging dataset and an academic citation dataset. …”
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Deep convolutional neural network model for classifying common bean leaf diseases by Dagne Walle Girmaw, Tsehay Wasihun Muluneh

“…However, the quality and quantity of this crop are heavily affected by different leaf diseases and affect crop growth. Currently, common bean disease detection is performed through expert visual observation. …”
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Dynamic graph convolutional networks with Temporal representation learning for traffic flow prediction by Aihua Zhang

“…Specifically, a temporal graph convolution block is specifically devised, treating historical time slots as graph nodes and employing graph convolution to process dynamic time series. …”
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Advancing semantic segmentation: Enhanced UNet algorithm with attention mechanism and deformable convolution. by Effat Sahragard, Hassan Farsi, Sajad Mohamadzadeh

“…This finding highlights the importance of exploring different attention mechanisms and their impact on segmentation performance. …”
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CloudNet: Ground‐Based Cloud Classification With Deep Convolutional Neural Network by Jinglin Zhang, Pu Liu, Feng Zhang, Qianqian Song

“…Abstract Clouds have an enormous influence on the Earth's energy balance, climate, and weather. Cloud types have different cloud radiative effects, which is an essential indicator of the cloud effect on radiation. …”
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Image super-resolution reconstruction network combining asymmetric convolution and feature distillation by ZHU Lei, FENG Da, ZHU Qiwei, ZHAO Han, WANG Qianqian

“…In order to further improve the image reconstruction effect of single image super-resolution (SISR) lightweight network, based on lightweight network RFDN, an image super-resolution reconstruction network combining asymmetric convolution and feature distillation was proposed. Firstly, asymmetric convolution was used to construct a feature extraction module, the asymmetric convolution of two different convolution kernels in parallel in the residual block enhances the feature extraction capability of the network. …”
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Diagnosis of abnormal sound in loudspeakers by integrated attention mechanism convolutional neural network by ZHOU Jinglei, WANG Xiaoming, LI Limin

“…Firstly, different types of abnormal sound signals were collected, and VMD was used to decompose the signals and extract the features of speaker abnormal sound, constructing labeled initial data. …”
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