Global Nuclear Explosion Discrimination Using a Convolutional Neural Network by Louisa Barama, Jesse Williams, Andrew V. Newman, Zhigang Peng

“…Even with limited training data, our model can accurately characterize most events recorded at regional and teleseismic distances, finding over 95% signals in the validation set. …”
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Real-Time Super Resolution Utilizing Dilation and Depthwise Separable Convolution by Che-Cheng Chang, Wen-Pin Chen, Yi-Wei Lin, Yu-Jhan Lin, Po-Jui Pan

“…Therefore, we designed a new dilation depthwise super-resolution (DDSR) model that is composed of dilation convolution, depthwise separable convolution, and residual connection, to overcome the predicaments. …”
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Convolutional Edge Constraint-Based U-Net for Salient Object Detection by Le Han, Xuelong Li, Yongsheng Dong

“…An accurate saliency map will be useful for subsequent tasks. However, in most saliency maps predicted by existing models, the objects regions are very blurred and the edges of objects are irregular. …”
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Detecting small seamounts in multibeam data using convolutional neural networks by Tobias Ziolkowski, Colin W. Devey, Agnes Koschmider

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Optimizing quantum convolutional neural network architectures for arbitrary data dimension by Changwon Lee, Israel F. Araujo, Dongha Kim, Junghan Lee, Siheon Park, Ju-Young Ryu, Ju-Young Ryu, Daniel K. Park, Daniel K. Park

“…Quantum convolutional neural networks (QCNNs) represent a promising approach in quantum machine learning, paving new directions for both quantum and classical data analysis. …”
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Sparse Convolution FPGA Accelerator Based on Multi-Bank Hash Selection by Jia Xu, Han Pu, Dong Wang

“…The main contributions are as follows: (1) Most neural network inference tasks are typically executed on general-purpose computing devices, which often fail to deliver high energy efficiency and are not well-suited for accelerating sparse convolutional models. …”
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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

“…Introduction:Given that agriculture has the most important role in ensuring food security (Johnston & Kilby,1989), it is necessary to prepare a map that shows the spatial distribution, land area, and type of crops cultivated with high accuracy (Cai et al., 2018). …”
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Deep spatio-temporal dependent convolutional LSTM network for traffic flow prediction by Jie Tang, Rong Zhu, Fengyun Wu, Xuansen He, Jing Huang, Xianlai Zhou, Yishuai Sun

“…Firstly, for spatial features, most scholars use convolutional neural networks (with fixed kernel size) to capture. …”
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Spatio-temporal transformer and graph convolutional networks based traffic flow prediction by Jin Zhang, Yimin Yang, Xiaoheng Wu, Sen Li

“…Despite substantial progress in this field, several challenges still remain. Firstly, most current methods rely on Graph Convolutional Networks (GCNs) to extract spatial correlations, typically using predefined adjacency matrices. …”
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Automatic melanoma detection using an optimized five-stream convolutional neural network by Vida Esmaeili, Mahmood Mohassel Feghhi, Hadi Seyedarabi

“…We suggest nine planes to grab the most vital information about skin lesions in any direction for accurate coding. …”
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Convolutional neural network for gesture recognition human-computer interaction system design. by Peixin Niu

“…Empirical findings demonstrate that our approach surpasses the accuracy achieved by most lightweight network models on publicly available datasets, all while maintaining real-time gesture interaction capabilities. …”
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Dual Convolution Neural Networks of Ensemble Learning with Attention Mechanism for Rice Classification by Cheng Linfeng

“…Image classification is one of the most classic fields. The aim of this project is to develop a dual convolutional neural network for ensemble learning based on the initial model and the res network model, and apply the ensemble model to the rice classification problem. …”
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Fault diagnosis algorithm based on multi-channel neighbor feature convolutional network by Huang Xiao, Hanqing Jian

“…Therefore, extracting the most representative features from multi-channel data is key to achieving highprecision fault diagnosis.MethodsTo address this issue, this paper proposes a fault diagnosis algorithm based on a multi-channel neighbor feature convolutional network. …”
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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

“…Medical image segmentation plays an important role in medical diagnosis and treatment. Most recent medical image segmentation methods are based on a convolutional neural network (CNN) or Transformer model. …”
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Integrating temporal convolutional networks with metaheuristic optimization for accurate software defect prediction. by Ahmed Abdelaziz, Alia Nabil Mahmoud, Vitor Santos, Mario M Freire

“…This study seeks to determine the most effective model for detecting defects in software projects. …”
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Analysis of the most Important Concepts Related to Social Distancing as A Result of COVID-19 Pandemic: A Review by Hiyam Hashem Saeed, Abdulbasit Alazzawia

“…This paper provides the first review of the substantial focus of convolutional neural networks (CNN)-based techniques. …”
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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

“… Lung cancer is one of the most serious and prevalent diseases, causing many deaths each year. …”
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Prediction Model of Material Properties Based on Feature Fusion and Convolutional Neural Network by SHI Jingchen, LIU Feining, WANG Wenjie, ZHAO Rui

“…Aiming at the problem that most machine learning models need a lot of prior knowledge and manual selection of feature vectors in the prediction of material properties, a convolutional neural network model OPCNN (Orbital of Electron and Periodic table CNN) is established by feature fusion based on two descriptors, electronic orbit matrix and periodic table method. …”
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Classification of Neuropsychiatric Disorders via Brain-Region-Selected Graph Convolutional Network by Zhenzhe Qin, Yongbo Li, Xiaoying Song, Li Chai

“…For the classification of patients with neuropsychiatric disorders based on rs-fMRI data, this paper proposed a Brain-Region-Selected graph convolutional network (BRS-GCN). In order to effectively identify the most significant biomarkers associated with disease, we designed a novel ROI pooling score function. …”
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Deep convolutional neural network model for classifying common bean leaf diseases by Dagne Walle Girmaw, Tsehay Wasihun Muluneh

“…Abstract Common bean is one of the most important crops used by Ethiopian farmers for export and local consumption. …”
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