Improvement of a Subpixel Convolutional Neural Network for a Super-Resolution Image by Muhammed Fatih Ağalday, Ahmet Çinar

“…In our proposed model, convolution layers are added to the efficient subpixel convolutional neural network (ESPCN) model, and in order to prevent the lost gradient value, we transfer the feature information of the current layer from the previous layer to the next upper layer. …”
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Few-shot traffic classification based on autoencoder and deep graph convolutional networks by Shengwei Xu, Jijie Han, Yilong Liu, Haoran Liu, Yijie Bai

“…As graph convolutional networks (GCNs) take into account not only the features of the data itself, but also the relationships among sets of data during classification. …”
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Optimization of Convolutional Neural Networks With Multi-Objective Function Metaheuristics for Melanoma Detection by Pamela Hermosilla, Ricardo Soto, Eric Monfroy, Emanuel Vega, Lucas Erazo, Valentina Guzman

“…Early and accurate detection of melanoma remains a critical challenge in medical imaging. Convolutional Neural Networks (CNNs) have demonstrated superior classification performance, often surpassing dermatologists in diagnostic accuracy. …”
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Coupled Spatial-Spectral Constrained Convolutional Fusion Network for Hyperspectral and Panchromatic images by Jingwei Chen

“…Therefore, a fusion method of HSI and panchromatic image (PAN) based on coupled spatial-spectral constrained convolution neural network is proposed in this paper to improve the spatial resolution of HSI and reduce the spectral distortion. …”
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Deep convolutional neural network model for classifying common bean leaf diseases by Dagne Walle Girmaw, Tsehay Wasihun Muluneh

“…As a result, in this paper, a novel deep convolutional neural network model is proposed for the automatic identification of common bean leaf diseases. …”
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Deep Learning-Based Interference Fringes Detection Using Convolutional Neural Network by Haowei Li, Chunxi Zhang, Ningfang Song, Huipeng Li

“…The FRPN, a deep convolutional neural network modified on Faster R-CNN, accurately recognizes the fringes with identification boxes. …”
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Rumor detection using dual embeddings and text-based graph convolutional network by Barsha Pattanaik, Sourav Mandal, Rudra M. Tripathy, Arif Ahmed Sekh

“…However, this often leads to the spread of unreliable or false information, such as harmful rumors. Currently, graph convolutional networks (GCNs), particularly TextGCN, have shown promise in text classification tasks, including rumor detection. …”
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Convolutional neural network model over encrypted data based on functional encryption by Chen WANG, Jiarun LI, Jian XU

“…Currently, homomorphic encryption, secure multi-party computation, and other encryption schemes are used to protect the privacy of sensitive data in outsourced convolutional neural network (CNN) models.However, the computational and communication overhead caused by the above schemes would reduce system efficiency.Based on the low cost of functional encryption, a new convolutional neural network model over encrypted data was constructed using functional encryption.Firstly, two algorithms based on functional encryption were designed, including inner product functional encryption and basic operation functional encryption algorithms to implement basic operations such as inner product, multiplication, and subtraction over encrypted data, reducing computational and communication costs.Secondly, a secure convolutional computation protocol and a secure loss optimization protocol were designed for each of these basic operations, which achieved ciphertext forward propagation in the convolutional layer and ciphertext backward propagation in the output layer.Finally, a secure training and classification method for the model was provided by the above secure protocols in a module-composable way, which could simultaneously protect the confidentiality of user data as well as data labels.Theoretical analysis and experimental results indicate that the proposed model can achieve CNN training and classification over encrypted data while ensuring accuracy and security.…”
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Hand-aware graph convolution network for skeleton-based sign language recognition by Juan Song, Huixuechun Wang, Jianan Li, Jian Zheng, Zhifu Zhao, Qingshan Li

“…Skeleton-based sign language recognition (SLR) is a challenging research area mainly due to the fast and complex hand movement. Currently, graph convolution networks (GCNs) have been employed in skeleton-based SLR and achieved remarkable performance. …”
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Advancements in Plant Pests Detection: Leveraging Convolutional Neural Networks for Smart Agriculture by Gopalakrishnan Nagaraj, Dakshinamurthy Sungeetha, Mohit Tiwari, Vandana Ahuja, Ajit Kumar Varma, Pankaj Agarwal

“…We developed a convolutional neural network (CNN)-based framework for identifying pest-borne diseases in tomato leaves using the Plant Village Dataset and the MobileNetV2 architecture. …”
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A novel recommender system using light graph convolutional network and personalized knowledge-aware attention sub-network by Rasoul Hassanzadeh, Vahid Majidnezhad, Bahman Arasteh

“…One of the contributions of the proposed method is to use a novel integration of light graph convolutional network (LightGCN) in RSs to efficiently manage common signals for user and item embeddings. …”
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Multimodal data fusion for Alzheimer's disease based on dynamic heterogeneous graph convolutional neural network and generative adversarial network by Xiaoyu Chen, Shuaiqun Wang, Wei Kong

“…To address these challenges, we propose a multi-modal data fusion method based on a Dynamic Heterogeneous Attention Network (DHAN) and Generative Adversarial Networks (GAN). …”
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Elephant Flows Detection Using Deep Neural Network, Convolutional Neural Network, Long Short-Term Memory, and Autoencoder by Getahun Wassie Geremew, Jianguo Ding

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An efficient approach for diagnosing faults in photovoltaic array using 1D-CNN and feature selection Techniques by Yousif Mahmoud Ali, Lei Ding, Shiyao Qin

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Optimization of deep learning architecture based on multi-path convolutional neural network algorithm by Chuan Zhou, Yan Liu, Xinghan An, Xiyao Xu, Hao Wang

“…Abstract Current multi-stream convolutional neural network (MSCNN) exhibits notable limitations in path cooperation, feature fusion, and resource utilization when handling complex tasks. …”
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Integration of unpaired single cell omics data by deep transfer graph convolutional network. by Yulong Kan, Yunjing Qi, Zhongxiao Zhang, Xikeng Liang, Weihao Wang, Shuilin Jin

“…Here, we present a robust deep transfer model based graph convolutional network, scTGCN, which achieves versatile performance in preserving biological variation, while achieving integration hundreds of thousands cells in minutes with low memory consumption. …”
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PhyICNet: Physics-informed interactive learning convolutional recurrent network for spatiotemporal dynamics by Ruohan Cao, Jin Su, Jinqian Feng, Qin Guo

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Advancements in the Application of Convolutional Neural Networks in Ultrasound Imaging for Breast Cancer Diagnosis and Treatment by An Zichen, Li Fan

“…This review focuses on the application of convolutional neural networks (CNNs) within DL technology in the field of breast US. …”
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An Extensive Study of Convolutional Neural Networks: Applications in Computer Vision for Improved Robotics Perceptions by Ravi Raj, Andrzej Kos

“…Convolutional neural networks (CNNs), a type of artificial neural network (ANN) in the deep learning (DL) domain, have gained popularity in several computer vision applications and are attracting research in other fields, including robotic perception. …”
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MagNet: Automated Magnetic Mineral Grain Morphometry Using Convolutional Neural Network by Zhaowen Pei, Liao Chang, Pengfei Xue, Richard J. Harrison

“…This framework, based on a convolutional neural network, performs well in the recognition and classification of magnetofossil nanoparticles in transmission electron microscopy images after training and testing. …”
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