Identifying ADHD-Related Abnormal Functional Connectivity with a Graph Convolutional Neural Network by Yilin Hu, Junling Ran, Rui Qiao, Jiayang Xu, Congming Tan, Liangliang Hu, Yin Tian

“…The neural mechanisms underlying ADHD remain inadequately understood, and current approaches do not well link neural networks and attention networks within brain networks. …”
Get full text

Effective Skin Cancer Diagnosis Through Federated Learning and Deep Convolutional Neural Networks by Mabrook S. Al-Rakhami, Salman A. AlQahtani, Abdulaziz Alawwad

“…Early detection is crucial for successful treatment, and deep learning techniques, particularly deep convolutional neural networks (DCNNs), have shown tremendous potential in this area. …”
Get full text

A method of fatigue life prediction for rubber materials based on convolutional neural network by Jiao Li, Qi Li, Feng Wang, Liuxiangzi Yang, Mingshan Lou

“…Aiming at resolving these limitations, a novel fatigue life prediction approach for rubber materials utilizing convolutional neural networks (CNN) was introduced. Dumbbell-shaped rubber specimens, vulcanized from rubber materials, were subjected to uniaxial tensile fatigue tests under varying temperatures, hardness levels, and load conditions. …”
Get full text

Comparative exploration of deep convolutional neural networks using real-time endoscopy images by Subhashree Mohapatra, Pukhraj Singh Jeji, Girish Kumar Pati, Manohar Mishra, Tripti Swarnkar

“…Until now various deep convolutional neural networks are designed and trained for the purpose of classifying different medical conditions related to the domain of gastroenterology. …”
Get full text

Estimation of rebar corrosion level using magnetic sensor array with convolutional neural network by Yuji Ogata, Tomonori Yanagida, Bunichi Kakinuma, Koichiro Kobayashi

“…Moreover, we utilized a convolutional neural network (CNN) to estimate the corrosion level of a rebar with unknown depth. …”
Get full text

Highway subgrade stability prediction model based on depth separation convolutional fusion network by Yubian Wang

“…The method can quickly and effectively identify the adjacent supervoxels in the convolution kernel, effectively reduce the number of parameters of the network, and alleviate the problems of overfitting and long training time to improve computational efficiency. …”
Get full text

Lightweight Multiscale Spatio-Temporal Graph Convolutional Network for Skeleton-Based Action Recognition by Zhiyun Zheng, Qilong Yuan, Huaizhu Zhang, Yizhou Wang, Junfeng Wang

“…Graph Convolutional Networks (GCN) have gained popularity in this discipline due to their capacity to efficiently process graph-structured data. …”
Get full text

Multi-Scale Residual Convolutional Neural Network with Hybrid Attention for Bearing Fault Detection by Yanping Zhu, Wenlong Chen, Sen Yan, Jianqiang Zhang, Chenyang Zhu, Fang Wang, Qi Chen

“…This paper proposes an advanced deep convolutional neural network model for motor bearing fault detection that was designed to overcome the limitations of traditional models in feature extraction, accuracy, and generalization under complex operating conditions. …”
Get full text

Research on the roadheader cutting control system based on convolutional neural network and fuzzy PID by LI Yingna, CUI Yanping, AN Boshuo, LIU Baijian, JIN Jianwei

“…In response to the insufficient adaptability and low system stability of cantilever roadheader when facing changes in coal and rock hardness during tunneling, a roadheader cutting control system based on convolutional neural networks (CNN) and fuzzy PID is proposed. …”
Get full text

Spatial Multifeature and Dual-Layer Multihop Graph Convolution Networks for Hyperspectral Image Classification by Xiangyue Yu, Ning Li, Di Wu, Zheng Li, Zhenyuan Wu, Ximing Ma

“…Hyperspectral image (HSI) classification constitutes a crucial research direction within the domain of remote sensing. Convolutional neural networks (CNNs) and graph convolutional network (GCN) have exhibited outstanding classification performance in this field, emerging as current research focuses. …”
Get full text

Enhanced neurological anomaly detection in MRI images using deep convolutional neural networks by Ahmed Mateen Buttar, Zubair Shaheen, Abdu H. Gumaei, Mogeeb A. A. Mosleh, Mogeeb A. A. Mosleh, Indrajeet Gupta, Samah M. Alzanin, Muhammad Azeem Akbar

“…This study introduces a deep learning framework designed to automate neuro-diagnostics, addressing the limitations of current manual interpretation methods, which are often time-consuming and prone to variability.MethodsWe propose a specialized deep convolutional neural network (DCNN) framework aimed at detecting and classifying neurological anomalies in MRI data. …”
Get full text

Convolutional neural network based on transfer learning for discriminating the fermentation degree of black tea by Xuesong Zhu, Zezhong Ding, Mei Wang, Shanjian Liu, Yulong Chen, Chunwang Dong

“…Based on this, this paper introduces a lightweight convolutional neural network utilizing transfer learning to assess the fermentation level of black tea. …”
Get full text

S-DCNN: prediction of ATP binding residues by deep convolutional neural network based on SMOTE by Sixi Hao, Sixi Hao, Cai-Yan Li, Xiuzhen Hu, Zhenxing Feng, Gaimei Zhang, Caiyun Yang, Huimin Hu

“…As a significant protein-binding ligand, ATP plays a crucial role in various biological processes. Currently, the precise prediction of ATP binding residues remains challenging.MethodsBased on the sequence information, this paper introduces a method called S-DCNN for predicting ATP binding residues, utilizing a deep convolutional neural network (DCNN) enhanced with the synthetic minority over-sampling technique (SMOTE).ResultsThe incorporation of additional feature parameters such as dihedral angles, energy, and propensity factors into the standard parameter set resulted in a significant enhancement in prediction accuracy on the ATP-289 dataset. …”
Get full text

Establishment and Test Effect of Artificial Intelligence Optimization Model Based on Convolutional Neural Network by Chunrong Zhou, Zhenghong Jiang

“…Convolutional neural networks (CNNs) are often used in tasks involving vision processing, and unclear images can hinder the performance of convolutional neural networks and increase its computational time. …”
Get full text

Deep Unified Model For Face Recognition Based on Convolution Neural Network and Edge Computing by Muhammad Zeeshan Khan, Saad Harous, Saleet Ul Hassan, Muhammad Usman Ghani Khan, Razi Iqbal, Shahid Mumtaz

“…This paper proposes an algorithm for face detection and recognition based on convolution neural networks (CNN), which outperform the traditional techniques. …”
Get full text

Region search based on hybrid convolutional neural network in optical remote sensing images by Shoulin Yin, Ye Zhang, Shahid Karim

“…First, fully convolutional network is adopted to produce all the candidate regions that contain the possible object regions. …”
Get full text

Automatic Fault Classification in Photovoltaic Modules Using Denoising Diffusion Probabilistic Model, Generative Adversarial Networks, and Convolutional Neural Networks by Carlos Roberto da Silveira Junior, Carlos Eduardo Rocha Sousa, Ricardo Henrique Fonseca Alves

“…Deep convolutional neural networks (CNNs) are machine learning algorithms that perform tasks involving images, such as image classification and object recognition. …”
Get full text

Capturing Global Structural Features and Global Temporal Dependencies in Dynamic Social Networks Using Graph Convolutional Networks for Enhanced Analysis by Ling Wu, Boen Li, Kun Guo, Qishan Zhang

“…Graph convolutional networks (GCNs) are widely used for learning social network embeddings and social network analysis. …”
Get full text

Alternating current servo motor and programmable logic controller coupled with a pipe cutting machine based on human-machine interface using dandelion optimizer algorithm - attention pyramid convolution neural network by Santosh Prabhakar Agnihotri, Mandar Padmakar Joshi

Get full text

Revealing job accessibility supported by public transit services using a graph convolutional network by Meihan Jin, Leiyu Liu, Yongxi Gong, Yu Liu, Christophe Claramunt

“…A graph convolutional network clustering method is applied to reveal the network characteristics of the public transit supportability for job accessibility. …”
Get full text