Research on lightweight non-intrusive load disaggregation model for edge computing by YE Canshen, LUO Dehan, HE Jiafeng

“…In addition, the design of different decoders is investigated in this paper, and the depthwise separable convolution is used to improve the residual block in the upsampling layer and reduce the number of kernels in the convolution layer, so that the model has fewer parameters and requires less computation while ensuring good load disaggregation performance. …”
Get full text

Multilevel Feature Fusion-Based GCN for Rumor Detection with Topic Relevance Mining by Shenyu Chen, Meng Li, Weifeng Yang

“…In this paper, we propose a novel graph convolution network model, named multilevel feature fusion-based graph convolution network (MFF-GCN) which can employ multiple streams of GCNs to learn different level features of rumor data, respectively. …”
Get full text

A Study on Energy Consumption in AI-Driven Medical Image Segmentation by R. Prajwal, S. J. Pawan, Shahin Nazarian, Nicholas Heller, Christopher J. Weight, Vinay Duddalwar, C.-C. Jay Kuo

“…To address these aspects, we evaluated three variants of convolution—Standard Convolution, Depthwise Convolution, and Group Convolution—combined with optimization techniques such as Mixed Precision and Gradient Accumulation. …”
Get full text

Small Ship Detection Based on Improved Neural Network Algorithm and SAR Images by Jiaqi Li, Hongyuan Huo, Li Guo, De Zhang, Wei Feng, Yi Lian, Long He

“…Secondly, multiple Depthwise Separable Convolution layers are added to the SPPF (Spatial Pyramid Pooling-Fast) structure. …”
Get full text

Notice of Violation of IEEE Publication Principles: Ground-Based Cloud Image Recognition System Based on Multi-CNN and Feature Screening and Fusion by Ma Jingyi, Tiejun Zhang, Jing Guodong, Yan Wenjun, Yang Bin

“…With the popularity of convolutional neural networks in image processing, ground-based cloud image recognition algorithms based on convolutional neural network have become a research focus. …”
Get full text

Learner preferences prediction with mixture embedding of knowledge and behavior graph by Xiaoguang LI, Lei GONG, Xiaoli LI, Xin ZHANG, Ge YU

“…To solve the problems of inaccurate prediction of learner preference and insufficient utilization of structural information in the knowledge recommendation model, for the knowledge structure and learner behavior structure in the learner’s preference prediction model, the model of learner preferences predication with mixture embedding of knowledge and behavior graph was proposed.First, considering using graph convolution network (GCN) to fit structural information, GCN was extended to knowledge graph and behavior graph, the purpose of which was to obtain learners’ overall learning pattern and individual learning pattern.Then, the difference between knowledge structure and behavior structure was used to fit learners’ individual preferences, and recurrent neural network was used to encode and decode learners’ preferences to obtain the distribution of learners’ preference distribution.The experimental results on the real datasets demonstrate that the proposed model has a good effect on predicting learner preferences.…”
Get full text

Deep joint learning diagnosis of Alzheimer’s disease based on multimodal feature fusion by Jingru Wang, Shipeng Wen, Wenjie Liu, Xianglian Meng, Zhuqing Jiao

“…The other branch learned the position information of brain regions with different changes in the different categories of subjects’ brains by introducing attention convolution, and then obtained the discriminative probability information from locations via convolution and global average pooling. …”
Get full text

Deep learning for predicting myopia severity classification method by WangMeiYu Xing, XiaoNa Li, JingShu Ni, YuanZhi Zhang, ZhongSheng Li, Yong Liu, YiKun Wang, Yao Huang

“…To improve the efficiency of myopia screening, this paper proposes a deep learning model, X-ENet, which combines the advantages of depthwise separable convolution and dynamic convolution to classify different severities of myopia. …”
Get full text

High resolution remote sensing image object detection algorithm based on improved YOLOv8 by ZHANG Xia, QIAO Huanyu, CAO Feng

“…Firstly, dynamic snake convolution was incorporated to make the algorithm detect objects with different scales and directions better; Secondly, in order to enable the algorithm to capture the global context information in the image with complex background, inverted residual mobile block was combined with Shift-Wise convolution . …”
Get full text

A Multimodel Fusion Method for Cardiovascular Disease Detection Using ECG by Guanghui Song, Jiajian Zhang, Dandan Mao, Genlang Chen, Chaoyi Pang

“…A record quality filter was designed to judge ECG signal quality, and a random forest method, a multilayer perceptron, and a residual neural network (RESNET)-based convolutional neural network were implemented to provide baselines for ECG record classification according to three different principles. …”
Get full text

A Deep Paraphrase Identification Model Interacting Semantics with Syntax by Leilei Kong, Zhongyuan Han, Yong Han, Haoliang Qi

“…Then, DPIM-ISS learns the paraphrase pattern from this representation interacting the semantics with syntax by exploiting a convolutional neural network with convolution-pooling structure. …”
Get full text

DCFE-YOLO: A novel fabric defect detection method. by Lei Zhou, Bingya Ma, Yanyan Dong, Zhewen Yin, Fan Lu

“…Finally, the feature fusion network integrates Partial Convolution and Efficient Multi-scale Attention, optimizing the fusion of information across different feature levels and spatial scales, which enhances the richness and accuracy of feature representations while reducing computational complexity. …”
Get full text

Analysis of preprocessing for Generative Adversarial Networks: A case study on color fundoscopy to fluorescein angiography image-to-image translation by Veena K.M., Veena Mayya, Rashmi Naveen Raj, Sulatha V. Bhandary, Uma Kulkarni

“…This study examines the impact of five different image preprocessing techniques - Green Channel, CLAHE on Green Channel, CLAHE on RGB channels, Green Channel Gaussian Convolution, and RGB Gaussian Convolution - on five different GAN variants: CycleGAN, Pix2Pix GAN, CUT GAN, FastCut GAN, and NICE GAN. …”
Get full text

EDPNet: A Transmission Line Ice-Thickness Recognition End-Side Network Based on Efficient Dynamic Perception by Yangyang Jiao, Yu Zhang, Yinke Dou, Liangliang Zhao, Qiang Liu

“…Firstly, a lightweight multidimensional recombination convolution (LMRC) is designed to split the ordinary convolution for lightweight design and extract feature information of different scales for reorganization. …”
Get full text

Research on Fault Diagnosis of Rotating Parts Based on Transformer Deep Learning Model by Zilin Zhang, Yaohua Deng, Xiali Liu, Jige Liao

“…The experimental results on three different datasets indicate that the proposed model achieved high accuracy in fault diagnosis with relatively short data windows. …”
Get full text

LCCDMamba: Visual State Space Model for Land Cover Change Detection of VHR Remote Sensing Images by Junqing Huang, Xiaochen Yuan, Chan-Tong Lam, Yapeng Wang, Min Xia

“…The proposed MISF comprises multi-scale feature aggregation (MSFA), which utilizes strip convolution to aggregate multiscale local change information of bitemporal land cover features, and residual with SS2D (RSS) which employs residual structure with SS2D to capture global feature differences of bitemporal land cover features. …”
Get full text

Security application of intrusion detection model based on deep learning in english online education by Xue Li, Yugui Zhang

“…This model uses one dimensional convolution to construct a multi scale convolution structure to extract network data feature information of different scales. …”
Get full text

CBLN-YOLO: An Improved YOLO11n-Seg Network for Cotton Topping in Fields by Yufei Xie, Liping Chen

“…At the same time, CBLN-YOLO also shows strong robustness under different weather and time periods, and its recognition speed reaches 135 frames per second, which provides strong support for cotton top bud positioning in the field environment.…”
Get full text

CWMS-GAN: A small-sample bearing fault diagnosis method based on continuous wavelet transform and multi-size kernel attention mechanism. by Shun Yu, Zi Li, Jialin Gu, Runpu Wang, Xiaoyu Liu, Lin Li, Fusen Guo, Yuheng Ren

“…Specifically, this study proposes a continuous wavelet convolution strategy (CWCL) instead of the traditional convolution operation in GAN, which can additionally capture the signal's frequency domain features. …”
Get full text

An Antinoise Fault Diagnosis Method Based on Multiscale 1DCNN by Jie Cao, Zhidong He, Jinhua Wang, Ping Yu

“…MSK has five convolutional kernels with different sizes, and those kernels are used to extract features with varying resolutions in the original signal. …”
Get full text