Video Tactical Intelligence Analysis Method of Karate Competition Based on Convolutional Neural Network by Jun Zhong, Jian Xu

“…Therefore, based on the convolutional neural network, this paper establishes a new graph convolution model for automatic intelligent analysis of karate athletes’ technical action recognition, action frequency statistics, and trajectory tracking. …”
Get full text

EFFICIENCY AND ACCURACY OF CONVOLUTIONAL AND FOURIER TRANSFORM LAYERS IN NEURAL NETWORKS FOR MEDICAL IMAGE CLASSIFICATION by Fauzi Nafi'udin, Hasih Pratiwi, Etik Zukhronah

“…In an era where information flow is moving at a rapid pace, image data processing is becoming increasingly important as technology advances, including in healthcare. Convolutional Neural Network (CNN) has been a common approach in image classification, but the larger the volume of data and the complexity of the task, the more expensive the computational cost of CNN. …”
Get full text

Decom-UNet3+: A Retinal Vessel Segmentation Method Optimized With Decomposed Convolutions by Qun Li, Juntao Zhang, Licheng Hua, Songyin Fu, Chenjie Gu

“…To address this issue, we propose Decom-UNet3+, a model that optimizes the encoders by employing decomposed convolutions. Specifically, the encoders replace standard convolutional layers with asymmetric convolutions and depthwise separable convolutions, reducing the number of parameters while enhancing capability for feature extraction. …”
Get full text

A dual-branch model combining convolution and vision transformer for crop disease classification. by Qingduan Meng, Jiadong Guo, Hui Zhang, Yaoqi Zhou, Xiaoling Zhang

“…To address these issues, this paper proposes a dual-branch model for crop disease classification, which combines Convolutional Neural Network (CNN) with Vision Transformer (ViT). …”
Get full text

Detection of fasting blood sugar using a microwave sensor and convolutional neural network by Mohammad Amir Sattari, Mohsen Hayati

“…To interpret the complex, non-linear features of the sensor response, a convolutional neural network (CNN) was developed and trained using the entire dataset. …”
Get full text

Application of Dual-Stage Attention Temporal Convolutional Networks in Gas Well Production Prediction by Xianlin Ma, Long Zhang, Jie Zhan, Shilong Chang

“…This paper introduces a Dual-Stage Attention Temporal Convolutional Network (DA-TCN) model to enhance the accuracy and efficiency of gas production forecasting, particularly for wells in tight sandstone reservoirs. …”
Get full text

Bearing Fault Detection and Classification Based on Temporal Convolutions and LSTM Network in Induction Machine by Mohammad Hoseintabar Marzebali, Saeed Hasani Borzadaran, Hoda Mashayekhi, Valiollah Mashayekhi

“…In this paper, a deep learning approach based on a combination of temporal convolutions and Long Short Term Memory (LSTM) network is used for fault diagnosis. …”
Get full text

GMFLDA: Improved Prediction of lncRNA-Disease Association via Graph Convolutional Network by Kwangsu Kim, Jihwan Ha

“…In this study, we present GMFLDA, an advanced machine learning framework for inferring lncRNA-disease associations (LDA) by synergizing graph convolutional networks (GCNs) with deep matrix factorization. …”
Get full text

A High-Speed Finger Vein Recognition Network with Multi-Scale Convolutional Attention by Ziyun Zhang, Peng Liu, Chen Su, Shoufeng Tong

“…Existing research primarily focuses on improving recognition accuracy; however, this often comes at the cost of increased model complexity, which, in turn, affects recognition efficiency, making it difficult to balance accuracy and speed in practical applications. …”
Get full text

A lightweight fabric defect detection with parallel dilated convolution and dual attention mechanism by Zheqing Zhang, Kezhong Lu, Gaoming Yang

“…In order to capture multi-scale contextual information, we designed a parallel dilated convolution downsampling (PDCD) block to replace the conventional downsampling block in the backbone. …”
Get full text

Real-Time Transformer Detection of Underwater Objects Based on Lightweight Gated Convolutional Network by Yuhui LI, Huixia CUI, Yaomin LI, Senping JIA

“…To address the challenges in underwater object detection algorithms, including difficult image feature processing, redundant model architectures, and excessive parameter numbers, this paper proposed a real-time Transformer detection method for underwater objects based on a lightweight gated convolutional network. This method first constructed a convolutional gated linear unit based on the gating mechanism to dynamically modulate feature transmission. …”
Get full text

FCSwinU: Fourier Convolutions and Swin Transformer UNet for Hyperspectral and Multispectral Image Fusion by Rumei Li, Liyan Zhang, Zun Wang, Xiaojuan Li

“…The fusion of low-resolution hyperspectral images (LR-HSI) with high-resolution multispectral images (HR-MSI) provides a cost-effective approach to obtaining high-resolution hyperspectral images (HR-HSI). …”
Get full text

Classifying early apple scab infections in multispectral imagery using convolutional neural networks by Alexander J. Bleasdale, J. Duncan Whyatt

“…Multispectral imaging systems combined with deep learning classification models can be cost-effective tools for the early detection of apple scab (Venturia inaequalis) disease in commercial orchards. …”
Get full text

A Low-Cost AI-Powered System for Early Detection of Diabetic Retinopathy and Ocular Diseases in Resource Limited Settings by Harpreet Vohra, Mohammad Kamrul Hasan, Siti Norul Huda Sheikh Abdullah, Shayla Islam, Abdul Hadi Abd Rahman, Shreya N. Bhojake, Dhruv Bobal, Awal Kaur Gandhi, Muskan, Chandramohan Dhasarathan

“…The system features a cost-effective imaging setup utilizing a 28D lens and a camera to capture high-quality fundus images. …”
Get full text

PGDRT: Prediction Demand Based on Graph Convolutional Network for Regional Demand-Responsive Transport by Eunkyeong Lee, Hosik Choi, Do-Gyeong Kim

“…In this study, a graph convolutional network model that performs demand prediction using spatial and temporal information was developed. …”
Get full text

Convolutional Neural Networks for Real Time Classification of Beehive Acoustic Patterns on Constrained Devices by Antonio Robles-Guerrero, Salvador Gómez-Jiménez, Tonatiuh Saucedo-Anaya, Daniela López-Betancur, David Navarro-Solís, Carlos Guerrero-Méndez

“…Recent research has demonstrated the effectiveness of convolutional neural networks (CNN) in assessing the health status of bee colonies by classifying acoustic patterns. …”
Get full text

Spatiotemporal wind speed forecasting using conditional local convolution and multidimensional meteorology features by Meng Wang, Juanle Wang, Mingming Yu, Fei Yang

“…This model addresses uniform influence model weight issue by redesigning convolution kernels to better capture local meteorological features and integrating multiple influencing factors. …”
Get full text

Reparameterized Feature Aggregation Convolutional Neural Network for Remote Sensing Scene Image Classification by Cuiping Shi, Mengxiang Ding, Liguo Wang

“…In this study, a re-parameterized feature aggregation convolutional neural network (RepFACNN) is proposed. …”
Get full text

System Development for Liquid Chemicals Point Injection Based on Convolutional Neural Network Models by V. S. Semenyuk, E. A. Nikitin

“…When developing the system, they used the U-net-algorithm of convolutional neural networks, as well as data displaying diseases of winter and spring wheat – brown rust and powdery mildew. …”
Get full text

ASCDet: cross-space UAV object detection method guided by adaptive sparse convolution by Gui Cheng, Xubin Feng, Yan Tian, Meilin Xie, Chaoya Dang, Qing Ding, Zhenfeng Shao

“…These masks guide cross-space object detection through sparse convolutions, while a global context enhancement strategy within the sparse convolution module enriches the contextual information, maintaining detection accuracy. …”
Get full text