Pixel-Based Change Detection in Moving-Camera Videos Using Twin Convolutional Features on a Data-Constrained Scenario by Luiz G. C. Tavares, Allan F. Da Silva, Rafael Padilla, Lucas A. Thomaz, Sergio L. Netto, Eduardo A. B. Da Silva

Get full text

Feature extraction and classification of digital rock images via pre-trained convolutional neural network and unsupervised machine learning by Masashige Shiga, Masao Sorai, Tetsuya Morishita, Masaatsu Aichi, Naoki Nishiyama, Takashi Fujii

“…By visualizing the spatial distribution of these patterns and quantifying their characteristics, we gained insights into the microstructural differences between rock samples, providing an effective tool for interpreting the classification results and understanding the underlying factors that differentiate various rock types.…”
Get full text

Utilizing GCN-Based Deep Learning for Road Extraction from Remote Sensing Images by Yu Jiang, Jiasen Zhao, Wei Luo, Bincheng Guo, Zhulin An, Yongjun Xu

“…To address these issues, this paper proposes an improved model based on Graph Convolutional Networks (GCNs), named FR-SGCN (Hierarchical Depth-wise Separable Graph Convolutional Network Incorporating Graph Reasoning and Attention Mechanisms). …”
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

“…However, to train a model effectively to recognize different patterns, it is crucial to have a sufficiently balanced dataset. …”
Get full text

Fault Diagnosis Method of Rolling Bearing Based on 1D Multi-Channel Improved Convolutional Neural Network in Noisy Environment by Huijuan Guo, Dongzhi Ping, Lijun Wang, Weijie Zhang, Junfeng Wu, Xiao Ma, Qiang Xu, Zhongyu Lu

“…By introducing BiLSTM, an attention mechanism and a local sparse structure of a two-channel Convolutional Neural Network, the feature information of the noisy timing signal is fully extracted at different scales while reducing the computational parameters. …”
Get full text

Combining convolutional neural network with transformer to improve YOLOv7 for gas plume detection and segmentation in multibeam water column images by Wenguang Chen, Xiao Wang, Junjie Chen, Jialong Sun, Guozhen Zha

“…However, the gas plume in the image is often affected by the seafloor environment and exhibits sparse texture and changing motion, making traditional detection and segmentation methods more time-consuming and labor-intensive. The emergence of convolutional neural networks (CNNs) alleviates this problem, but the local feature extraction of the convolutional operations, while capturing detailed information well, cannot adapt to the elongated morphology of the gas plume target, limiting the improvement of the detection and segmentation accuracy. …”
Get full text

A text classification method by integrating mobile inverted residual bottleneck convolution networks and capsule networks with adaptive feature channels by Tao Jin, Jiaming Liu

“…A Capsule Network is designed to adaptively adjust the importance of different feature channels, including N-gram convolutional layers, selective kernel network layers, primary capsule layers, convolutional capsule layers, and fully connected capsule layers, aiming to enhance the model’s ability to capture semantic information of text across different feature channels. …”
Get full text

Enhancing Skin Disease Diagnosis Through Fine-Tune Convolutional Neural Network: A Comparative Study with Multi-class Approach by Najnin Akter Ringky, Abu Kowshir Bitto, Khalid Been Md. Badruzzaman Biplob, Md. Fazla Elahe, Musabbir Hasan Sammak, Tapushe Rabaya Toma

Get full text

Enhancing Skin Disease Diagnosis Through Fine-Tune Convolutional Neural Network: A Comparative Study with Multi-class Approach by Najnin Akter Ringky, Abu Kowshir Bitto, Khalid Been Md. Badruzzaman Biplob, Md. Fazla Elahe, Musabbir Hasan Sammak, Tapushe Rabaya Toma

Get full text

Skin lesion segmentation with a multiscale input fusion U-Net incorporating Res2-SE and pyramid dilated convolution by Zhihui Liu, Jie Hu, Xulu Gong, Fuzhong Li

“…The PDC module captures image information at different receptive fields through pyramid dilated convolution, improving segmentation accuracy. …”
Get full text

Graphical Empirical Mode Decomposition–Convolutional Neural Network-Based Expert System for Early Corrosion Detection in Truss-Type Bridges by Alan G. Lujan-Olalde, Angel H. Rangel-Rodriguez, Andrea V. Perez-Sanchez, Martin Valtierra-Rodriguez, Jose M. Machorro-Lopez, Juan P. Amezquita-Sanchez

“…The evaluation considers three different corrosion levels: (1) incipient, (2) moderate, and (3) severe, along with a healthy condition. …”
Get full text

Automated Detection of Gibbon Calls From Passive Acoustic Monitoring Data Using Convolutional Neural Networks in the “Torch for R” Ecosystem by Dena J. Clink, Jinsung Kim, Hope Cross‐Jaya, Abdul Hamid Ahmad, Moeurk Hong, Roeun Sala, Hélène Birot, Cain Agger, Thinh Tien Vu, Hoa Nguyen Thi, Thanh Nguyen Chi, Holger Klinck

“…The “torch for R” ecosystem has made deep learning with convolutional neural networks (CNNs) accessible for R users. …”
Get full text

Multi-Branch Convolutional Neural Network Architecture for Glaucoma Diagnosis Using Optical Coherence Tomography Biomarkers and Synthetic Image Simulation by Ph. V. Usenko, A. M. Prudnik

“…This paper presents a multi-branch convolutional neural network designed for glaucoma diagnosis using optical coherence tomography biomarkers and synthetic image simulations. …”
Get full text

Indonesian Lip-Reading Detection and Recognition Based on Lip Shape Using Face Mesh and Long-Term Recurrent Convolutional Network by null Aripin, Abas Setiawan

“…This study proposes an enhanced lip-reading system trained using the long-term recurrent convolutional network (LRCN) considering eight different types of lip shapes. …”
Get full text

Enhancing the Transformer Model with a Convolutional Feature Extractor Block and Vector-Based Relative Position Embedding for Human Activity Recognition by Xin Guo, Young Kim, Xueli Ning, Se Dong Min

“…Therefore, we proposed using multi-layer convolutional layers as a Convolutional Feature Extractor Block (CFEB). …”
Get full text

Prediction of Shear Wave Velocity Based on a Hybrid Network of Two-Dimensional Convolutional Neural Network and Gated Recurrent Unit by Tengfei Chen, Gang Gao, Peng Wang, Bin Zhao, Yonggen Li, Zhixian Gui

“…However, these algorithms focus either on spatial feature extraction for different physical properties of rocks or on sequential feature extraction in the depth direction of rocks. …”
Get full text

Convolutional Neural Network–Vision Transformer Architecture with Gated Control Mechanism and Multi-Scale Fusion for Enhanced Pulmonary Disease Classification by Okpala Chibuike, Xiaopeng Yang

“…Background/Objectives: Vision Transformers (ViTs) and convolutional neural networks (CNNs) have demonstrated remarkable performances in image classification, especially in the domain of medical imaging analysis. …”
Get full text

Research on Land Use and Land Cover Information Extraction Methods for Remote Sensing Images Based on Improved Convolutional Neural Networks by Xue Ding, Zhaoqian Wang, Shuangyun Peng, Xin Shao, Ruifang Deng

“…To address the challenges that convolutional neural networks (CNNs) face in extracting small objects and handling class imbalance in remote sensing imagery, this paper proposes a novel spatial contextual information and multiscale feature fusion encoding–decoding network, SCIMF-Net. …”
Get full text

Investigating a three-dimensional convolution recognition model for acoustic emission signal analysis during uniaxial compression failure of coal by Tao Wang, Zishuo Liu, Liyuan Liu

“…DenseNet + GC + SE showed a probability distribution focusing on different risk levels. By integrating group convolution and SE modules, this model significantly reduced both model and time complexity while preserving precision, enhancing efficiency. …”
Get full text

Image-based soft drink type classification and dietary assessment system using deep convolutional neural network with transfer learning by Rubaiya Hafiz, Mohammad Reduanul Haque, Aniruddha Rakshit, Mohammad Shorif Uddin

“…The experiment confirms that our system can detect and recognize different types of drinks with an accuracy of 98.51%.…”
Get full text