GCN-Transformer: Graph Convolutional Network and Transformer for Multi-Person Pose Forecasting Using Sensor-Based Motion Data by Romeo Šajina, Goran Oreški, Marina Ivašić-Kos

“…Unlike other models with performances that fluctuate across datasets, GCN-Transformer performs consistently, proving its robustness in multi-person pose forecasting and providing an excellent foundation for the application of GCN-Transformer in different domains.…”
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Real-Time Detection of Meningiomas by Image Segmentation: A Very Deep Transfer Learning Convolutional Neural Network Approach by Debasmita Das, Chayna Sarkar, Biswadeep Das

“…The VGG network that we have constructed with very small convolutional filters consists of 13 convolutional layers and 3 fully connected layers. …”
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Automated Detection of High Frequency Oscillations in Intracranial EEG Using the Combination of Short-Time Energy and Convolutional Neural Networks by Dakun Lai, Xinyue Zhang, Kefei Ma, Zichu Chen, Wenjing Chen, Heng Zhang, Han Yuan, Lei Ding

“…A new methodology is presented in this paper for the automated detection of HFOs based on their 2D time–frequency map employing the short-time energy (STE) estimation and the convolutional neural network (CNN) classification algorithm. …”
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Development and verification of a convolutional neural network-based model for automatic mandibular canal localization on multicenter CBCT images by Xiao Pan, Chengtao Wang, Xuhui Luo, Qi Dong, Haiyang Sun, Wentao Zhang, Hongyan Qu, Runzhi Deng, Zitong Lin

“…Abstract Objectives Development and verification of a convolutional neural network (CNN)-based deep learning (DL) model for mandibular canal (MC) localization on multicenter cone beam computed tomography (CBCT) images. …”
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RMCNet: A Liver Cancer Segmentation Network Based on 3D Multi-Scale Convolution, Attention, and Residual Path by Zerui Zhang, Jianyun Gao, Shu Li, Hao Wang

“…However, liver cancer presents challenges such as significant differences in tumor size, shape, and location, which can affect segmentation accuracy. …”
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Multi-convolutional neural network brain image denoising study based on feature distillation learning and dense residual attention by Huimin Qu, Haiyan Xie, Qianying Wang

“…Due to the complexity of the brain's structure and minor density differences, noise can increase diagnosis difficulty, so high-quality images are essential for disease detection, prognosis assessment, and treatment plan development. …”
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VNet: An End-to-End Fully Convolutional Neural Network for Road Extraction From High-Resolution Remote Sensing Data by Arnick Abdollahi, Biswajeet Pradhan, Abdullah Alamri

“…In the present study, we introduce a new deep learning-based convolutional network called VNet model to produce a high-resolution road segmentation map. …”
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One-Dimensional Convolutional Neural Network for Automated Kimchi Cabbage Downy Mildew Detection Using Aerial Hyperspectral Images by Yang Lyu, Lukas Wiku Kuswidiyanto, Pingan Wang, Hyun-Ho Noh, Hee-Young Jung, Xiongzhe Han

“…Spectral analysis of the late and early stages of downy mildew infection revealed notable differences in the red-edge band, with infected plants exhibiting increased red-edge reflectance. …”
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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

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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.…”
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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. …”
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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. …”
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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

“…Then, the C-BiFormer module is proposed, which can achieve effective collaboration between local feature extraction and global semantic modeling while reducing computing resources, and enhance the multi-scale feature extraction capability of the model. Finally, two different depths of networks are designed by stacking C-BiFormer modules with different numbers of layers. …”
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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. …”
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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

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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

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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. …”
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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. …”
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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

“…Our specific goals include (1) present a method for automated detection of gibbon calls from PAM data using the “torch for R” ecosystem, (2) conduct a series of benchmarking experiments and compare the results of six CNN architectures; and (3) investigate how well the different architectures perform on data sets of the female calls from two different gibbon species: the northern gray gibbon (Hylobates funereus) and the southern yellow‐cheeked crested gibbon (Nomascus gabriellae). …”
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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. …”
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