Insights into gait performance in Parkinson's disease via latent features of deep graph neural networks by Jiecheng Wu, Jiecheng Wu, Ning Su, Xinjin Li, Xinjin Li, Chao Yao, Jipeng Zhang, Xucheng Zhang, Wei Sun

“…This allowed us to explore how the model's parameters (different ST-GCN Layers) could assist clinicians in understanding.ResultsThe dataset used to evaluate the model in this paper includes motion data from 65 PD participants and 77 healthy control participants. …”
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Fracture identification and 3D reconstruction of coal-rock combinations based on VRA-UNet network by Dengke WANG, Longhang WANG, Yaguang QIN, Le WEI, Tanggen CAO, Wenrui LI, Lu LI, Xu CHEN, Yuling XIA

“…Finally, an asymmetric atrous pyramid module (AC-ASPP) utilizing convolution kernels of different scales is added at the end of the downsampling, which reduced the computational complexity and improved the computational efficiency of the model while keeping the receptive field unchanged. …”
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Comparative Analysis of AI Models for Atypical Pigmented Facial Lesion Diagnosis by Alessandra Cartocci, Alessio Luschi, Linda Tognetti, Elisa Cinotti, Francesca Farnetani, Aimilios Lallas, John Paoli, Caterina Longo, Elvira Moscarella, Danica Tiodorovic, Ignazio Stanganelli, Mariano Suppa, Emi Dika, Iris Zalaudek, Maria Antonietta Pizzichetta, Jean Luc Perrot, Gabriele Cevenini, Ernesto Iadanza, Giovanni Rubegni, Harald Kittler, Philipp Tschandl, Pietro Rubegni

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Fault diagnosis and inference of hoist main bearing based on transfer learning and ontology by Fei DONG, Di ZHANG, Kunpeng GE, Junjie CHEN, Xinyue XU

“…To overcome the challenges still faced by data-driven hoist main bearing fault diagnosis methods, including data imbalance due to a lack of fault samples under real operating conditions, diagnostic performance degradation of fault diagnosis models caused by significant differences in data sample distribution under varying conditions, single fault diagnosis function, and a lack of reasoning analysis and localization for the causes of hoist main bearing system failures, a new fault diagnosis and reasoning method for hoist main bearing systems is studied, which includes two aspects: ① Bearing fault diagnosis based on convolutional neural network transfer learning and domain adaptation. …”
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A fault diagnosis method for rolling bearings in open-set domain adaptation with adversarial learning by Tongfei Lei, Feng Pan, Jiabei Hu, Xu He, Bing Li

“…Abstract The closed-set assumption often fails in practical industrial applications, especially considering diverse working conditions where the data distribution may differ significantly. In light of this, a domain adaptation method with adversarial learning is designed for open-set fault diagnosis. …”
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HTC-HAD: A Hybrid Transformer-CNN Approach for Hyperspectral Anomaly Detection by Minghua Zhao, Wen Zheng, Jing Hu

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Deep machine learning identified fish flesh using multispectral imaging by Zhuoran Xun, Xuemeng Wang, Hao Xue, Qingzheng Zhang, Wanqi Yang, Hua Zhang, Mingzhu Li, Shangang Jia, Jiangyong Qu, Xumin Wang

“…We found that nCDA images transformed from MSI data showed significant differences in flesh splices of the 20 fish species. …”
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Deep Learning for Glioblastoma Multiforme Detection from MRI: A Statistical Analysis for Demographic Bias by Kebin Contreras, Julio Gutierrez-Rengifo, Oscar Casanova-Carvajal, Angel Luis Alvarez, Patricia E. Vélez-Varela, Ana Lorena Urbano-Bojorge

“…This study presents a convolutional neural network (CNN) specifically optimised for GBM detection from T1-weighted magnetic resonance imaging (MRI), with systematic evaluations of layer depth, activation functions, and hyperparameters. …”
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RDAU-Net: A U-Shaped Semantic Segmentation Network for Buildings near Rivers and Lakes Based on a Fusion Approach by Yipeng Wang, Dongmei Wang, Teng Xu, Yifan Shi, Wenguang Liang, Yihong Wang, George P. Petropoulos, Yansong Bao

“…To address the above issues, the present study proposes the design of a U-shaped segmentation network of buildings called RDAU-Net that works through extraction and fuses a convolutional neural network and a transformer to segment buildings. …”
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DreamOn: a data augmentation strategy to narrow the robustness gap between expert radiologists and deep learning classifiers by Luc Lerch, Luc Lerch, Lukas S. Huber, Lukas S. Huber, Amith Kamath, Alexander Pöllinger, Aurélie Pahud de Mortanges, Verena C. Obmann, Florian Dammann, Walter Senn, Walter Senn, Mauricio Reyes, Mauricio Reyes, Mauricio Reyes

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Subtypes detection of papillary thyroid cancer from methylation assay via Deep Neural Network by Andrea Colacino, Andrea Soricelli, Michele Ceccarelli, Ornella Affinito, Monica Franzese

“…Methods: To address this issue, we first performed a pan-cancer analysis to train a convolutional 1-D Neural Network (CNN) using supervised learning. …”
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Impacted lower third molar classification and difficulty index assessment: comparisons among dental students, general practitioners and deep learning model assistance by Paniti Achararit, Chawan Manaspon, Chavin Jongwannasiri, Promphakkon Kulthanaamondhita, Chumpot Itthichaisri, Soranun Chantarangsu, Thanaphum Osathanon, Ekarat Phattarataratip, Kraisorn Sappayatosok

“…The aim of this study was to evaluate the effectiveness of a convolutional neural network (CNN) in determining the angulation, position, classification and difficulty index (DI) of ILTM. …”
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Fault diagnosis method for rigid guides in vertical shaft hoisting systems by WANG Jianfeng, JIN Yuanzhi, ZHANG Yong, WANG Yongzhen, HE Jiacong

“…The network extracted multi-scale features through parallel multi-scale convolutions, enhancing its ability to perceive signal features at different scales. …”
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A dual-phase deep learning framework for advanced phishing detection using the novel OptSHQCNN approach by Srikanth Meda, Vangipuram Sesha Srinivas, Killi Chandra Bhushana Rao, Repudi Ramesh, Narasimha Rao Yamarthi

“…Background Phishing attacks are now regarded as one of the most prevalent cyberattacks that often compromise the security of different communication and internet networks. Phishing websites are created with the goal of generating cyber threats in order to ascertain the user’s financial information. …”
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Improved YOLOv8-Based Algorithm for Citrus Leaf Disease Detection by Zhengbing Zheng, Yibang Zhang, Luchao Sun

“…The proposed approach uses YOLOv8n as the base model and introduces adaptive convolution into the Backbone, allowing the model to dynamically prioritize different disease features. …”
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Signal Processing for Transient Flow Rate Determination: An Analytical Soft Sensor Using Two Pressure Signals by Faras Brumand-Poor, Tim Kotte, Enrico Gaspare Pasquini, Katharina Schmitz

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Intelligent recognition algorithm and application of coal mine overhead passenger device based on multiscale feature fusion by Beijing XIE, Heng LI, Hang DONG, Zheng LUAN, Ben ZHANG, Xiaoxu LI

“…In the feature extraction stage, the partial convolution of the backbone network C2f module is replaced by deformable convolution, and a novel C2f_DCN module is designed. …”
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Directed Knowledge Graph Embedding Using a Hybrid Architecture of Spatial and Spectral GNNs by Guoqiang Hou, Qiwen Yu, Fan Chen, Guang Chen

“…To address this limitation, a directed spectral graph transformer (DSGT), a hybrid architecture model, is constructed by integrating the graph transformer and directed spectral graph convolution networks. The graph transformer leverages multi-head attention mechanisms to capture the global connectivity of the feature graph from different perspectives in the spatial domain, which bridges the gap between frequency responses and, further, naturally couples the graph transformer and directed graph convolutional neural networks (GCNs). …”
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Feature enhanced cascading attention network for lightweight image super-resolution by Feng Huang, Hongwei Liu, Liqiong Chen, Ying Shen, Min Yu

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Pedestrian Trajectory Prediction via Window Attention and Spatial Graph Interaction Network by Xiang Gu, Chao Li, Jie Yang, Jing Wang, Qiwei Huang

“…Finally, a multi-scale dilated convolution network is employed for future trajectory generation, capturing multi-scale spatiotemporal features through dilated convolutions to enhance prediction accuracy and robustness. …”
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