CGLCS-Net: Addressing Multi-Temporal and Multi-Angle Challenges in Remote Sensing Change Detection by Ke Liu, Hang Xue, Caiyi Huang, Jiaqi Huo, Guoxuan Chen

“…GLCAS dynamically selects receptive fields at different feature extraction stages through a joint pooling attention mechanism and depthwise separable convolution, enhancing global context and local feature extraction capabilities and improving feature representation for multi-scale and irregular change regions. …”
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A Voxelized Transformer-Based Neural Network for 3D Reconstruction From Multi-Energy SEM Backscattered Electrons by Caizhi Zheng, Ronghan Hong, Hao-Jie Hu, Qing Huo Liu

“…Two numerical cases are used to respectively verify the inversion performance of the proposed method for different depths and different feature scales. The reconstruction results show that the proposed BSE-VoxNets can reach a resolution of 2 nm in both the horizontal and vertical dimensions.…”
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Impact of large language models and vision deep learning models in predicting neoadjuvant rectal score for rectal cancer treated with neoadjuvant chemoradiation by Hyun Bin Kim, Hong Qi Tan, Wen Long Nei, Ying Cong Ryan Shea Tan, Yiyu Cai, Fuqiang Wang

“…For CT scans, two different approaches with convolutional neural network were utilized to tackle the 3D scan entirely or tackle it slice by slice. …”
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A wireless sensor data-based coal mine gas monitoring algorithm with least squares support vector machines optimized by swarm intelligence techniques by Peng Chen, Yonghong Xie, Pei Jin, Dezheng Zhang

“…Using the popular deep neural networks, convolutional neural network and long short-term memory model, as comparisons, a number of experiments are carried out on several UCI machine learning datasets with different features. …”
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RGB imaging-based detection of rice leaf blast spot and resistance evaluation at the canopy scale by XIE Pengyao, FU Haowei, TANG Zheng, MA Zhihong, CEN Haiyan

“…Therefore, this study aims to identify and detect rice leaf blast spots based on RGB imaging of rice canopy combined with mask regions with convolutional neural network (Mask-RCNN), and develop multiple classification models to quantify the number of disease spots and evaluate the association between the number of disease spots and the resistance level by analyzing the quantitative information of different categories of disease spots in RGB images of rice. …”
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3D animation design image detail enhancement based on intelligent fuzzy algorithm by Pu Haitao, Pu Yuang

“…When processing high texture 3D animations, the method cannot effectively optimize for different areas, significantly affecting image quality and detail representation. …”
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A Dynamic Spatio-Temporal Deep Learning Model for Lane-Level Traffic Prediction by Bao Li, Quan Yang, Jianjiang Chen, Dongjin Yu, Dongjing Wang, Feng Wan

“…Specifically, we take advantage of the graph convolutional network (GCN) with a data-driven adjacent matrix for spatial feature modeling and treat different lanes of the same road segment as different nodes. …”
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Chess Position Evaluation Using Radial Basis Function Neural Networks by Dimitrios Kagkas, Despina Karamichailidou, Alex Alexandridis

“…The proposed approach introduces models based on the radial basis function (RBF) neural network architecture trained with the fuzzy means algorithm, in conjunction with a novel set of input features; different methods of network training are also examined and compared, involving the multilayer perceptron (MLP) and convolutional neural network (CNN) architectures and a different set of input features. …”
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Identification of Subtypes of Post-Stroke and Neurotypical Gait Behaviors Using Neural Network Analysis of Gait Cycle Kinematics by Andrian Kuch, Nicolas Schweighofer, James M. Finley, Alison McKenzie, Yuxin Wen, Natalia Sanchez

“…We first trained a Convolutional Neural Network and a Temporal Convolutional Network to extract features that distinguish impaired from neurotypical gait. …”
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OEM-HWNet: A Prior Knowledge-Guided Network for Pavement Interlayer Distress Detection Based on Computer Vision Using GPR by Congde Lu, Senguo Cao, Xiao Wang, Guanglai Jin, Siqi Wang, Wenlong Cai

“…Firstly, an object enhancement module based on prior knowledge was designed to locate the regions of interlayer distress and enhance their characteristics. Then, wavelet convolution was added to increase the receptive field of the network and enhance the ability to capture low-frequency information. …”
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Synthesizing field plot and airborne remote sensing data to enhance national forest inventory mapping in the boreal forest of Interior Alaska by Pratima Khatri-Chhetri, Hans-Erik Andersen, Bruce Cook, Sean M. Hendryx, Liz van Wagtendonk, Van R. Kane

“…To achieve this goal, we compared the performance of two advanced modeling approaches, the convolutional neural network (CNN) and the XGBoost model. …”
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Automated analysis of high‐content microscopy data with deep learning by Oren Z Kraus, Ben T Grys, Jimmy Ba, Yolanda Chong, Brendan J Frey, Charles Boone, Brenda J Andrews

“…We also demonstrate the ability of DeepLoc to classify highly divergent image sets, including images of pheromone‐arrested cells with abnormal cellular morphology, as well as images generated in different genetic backgrounds and in different laboratories. …”
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On the improvement of schizophrenia detection with optical coherence tomography data using deep neural networks and aggregation functions by Paweł Karczmarek, Małgorzata Plechawska-Wójcik, Adam Kiersztyn, Adam Domagała, Agnieszka Wolinska, Steven M. Silverstein, Kamil Jonak, Paweł Krukow

“…Among over 300, 000 different variants of tested aggregation operators, a few versions provided satisfactory results.…”
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Highly Robust Synthetic Aperture Radar Target Recognition Method Based on Simulation Data Training by Liping Hu, Canming Yao, Jian Huang, Jinfan Liu, Guanyong Wang

“…There are inevitable differences between the simulated and measured SAR images, which will affect the recognition performance. …”
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Mapping herbaceous wetlands using combined phenological and hydrological features from time-series Sentinel-1/2 imagery by Zhaolong Yang, Xiaodong Na

“…However, the classification results of herbaceous marshes differ significantly using single date imagery because of the different vegetation phenological rhythm and hydrological condition. …”
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State-of-Health Estimation of Lithium-Ion Batteries Based on Electrochemical Impedance Spectroscopy Features and Fusion Interpretable Deep Learning Framework by Bohan Shao, Jun Zhong, Jie Tian, Yan Li, Xiyu Chen, Weilin Dou, Qiangqiang Liao, Chunyan Lai, Taolin Lu, Jingying Xie

“…This paper introduces a network architecture called CGMA-Net (Convolutional Gated Multi-Attention Network), which is designed to effectively address the issue of battery capacity degradation. …”
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Joint Spectral Information and Spatial Details for Road Extraction From Optical Remote-Sensing Images by Yuzhun Lin, Jie Rui, Fei Jin, Shuxiang Wang, Xibing Zuo, Xiao Liu

“…A polarized self-attention mechanism was finally introduced at different levels of the fusion branch to reduce information loss during feature extraction, and operations, such as connected convolution and nonlinear activation, were later connected to complete the road prediction. …”
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Distinguishing Difficulty Imbalances in Strawberry Ripeness Instances in a Complex Farmland Environment by Yang Gan, Xuefeng Ren, Huan Liu, Yongming Chen, Ping Lin

“…Firstly, a partial convolution-based compact inverted block is developed, which significantly enhances the feature extraction capability of the model. …”
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TCE-YOLOv5: Lightweight Automatic Driving Object Detection Algorithm Based on YOLOv5 by Han Wang, Zhenwei Yang, Qiaoshou Liu, Qiang Zhang, Honggang Wang

“…Firstly, the Bottleneck convolution kernel channels in the C3 module were grouped to greatly reduce the number of parameters. …”
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DSCONV-GAN: a UAV-BASED model for Verticillium Wilt disease detection in Chinese cabbage in complex growing environments by Jun Zhang, Dongfang Zhang, Jingyan Liu, Yuhong Zhou, Xiaoshuo Cui, Xiaofei Fan

“…Based on YOLOv8, with the addition of the dynamic snake convolution (DSConv) module and the improved loss function maximum possible distance intersection-over-union (MPDIoU), we acquired enhanced complex structures and global characteristics in Chinese cabbage images under different growth conditions. …”
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