Ribosome phenotypes for rapid classification of antibiotic-susceptible and resistant strains of Escherichia coli by Alison Farrar, Piers Turner, Hafez El Sayyed, Conor Feehily, Stelios Chatzimichail, Sammi Ta, Derrick Crook, Monique Andersson, Sarah Oakley, Lucinda Barrett, Christoffer Nellåker, Nicole Stoesser, Achillefs Kapanidis

“…Here, we use the spatial distribution of fluorescently labelled ribosomes to detect intracellular changes associated with antibiotic susceptibility in E. coli cells using a convolutional neural network (CNN). By using ribosome-targeting probes, one fluorescence image provides data for cell segmentation and susceptibility phenotyping. …”
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Multi-View Contrastive Fusion POI Recommendation Based on Hypergraph Neural Network by Luyao Hu, Guangpu Han, Shichang Liu, Yuqing Ren, Xu Wang, Ya Liu, Junhao Wen, Zhengyi Yang

“…The model first constructs three distinct hypergraphs, representing interaction, trajectory, and geographical location, capturing the complex relationships and high-order dependencies between users and POIs from different perspectives. Subsequently, a targeted hypergraph convolutional network is designed for aggregation and propagation, learning the latent factors within each view. …”
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Advancement in Graph Neural Networks for EEG Signal Analysis and Application: A Review by S. M. Atoar Rahman, Md Ibrahim Khalil, Hui Zhou, Yu Guo, Ziyun Ding, Xin Gao, Dingguo Zhang

“…Electroencephalography (EEG) can non-invasively measure neuronal events and reflect brain activity at different locations on the scalp. Early studies for EEG signal processing have focused more on extracting EEG temporal features and considered the topology of EEG channels less due to the limitation of rich spatial information. …”
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Robust Automated Mouse Micro-CT Segmentation Using Swin UNEt TRansformers by Lu Jiang, Di Xu, Qifan Xu, Arion Chatziioannou, Keisuke S. Iwamoto, Susanta Hui, Ke Sheng

“…Further evaluation on an external mouse dataset acquired on a different micro-CT with lower kVp and higher imaging noise was also employed to assess model robustness and generalizability. …”
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FetalMovNet: A Novel Deep Learning Model Based on Attention Mechanism for Fetal Movement Classification in US by Musa Turkan, Emre Dandil, Furkan Erturk Urfali, Mehmet Korkmaz

“…To evaluate FetalMovNet, we construct a new dataset containing fetal movements in US across seven different anatomical structures-head, body, arm, hand, heart, leg, and foot. …”
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Multi-scale extreme climate disaster prediction model integrated with ConvLSTM: taking rainstorm and flood disaster as an example by Lei He, Yunhao He, Yongqiang Xia, Yuxia Li, Bin Liu, Siqi Zhang, Cunjie Zhang

“…Firstly, four disaster indicators (population disaster index, housing disaster index, agricultural disaster index and economic disaster index) were introduced to reflect different losses, which could form a comprehensive disaster index to quantify the overall loss degree; Second, with raster data and VGGNet, a lightweight regression convolutional neural network model VGG-Light was proposed to solve these problem; Third, focused on impact of precipitation on disaster situations, the ConvLSTM module was used to capture the spatiotemporal characteristics of precipitation data, and then the TSVGG-Light model was presented for feature fusion. …”
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ViViT-Prob: A Radar Echo Extrapolation Model Based on Video Vision Transformer and Spatiotemporal Sparse Attention by Yunan Qiu, Bingjian Lu, Wenrui Xiong, Zhenyu Lu, Le Sun, Yingjie Cui

“…The model takes historical sequences as input and initially maps them into a fixed-dimensional vector space through 3D convolutional patch encoding. Subsequently, a multi-head spatiotemporal fusion module with sparse attention encodes these vectors, effectively capturing spatiotemporal relationships between different regions in the sequences. …”
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Spectral Super-Resolution Reconstruction of Multispectral Remote Sensing Images via Clustering-Based Spectral Feature by Wang Benlin, Yu Qinglin, Wang Zuo, Li Weitao, Wang Yong, Liu Huan, Gu Shuangxi, Zhang Lingling, Lv Dong

“…To address this, we proposed a jointly fused convolutional neural network for spectral super-resolution (JF-CNNSSR), leveraging spectral reflectance variations across different land cover types. …”
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Tree semantic segmentation from aerial image time series by Venkatesh Ramesh, Arthur Ouaknine, David Rolnick

“…Effective monitoring of different tree species is essential to understanding and improving the health and biodiversity of forests. …”
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RNN and GNN based prediction of agricultural prices with multivariate time series and its short-term fluctuations smoothing effect by Youngho Min, Young Rock Kim, YunKyong Hyon, Taeyoung Ha, Sunju Lee, Jinwoo Hyun, Mi Ra Lee

“…In this investigation, we applied five different smoothing time window lengths to evaluate the effect of mitigating short-term fluctuations on the predictive performance of the models. …”
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APO-CViT: A Non-Destructive Estrus Detection Method for Breeding Pigs Based on Multimodal Feature Fusion by Jinghan Cai, Wenzheng Liu, Tonghai Liu, Fanzhen Wang, Zhihan Li, Xue Wang, Hua Li

“…By integrating the Vision Transformer and convolutional neural networks, the model extracted and fused features from multimodal data. …”
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Deep learning approach based on a patch residual for pediatric supracondylar subtle fracture detection by Qingming Ye, Zhilu Wang, Yi Lou, Yang Yang, Jue Hou, Zheng Liu, Weiguang Liu, Jiayu Li

“…However, their diagnosis can be particularly challenging due to the anatomical characteristics and imaging features of the pediatric skeleton. In recent years, convolutional neural networks (CNNs) have achieved notable success in medical image analysis, though their performance typically relies on large-scale, high-quality labeled datasets. …”
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A Violet‐Light‐Responsive ReRAM Based on Zn2SnO4/Ga2O3 Heterojunction as an Artificial Synapse for Visual Sensory and In‐Memory Computing by Saransh Shrivastava, Wei‐Sin Dai, Stephen Ekaputra Limantoro, Hans Juliano, Tseung‐Yuen Tseng

“…Classification of three‐channeled images corrupted with different levels (0.15–0.9) of Gaussian noise is achieved by simulating a convolutional neural network (CNN). …”
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Soil Condition Classification Based on Natural Water Content Using Computer Vision Technique by Mark Miller, Yong Fang, Yubo Wang, Sergey Kharitonov, Vladimir Akulich

“…Second, the resulting dataset after preprocessing was processed by convolutional neural network algorithms during deep learning; the transfer learning technique was used to obtain better results. …”
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Research on insider threat detection based on personalized federated learning and behavior log analysis by Xiaoyun Ye, Faqin Luo, Huangrongbin Cui, Jinlong Wang, Xiaoyun Xiong, Wencui Zhang, Jiawei Yu, Wenhao Zhao

“…Drawing on the DeepInsight concept, we convert different data types into image formats for use with Convolutional Neural Networks (CNNs) to train insider threat detection models. …”
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Ensemble reconstruction of missing satellite data using a denoising diffusion model: application to chlorophyll <i>a</i> concentration in the Black Sea by A. Barth, J. Brajard, A. Alvera-Azcárate, B. Mohamed, C. Troupin, J.-M. Beckers

“…They are however affected by clouds (among others), which severely reduce their spatial coverage. Different methods have been proposed in the literature to reconstruct missing data in satellite observations. …”
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Joint feature representation optimization and anti-occlusion for robust multi-vessel tracking in inland waterways by Shenjie Zou, Jin Liu, Xiliang Zhang, Zhongdai Wu, Jing Liu, Bing Han

“…Finally, a bidirectional feature pyramid network (BiFPN) is utilized to fuse vessel appearance features from different scales, enhancing the capability to learn cross-scale features of vessels to some extent. …”
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Horizontal and Vertical Part-Wise Feature Extraction for Cross-View Gait Recognition by Md. Zasim Uddin, Kamrul Hasan, Md Atiqur Rahman Ahad, Fady Alnajjar

“…A novel method is proposed that integrates the parts generated according to transverse and sagittal planes utilizing three-dimensional and two-dimensional convolutional neural networks for robust feature extraction. …”
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Transferability Evaluation in Wi-Fi Intrusion Detection Systems Through Machine Learning and Deep Learning Approaches by Saud Yonbawi, Adil Afzal, Muhammad Yasir, Muhammad Rizwan, Natalia Kryvinska

“…A comprehensive evaluation involving Multilayer Perceptron(MLP), and Convolutional Neural Networks (CNN) models has been executed, uncovering that CNN conspicuously outshines the MLP model.…”
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Manifold and spatiotemporal learning on multispectral unoccupied aerial system imagery for phenotype prediction by Fared Farag, Trevis D. Huggins, Jeremy D. Edwards, Anna M. McClung, Ahmed A. Hashem, Jason L. Causey, Emily S. Bellis

“…We observed similar performance on a held‐out growing season for a spatiotemporal model (a three‐dimensional convolutional neural network) trained on raw images compared to simpler workflows using dimension reduction of manually extracted features from temporal imagery (i.e., vegetation indices and image texture properties). …”
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