Use of Vision Transformer to Classify Sea Surface Phenomena in SAR Imagery by Junfei Xia, Roland Romeiser, Wei Zhang, Tamay Ozgokmen

“…In addition, our study is the first to apply a pretrained ViT model to a dataset with different polarizations and spatial resolutions—the AI4Arctic Sea Ice Challenge dataset—to rigorously assess model adaptability. …”
Get full text

Automated note annotation after bioacoustic classification: Unsupervised clustering of extracted acoustic features improves detection of a cryptic owl by Callan Alexander, Robert Clemens, Paul Roe, Susan Fuller

“…Adaptation of these methods to other species and vocalisations may facilitate improved detection and investigation of vocal characteristics across different populations or regions.…”
Get full text

A Comparative Study of Lesion-Centered and Severity-Based Approaches to Diabetic Retinopathy Classification: Improving Interpretability and Performance by Gang-Min Park, Ji-Hoon Moon, Ho-Gil Jung

“…Third, we analyze how various model architectures and classification strategies perform under different labeling schemes. Finally, we evaluate decision-making differences between labeling methods using visualization techniques. …”
Get full text

Multiphysics property prediction from hyperspectral drill core data by A. V. Kamath, S. T. Thiele, M. Kirsch, R. Gloaguen

“…Our results show that, with careful preprocessing and thorough data cleaning, differences in resolution can be overcome to learn the relationship between hyperspectral data and petrophysics. …”
Get full text

Untrained perceptual loss for image denoising of line-like structures in MR images. by Elisabeth Pfaehler, Daniel Pflugfelder, Hanno Scharr

“…The uPL network's initialization is not important (e.g. for MR root images SSIM differences of 0.01 occur across initializations, while network depth and pooling operations impact denoising performance slightly more (SSIM of 0.83 for 5 convolutional layers and kernel size 3 vs. 0.86 for 5 convolutional layers and kernel size 5 for the root dataset). …”
Get full text

Intrinsic factors influence a physiological measure in a forest bird community: adults and females have higher H/L ratios than juveniles and males by Finja Strehmann, Markus Vogelbacher, Clara Guckenbiehl, Yvonne R. Schumm, Juan F. Masello, Petra Quillfeldt, Nikolaus Korfhage, Hicham Bellafkir, Markus Mühling, Bernd Freisleben, Nina Farwig, Dana G. Schabo, Sascha Rösner

“…As physiological measure, we used the heterophil to lymphocyte (H/L) ratio of individuals belonging to different species in the forest bird community, which was assessed using a novel deep learning approach based on convolutional neural networks (CNNs) applied to whole blood smear scans. …”
Get full text

Narrowband Radar Micromotion Targets Recognition Strategy Based on Graph Fusion Network Constructed by Cross-Modal Attention Mechanism by Yuanjie Zhang, Ting Gao, Hongtu Xie, Haozong Liu, Mengfan Ge, Bin Xu, Nannan Zhu, Zheng Lu

“…The network first adopts convolutional neural networks (CNNs) to extract unimodal features from RCSs, TF images, and CVDs independently. …”
Get full text

A Multi-Spatial Scale Ocean Sound Speed Prediction Method Based on Deep Learning by Yu Liu, Benjun Ma, Zhiliang Qin, Cheng Wang, Chao Guo, Siyu Yang, Jixiang Zhao, Yimeng Cai, Mingzhe Li

“…The core concept involves accounting for the coupling effects among various spatial scales while extracting temporal and spatial information from the data and assigning appropriate weights to different spatiotemporal entities. Furthermore, we introduce an interpolation method for ocean temperature and salinity data based on the KNN algorithm to enhance dataset resolution. …”
Get full text

A systematic review of deep learning methods for community detection in social networks by Mohamed El-Moussaoui, Mohamed Hanine, Ali Kartit, Monica Garcia Villar, Monica Garcia Villar, Monica Garcia Villar, Helena Garay, Helena Garay, Helena Garay, Isabel de la Torre Díez

“…It also examines the variety of social networks, datasets, evaluation metrics, and employed frameworks in these studies.DiscussionHowever, the analysis highlights several challenges, such as scalability, understanding how the models work (interpretability), and the need for solutions that can adapt to different types of networks. These issues stand out as important areas that need further attention and deeper research. …”
Get full text

Ultra-short-term Probabilistic Forecasting of Distributed Photovoltaic Power Generation Based on Hierarchical Correlation Modeling by Can CHEN, Zinuo SU, Yuan MA, Jialin LIU, Yuqing WANG, Fei WANG

“…On this basis, a hierarchical graph structure is constructed to simultaneously model the intra-subregion and inter-subregion spatio-temporal correlations, enabling effective utilization of correlation information across different hierarchical levels. Then, a probabilistic forecasting model based on hierarchical graph convolutional neural networks (GCNs) is proposed to mine deep spatio-temporal correlation features between PV power stations, thereby enhancing the accuracy of ultra-short-term probabilistic forecasting of regional distributed PV power. …”
Get full text

GU-Net3+: A Global-Local Feature Fusion Algorithm for Building Extraction in Remote Sensing Images by Yali Liu, Cui Ni, Peng Wang, Dongqing Yang, Hexin Yuan, Chao Ma

“…Finally, a Global Cross-Attention module is developed to aggregate global features from different spatial locations, which are then fused with local features from skip connections as input to the decoder, enhancing information exchange across multi-scale and hierarchical features to improve building extraction accuracy. …”
Get full text

Fine-Scale Small Water Body Uncovered by GF-2 Remote Sensing and Multifeature Deep Learning Model by Yixin Jiang, Chunlin Wang, Zhaji Huang, Dandan Li, Biao Wang, Yanlan Wu, Hui Liu, Zihan Liu

“…Spatial characteristics of small water bodies in different urban zones and their relationship with overall urban water resources are then analyzed. …”
Get full text

Breast Tumor Detection and Diagnosis Using an Improved Faster R-CNN in DCE-MRI by Haitian Gui, Han Jiao, Li Li, Xinhua Jiang, Tao Su, Zhiyong Pang

“…We adopted Faster RCNN as the architecture, introduced ROI aligning to minimize quantization errors and feature pyramid network (FPN) to extract different resolution features, added a bounding box quadratic regression feature map extraction network and three convolutional layers to reduce interference from tumor surrounding information, and extracted more accurate and deeper feature maps. …”
Get full text

Validation of a Swine Cough Monitoring System Under Field Conditions by Luís F. C. Garrido, Gabriel S. T. Rodrigues, Leandro B. Costa, Diego J. Kurtz, Ruan R. Daros

“…It is recommended to test the technology in other environments to evaluate the effectiveness in different farm settings.…”
Get full text

Novel Neural Networks for Camera Calibration in Underwater Environments by Cristian H. Sanchez-Saquin, Leonardo Barriga-Rodriguez, Leonardo A. Baldenegro-Perez, Guillermo Ronquillo-Lomeli, Noe A. Rodriguez-Olivares

“…Three distinct scenes clean, green and blue waters were used to study the network performance under different lighting and color conditions. For network training, the Mean Squared Error (MSE) was used as the loss function, and the <inline-formula> <tex-math notation="LaTeX">$L2$ </tex-math></inline-formula> norm was applied to the dense layers for 256 epochs. …”
Get full text

Reverse design of solid propellant grain based on deep learning: Imaging internal ballistic data by Lin Sun, Xiangyu Peng, Yang Liu, Shu Long, Weihua Hui, Ran Wei, Futing Bao

“…This paper conducts comparative experiments across various neural network models, validating the effectiveness of the feature extraction method that transforms internal ballistic time-series data into images, as well as its generalization capability across different CNN architectures. Ignition tests were performed based on the predicted propellant grain. …”
Get full text

Enhanced prediction of heat transfer in jet impingement cooling using an artificial intelligence: A case study by Mehmet Berkant Özel, Ufuk Durmaz, Muhammed Ali Nur Öz, Ahmet Ümit Tepe, Cemil Öz, Ünal Uysal, Orhan Yalçinkaya, Ali Cemal Beni̇m, Norah Alomayrah, M.S. Al-Buriahi

“…Jet impingement cooling was examined with four different Reynolds numbers (16250, 21700, 27100, 36250) and six dimensionless gaps between the jet and the target surface (G/D = 1, 2, 3, 4, 5, and 6). …”
Get full text

MS-YOLOv8: multi-scale adaptive recognition and counting model for peanut seedlings under salt-alkali stress from remote sensing by Fan Zhang, Longgang Zhao, Longgang Zhao, Dongwei Wang, Jiasheng Wang, Igor Smirnov, Juan Li

“…First, a lightweight adaptive feature fusion module (called MSModule) is constructed, which groups the channels of input feature maps and feeds them into different convolutional layers for multi-scale feature extraction. …”
Get full text

Coupling Deep Learning and Physically Based Hydrological Models for Monthly Streamflow Predictions by Wenxin Xu, Jie Chen, Gerald Corzo, Chong‐Yu Xu, Xunchang John Zhang, Lihua Xiong, Dedi Liu, Jun Xia

“…It also saves decision‐makers the time and effort of trying different combinations of predictors, which is indispensable when building DL models. …”
Get full text

HSD²Former: Hybrid-Scale Dual-Domain Transformer with Crisscrossed Interaction for Hyperspectral Image Classification by Binxin Luo, Meihui Li, Yuxing Wei, Haorui Zuo, Jianlin Zhang, Dongxu Liu

“…D²MSCE supersedes conventional patch embedding to generate spectral and spatial tokens at different scales, effectively enriching the diversity of spectral-spatial features. …”
Get full text