Efficient-Deformable-DETR: Enhancing underwater small target detection in complex environments by Xie Haozheng, Xin Guanghong, Kang Shuo, Xie Qiongdan

“…By introducing global response normalization and a fully convolutional mask autoencoder framework, the model’s feature aggregation ability and detection accuracy are enhanced. …”
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Data‐Driven Predictions of Peak Warming Under Rapid Decarbonization by Noah S. Diffenbaugh, Elizabeth A. Barnes

“…Abstract The severe impacts associated with recent record‐setting annual global temperatures elevate the need to accurately predict the hottest conditions that could occur even if the most ambitious decarbonization goals are achieved. We use convolutional neural networks (CNNs) to predict peak global warming from recent observed temperature maps and future cumulative CO2 emissions. …”
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Scene Understanding Based on High-Order Potentials and Generative Adversarial Networks by Xiaoli Zhao, Guozhong Wang, Jiaqi Zhang, Xiang Zhang

“…In this study, we propose a semantic segmentation framework based on classic generative adversarial nets (GAN) to train a fully convolutional semantic segmentation model along with an adversarial network. …”
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Towards automated and real-time multi-object detection of anguilliform fishes from sonar data using YOLOv8 deep learning algorithm by Tao Huang, Xiaoqin Zang, Grigoriy Kondyukov, Zhangshuan Hou, Guanze Peng, Joachim Pander, Josef Knott, Juergen Geist, Meklit Berihun Melesse, Paul Jacobson, Zhiqun Daniel Deng

“…Leveraging imaging sonar as a reliable technology for fish passage monitoring in dark, turbid and high-flow environments, field data are acquired using imaging sonar and then converted to single sonar frames/images for subsequent analysis. In this study, a framework based on the “You Only Look Once” Version 8 (YOLOv8)-based convolutional neural network is proposed for multi-object detection of eels and non-eel fish using the sonar images after image subtraction and additional wavelet denoising. …”
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Autonomous International Classification of Diseases Coding Using Pretrained Language Models and Advanced Prompt Learning Techniques: Evaluation of an Automated Analysis System Usin... by Yan Zhuang, Junyan Zhang, Xiuxing Li, Chao Liu, Yue Yu, Wei Dong, Kunlun He

“…Additionally, we evaluated the framework’s performance under different prompt learning and fine-tuning settings. …”
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A dual-branch deep learning model based on fNIRS for assessing 3D visual fatigue by Yan Wu, Yan Wu, Yan Wu, TianQi Mu, SongNan Qu, XiuJun Li, XiuJun Li, XiuJun Li, Qi Li, Qi Li, Qi Li

“…A transformer was integrated into the convolutional network to enhance long-range feature extraction. …”
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Dual-coding Contrastive Learning Based on the ConvNeXt and ViT Models for Morphological Classification of Galaxies in COSMOS-Web by Shiwei Zhu, Guanwen Fang, Chichun Zhou, Jie Song, Zesen Lin, Yao Dai, Xu Kong

“…In our previous works, we proposed a machine learning framework named USmorph for efficiently classifying galaxy morphology. …”
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A novel method for soil organic carbon prediction using integrated ‘ground-air-space’ multimodal remote sensing data by Yilin Bao, Xiangtian Meng, Huanjun Liu, Mengyuan Xu, Mingchang Wang

“…Then, joint spatial-spectral features were extracted by 3D convolutional neural network. Based on this framework, we developed Model (i), which integrates SOC data with spatial-spectral resolution downscaling (SSD) image; Model (ii), which integrates SOC data, UAV image with spatial resolution downscaling (SD) image; and Model (iii), which integrates SOC data, UAV image with SSD image. …”
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MS3OSD: A Novel Deep Learning Approach for Oil Spills Detection Using Optical Satellite Multisensor Spatial-Spectral Fusion Images by Kai Du, Yi Ma, Zhongwei Li, Rongjie Liu, Zongchen Jiang, Junfang Yang

“…The framework uses parallel branches, including a convolutional neural network and a vision transformer, to extract surrounding spatial features and central spectral features from the fused data. …”
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Automatic detection and prediction of epileptic EEG signals based on nonlinear dynamics and deep learning: a review by Shixiao Tan, Zhen Tang, Qiang He, Ying Li, Yuliang Cai, Jiawei Zhang, Di Fan, Zhenkai Guo

“…It emphasizes the need to integrate algebraic topology and graph convolutional deep learning to address multiscale dynamics, and proposes a unified framework for regulatory-compliant clinical translation that bridges the gap between research innovations and real-world clinical deployment, while outlining future research priorities focused on multimodal data fusion and regulatory-compliant validation frameworks.…”
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Medical slice transformer for improved diagnosis and explainability on 3D medical images with DINOv2 by Gustav Müller-Franzes, Firas Khader, Robert Siepmann, Tianyu Han, Jakob Nikolas Kather, Sven Nebelung, Daniel Truhn

“…We introduce the Medical Slice Transformer (MST) framework to adapt 2D self-supervised models for 3D medical image analysis. …”
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Differentiating localized autoimmune pancreatitis and pancreatic ductal adenocarcinoma using endoscopic ultrasound images with deep learning by Hitomi Nakamura, Motohisa Fukuda, Akiko Matsuda, Naohiko Makino, Hirohito Kimura, Yu Ohtaki, Yoshihito Nawa, Soushi Oyama, Yuya Suzuki, Toshikazu Kobayashi, Tetsuya Ishizawa, Yasuharu Kakizaki, Yoshiyuki Ueno

“…Hence, we developed a special cross‐validation framework to search for effective methodologies of deep learning in distinguishing autoimmune pancreatitis from pancreatic ductal adenocarcinoma on endoscopic ultrasound images. …”
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EPI-DynFusion: enhancer-promoter interaction prediction model based on sequence features and dynamic fusion mechanisms by Ao Zhang, Jianhua Jia, Mingwei Sun, Xin Wei

“…Furthermore, we incorporate the Convolutional Block Attention Module (CBAM) to enhance the model’s ability to focus on informative regions. …”
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GNODEVAE: a graph-based ODE-VAE enhances clustering for single-cell data by Zeyu Fu, Chunlin Chen, Song Wang, Junping Wang, Shilei Chen

“…Through systematic evaluation across 10 graph convolutional layers, GAT demonstrated optimal performance, achieving average ARI advantages of 0.108 and 0.112 over alternative graph convolutional layers in VGAE and GNODEVAE architectures respectively, along with ASW advantages of 0.047 and 0.098. …”
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A multimodal educational robots driven via dynamic attention by An Jianliang, An Jianliang

“…In addition, the model integrates a VGG19-based convolutional network for image feature extraction and utilizes a dynamic attention mechanism to dynamically focus on relevant parts of multimodal inputs. …”
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Bayesian optimization of biodegradable polymers via machine learning driven features from low-field NMR data by Ryo Fujita, Yoshifumi Amamoto, Jun Kikuchi

“…This might be potentially insightful for the feasibility of a framework to accelerate polymer development through low-field NMR with minimal property data.…”
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Improved Collaborative Representation Classifier Based on l2-Regularized for Human Action Recognition by Shirui Huo, Tianrui Hu, Ce Li

“…In this paper, a unified improved collaborative representation framework is proposed in which the probability that a test sample belongs to the collaborative subspace of all classes can be well defined and calculated. …”
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Advancing precision dentistry: the integration of multi-omics and cutting-edge imaging technologies—a systematic review by Neelam Das

“…A total of 50 studies published between 2015 and 2024 were selected using a PICOS framework. Analytical tools included meta-analysis (Forest and Funnel plots), risk of bias assessment, VOS viewer-based bibliometric mapping, and GRADE evidence grading.ResultsMulti-omics approaches revealed key biomarkers such as TP53, IL-1, and MMPs in early diagnosis. …”
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Enhancing Malware Detection via RGB Assembly Visualization and Hybrid Deep Learning Models by Esra Eroğlu Demirkan, Murat Aydos

“…This research introduces a novel image-based malware classification framework that uses hybrid-model Convolutional Neural Networks to process RGB images generated from assembly code. …”
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Improving Climate Bias and Variability via CNN‐Based State‐Dependent Model‐Error Corrections by William E. Chapman, Judith Berner

“…Abstract We develop an approach to correct biases in the atmospheric component of the Community Earth System Model using convolutional neural networks (CNNs) to create a corrective model parameterization for online bias reduction. …”
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