3-D UXSE-Net for Seismic Channel Detection Based on Satellite Image Enhanced Synthetic Datasets by Xinke Zhang, Yihuai Lou, Naihao Liu, Daosheng Ling, Yunmin Chen

“…The model generates improved feature representations that enhance performance by combining convolutional neural networks for local feature extraction and Transformer-based modules for capturing global context. …”
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Enhancing Unmanned Aerial Vehicle Object Detection via Tensor Decompositions and Positive–Negative Momentum Optimizers by Ruslan Abdulkadirov, Pavel Lyakhov, Denis Butusov, Nikolay Nagornov, Dmitry Reznikov, Anatoly Bobrov, Diana Kalita

“…We verified the performance of the proposed and known neural networks on different optimizers. It is shown that the proposed neural network accelerates the solving object detection problem by 44–52%. …”
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Randomization-Driven Hybrid Deep Learning for Diabetic Retinopathy Detection by A. M. Mutawa, G. R. Hemalakshmi, N. B. Prakash, M. Murugappan

“…This study pioneers an innovative framework, using Multi-Scale Discriminative Robust Local Binary Pattern (MS-DRLBP) features, combined with a hybrid Convolutional Neural Network-Radial Basis Function (CNN-RBF) classifier, to enhance the detection of DR. …”
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The spatial–temporal evolution characteristics and influencing factors of coordinated development in the Yellow River Basin: Based on the perspective of flood-sediment transport, e... by Ni Geng, Guiliang Tian, Hengquan Zhang

“…Secondly, Grey relational analysis and obstacle degree (OD) model were developed to identify the major factors affecting the coordinated development of the FES system. Finally, the Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM) model was selected to predict the CCD under different scenarios. …”
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On the effectiveness of neural operators at zero-shot weather downscaling by Saumya Sinha, Brandon Benton, Patrick Emami

“…., 8x and 15x) across data from different simulations: the European Centre for Medium-Range Weather Forecasts Reanalysis version 5 (ERA5) and the Wind Integration National Dataset Toolkit. …”
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Medium- Long-Term Runoff Forecasting Using Interpretable Hybrid Machine Learning Model for Data-Scarce Regions by YOU Yu-jun, BAI Yun-gang, LU Zhen-lin, ZHANG Jiang-hui, CAO Biao, LI Wen-zhong, YU Qi-ying

“…[Methods] Based on historical precipitation, temperature, and runoff sequences from the Yulongkashi River, a Convolutional Neural Network-Bidirectional Gated Recurrent Unit-Attention (CNN-BiGRU-Attention) model was developed. …”
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Feature Fusion Graph Consecutive-Attention Network for Skeleton-Based Tennis Action Recognition by Pawel Powroznik, Maria Skublewska-Paszkowska, Krzysztof Dziedzic, Marcin Barszcz

“…They are employed to extract joint information at different scales from the motion capture data. Due to focusing on relevant components, the model enriches the network’s comprehension of tennis motion data representation and allows for a more invested representation. …”
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Ground Segmentation for LiDAR Point Clouds in Structured and Unstructured Environments Using a Hybrid Neural–Geometric Approach by Antonio Santo, Enrique Heredia, Carlos Viegas, David Valiente, Arturo Gil

“…This paper introduces a hybrid framework that synergizes multi-resolution polar discretization with sparse convolutional neural networks (SCNNs) to address these challenges. …”
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Deepfake Image Forensics for Privacy Protection and Authenticity Using Deep Learning by Saud Sohail, Syed Muhammad Sajjad, Adeel Zafar, Zafar Iqbal, Zia Muhammad, Muhammad Kazim

“…The experimental results reveal a near-perfect accuracy of over 99% across different architectures, highlighting their effectiveness in forensic investigations.…”
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TCBGY net for enhanced wear particle detection in ferrography using self attention and multi scale fusion by Lei He, Haijun Wei, Cunxun Sun

“…Secondly, we introduce the convolutional block attention module (CBAM) into the neck network to enhance salience for detecting wear particles while suppressing irrelevant information interference. …”
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Developing methodologies to study perceived sound qualities of violins* by Fritz Claudia, Stoppani George, Igartua Unai, Woodhouse Jim

“…The hybrid synthesis was found to be the most effective of the three in highlighting instrument differences, capturing properties perceived in the actual instruments, and correlating with radiation measurements.…”
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Automated Foveal Avascular Zone Segmentation in Optical Coherence Tomography Angiography Across Multiple Eye Diseases Using Knowledge Distillation by Peter Racioppo, Aya Alhasany, Nhuan Vu Pham, Ziyuan Wang, Giulia Corradetti, Gary Mikaelian, Yannis M. Paulus, SriniVas R. Sadda, Zhihong Hu

“…Although several automated FAZ detection and segmentation algorithms have been developed for use with OCTA, their performance can vary significantly due to differences in data accessibility of OCTA in different retinal pathologies, and differences in image quality in different subjects and/or different OCTA devices. …”
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Modulation Format Identification Method Based on Multi-Feature Input Hybrid Neural Network by Zhiqi Huang, Xiangjun Xin, Qi Zhang, Haipeng Yao, Feng Tian, Fu Wang

“…The method enhances MFI accuracy by leveraging features of different modulation formats and representations at different neural network levels. …”
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Role of Biological Age in the Determination of Long‐Term Cause‐Specific Death Following Percutaneous Coronary Interventions by Mandeep Singh, Paul A. Friedman, Rajiv Gulati, Abdallah El Sabbagh, Bradley R. Lewis, Amrit Kanwar, Claire E. Raphael, Mohammed A. Al‐Hijji, Zachi I. Attia, Atta Behfar, James L. Kirkland

“…Age‐Gap was calculated as the difference between chronological age and age estimated by artificial intelligence ECG using a convolutional neural network. …”
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Oral atogepant mitigates spreading depolarization-induced pain and anxiety behavior in mice by Melih Z. Kaya, Pradeep Banerjee, Cenk Ayata, Andrea M. Harriott

“…Mouse grimace was then quantified using PainFace, an open convolutional neural network platform. Lastly, anxiety‐like behavior was examined in an open field. …”
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An improved ViT model for music genre classification based on mel spectrogram. by Pingping Wu, Weijie Gao, Yitao Chen, Fangfang Xu, Yanzhe Ji, Juan Tu, Han Lin

“…In this paper, an improved ViT model is proposed to extract more comprehensive music genre features from Mel spectrograms by leveraging the strengths of both convolutional neural networks and Transformers. Also, the paper incorporates a channel attention mechanism by amplifying differences between channels within the Mel spectrograms of individual music genres, thereby facilitating more precise classification. …”
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COSMIC-2 RFI Prediction Model Based on CNN-BiLSTM-Attention for Interference Detection and Location by Cheng-Long Song, Rui-Min Jin, Chao Han, Dan-Dan Wang, Ya-Ping Guo, Xiang Cui, Xiao-Ni Wang, Pei-Rui Bai, Wei-Min Zhen

“…Research shows that the SNR in different GNSS signal channels shows a correlated change under the influence of RFI. …”
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Development of a deep learning model for automated detection of calcium pyrophosphate deposition in hand radiographs by Thomas Hügle, Elisabeth Rosoux, Guillaume Fahrni, Deborah Markham, Tobias Manigold, Fabio Becce

“…CPPD presence was then predicted using a convolutional neural network. We tested seven CPPD models, each with a different combination of sites out of TFCC, MCP-2 and MCP-3. …”
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A Deep Learning Framework for Using Search Engine Data to Predict Influenza-Like Illness and Distinguish Epidemic and Nonepidemic Seasons: Multifeature Time Series Analysis by Ji Li, Xiangyu Yan, Xingjie Chu, Ying Zhang, Guoliang Liu, Lin Li, Yue Li, Xiaochun Dong, Zihan Mei, Zhengkun Liu, Jinyue Yuan, Xiaohan Sun, Chunxia Cao

“…ObjectiveThe aim of this study was to propose a deep learning framework for different influenza epidemic states based on Baidu index and percentage of influenza-like illness (ILI%). …”
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A Sensor Data Prediction and Early-Warning Method for Coal Mining Faces Based on the MTGNN-Bayesian-IF-DBSCAN Algorithm by Mingyang Liu, Xiaodong Wang, Wei Qiao, Hongbo Shang, Zhenguo Yan, Zhixin Qin

“…By constructing a graph structure based on sensor spatial dependencies and utilizing temporal convolutional layers to capture long short-term time-series features, the high-precision dynamic prediction of gas concentrations is achieved via the MTGNN. …”
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