Results 121 to 130 of about 24,295 (253)

Gate‐Align‐SED: Semi‐Supervised Sound Event Detection via Adaptive Feature Gating and Cross‐Task Alignment in Situation Awareness

Advanced Intelligent Systems, EarlyView.
Overview of the proposed Gate‐Align‐SED, including two stages of training: (1) Mean‐Teacher SSL Training; and (2) Enhancer Model Training. In complex real‐world environments such as disaster monitoring, effective sound event detection (SED) is often hindered by the presence of noise and limited labeled data.
Jieli Chen, Li‐Minn Ang, Chee Shen Lim, Kah Phooi Seng, Jeremy Smith   +4 more
wiley   +1 more source

Prediction of Properties of Polymer Composite Formulations Using Ensemble Models With Feature Generation

Journal of Applied Polymer Science, EarlyView.
This study develops an interpretable machine‐learning framework to predict multiple properties of polymer composites based on composition and processing variables. By combining ensemble models with composition‐based feature generation and SHAP‐based interpretation, the approach reveals composition‐property relationships and supports efficient multi ...
Dong Ryeol Shin, Sung Kwang Lee
wiley   +1 more source

Transfer Learning Approaches in Bioprocess Engineering: Opportunities and Challenges

Biotechnology and Bioengineering, EarlyView.
ABSTRACT Transfer learning (TL) has recently emerged as a promising approach to overcoming one of the key limitations of bioprocess engineering: data scarcity. By leveraging knowledge from one bioprocess to another, TL allows existing models and data sets to be reused efficiently, accelerating process development, improving prediction accuracy, and ...
Daniel Barón Díaz, Anna‐Lena Drommershausen, Alexander Grünberger, Dirk Holtmann   +3 more
wiley   +1 more source

GNN-EADD: Graph Neural Network-Based E-Commerce Anomaly Detection via Dual-Stage Learning

IEEE Access
E-commerce platforms face significant challenges in detecting anomalous products, including counterfeit goods and fraudulent listings, which can undermine user trust and platform integrity.
Zhouhang Shao, Xuran Wang, Enkai Ji, Shiyang Chen, Jin Wang   +4 more
doaj   +1 more source

LPS-GNN : Deploying Graph Neural Networks on Graphs with 100-Billion Edges

ACM Transactions on Knowledge Discovery from Data
Graph Neural Networks (GNNs) have emerged as powerful tools for various graph mining tasks, yet existing scalable solutions often struggle to balance execution efficiency with prediction accuracy. These difficulties stem from iterative message-passing techniques, which place significant computational demands and require extensive GPU memory ...
Xu Cheng, Liang Yao, Feng He, Yukuo Cen, Yufei He, Chenhui Zhang, Wenzheng Feng, Hongyun Cai, Jie Tang   +8 more
openaire   +2 more sources

Addressing Small Data Challenges in Biopharmaceutical Development and Manufacturing: A Mini Review of Multi‐Fidelity Techniques

Biotechnology and Bioengineering, EarlyView.
ABSTRACT The growing demand for biopharmaceutical products reflects their effectiveness in medical treatments. However, developing new biopharmaceuticals remains a major bottleneck, often taking up to a decade before market approval. Machine learning (ML) models have the potential to accelerate this process, but their success depends on access to large
Mohammad Golzarijalal, Uwe Aickelin, Ellen Otte   +2 more
wiley   +1 more source

Toward Representing Identical Privacy-Preserving Graph Neural Network via Split Learning

IEEE Access
In recent years, the fast rise in number of studies on graph neural network (GNN) has put it from the theories research to the real-world application stage.
Yiming Fang, Huiyun Jiao, Risheng Huang
doaj   +1 more source

Machine Learning Paradigm for Advanced Battery Electrolyte Development

Carbon Energy, EarlyView.
Electrolyte materials determine ion transport kinetics within the bulk and interphases, ultimately influencing the performance of battery systems. As data‐driven paradigms increasingly reshape materials discovery, this review provides an application‐oriented exploration of the intersection between machine learning and electrolyte science. By evaluating
Chang Su, Mengfan Pei, Keyu Chen, Xigao Jian, Fangyuan Hu   +4 more
wiley   +1 more source

Reconstruction of Radio Environment Map Based on Multi-Source Domain Adaptive of Graph Neural Network for Regression

Sensors
The graph neural network (GNN) has shown outstanding performance in processing unstructured data. However, the downstream task performance of GNN strongly depends on the accuracy of data graph structural features and, as a type of deep learning (DL ...
Xiaomin Wen, Shengliang Fang, Youchen Fan   +2 more
doaj   +1 more source

Real‐time monitoring of tunnel structures using digital twin and artificial intelligence: A short overview

Deep Underground Science and Engineering, EarlyView.
How artificial intelligence (AI) and digital twin (DT) technologies are revolutionizing tunnel surveillance, offering proactive maintenance strategies and enhanced safety protocols. It explores AI's analytical power and DT's virtual replicas of infrastructure, emphasizing their role in optimizing maintenance and safety in tunnel management.
Mohammad Afrazi, Danial Jahed Armaghani, Hossein Afrazi, Hadi Fattahi, Pijush Samui   +4 more
wiley   +1 more source