Results 171 to 180 of about 39,453 (246)

From Local Patterns to Global Context: A Multimodal Deep Learning Approach for Complex Power Quality Disturbance Recognition

Artificial Intelligence for Engineering, EarlyView.
A multimodal deep learning framework that combines time–frequency feature extraction, lightweight Transformer‐based global dependency modelling and signal‐based cross‐attention is proposed for recognising complex power quality disturbances. The framework effectively integrates local and long‐range representations, enabling robust and accurate ...
Jiajian Lin, Hadi Nabipour Afrouzi, Mehran Motamed Ektesabi, Jalal Tavalaei   +3 more
wiley   +1 more source

Assessment of Android Network Positioning as an Alternate Source for Robust PNT. [PDF]

Sensors (Basel)
Chun J, Spagnolli J, Holmes T, Akos D.
europepmc   +1 more source

Review on enhancing clinical decision support system using machine learning

CAAI Transactions on Intelligence Technology, EarlyView.
Abstract Clinical decision‐making is a complex patient‐centred process. For an informed clinical decision, the input data is very thorough ranging from detailed family history, environmental history, social history, health‐risk assessments, and prior relevant medical cases.
Anum Masood, Usman Naseem, Junaid Rashid, Jungeun Kim, Imran Razzak   +4 more
wiley   +1 more source

Automated abstraction of clinical parameters of multiple myeloma from real-world clinical notes using large language models. [PDF]

BMC Med Inform Decis Mak
Del Campo AO, Lituiev D, Varma G, Manoharan M, Kumar Ravi S, Aman A, Kansagra A, Greshock J, Venkatakrishnan AJ, Batavia AS.   +9 more
europepmc   +1 more source

HPoolGCL: Augmentation‐Free Cross‐Granularity Graph Contrastive Learning With Hierarchical Pooling

CAAI Transactions on Intelligence Technology, EarlyView.
ABSTRACT Graph contrastive learning (GCL) has emerged as a dominant paradigm for self‐supervised representation learning for attributed graph data. However, existing GCL methods heavily rely on empirical graph data augmentation, which may distort intrinsic graph semantics and produce poor generalisation without carefully chosen or designed augmentation
Fenglin Cen, Shijie Liu, Quan Feng, Tiantian He, Ji Xu   +4 more
wiley   +1 more source

Lung cancer survivorship: natural language processing for automated abstraction of follow-up computed tomography indication. [PDF]

Transl Lung Cancer Res
Grambozov B, Ruznic E, Zehentmayr F.
europepmc   +1 more source

Generating Compressed Counterfactual Hard Negative Samples for Graph Contrastive Learning

CAAI Transactions on Intelligence Technology, EarlyView.
ABSTRACT Graph contrastive learning (GCL) relies on acquiring high‐quality positive and negative samples to learn the structural semantics of the input graph. Previous approaches typically sampled negative samples from the same training batch or an irrelevant external graph.
Haoran Yang, Hongxu Chen, Xiangyu Zhao, Sixiao Zhang, Xiangguo Sun, Qian Li, Hongzhi Yin, Guandong Xu   +7 more
wiley   +1 more source

Assessing pediatric clinician adherence to the guidelines for prevention of peanut allergy: a natural language processing study. [PDF]

BMC Med Inform Decis Mak
Wong AF, Bilaver LA, Jiang J, Luo Y, Gupta RS, Rosenman M, Carroll MS.   +6 more
europepmc   +1 more source

Multitype Game Optimisation: A Two‐Stage Fine‐Tuning Framework for Multi‑Game Optimisation With Large Language Models

CAAI Transactions on Intelligence Technology, EarlyView.
ABSTRACT Large language models (LLMs) have made remarkable advances in natural language processing, demonstrating great potential in modelling structured sequences. However, adapting these capabilities to machine gaming tasks such as Go remains challenging due to limitations in strategy generalisation and optimisation efficiency.
Xiali Li, Jingshi Gu, Feifan He, Yang Xiao, Yuanli Jia, Ping Lan   +5 more
wiley   +1 more source

Enhancing nurses' psychological empowerment through NLP: a mixed-methods study. [PDF]

BMC Nurs
Şen Olgay S, Uğur E.
europepmc   +1 more source