Results 201 to 210 of about 57,585 (308)

Enhancing generalized spectral clustering with embedding Laplacian graph regularization

CAAI Transactions on Intelligence Technology, EarlyView.
Abstract An enhanced generalised spectral clustering framework that addresses the limitations of existing methods by incorporating the Laplacian graph and group effect into a regularisation term is presented. By doing so, the framework significantly enhances discrimination power and proves highly effective in handling noisy data.
Hengmin Zhang, Jian Yang, Bob Zhang, Yang Tang, Wenli Du, Bihan Wen   +5 more
wiley   +1 more source

Laplacian reconstructive network for guided thermal super-resolution. [PDF]

Sci Rep
Kasliwal A, Gakhar I, Kamani A, Seth P, Verma U.   +4 more
europepmc   +1 more source

Boosted unsupervised feature selection for tumor gene expression profiles

CAAI Transactions on Intelligence Technology, EarlyView.
Abstract In an unsupervised scenario, it is challenging but essential to eliminate noise and redundant features for tumour gene expression profiles. However, the current unsupervised feature selection methods treat all samples equally, which tend to learn discriminative features from simple samples.
Yifan Shi, Kaixiang Yang, Mengzhi Wang, Zhiwen Yu, Huanqiang Zeng, Yang Hu   +5 more
wiley   +1 more source

KA-IHO: A Kinematic-Aware Improved Hippo Optimization Algorithm for Collision-Free Mobile Robot Path Planning in Complex Grid Environments. [PDF]

Sensors (Basel)
Yuan C, Cai Y, Que H, Pei Y, Zhang X, Xie J, Zhang Q, Mu L, Qiao F.   +8 more
europepmc   +1 more source

Short‐Term Multi‐Horizon Line Loss Rate Forecasting of a Distribution Network Using Attention‐GCN‐LSTM

CAAI Transactions on Intelligence Technology, EarlyView.
ABSTRACT Accurately predicting line loss rates is crucial for effective management in distribution networks, particularly for short‐term multihorizon forecasts ranging from 1 hour to 1 week. In this study, we propose attention‐GCN–LSTM, a novel method that integrates graph convolutional networks (GCN), long short‐term memory (LSTM) and a three‐level ...
Jie Liu, Yijia Cao, Yong Li, Yixiu Guo, Wei Deng   +4 more
wiley   +1 more source

Predictive modeling for physicochemical properties of β-lactam antibiotics through eigenvalue based topological indices and non linear regression techniques. [PDF]

Sci Rep
Yuvaraj A, Kalaimurugan G, Thamizhmaran R, Tajudeen MM, Contreras RC.   +4 more
europepmc   +1 more source

ANPGT: Towards Adaptive Node Property Extraction and Integration

CAAI Transactions on Intelligence Technology, EarlyView.
ABSTRACT Graph transformers (GTs) with elaborate positional/structural encodings (PEs/SEs) have excelled in graph representation learning, especially in graph‐level tasks. However, their potential in large‐scale node classification remains untapped for several reasons: (i) Current PEs/SEs are insufficient in modelling large‐scale real‐world graphs ...
Qin Chen, Yuanyi Ren, Guojie Song, Liang Wang, Bo Zheng   +4 more
wiley   +1 more source

Invariance Principle for Lifts of Geodesic Random Walks. [PDF]

J Theor Probab
Junné J, Redig F, Versendaal R.
europepmc   +1 more source

Realistic PET image synthesis from MRI for automated inference of brain atrophy and Alzheimer's. [PDF]

iScience
Theodorou B, Dadu A, Avants B, Nalls M, Sun J, Faghri F.   +5 more
europepmc   +1 more source

Edge‐Channel Aggregation Network and Two‐Stage Fine Tuning Scheme for Handwritten Dongba Character Recognition

CAAI Transactions on Intelligence Technology, EarlyView.
ABSTRACT Handwritten Dongba Character Recognition (HDCR) contains a large number of visually similar characters with subtle and fragile edge cues, posing severe challenges to feature learning. To address this issue, an Edge Channel Aggregation Network (EdgeCANet) model is proposed.
Xiali Li, Yang Xiao, Xiaojun Bi, Yanlong Luo   +3 more
wiley   +1 more source