Results 201 to 210 of about 93,522 (265)

From prediction to intervention: Paradigm shifts in causal AI for precision medicine and large‐scale cohorts

VIEW, EarlyView.
Large‐scale cohorts and multimodal biomedical data have enabled powerful predictive models for clinical risk stratification, but prediction alone cannot guide effective interventions. This review introduces causal artificial intelligence as a design‐first framework that integrates target trial emulation, causal discovery, and robust effect estimation ...
Linlin Cao, Yudian Xu, Yifan Chen, Honghao Huang, Wei Xu, Jun Pu   +5 more
wiley   +1 more source

Hybrid machine learning models for enhanced arrhythmia detection from ECG signals using autoencoder and convolution features. [PDF]

PLoS One
Biswas S, Sahoo PK, Kumar B, Rath A, Jain P, Panda G, Liu H, Wang X.   +7 more
europepmc   +1 more source

Image and video analysis using graph neural network for Internet of Medical Things and computer vision applications

CAAI Transactions on Intelligence Technology, EarlyView.
Abstract Graph neural networks (GNNs) have revolutionised the processing of information by facilitating the transmission of messages between graph nodes. Graph neural networks operate on graph‐structured data, which makes them suitable for a wide variety of computer vision problems, such as link prediction, node classification, and graph classification.
Amit Sharma, Ashutosh Sharma, Alexey Tselykh, Alexander Bozhenyuk, Byung‐Gyu Kim   +4 more
wiley   +1 more source

Haplotype-based autoencoders can reduce the dataset dimension and estimate haplotype block effects in different crop species. [PDF]

BMC Bioinformatics
Heilmann PG, Grosch E, Frisch M, Herrmann M, Beuch S, Kurra V, Mascher M, Avni R, Oldach K, Röhrs I, Hanemann A, Mehta RR, Reinbrecht C, Serfling A, Stahl A, Stucke M, Abbadi A, Kox T, Zenke-Philippi C.   +18 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

One Patch Is All You Need: Joint Surface Material Reconstruction and Classification from Minimal Visual Cues. [PDF]

Sensors (Basel)
Penchala S, Money G, Marques G, Wood S, Kirschman J, Atkison T, Rahimi S, Amiri Golilarz N.   +7 more
europepmc   +1 more source

Combining kernelised autoencoding and centroid prediction for dynamic multi‐objective optimisation

CAAI Transactions on Intelligence Technology, EarlyView.
Abstract Evolutionary algorithms face significant challenges when dealing with dynamic multi‐objective optimisation because Pareto optimal solutions and/or Pareto optimal fronts change. The authors propose a unified paradigm, which combines the kernelised autoncoding evolutionary search and the centroid‐based prediction (denoted by KAEP), for solving ...
Zhanglu Hou, Juan Zou, Gan Ruan, Yuan Liu, Yizhang Xia   +4 more
wiley   +1 more source

An efficient framework for protein-protein interaction prediction by integrating stacked denoising autoencoders and random ferns. [PDF]

iScience
Wang Z, Wang L, Li Y, You ZH, Li YC.
europepmc   +1 more source

DrLS: Distortion‐Resistant Lossless Steganography via Colour Depth Interpolation

CAAI Transactions on Intelligence Technology, EarlyView.
ABSTRACT The lossless data steganography is to hide a certain amount of information into a container image. Previous lossless steganography methods fail to strike a balance between capacity, imperceptibility, accuracy, and robustness, commonly vulnerable to distortion on container images.
Youmin Xu, Xuanyu Zhang, Xiandong Meng, Jian Zhang   +3 more
wiley   +1 more source

"A novel adaptive gesture recognition framework for bionic hands using Stacked Autoencoder (SAE), Adaptive Bayesian Feature Selection (ABFS), MODWT, and Hybrid sEMG-MMG Sensor Modality". [PDF]

MethodsX
Yadav AP, Patil SR.
europepmc   +1 more source