Results 101 to 110 of about 181,047 (259)

scPML: pathway-based multi-view learning for cell type annotation from single-cell RNA-seq data. [PDF]

Commun Biol, 2023
Du ZH, Hu WL, Li JQ, Shang X, You ZH, Chen ZZ, Huang YA.   +6 more
europepmc   +1 more source

Machine Learning‐Supported Analysis for Predicting and Visualizing Nonlinear Relationships Between Material Properties in Electroplated Chromium Layers

Advanced Engineering Materials, EarlyView.
This study applies machine learning regression to predict chromium layer thickness in decorative trivalent chromium electroplating, using 441 experiments from laboratory‐scale (1L) and pilot‐scale (14L) setups. Tree‐based models, particularly CatBoost, outperformed linear regression by capturing nonlinear parameter interactions (R2$R^2$ up to 0.77 ...
Christoph Baumer, Christopher Mai, Luca Eisentraut, Andreas Bund, Ricardo Buettner   +4 more
wiley   +1 more source

Structure-guided decoupled contrastive framework for partial multi-view incomplete multi-label classification

Journal of King Saud University: Computer and Information Sciences
Recently, multi-view multi-label learning has gained significant attention due to its applicability in various domains. However, due to the limitations of data collection and the subjectivity of manual labeling, multi-view multi-label learning often ...
Linqian Yang, Tao Zhuang, Xing Jin, Tianyang Cao, Xiyin Wu   +4 more
doaj   +1 more source

A Practical Noise2Noise Denoising Pipeline for High‐Throughput Raman Spectroscopy

Advanced Engineering Materials, EarlyView.
A lightweight and reproducible denoising pipeline for high‐throughput Raman spectroscopy is introduced, based on a 1D convolutional autoencoder trained with a Noise2Noise strategy. Using only repeated short‐exposure acquisitions, the method suppresses stochastic noise without reference spectra, enabling reliable spectral reconstruction while preserving
David Martin‐Calle, Cesar Alvarez‐Llamas, Vincent Motto‐Ros, Christophe Dujardin, Jérémie Margueritat, David Rodney   +5 more
wiley   +1 more source

Twin Contrastive Learning for Multi-View Clustering

IEEE Access
Multi-view Clustering (MVC) utilizes the complementary and consistent information between multiple views or modal data to more comprehensively reveal the inherent structure and patterns of data. However, mining the invariance and complementarity of multi-
Ansi Zhang, Yang Zheng, Xiaohua Wang, Jie Hu   +3 more
doaj   +1 more source

Self-paced Multi-view Learning for CT-based severity assessment of COVID-19. [PDF]

Biomed Signal Process Control, 2023
Liu Y, Chen B, Zhang Z, Yu H, Ru S, Chen X, Lu G.   +6 more
europepmc   +1 more source

New AI‐Assisted Approach for Expanding the Solution Space: Application to Lattice Structure Design

Advanced Engineering Materials, EarlyView.
This work introduces an innovative framework for designing structured materials by ex panding the design space through reparameterization of qualitative variables into continuous structural descriptors. Combined with machine‐learning‐based prediction and multi‐objective optimization, the approach enables the discovery of novel lattice architectures ...
G. H. Gahimbare, Y. Beideler, D. Chune, R. De Guio, T. Roland, H. Chibane   +5 more
wiley   +1 more source

Incomplete multi-view partial multi-label classification via deep semantic structure preservation

Complex & Intelligent Systems
Recent advances in multi-view multi-label learning are often hampered by the prevalent challenges of incomplete views and missing labels, common in real-world data due to uncertainties in data collection and manual annotation.
Chaoran Li, Xiyin Wu, Pai Peng, Zhuhong Zhang, Xiaohuan Lu   +4 more
doaj   +1 more source

scAWMV: an adaptively weighted multi-view learning framework for the integrative analysis of parallel scRNA-seq and scATAC-seq data. [PDF]

Bioinformatics, 2023
Zeng P, Ma Y, Lin Z.
europepmc   +1 more source

All‐in‐One Analog AI Hardware: On‐Chip Training and Inference with Conductive‐Metal‐Oxide/HfOx ReRAM Devices

Advanced Functional Materials, EarlyView.
An all‐in‐one analog AI accelerator is presented, enabling on‐chip training, weight retention, and long‐term inference acceleration. It leverages a BEOL‐integrated CMO/HfOx ReRAM array with low‐voltage operation (<1.5 V), multi‐bit capability over 32 states, low programming noise (10 nS), and near‐ideal weight transfer.
Donato Francesco Falcone, Victoria Clerico, Wooseok Choi, Tommaso Stecconi, Folkert Horst, Laura Bégon‐Lours, Matteo Galetta, Antonio La Porta, Nikhil Garg, Fabien Alibart, Bert Jan Offrein, Valeria Bragaglia   +11 more
wiley   +1 more source