Results 201 to 210 of about 136,861 (303)

Leveraging productivity indicators for anomaly detection in swine breeding herds with unsupervised learning. [PDF]

Front Vet Sci
Pedro Mil-Homens M, Wang C, Trevisan G, Dórea F, Linhares DCL, Holtkamp D, Silva GS.   +6 more
europepmc   +1 more source

Resource‐Aware Contrastive Scattering Meta‐Learning for Efficient Few‐Shot Acoustic Anomaly Detection

Advanced Intelligent Systems, EarlyView.
This paper introduces a resource‐aware Contrastive Scattering Meta‐Learning (CSML) framework for acoustic anomaly detection. By leveraging training‐free wavelet scattering and metric‐based meta‐learning, the model achieves competitive performance with only 50 K learnable parameters—a 98% reduction compared to state‐of‐the‐art frameworks—enabling ...
Rami Zewail, Bassem Mokhtar
wiley   +1 more source

Single-station analysis of Campi Flegrei (Italy) seismic signals using multiscale entropy and unsupervised learning. [PDF]

Sci Rep
Grimaldi A, Amoroso O, Scarpetta S, Convertito V, Napolitano F, Messuti G, Capuano P, Nardone L, Gaudiosi G, Galluzzo D.   +9 more
europepmc   +1 more source

VALIANT: A Vision‐Authenticity Language Framework Through Integrated Experts and Aligned Numerical‐Textual Descriptors for Citri Reticulatae Pericarpium

Advanced Intelligent Systems, EarlyView.
Visual features, numerical descriptors, and controlled textual attributes extracted from smartphone images of Chenpi are integrated by VALIANT, a tailored multimodal framework for simultaneous storage‐age classification and authenticity verification. The workflow distinguishes genuine products from suspicious standard operating procedure mimics while ...
Simon C. K. Chan, Bingxin Huang, Hannah H. Kim, Wentao Yu, Yinqian Luo, Terence T. W. Wong   +5 more
wiley   +1 more source

Enhancing Enzyme Activity With Mutation Combinations Guided by Few‐Shot Learning and Causal Inference

Angewandte Chemie, EarlyView.
We present an integrated workflow that predicts activity‐enhancing mutation combinations from minimal experimental data. By proposing in vivo unit yield (yield/expression) as a surrogate for kcat/Km through causal inference, and visualizing local activity landscape, it effectively guides product yield improvement. ABSTRACT Designing enzyme sequences to
Lin Guo, Xiaoguang Yan, Yali Lu, Shengxin Nie, Mingyue Ge, Yukun Li, Weiguo Li, Xiaochun Zhang, Dongmei Liang, Yihan Zhao, Hongxiao Tan, Xiling Chen, Shilong Fan, Yefeng Tang, Jianjun Qiao, Boxue Tian   +15 more
wiley   +2 more sources

PROTECT: Protein circadian time prediction using unsupervised learning. [PDF]

iScience
Ansary Ogholbake A, Cheng Q.
europepmc   +1 more source

An Integrated and Robust Deep Learning Framework for Denoising and Analyzing Single‐Cell Spatial Transcriptomics

Advanced Intelligent Systems, EarlyView.
Single‐cell Spatial Transcriptomics Analysis and Denoising Engine is introduced as a unified deep learning framework that jointly performs denoising, clustering, and gene prioritization in spatial transcriptomics. By integrating linear and nonlinear representations within a dual‐channel architecture, it improves robustness and accuracy, uncovers ...
Yaxuan Cui, Yang Cui, Ruheng Wang, Xin Zeng, Zheyong Zhu, Ding Yi, Ruihua Guo, Tetsuya Sakurai, Kenta Nakai, Xiucai Ye, Leyi Wei, Yong Chen   +11 more
wiley   +1 more source

An Unsupervised Learning Algorithm for the Automatic Classification of Coronary Artery Lesions. [PDF]

Cureus
Szopinska J, Regulski PA, Mazurek M, Grabowski M, Wendykier P, Jozwiak R.   +5 more
europepmc   +1 more source

MDSC: Unsupervised Multipath Decomposition and Synergistic Correction for Efficient Low‐Light Image Enhancement with Detail‐Aware Smoothing

Advanced Intelligent Systems, EarlyView.
This work proposes MDSC, an unsupervised low‐light enhancement framework integrating three core innovations: detail‐aware smoothing, multipath decomposition, and synergistic correction. It suppresses noise, handles rapid illumination variations, and prevents reflectance‐contrast amplification inherent to Retinex separation.
Yong Cheng, De Yu, Yongqi Yuan, Mengjie Ye, Huan Wang, Qian Zhang, Ge Jin   +6 more
wiley   +1 more source

Exploring unsupervised learning techniques for early detection of myocardial ischemia in type 2 diabetes. [PDF]

Front Endocrinol (Lausanne)
Liu B, Hou YJ, Wu P, Han X, Qi H, Yang XY, Wu ZF, Li SJ.   +7 more
europepmc   +1 more source