Results 101 to 110 of about 1,171,833 (351)

Comparative Analysis of Anomaly Detection Approaches in Firewall Logs: Integrating Light-Weight Synthesis of Security Logs and Artificially Generated Attack Detection

Sensors
Detecting anomalies in large networks is a major challenge. Nowadays, many studies rely on machine learning techniques to solve this problem. However, much of this research depends on synthetic or limited datasets and tends to use specialized machine ...
Adrian Komadina, Ivan Kovačević, Bruno Štengl, Stjepan Groš   +3 more
doaj   +1 more source

Tidal debris from Omega Centauri discovered with unsupervised machine learning

, 2023
Kris Youakim, K. Lind, Iryna Kushniruk
openalex   +1 more source

Integrating Spatial Proteogenomics in Cancer Research

Advanced Science, EarlyView.
Xx xx. ABSTRACT Background: Spatial proteogenomics marks a paradigm shift in oncology by integrating molecular analysis with spatial information from both spatial proteomics and other data modalities (e.g., spatial transcriptomics), thereby unveiling tumor heterogeneity and dynamic changes in the microenvironment.
Yida Wang, Yang Wu, Feng Zhang, Parthiban Periasamy, Haiyue You, Denise Goh, Rachel Elizabeth Ann Fincham, Xin Ning, Danping Wu, Lu Liu, Ying Jiang, Zhiwen Qian, Joe Yeong, Yan Zhang   +13 more
wiley   +1 more source

Advancing Precision Nutrition Through Multimodal Data and Artificial Intelligence

Advanced Science, EarlyView.
Individual responses to food vary dramatically, challenging traditional dietary advice. This review explores how the unique genetic makeup, gut microbiome, and brain activity shape host metabolic health. We examine how artificial intelligence integrates these multimodal data to predict individualized dietary needs, moving beyond one‐size‐fits‐all ...
Yuanqing Fu, Ke Zhang, Zelei Miao, Gaoyi Yang, Yujing Huang, Ju‐Sheng Zheng   +5 more
wiley   +1 more source

Performance of unsupervised machine learning methods using chi-squared weights for LiDAR point cloud filtering in urban areas

, 2021
Alper Şen, Barış Süleymanoğlu, Metin Soycan   +2 more
openalex   +2 more sources

Multi‐Scale Mapping of Gene Expression from Whole‐slide Images for Identifying Phenotype‐Associated Subpopulations

Advanced Science, EarlyView.
BiSCALE: A pathology‐driven deep learning framework for multi‐scale gene expression prediction from whole‐slide images. It accurately infers bulk and near‐cellular spot‐level expression, links predictions to clinical phenotypes, identifies disease‐associated niches, and enables applications in risk stratification and cell‐identity annotation, providing
Hailong Zheng, Jiajing Xie, Luqi Wang, Hailong Xie, Tong Zhi, Yujia Guo, Bofeng Zhu, Dong Wang, Yu Chen   +8 more
wiley   +1 more source

Using Unsupervised Machine Learning to make new discoveries in space data

, 2023
Giovanni Lapenta, F. Califano, Romain Dupuis, Maria Elena Innocenti, Giorgio Pedrazzi   +4 more
openalex   +2 more sources

Unsupervised machine learning for exploratory data analysis in imaging mass spectrometry

Mass spectrometry reviews (Print), 2019
Imaging mass spectrometry (IMS) is a rapidly advancing molecular imaging modality that can map the spatial distribution of molecules with high chemical specificity.
N. Verbeeck, R. Caprioli, R. Van de Plas
semanticscholar   +1 more source

Unsupervised Hierarchical Symbolic Regression for Interpretable Property Modeling in Complex Multi‐Variable Systems

Advanced Science, EarlyView.
UHSR translates complex chemical behavior into clear and explainable equations. Applied to thin‐layer chromatography, it automatically uncovers the mathematical rules linking a molecule's structure to its polarity. This approach matches the accuracy of advanced AI while providing interpretable results, earning greater trust from chemists. The method is
Siyu Lou, Chengchun Liu, Dongxiao Zhang, Yuntian Chen, Fanyang Mo   +4 more
wiley   +1 more source

Unsupervised Machine Learning for Identifying Challenging Behavior Profiles to Explore Cluster-Based Treatment Efficacy in Children With Autism Spectrum Disorder: Retrospective Data Analysis Study [PDF]

, 2021
Julie Gardner-Hoag, Marlena N. Novack, Chelsea Parlett-Pelleriti, Elizabeth S. Stevens, Dennis R. Dixon, Erik Linstead   +5 more
openalex   +1 more source