Results 241 to 250 of about 1,171,833 (351)

Automated CLL cell population detection using a weakly supervised approach and CLL MRD flow cytometry data

Cytometry Part B: Clinical Cytometry, EarlyView.
Abstract Minimal/measurable residual disease detection is routinely performed as part of post‐diagnostic treatment plans for many types of cancer, for which multiparameter flow cytometry is one possible modality frequently used. We propose a machine learning approach for binary prediction of minimal residual disease status with flow cytometry data. Our
Wikum Dinalankara, Chandler Sy, Jiani Chai, Paul B. Barone, Luigi Marchionni, Paul D. Simonson   +5 more
wiley   +1 more source

Dynamic machine learning for supervised and unsupervised classification

, 2016
Adela-Maria Sîrbu
openalex   +1 more source

Unsupervised structural damage detection and localization using deep learning and machine learning

, 2021
Zilong Wang
openalex   +1 more source

Real‐time monitoring of tunnel structures using digital twin and artificial intelligence: A short overview

Deep Underground Science and Engineering, EarlyView.
How artificial intelligence (AI) and digital twin (DT) technologies are revolutionizing tunnel surveillance, offering proactive maintenance strategies and enhanced safety protocols. It explores AI's analytical power and DT's virtual replicas of infrastructure, emphasizing their role in optimizing maintenance and safety in tunnel management.
Mohammad Afrazi, Danial Jahed Armaghani, Hossein Afrazi, Hadi Fattahi, Pijush Samui   +4 more
wiley   +1 more source

Biokinetic Profiles in Patellofemoral Pain Patients During a Step-Down Task: An Unsupervised Machine Learning Approach. [PDF]

Orthop J Sports Med
Metsavaht L, Leporace G, Gonzalez FF, Guadagnin EC, Mameri E, Gustafson JA, Yanke A, Chahla J, Luzo M.   +8 more
europepmc   +1 more source

Unsupervised Machine Learning for Osteoporosis Diagnosis Using Singh Index Clustering on Hip Radiographs [PDF]

M. Vimaladevi, K. Vijaya, Abhay Lal, S R Rithika, S. Subramaniam, Mohamed Sameer   +5 more
openalex   +1 more source

A review on rockburst prediction and prevention to shape an ontology‐based framework for better decision‐making for underground excavations

Deep Underground Science and Engineering, EarlyView.
A scientometric analysis of 2449 journal articles and a comprehensive review of 336 papers were conducted, discussing and identifying challenges and research gaps in rockburst prediction and prevention and proposing an ontology‐based framework for better decision‐making in underground excavations. Abstract With underground engineering projects becoming
Hongchuan Yan, Haijiang Li, Hongqiang Xie, Mingli Xiao, Jianliang Pei, Huaizhong Liu, Jiangda He   +6 more
wiley   +1 more source

Using unsupervised machine learning methods to cluster cardio-metabolic profile of the middle-aged and elderly Chinese with general and central obesity. [PDF]

BMC Cardiovasc Disord
Xue Y, Song M, Ung COL, Hu H.
europepmc   +1 more source

Quantifying cell densities and biovolumes of phytoplankton communities and functional groups using scanning flow cytometry, machine learning and unsupervised clustering

, 2018
Mridul K. Thomas, Simone Fontana, Marta Reyes, Francesco Pomati   +3 more
openalex   +2 more sources

Gaborlet‐guided sparse filtering: A novel intelligent method for lithology identification by vibration signals while drilling

Deep Underground Science and Engineering, EarlyView.
The flowchart illustrates rock specimen testing, vibration signal acquisition, and feature extraction with Gaborlet and sparse filtering for classification. Abstract Traditional lithology identification methods mainly rely on core sampling and well‐logging data.
Jian Hao, Danqi Zhang, Zhenqi Song, Jinrui Wang, Qi Wang, Heqing Liu   +5 more
wiley   +1 more source