Results 41 to 50 of about 8,272,458 (291)

Exploring the Synergy between two Modular Learning Techniques for Automated Planning [PDF]

In the last decade the emphasis on improving the operational performance of domain independent automated planners has been in developing complex techniques which merge a range of different strategies.
Chrpa, Lukáš, Vallati, Mauro, McCluskey, T.L., Fuentetaja, Raquel   +3 more
core   +2 more sources

Automated Prediction of Crack Propagation Using H2O AutoML

Sensors, 2023
Crack propagation is a critical phenomenon in materials science and engineering, significantly impacting structural integrity, reliability, and safety across various applications.
Intisar Omar, Muhammad Khan, Andrew Starr, Khaled Abou Rok Ba   +3 more
doaj   +1 more source

The impact of leukocyte‐associated RNA retention on platelet RNA‐based cancer detection

Molecular Oncology, EarlyView.
Platelets carry RNA signals that may help detect cancer, but platelet samples can also retain RNA from white blood cells. We found that this retained white blood cell signal did not significantly affect machine learning cancer classification, suggesting that the cancer‐related RNA signal in platelets is largely independent of leukocyte‐associated RNA ...
Michał Sieczczyński, Krzysztof Pastuszak, Anna Samelak‐Czajka, Zuzanna Cemka, Paulina Jackowiak, Tomasz Stefaniak, Sylwia Łapińska‐Szumczyk, Jacek Jassem, Anna J. Żaczek, Matthew T. Rondina, Myron G. Best, Sjors G. J. G. In 't Veld, Anna Supernat   +12 more
wiley   +1 more source

AutoPrognosis 2.0: Democratizing diagnostic and prognostic modeling in healthcare with automated machine learning

PLOS Digital Health, 2023
Diagnostic and prognostic models are increasingly important in medicine and inform many clinical decisions. Recently, machine learning approaches have shown improvement over conventional modeling techniques by better capturing complex interactions ...
Fergus Imrie, Bogdan Cebere, Eoin F. McKinney, Mihaela van der Schaar   +3 more
doaj   +2 more sources

Directed evolution of enzymes at the crossroads of tradition and innovation

FEBS Open Bio, EarlyView.
An iterative cycle of data‐driven enzyme optimization comprising four stages: genetic diversification of a template enzyme, expression of protein variants, high‐throughput evaluation, and machine‐learning‐guided redesign of the next variant library.
Maria Tomkova, Andrej Mirossay, Erik Sedlak   +2 more
wiley   +1 more source

Recent advancements in machine learning for bone marrow cell morphology analysis

Frontiers in Medicine
As machine learning progresses, techniques such as neural networks, decision trees, and support vector machines are being increasingly applied in the medical domain, especially for tasks involving large datasets, such as cell detection, recognition ...
Yifei Lin, Yifei Lin, Qingquan Chen, Qingquan Chen, Tebin Chen   +4 more
doaj   +1 more source

Multi-Agent Automated Machine Learning

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023
In this paper, we propose multi-agent automated machine learning (MA2ML) with the aim to effectively handle joint optimization of modules in automated machine learning (AutoML). MA2ML takes each machine learning module, such as data augmentation (AUG), neural architecture search (NAS), or hyper-parameters (HPO), as an agent and the final performance as
Zhaozhi Wang, Kefan Su, Jian Zhang 0018, Huizhu Jia, Qixiang Ye, Xiaodong Xie, Zongqing Lu 0002   +6 more
openaire   +4 more sources

Combining Automated Microfluidic Experimentation with Machine Learning for Efficient Polymerization Design [PDF]

, 2020
Understanding polymerization reactions has challenges relating to the complexity of the systems, hazards associated with the reagents, environmental footprint of the operations, and the highly non-linear topologies of reaction spaces.
Ryan L., Hartman, Benjamin, Rizkin, Albert, Shkolnik, Neil, Ferraro   +3 more
core   +2 more sources

RNA Sequencing Resolves Cryptic Pathogenic Variants in Mitochondrial Disease

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Mitochondrial diseases are the most common inherited metabolic disorders, characterized by pronounced clinical and genetic heterogeneity that complicates molecular diagnosis. Although DNA‐based sequencing approaches have become standard in genetic testing, up to half of patients remain without a definitive diagnosis.
Zhimei Liu, Xin Duan, Fatemeh Peymani, Jia Wang, Chengjia Bao, Chaolong Xu, Ying Zou, Zixuan Zhang, Yunxi Zhang, Tongyue Li, Martin Pavlov, Junling Wang, Minhan Song, Tianyu Song, Xiaodi Han, Mingxi Sun, Danmin Shen, Ruoyu Duan, Huafang Jiang, Manting Xu, Holger Prokisch, Fang Fang   +21 more
wiley   +1 more source

Structure Learning and Hyperparameter Optimization Using an Automated Machine Learning (AutoML) Pipeline

Information, 2023
In this paper, we built an automated machine learning (AutoML) pipeline for structure-based learning and hyperparameter optimization purposes. The pipeline consists of three main automated stages. The first carries out the collection and preprocessing of
Konstantinos Filippou, George Aifantis, George A. Papakostas, George E. Tsekouras   +3 more
doaj   +1 more source