Results 41 to 50 of about 11,467,392 (329)

Supervised machine learning and active learning in classification of radiology reports [PDF]

Journal of the American Medical Informatics Association, 2014
This paper presents an automated system for classifying the results of imaging examinations (CT, MRI, positron emission tomography) into reportable and non-reportable cancer cases. This system is part of an industrial-strength processing pipeline built to extract content from radiology reports for use in the Victorian Cancer Registry.In addition to ...
Dung H M, Nguyen, Jon D, Patrick
openaire   +2 more sources

Using Machine Learning to Analyze Merger Activity [PDF]

Frontiers in Applied Mathematics and Statistics, 2021
An unprecedented amount of access to data, “big data (or high dimensional data),” cloud computing, and innovative technology have increased applications of artificial intelligence in finance and numerous other industries. Machine learning is used in process automation, security, underwriting and credit scoring, algorithmic trading and robo-advisory. In
Tiffany Jiang, Tiffany Jiang
openaire   +2 more sources

Active-Learning Approaches for Landslide Mapping Using Support Vector Machines

Remote Sensing, 2021
Ex post landslide mapping for emergency response and ex ante landslide susceptibility modelling for hazard mitigation are two important application scenarios that require the development of accurate, yet cost-effective spatial landslide models.
Zhihao Wang, Alexander Brenning
doaj   +1 more source

Yoked learning in molecular data science

Artificial Intelligence in the Life Sciences
Active machine learning is an established and increasingly popular experimental design technique where the machine learning model can request additional data to improve the model's predictive performance. It is generally assumed that this data is optimal
Zhixiong Li, Yan Xiang, Yujing Wen, Daniel Reker   +3 more
doaj   +1 more source

Machine Learning Approaches to Retrieve High-Quality, Clinically Relevant Evidence From the Biomedical Literature: Systematic Review

JMIR Medical Informatics, 2021
BackgroundThe rapid growth of the biomedical literature makes identifying strong evidence a time-consuming task. Applying machine learning to the process could be a viable solution that limits effort while maintaining accuracy.
Wael Abdelkader, Tamara Navarro, Rick Parrish, Chris Cotoi, Federico Germini, Alfonso Iorio, R Brian Haynes, Cynthia Lokker   +7 more
doaj   +1 more source

Machine-learned interatomic potentials by active learning: amorphous and liquid hafnium dioxide

npj Computational Materials, 2020
We propose an active learning scheme for automatically sampling a minimum number of uncorrelated configurations for fitting the Gaussian Approximation Potential (GAP).
G. Sivaraman, A. Krishnamoorthy, Matthias Baur, Christian Holm, M. Stan, Gábor Csányi, C. Benmore, Á. Vázquez-Mayagoitia   +7 more
semanticscholar   +1 more source

The Role of Active Learning in Modern Machine Learning

CoRR
Even though Active Learning (AL) is widely studied, it is rarely applied in contexts outside its own scientific literature. We posit that the reason for this is AL's high computational cost coupled with the comparatively small lifts it is typically able to generate in scenarios with few labeled points.
Thorben Werner, Lars Schmidt-Thieme, Vijaya Krishna Yalavarthi   +2 more
openaire   +2 more sources

Multi-Class Adaptive Active Learning for Predicting Student Anxiety

IEEE Access
This research delves into applying active and machine learning techniques to predict student anxiety. This research explores how these technologies can be explored to understand and predict student anxiety levels.
Ahmad Almadhor, Sidra Abbas, Gabriel Avelino Sampedro, Shtwai Alsubai, Stephen Ojo, Abdullah Al Hejaili, Lubomira Strazovska   +6 more
doaj   +1 more source

Accelerating crystal structure prediction by machine-learning interatomic potentials with active learning [PDF]

Physical review B, 2018
We propose a methodology for crystal structure prediction that is based on the evolutionary algorithm USPEX and the machine-learning interatomic potentials actively learning on-the-fly.
E. Podryabinkin, E. Tikhonov, A. Shapeev, A. Oganov   +3 more
semanticscholar   +1 more source

Active learning on stacked machine learning techniques for predicting compressive strength of alkali-activated ultra-high-performance concrete

Archives of Civil and Mechanical Engineering
Conventional ultra-high performance concrete (UHPC) has excellent development potential. However, a significant quantity of CO2 is produced throughout the cement-making process, which is in contrary to the current worldwide trend of lowering emissions ...
Farzin Kazemi, Torkan Shafighfard, R. Jankowski, Doo-Yeol Yoo   +3 more
semanticscholar   +1 more source