Results 91 to 100 of about 977,116 (306)
Enhancing diabetes risk prediction through focal active learning and machine learning models.
PLoS ONETo improve the effectiveness of diabetes risk prediction, this study proposes a novel method based on focal active learning strategies combined with machine learning models.
Wangyouchen Zhang +4 more
doaj +2 more sources
APL Machine Learning, 2023 The discovery of the molecular candidates for application in drug targets, biomolecular systems, catalysts, photovoltaics, organic electronics, and batteries necessitates the development of machine learning algorithms capable of rapid exploration of ...
Ayana Ghosh +2 more
doaj +1 more source
Machine Learning for Interactive Systems: Challenges and Future Trends [PDF]
, 2014 National audienceMachine learning has been introduced more than 40 years ago in interactive systems through speech recognition or computer vision. Since that, machine learning gained in interest in the scientific community involved in human- machine ...
Lopes, Manuel, Pietquin, Olivier
core +2 more sources
Portable Low‐Field Magnetic Resonance Imaging in People With Human Immunodeficiency Virus
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective The aging population of people with HIV (PWH) raises heightened concerns regarding accelerated aging and dementia. Portable, low‐field MRI (LF‐MRI) is an innovative technology that could enhance access and facilitate routine monitoring of PWH.
Annabel Sorby‐Adams +14 more
wiley +1 more source
Active learning model used for android malware detection
Machine Learning with ApplicationsSmartphones have become one of the main products in today’s world. However, the security risks of smartphones are high compared with those of other devices. Smartphone users face threats to their privacy and property protection.
Md․Habibullah Shakib +5 more
doaj +1 more source
A surrogate modeling and adaptive sampling toolbox for computer based design [PDF]
, 2010 An exceedingly large number of scientific and engineering fields are confronted with the need for computer simulations to study complex, real world phenomena or solve challenging design problems.
Couckuyt, Ivo +4 more
core
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Background Myasthenia gravis (MG) is a rare disorder characterized by fluctuating muscle weakness with potential life‐threatening crises. Timely interventions may be delayed by limited access to care and fragmented documentation. Our objective was to develop predictive algorithms for MG deterioration using multimodal telemedicine data ...
Maike Stein +7 more
wiley +1 more source
regAL: Python package for active learning of regression problems
Machine Learning: Science and TechnologyIncreasingly more research areas rely on machine learning methods to accelerate discovery while saving resources. Machine learning models, however, usually require large datasets of experimental or computational results, which in certain fields—such as ...
Elizaveta Surzhikova, Jonny Proppe
doaj +1 more source
Hospital Readmission After Traumatic Brain Injury Hospitalization in Community‐Dwelling Older Adults
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective To examine the risk of hospital readmission after an index hospitalization for TBI in older adults. Methods Using data from the Atherosclerosis Risk in Communities (ARIC) study, we used propensity score matching of individuals with an index TBI‐related hospitalization to individuals with (1) non‐TBI hospitalizations (primary analysis)
Rachel Thomas +7 more
wiley +1 more source
ICE: Enabling Non-Experts to Build Models Interactively for Large-Scale Lopsided Problems
, 2014 Quick interaction between a human teacher and a learning machine presents numerous benefits and challenges when working with web-scale data. The human teacher guides the machine towards accomplishing the task of interest.
Aparna Lakshmiratan +10 more
core