Unsupervised machine learning of topological phase transitions from experimental data [PDF]
Machine Learning: Science and Technology, 2021 Identifying phase transitions is one of the key challenges in quantum many-body physics. Recently, machine learning methods have been shown to be an alternative way of localising phase boundaries from noisy and imperfect data without the knowledge of the
Niklas Käming +6 more
semanticscholar +1 more source
Data-Driven Signal–Noise Classification for Microseismic Data Using Machine Learning
Energies, 2021 It is necessary to monitor, acquire, preprocess, and classify microseismic data to understand active faults or other causes of earthquakes, thereby facilitating the preparation of early-warning earthquake systems.
Sungil Kim +3 more
doaj +1 more source
Big Data and Cognitive Computing, 2022 The purpose of this paper is to reveal how social network marketing (SNM) can affect consumers’ purchase behavior (CPB). We used the combination of structural equation modeling (SEM) and unsupervised machine learning approaches as an innovative method ...
P. Ebrahimi +5 more
semanticscholar +1 more source
Identifying multiple sclerosis subtypes using unsupervised machine learning and MRI data
Nature Communications, 2021 Multiple sclerosis (MS) can be divided into four phenotypes based on clinical evolution. The pathophysiological boundaries of these phenotypes are unclear, limiting treatment stratification.
A. Eshaghi +11 more
semanticscholar +1 more source
Unsupervised machine learning for discovery of promising half-Heusler thermoelectric materials
npj Computational Materials, 2022 Thermoelectric materials can be potentially applied to waste heat recovery and solid-state cooling because they allow a direct energy conversion between heat and electricity and vice versa.
Xue Jia +14 more
semanticscholar +1 more source
Railway defect detection based on track geometry using supervised and unsupervised machine learning
Structural Health Monitoring, 2022 Track quality affects passenger comfort and safety. To maintain the quality of the track, track geometry and track component defects are inspected routinely. Track geometry is inspected using a track geometry car (TGC).
J. Sresakoolchai, S. Kaewunruen
semanticscholar +1 more source
Towards Model Generalization for Intrusion Detection: Unsupervised Machine Learning Techniques
Journal of Network and Systems Management, 2021 Through the ongoing digitization of the world, the number of connected devices is continuously growing without any foreseen decline in the near future.
Miel Verkerken +4 more
semanticscholar +1 more source
Applications of Unsupervised Machine Learning in Autism Spectrum Disorder Research: a Review
Review Journal of Autism and Developmental Disorders, 2022 Large amounts of autism spectrum disorder (ASD) data is created through hospitals, therapy centers, and mobile applications; however, much of this rich data does not have pre-existing classes or labels.
Chelsea Parlett-Pelleriti +3 more
semanticscholar +1 more source
Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges
IEEE Access, 2019 While machine learning and artificial intelligence have long been applied in networking research, the bulk of such works has focused on supervised learning.
Muhammad Usama +7 more
doaj +1 more source
Adversarial Learning Approach to Unsupervised Labeling of Fine Art Paintings
IEEE Access, 2021 An automatic classification of fine art images is limited by the scarcity of high-quality labels made by art experts. This study aims to provide meaningful automatic labeling of fine art paintings (machine labeling) without the need for human annotation.
Catherine Sandoval +2 more
doaj +1 more source