Results 71 to 80 of about 137,057 (303)
CBARS: cluster based classification for activity recognition systems [PDF]
, 2012 Activity recognition focuses on inferring current user activities by leveraging sensory data available on today’s sensor rich environment. Supervised learning has been applied pervasively for activity recognition.
Gaber, Mohamed Medhat +11 more
core +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
Unsupervised Learning Bioreactor Regimes [PDF]
Computers & Chemical EngineeringEfficient operation of bioreactors is crucial for the success of biomanufacturing processes. Traditional Computational Fluid Dynamics (CFD) simulations provide detailed insights but often involve lengthy computation times and complexity, hindering their practicality for real-time applications.
Víctor Puig I Laborda +4 more
openaire +3 more sources
A Practical Noise2Noise Denoising Pipeline for High‐Throughput Raman Spectroscopy
Advanced Engineering Materials, EarlyView.A lightweight and reproducible denoising pipeline for high‐throughput Raman spectroscopy is introduced, based on a 1D convolutional autoencoder trained with a Noise2Noise strategy. Using only repeated short‐exposure acquisitions, the method suppresses stochastic noise without reference spectra, enabling reliable spectral reconstruction while preserving
David Martin‐Calle +5 more
wiley +1 more source
Unsupervised two-class & multi-class support vector machines for abnormal traffic characterization. [PDF]
, 2009 Although measurement-based real-time traffic classification has received considerable research attention, the timing constraints imposed by the high accuracy requirements and the learning phase of the algorithms employed still remain a challenge. In this
Kim, Hyun-chul +7 more
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Explainable Unsupervised Machine Learning for Cyber-Physical Systems
IEEE Access, 2021 Cyber-Physical Systems (CPSs) play a critical role in our modern infrastructure due to their capability to connect computing resources with physical systems.
Chathurika S Wickramasinghe +4 more
doaj +1 more source
Advanced Functional Materials, EarlyView.Field‐free spin‐orbit torque domain‐wall synapses integrated with stochastic MTJ neurons enable compact hardware Boltzmann machines. Leveraging intrinsic stochasticity and multi‐level conductance, the system achieves efficient probabilistic learning with high accuracy, demonstrating a scalable spintronic platform for energy‐efficient edge AI.
Aijaz H. Lone +8 more
wiley +1 more source
Continual Unsupervised Representation Learning
CoRR, 2019 NeurIPS ...
Rao, D +5 more
openaire +4 more sources
Tuning Redox Dynamics in Cu‐Based Electrocatalysts: Effect of Secondary Metals
Advanced Functional Materials, EarlyView.Advanced analysis of operando X‐ray absorption spectroscopy data reveals that secondary metals affect the redox processes in Cu‐based catalysts for electrocatalytic CO2 reduction. This influences the amount of oxides formed under pulsed reaction conditions and the distribution of reaction products.
Martina Rüscher +17 more
wiley +1 more source
Unsupervised Relation Extraction for E-Learning Applications [PDF]
, 2011 A thesis submitted in partial fulfilment of the requirements of the University of Wolverhampton for the degree of Doctor of PhilosophyIn this modern era many educational institutes and business organisations are adopting the e-Learning approach as it ...
Naveed Afzal, Afzal, Naveed
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