scadsfct > machine

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publications (hide)18
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2A methodology for direct parameter identification for experimental results using machine learning—Real world application to the highly non-linear deformation behavior of FRP
J. Gerritzen, A. Hornig, P. Winkler, and M. Gude. Computational Materials Science, (2024)
7 days ago by @scadsfct
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FRP
Real
application
behavior
deformation
direct
experimental
highly
identification
learning
machine
non-linear
parameter
results
using
world
FRPRealapplicationbehaviordeformationdirectexperimentalhighlyidentificationlearningmachinenon-linearparameterresultsusingworld
(0)
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1Chapter 5 - Artificial intelligence (AI) enhanced nanomotors and active matter
F. Cichos, S. Landin, and R. Pradip. Intelligent Nanotechnology, Elsevier, (2023)
a month ago by @scadsfct
show all tags
Active
Feedback
Machine
Multi
Optical
Reinforcement
agent
control
control,
learning,
particles,
reinforcement
ActiveFeedbackMachineMultiOpticalReinforcementagentcontrolcontrol,learning,particles,reinforcement
(0)
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1Stability selection enables robust learning of partial differential equations from limited noisy data
S. Maddu, B. Cheeseman, I. Sbalzarini, and C. Müller. (2019)
a month ago by @scadsfct
show all tags
(cs.LG),
(math.NA),
(physics.data-an),
Analysis
Analysis,
Computer
Data
FOS:
Learning
Machine
Mathematics,
Numerical
Physical
Probability
Statistics
and
information
sciences
sciences,
(cs.LG),(math.NA),(physics.data-an),AnalysisAnalysis,ComputerDataFOS:LearningMachineMathematics,NumericalPhysicalProbabilityStatisticsandinformationsciencessciences,
(0)
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1NDP-RANK: Prediction and ranking of NDP systems performance using machine learning
V. Iskandar, M. Abd El Ghany, and D. Goehringer. Microprocessors and Microsystems, (2023)
2 months ago by @scadsfct
show all tags
Design
Machine
Modeling,
Near-data
Prediction,
exploration
learning,
processing,
space
DesignMachineModeling,Near-dataPrediction,explorationlearning,processing,space
(0)
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1Common features in lncRNA annotation and classification: A survey
C. Klapproth, R. Sen, P. Stadler, S. Findeiß, and J. Fallmann. Noncoding RNA, 7 (4): 77 (December 2021)
2 months ago by @scadsfct
show all tags
classification
coding
extraction;
feature
learning
lncRNA;
machine
problems;
sequence;
classificationcodingextraction;featurelearninglncRNA;machineproblems;sequence;
(0)
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1Artificial intelligence in liver diseases: Improving diagnostics, prognostics and response prediction
D. Nam, J. Chapiro, V. Paradis, T. Seraphin, and J. Kather. JHEP Rep., 4 (4): 100443 (April 2022)
2 months ago by @scadsfct
show all tags
AI,
Artificial
CNN,
Communications
DICOM,
Diagnosis;
Digital
HCC,
Imaging
Individual
ML,
MVI,
Medicine;
NAFLD,
NASH,
Prognosis
Reporting
TACE,
TRIPOD,
Transparent
WSIs,
a
and
artificial
carcinoma;
chemoembolisation;
convolutional
data
deep
diagnostic
disease;
fatty
for
hepatocellular
images;
imaging;
in
integration
intelligence;
invasion;
learning;
liver
machine
microvascular
model
multimodal
multivariable
network;
neural
non-alcoholic
of
or
prediction
slide
steatohepatitis;
support
system;
transarterial
whole
AI,ArtificialCNN,CommunicationsDICOM,Diagnosis;DigitalHCC,ImagingIndividualML,MVI,Medicine;NAFLD,NASH,PrognosisReportingTACE,TRIPOD,TransparentWSIs,aandartificialcarcinoma;chemoembolisation;convolutionaldatadeepdiagnosticdisease;fattyforhepatocellularimages;imaging;inintegrationintelligence;invasion;learning;livermachinemicrovascularmodelmultimodalmultivariablenetwork;neuralnon-alcoholicoforpredictionslidesteatohepatitis;supportsystem;transarterialwhole
(0)
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1Automated algorithm selection on continuous black-box problems by combining Exploratory Landscape Analysis and machine learning
P. Kerschke, and H. Trautmann. Evol. Comput., 27 (1): 99--127 (2019)
2 months ago by @scadsfct
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Automated
algorithm
analysis;
black-box
continuous
exploratory
landscape
learning;
machine
optimization.
optimization;
selection;
single-objective
Automatedalgorithmanalysis;black-boxcontinuousexploratorylandscapelearning;machineoptimization.optimization;selection;single-objective
(0)
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1Automated algorithm selection: Survey and perspectives
P. Kerschke, H. Hoos, F. Neumann, and H. Trautmann. Evol. Comput., 27 (1): 3--45 (2019)
2 months ago by @scadsfct
show all tags
Automated
algorithm
analysis;
approaches;
automated
combinatorial
configuration;
continuous
data
exploratory
feature-based
landscape
learning;
machine
metalearning
optimisation;
selection;
streams.;
Automatedalgorithmanalysis;approaches;automatedcombinatorialconfiguration;continuousdataexploratoryfeature-basedlandscapelearning;machinemetalearningoptimisation;selection;streams.;
(0)
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1Proteomic profiling of low muscle and high fat mass: a machine learning approach in the KORA S4/FF4 study
M. Huemer, A. Bauer, A. Petrera, M. Scholz, S. Hauck, M. Drey, A. Peters, and B. Thorand. J. Cachexia Sarcopenia Muscle, 12 (4): 1011--1023 (August 2021)
2 months ago by @scadsfct
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Appendicular
Body
Fat
Machine
Muscle
Proteomics
fat
index;
learning;
mass
mass;
muscle
skeletal
AppendicularBodyFatMachineMuscleProteomicsfatindex;learning;massmass;muscleskeletal
(0)
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1Identifying digenic disease genes via machine learning in the Undiagnosed Diseases Network
S. Mukherjee, J. Cogan, J. Newman, J. Phillips, 3rd, R. Hamid, Undiagnosed Diseases Network, J. Meiler, and J. Capra. Am. J. Hum. Genet., 108 (10): 1946--1963 (October 2021)
2 months ago by @scadsfct
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Diseases
Network;
UDN;
Undiagnosed
clinical
digenic
disease
disease;
learning;
machine
oligogenic
prediction;
rare
topic_lifescience
DiseasesNetwork;UDN;Undiagnosedclinicaldigenicdiseasedisease;learning;machineoligogenicprediction;raretopic_lifescience
(0)
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1Structural damage identification of composite rotors based on fully connected neural networks and convolutional neural networks
V. Scholz, P. Winkler, A. Hornig, M. Gude, and A. Filippatos. Sensors (Basel), 21 (6): 2005 (March 2021)
2 months ago by @scadsfct
show all tags
(SHM)
composite
composites;
connected
convolutional
dense
fully
health
learning;
machine
monitoring
networks;
neural
rotors;
structural
(SHM)compositecomposites;connectedconvolutionaldensefullyhealthlearning;machinemonitoringnetworks;neuralrotors;structural
(0)
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1Impact of pre- and post-processing steps for supervised classification of colorectal cancer in hyperspectral images
M. Tkachenko, C. Chalopin, B. Jansen-Winkeln, T. Neumuth, I. Gockel, and M. Maktabi. Cancers (Basel), (April 2023)
2 months ago by @scadsfct
show all tags
cancer
cancer;
classification;
colorectal
convolutional
filter;
hyperspectral
imaging;
learning;
machine
median
networks;
post-processing;
pre-processing
cancercancer;classification;colorectalconvolutionalfilter;hyperspectralimaging;learning;machinemediannetworks;post-processing;pre-processing
(0)
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1CCR2 macrophage response determines the functional outcome following cardiomyocyte transplantation
P. Vasudevan, M. Wolfien, H. Lemcke, C. Lang, A. Skorska, R. Gaebel, A. Galow, D. Koczan, T. Lindner, W. Bergmann and 6 other author(s). Genome Med., 15 (1): 61 (August 2023)
2 months ago by @scadsfct
show all tags
Cell
Immunocompromised;
Machine
Macrophages;
Myocardial
Single-cell
infarction;
learning;
therapy;
topic_lifescience
CellImmunocompromised;MachineMacrophages;MyocardialSingle-cellinfarction;learning;therapy;topic_lifescience
(0)
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1Machine Learning Made Easy (MLme): a comprehensive toolkit for machine learning-driven data analysis
A. Akshay, M. Katoch, N. Shekarchizadeh, M. Abedi, A. Sharma, F. Burkhard, R. Adam, K. Monastyrskaya, and A. Gheinani. Gigascience, (January 2024)
2 months ago by @scadsfct
show all tags
AutoML;
analysis;
classification
data
learning;
machine
problems;
visualization
AutoML;analysis;classificationdatalearning;machineproblems;visualization
(0)
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1Stroke-GFCN: ischemic stroke lesion prediction with a fully convolutional graph network
A. Iporre-Rivas, D. Saur, K. Rohr, G. Scheuermann, and C. Gillmann. J. Med. Imaging (Bellingham), 10 (4): 044502 (July 2023)
2 months ago by @scadsfct
show all tags
graph
imaging;
learning;
machine
medical
multi-modal
networks;
neural
prediction
stroke
graphimaging;learning;machinemedicalmulti-modalnetworks;neuralpredictionstroke
(0)
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1MLcps: machine learning cumulative performance score for classification problems
A. Akshay, M. Abedi, N. Shekarchizadeh, F. Burkhard, M. Katoch, A. Bigger-Allen, R. Adam, K. Monastyrskaya, and A. Gheinani. Gigascience, (December 2022)
2 months ago by @scadsfct
show all tags
Python
classification
evaluation
evaluation;
learning;
machine
model
package;
problems;
score
unified
Pythonclassificationevaluationevaluation;learning;machinemodelpackage;problems;scoreunified
(0)
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1Explaining the Unexplainable: The Impact of Misleading Explanations on Trust in Unreliable Predictions for Hardly Assessable Tasks
M. Sadeghi, D. Pöttgen, P. Ebel, and A. Vogelsang. Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization, page 36–46. New York, NY, USA, Association for Computing Machinery, (2024)
2 months ago by @scadsfct
show all tags
XAI,
explainability,
learning,
machine
trust
XAI,explainability,learning,machinetrust
(0)
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1Multiclass prediction of different dementia syndromes based on multi-centric volumetric MRI imaging
L. Lampe, H. Huppertz, S. Anderl-Straub, F. Albrecht, T. Ballarini, S. Bisenius, K. Mueller, S. Niehaus, K. Fassbender, K. Fliessbach and 13 other author(s). NeuroImage Clin., 37 (103320): 103320 (January 2023)
2 months ago by @scadsfct
show all tags
Dementia;
Diagnosis;
MRI;
Machine
Neurodegeneration;
Volumetry
learning;
topic_lifescience
unit_test
Dementia;Diagnosis;MRI;MachineNeurodegeneration;Volumetrylearning;topic_lifescienceunit_test
(0)
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publications (hide)18
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2A methodology for direct parameter identification for experimental results using machine learning—Real world application to the highly non-linear deformation behavior of FRP

1Chapter 5 - Artificial intelligence (AI) enhanced nanomotors and active matter

1Stability selection enables robust learning of partial differential equations from limited noisy data

1NDP-RANK: Prediction and ranking of NDP systems performance using machine learning

1Common features in lncRNA annotation and classification: A survey

1Artificial intelligence in liver diseases: Improving diagnostics, prognostics and response prediction

1Automated algorithm selection on continuous black-box problems by combining Exploratory Landscape Analysis and machine learning

1Automated algorithm selection: Survey and perspectives

1Proteomic profiling of low muscle and high fat mass: a machine learning approach in the KORA S4/FF4 study

1Identifying digenic disease genes via machine learning in the Undiagnosed Diseases Network

1Structural damage identification of composite rotors based on fully connected neural networks and convolutional neural networks

1Impact of pre- and post-processing steps for supervised classification of colorectal cancer in hyperspectral images

1CCR2 macrophage response determines the functional outcome following cardiomyocyte transplantation

1Machine Learning Made Easy (MLme): a comprehensive toolkit for machine learning-driven data analysis

1Stroke-GFCN: ischemic stroke lesion prediction with a fully convolutional graph network

1MLcps: machine learning cumulative performance score for classification problems

1Explaining the Unexplainable: The Impact of Misleading Explanations on Trust in Unreliable Predictions for Hardly Assessable Tasks

1Multiclass prediction of different dementia syndromes based on multi-centric volumetric MRI imaging

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