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1A comparative patient-level prediction study in OMOP CDM: applicative potential and insights from synthetic data
N. Ahmadi, {. Nguyen, M. Sedlmayr, and M. Wolfien. Scientific reports, (Jan 27, 2024)
5 days ago by @scadsfct
show all tags
Databases,
Electronic
FIS_scads
Factual,
Health
Humans,
Informatics,
Learning,
Machine
Medical
Records
topic_lifescience
Databases,ElectronicFIS_scadsFactual,HealthHumans,Informatics,Learning,MachineMedicalRecordstopic_lifescience
(0)
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1Towards Standardizing Ophthalmic Data for Seamless Interoperability in Eye Care
K. Hoffmann, Y. Peng, T. Schlosser, G. Stolze, H. Langner, M. Susky, T. Meyer, M. Ritter, D. Kowerko, V. Kakkassery and 2 other author(s). Studies in health technology and informatics, (Aug 30, 2024)Publisher Copyright: © 2024 The Authors..
5 days ago by @scadsfct
show all tags
topic_lifescience
Diseases/therapy,
Electronic
Eye
FIS_scads
Germany,
Health
Humans,
Information
Interoperability/standards,
Learning,
Level
Machine
Ophthalmology
Records/standards,
Seven/standards,
topic_lifescienceDiseases/therapy,ElectronicEyeFIS_scadsGermany,HealthHumans,InformationInteroperability/standards,Learning,LevelMachineOphthalmologyRecords/standards,Seven/standards,
(0)
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1CCR2 macrophage response determines the functional outcome following cardiomyocyte transplantation
P. Vasudevan, M. Wolfien, H. Lemcke, {. Lang, A. Skorska, R. Gaebel, A. Galow, D. Koczan, T. Lindner, W. Bergmann and 6 other author(s). Genome medicine, (Aug 10, 2023)Publisher Copyright: © 2023, BioMed Central Ltd., part of Springer Nature..
5 days ago by @scadsfct
show all tags
topic_lifescience
Animals,
C57BL,
Cardiac/metabolism,
Cell
FIS_scads
Immunocompromised,
Inbred
Infarction,
Machine
Macrophages,
Macrophages/metabolism,
Mice,
Monocytes/metabolism
Myocardial
Myocytes,
Single-cell,
infarction,
learning,
therapy,
topic_lifescienceAnimals,C57BL,Cardiac/metabolism,CellFIS_scadsImmunocompromised,InbredInfarction,MachineMacrophages,Macrophages/metabolism,Mice,Monocytes/metabolismMyocardialMyocytes,Single-cell,infarction,learning,therapy,
(0)
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1Stability selection enables robust learning of differential equations from limited noisy data
S. Maddu, {. Cheeseman, {. Sbalzarini, and {. Müller. Proceedings of the Royal Society of London : Series A, Mathematical, physical and engineering sciences, (June 2022)Publisher Copyright: © 2022 Royal Society Publishing. All rights reserved..
5 days ago by @scadsfct
show all tags
topic_lifescience
FIS_scads
PAR
differential
equations,
learning
learning,
machine
proteins,
regression,
selection,
sparse
stability
statistical
theory
topic_lifescienceFIS_scadsPARdifferentialequations,learninglearning,machineproteins,regression,selection,sparsestabilitystatisticaltheory
(0)
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1RTASS: a RunTime Adaptable and Scalable System for Network-on-Chip-Based Architectures
N. Charaf, J. Haase, A. Kulisch, {. Elm, and D. Göhringer. 2023 26th Euromicro Conference on Digital System Design (DSD), page 585--592. IEEE, (Sep 8, 2023)Publisher Copyright: © 2023 IEEE.; 26th Euromicro Conference on Digital System Design, DSD 2023 ; Conference date: 06-09-2023 Through 08-09-2023.
5 days ago by @scadsfct
show all tags
topic_federatedlearn
Computer
Embedded
FIS_scads
Machine
Runtime,
Scalability,
Shape
algorithms,
architecture,
computing,
learning
vision,
topic_federatedlearnComputerEmbeddedFIS_scadsMachineRuntime,Scalability,Shapealgorithms,architecture,computing,learningvision,
(0)
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1Data-driven determination of plant growth stages for improved weather index insurance design
J. Zou, M. Odening, and O. Okhrin. Agricultural Finance Review, (2024)Publisher Copyright: © 2024, Jing Zou, Martin Odening and Ostap Okhrin..
5 days ago by @scadsfct
show all tags
topic_engineering
FIS_scads
Generalized
Machine
Plant
Temporal
Weather
additive
basis
growth
index
insurance
learning,
model,
risk,
stages,
topic_engineeringFIS_scadsGeneralizedMachinePlantTemporalWeatheradditivebasisgrowthindexinsurancelearning,model,risk,stages,
(0)
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1A 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, (September 2024)Publisher Copyright: © 2024 The Author(s).
5 days ago by @scadsfct
show all tags
topic_engineering
area_architectures
Constitutive
FIS_scads
Fiber
Machine
Neural
Parameter
identification
learning,
modeling,
networks,
plastics,
reinforced
topic_engineeringarea_architecturesConstitutiveFIS_scadsFiberMachineNeuralParameteridentificationlearning,modeling,networks,plastics,reinforced
(0)
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1Direct parameter identification for highly nonlinear strain rate dependent constitutive models using machine learning
J. Gerritzen, A. Hornig, P. Winkler, and M. Gude. ECCM21 - Proceedings of the 21st European Conference on Composite Materials, 3, page 1252--1259. European Society for Composite Materials (ESCM), (Jul 2, 2024)21st European Conference on Composite Materials, ECCM 21 ; Conference date: 02-07-2024 Through 05-07-2024.
5 days ago by @scadsfct
show all tags
topic_engineering
area_architectures
Convolutional
Direct
FIS_scads
Fiber
Machine
Strain
dependency,
identification,
learning,
networks,
neural
parameter
plastics
rate
reinforced
topic_engineeringarea_architecturesConvolutionalDirectFIS_scadsFiberMachineStraindependency,identification,learning,networks,neuralparameterplasticsratereinforced
(0)
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1Identifying Secondary School Students' Misconceptions about Machine Learning: An Interview Study
E. Marx, C. Witt, and T. Leonhardt. WiPSCE '24: Proceedings of the 19th WiPSCE Conference on Primary and Secondary Computing Education Research, page 1--10. Association for Computing Machinery, (Sep 16, 2024)Publisher Copyright: © 2024 Copyright held by the owner/author(s)..
5 days ago by @scadsfct
show all tags
area_responsibleai
FIS_scads
artificial
conceptions
intelligence,
interview
learning,
machine
mental
misconceptions,
models,
qualitative
research,
students
study,
area_responsibleaiFIS_scadsartificialconceptionsintelligence,interviewlearning,machinementalmisconceptions,models,qualitativeresearch,studentsstudy,
(0)
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1Brief Summary of Existing Research on Students’ Conceptions of AI
E. Marx, T. Leonhardt, and N. Bergner. (October 2022)Publisher Copyright: © 2022 Owner/Author.; WiPSCE 2022 ; Conference date: 31-10-2022 Through 02-11-2022.
5 days ago by @scadsfct
show all tags
area_responsibleai
FIS_scads
artificial
beliefs,
conceptions,
education,
ideas,
intelligence,
k-12
learning,
machine
mental
models,
preconceptions
area_responsibleaiFIS_scadsartificialbeliefs,conceptions,education,ideas,intelligence,k-12learning,machinementalmodels,preconceptions
(0)
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2How Interpretable Machine Learning Can Benefit Process Understanding in the Geosciences
S. Jiang, {. Sweet, G. Blougouras, A. Brenning, W. Li, M. Reichstein, J. Denzler, W. Shangguan, G. Yu, F. Huang and 1 other author(s). Earth's Future, (July 2024)Publisher Copyright: © 2024. The Author(s)..
5 days ago by @scadsfct
show all tags
FIS_scads
XAI
big
data,
discovery,
interpretability,
interpretable
knowledge
learning,
machine
FIS_scadsXAIbigdata,discovery,interpretability,interpretableknowledgelearning,machine
(0)
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2Assessing the Impact of Image Dataset Features on Privacy-Preserving Machine Learning
L. Lange, M. Heykeroth, and E. Rahm. arXiv preprint arXiv:2409.01329, (September 2024)
5 days ago by @scadsfct
show all tags
area_responsibleai
area_bigdata
(cs.CR),
(cs.CV),
(cs.LG),
Computer
Cryptography
FOS:
Learning
Machine
Pattern
Recognition
Security
Vision
and
ep
information
sciences
area_responsibleaiarea_bigdata(cs.CR),(cs.CV),(cs.LG),ComputerCryptographyFOS:LearningMachinePatternRecognitionSecurityVisionandepinformationsciences
(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)
2 months ago by @scadsfct
show all tags
topic_physchemistry
Active
Feedback
Machine
Multi
Optical
Reinforcement
agent
control
control,
learning,
particles,
reinforcement
topic_physchemistryActiveFeedbackMachineMultiOpticalReinforcementagentcontrolcontrol,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)
2 months 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
topic_federatedlearn
Design
Machine
Modeling,
Near-data
Prediction,
exploration
learning,
processing,
space
topic_federatedlearnDesignMachineModeling,Near-dataPrediction,explorationlearning,processing,space
(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)
3 months ago by @scadsfct
show all tags
topic_lifescience
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
topic_lifescienceAI,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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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)
3 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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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)
3 months ago by @scadsfct
show all tags
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)
3 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)
3 months ago by @scadsfct
show all tags
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)
3 months ago by @scadsfct
show all tags
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)
3 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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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)
3 months ago by @scadsfct
show all tags
topic_visualcomputing
graph
imaging;
learning;
machine
medical
multi-modal
networks;
neural
prediction
stroke
topic_visualcomputinggraphimaging;learning;machinemedicalmulti-modalnetworks;neuralpredictionstroke
(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)
3 months ago by @scadsfct
show all tags
topic_visualcomputing
XAI,
explainability,
learning,
machine
trust
topic_visualcomputingXAI,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)
3 months ago by @scadsfct
show all tags
topic_neuroinspired
Dementia;
Diagnosis;
MRI;
Machine
Neurodegeneration;
Volumetry
learning;
topic_lifescience
unit_test
topic_neuroinspiredDementia;Diagnosis;MRI;MachineNeurodegeneration;Volumetrylearning;topic_lifescienceunit_test
(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)
3 months ago by @scadsfct
show all tags
topic_lifescience
cancer
cancer;
classification;
colorectal
convolutional
filter;
hyperspectral
imaging;
learning;
machine
median
networks;
post-processing;
pre-processing
topic_lifesciencecancercancer;classification;colorectalconvolutionalfilter;hyperspectralimaging;learning;machinemediannetworks;post-processing;pre-processing
(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)
3 months ago by @scadsfct
show all tags
topic_federatedlearn
Python
classification
evaluation
evaluation;
learning;
machine
model
package;
problems;
score
unified
topic_federatedlearnPythonclassificationevaluationevaluation;learning;machinemodelpackage;problems;scoreunified
(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)
3 months ago by @scadsfct
show all tags
topic_federatedlearn
AutoML;
analysis;
classification
data
learning;
machine
problems;
visualization
topic_federatedlearnAutoML;analysis;classificationdatalearning;machineproblems;visualization
(0)
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1A comparative patient-level prediction study in OMOP CDM: applicative potential and insights from synthetic data

1Towards Standardizing Ophthalmic Data for Seamless Interoperability in Eye Care

1CCR2 macrophage response determines the functional outcome following cardiomyocyte transplantation

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

1RTASS: a RunTime Adaptable and Scalable System for Network-on-Chip-Based Architectures

1Data-driven determination of plant growth stages for improved weather index insurance design

1A methodology for direct parameter identification for experimental results using machine learning — Real world application to the highly non-linear deformation behavior of FRP

1Direct parameter identification for highly nonlinear strain rate dependent constitutive models using machine learning

1Identifying Secondary School Students' Misconceptions about Machine Learning: An Interview Study

1Brief Summary of Existing Research on Students’ Conceptions of AI

2How Interpretable Machine Learning Can Benefit Process Understanding in the Geosciences

2Assessing the Impact of Image Dataset Features on Privacy-Preserving Machine Learning

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

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

1Common features in lncRNA annotation and classification: A survey

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

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

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

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

1MLcps: machine learning cumulative performance score for classification problems

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

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