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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 2023)
13 days ago by @scadsfct
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MLcps
cumulative
learning
machine
performance
MLcpscumulativelearningmachineperformance
(0)
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1Cost-Sensitive Best Subset Selection for Logistic Regression: A Mixed-Integer Conic Optimization Perspective
R. Knauer, and E. Rodner. KI 2023: Advances in Artificial Intelligence: 46th German Conference on AI, Berlin, Germany, September 26--29, 2023, Proceedings, page 114--129. Berlin, Heidelberg, Springer-Verlag, (2023)
13 days ago by @scadsfct
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best
conic
cost-sensitive
interpretable
learning
machine
meta-learning
mixed-integer
optimization
selection
subset
bestconiccost-sensitiveinterpretablelearningmachinemeta-learningmixed-integeroptimizationselectionsubset
(0)
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2Extendable hybrid approach to detect conscious states in a CLIS patient using machine learning
S. Adama, S. Wu, N. Nicolaou, and M. Bogdan. SNE Simul. Notes Eur., 32 (1): 37--45 (2022)
13 days ago by @scadsfct
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conscious
hybrid
learning
machine
patient
states
{CLIS}
conscioushybridlearningmachinepatientstates{CLIS}
(0)
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2Evaluating the DoC-Forest tool for Classifying the State of Consciousness in a Completely Locked-In Syndrome Patient
O. Kirsten, M. Bogdan, and S. Adama. 2023 7th International Conference on Imaging, Signal Processing and Communications (ICISPC), page 37-41. (2023)
13 days ago by @scadsfct
show all tags
Complexity
Computational
Consciousness
Information
Learning
Locked-In
Machine
Measures
Modeling
Neuroscience
Prediction
Predictive
Processing
Signal
Syndrome
Theory
Training
algorithms
and
data
learning
modeling
models
processing
ComplexityComputationalConsciousnessInformationLearningLocked-InMachineMeasuresModelingNeurosciencePredictionPredictiveProcessingSignalSyndromeTheoryTrainingalgorithmsanddatalearningmodelingmodelsprocessing
(0)
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1Machine Learning Based Mobile Capacity Estimation for Roadside Parking
A. Kramm, E. Peukert, A. Ludwig, and B. Franczyk. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, (2024)
a month ago by @scadsfct
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Based
Capacity
Estimation
Learning
Machine
Mobile
Parking
Roadside
BasedCapacityEstimationLearningMachineMobileParkingRoadside
(0)
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2Cloudy with a chance of precision: satellite’s autoconversion rates forecasting powered by machine learning
M. Novitasari, J. Quaas, and M. Rodrigues. Environmental Data Science, (2024)
a month ago by @scadsfct
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autoconversion
forecasting
learning
machine
rates
satellite
autoconversionforecastinglearningmachineratessatellite
(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).
2 months ago by @scadsfct
show all tags
area_architectures
topic_engineering
Constitutive
FIS_scads
Fiber
Machine
Neural
Parameter
identification
learning,
modeling,
networks,
plastics,
reinforced
area_architecturestopic_engineeringConstitutiveFIS_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.
2 months ago by @scadsfct
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area_architectures
topic_engineering
Convolutional
Direct
FIS_scads
Fiber
Machine
Strain
dependency,
identification,
learning,
networks,
neural
parameter
plastics
rate
reinforced
area_architecturestopic_engineeringConvolutionalDirectFIS_scadsFiberMachineStraindependency,identification,learning,networks,neuralparameterplasticsratereinforced
(0)
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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)
2 months 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..
2 months 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..
2 months 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..
2 months 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.
2 months 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..
2 months 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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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)..
2 months ago by @scadsfct
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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.
2 months 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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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)
2 months 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)
3 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)
3 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)
4 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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publications (hide)33
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1MLcps: machine learning cumulative performance score for classification problems

1Cost-Sensitive Best Subset Selection for Logistic Regression: A Mixed-Integer Conic Optimization Perspective

2Extendable hybrid approach to detect conscious states in a CLIS patient using machine learning

2Evaluating the DoC-Forest tool for Classifying the State of Consciousness in a Completely Locked-In Syndrome Patient

1Machine Learning Based Mobile Capacity Estimation for Roadside Parking

2Cloudy with a chance of precision: satellite’s autoconversion rates forecasting powered by machine learning

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

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

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

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

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

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