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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..
3 months ago by @scadsfct
show all tags
FIS_scads
Generalized
insurance
additive
model,
index
risk,
basis
Machine
stages,
Plant
learning,
Weather
Temporal
growth
topic_engineering
FIS_scadsGeneralizedinsuranceadditivemodel,indexrisk,basisMachinestages,Plantlearning,WeatherTemporalgrowthtopic_engineering
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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..
3 months ago by @scadsfct
show all tags
Animals,
FIS_scads
Infarction,
Macrophages,
Cardiac/metabolism,
Macrophages/metabolism,
Mice,
Myocytes,
C57BL,
Machine
learning,
Myocardial
topic_lifescience
Immunocompromised,
Single-cell,
infarction,
therapy,
Inbred
Cell
Monocytes/metabolism
Animals,FIS_scadsInfarction,Macrophages,Cardiac/metabolism,Macrophages/metabolism,Mice,Myocytes,C57BL,Machinelearning,Myocardialtopic_lifescienceImmunocompromised,Single-cell,infarction,therapy,InbredCellMonocytes/metabolism
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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)
6 months ago by @scadsfct
show all tags
convolutional
learning;
cancer
networks;
imaging;
classification;
pre-processing
post-processing;
cancer;
filter;
colorectal
median
machine
hyperspectral
topic_lifescience
convolutionallearning;cancernetworks;imaging;classification;pre-processingpost-processing;cancer;filter;colorectalmedianmachinehyperspectraltopic_lifescience
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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)
6 months ago by @scadsfct
show all tags
convolutional
(SHM)
monitoring
learning;
neural
networks;
rotors;
health
dense
composites;
connected
structural
composite
machine
fully
convolutional(SHM)monitoringlearning;neuralnetworks;rotors;healthdensecomposites;connectedstructuralcompositemachinefully
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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)
6 months ago by @scadsfct
show all tags
XAI,
learning,
explainability,
machine
topic_visualcomputing
trust
XAI,learning,explainability,machinetopic_visualcomputingtrust
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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)
3 months ago by @scadsfct
show all tags
rates
forecasting
machine
autoconversion
learning
satellite
yaff
ratesforecastingmachineautoconversionlearningsatelliteyaff
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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)
6 months ago by @scadsfct
show all tags
a
disease;
HCC,
or
intelligence;
liver
multivariable
non-alcoholic
multimodal
ML,
artificial
WSIs,
images;
prediction
support
integration
TACE,
DICOM,
AI,
network;
deep
neural
Digital
Diagnosis;
MVI,
transarterial
fatty
topic_lifescience
microvascular
convolutional
in
learning;
imaging;
Communications
invasion;
NAFLD,
chemoembolisation;
hepatocellular
Reporting
steatohepatitis;
Transparent
Individual
slide
machine
Prognosis
Artificial
data
for
NASH,
whole
Medicine;
TRIPOD,
of
CNN,
and
Imaging
carcinoma;
diagnostic
system;
model
adisease;HCC,orintelligence;livermultivariablenon-alcoholicmultimodalML,artificialWSIs,images;predictionsupportintegrationTACE,DICOM,AI,network;deepneuralDigitalDiagnosis;MVI,transarterialfattytopic_lifesciencemicrovascularconvolutionalinlearning;imaging;Communicationsinvasion;NAFLD,chemoembolisation;hepatocellularReportingsteatohepatitis;TransparentIndividualslidemachinePrognosisArtificialdataforNASH,wholeMedicine;TRIPOD,ofCNN,andImagingcarcinoma;diagnosticsystem;model
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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)
6 months ago by @scadsfct
show all tags
disease;
prediction;
disease
learning;
Network;
oligogenic
Diseases
clinical
digenic
rare
machine
topic_lifescience
Undiagnosed
UDN;
disease;prediction;diseaselearning;Network;oligogenicDiseasesclinicaldigenicraremachinetopic_lifescienceUndiagnosedUDN;
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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)..
3 months ago by @scadsfct
show all tags
FIS_scads
models,
students
mental
misconceptions,
artificial
research,
intelligence,
qualitative
study,
learning,
machine
interview
area_responsibleai
conceptions
FIS_scadsmodels,studentsmentalmisconceptions,artificialresearch,intelligence,qualitativestudy,learning,machineinterviewarea_responsibleaiconceptions
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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.
3 months ago by @scadsfct
show all tags
FIS_scads
models,
education,
mental
preconceptions
artificial
ideas,
beliefs,
intelligence,
k-12
learning,
machine
conceptions,
area_responsibleai
FIS_scadsmodels,education,mentalpreconceptionsartificialideas,beliefs,intelligence,k-12learning,machineconceptions,area_responsibleai
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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)
5 months ago by @scadsfct
show all tags
(physics.data-an),
Analysis,
Probability
Data
sciences,
Numerical
Statistics
FOS:
Machine
Physical
sciences
Analysis
(cs.LG),
Learning
(math.NA),
Mathematics,
and
Computer
information
(physics.data-an),Analysis,ProbabilityDatasciences,NumericalStatisticsFOS:MachinePhysicalsciencesAnalysis(cs.LG),Learning(math.NA),Mathematics,andComputerinformation
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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..
3 months ago by @scadsfct
show all tags
FIS_scads
PAR
statistical
proteins,
equations,
learning
selection,
sparse
differential
regression,
learning,
machine
topic_lifescience
stability
theory
FIS_scadsPARstatisticalproteins,equations,learningselection,sparsedifferentialregression,learning,machinetopic_lifesciencestabilitytheory
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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)
3 months ago by @scadsfct
show all tags
ep
FOS:
Machine
sciences
(cs.LG),
Security
Learning
(cs.CV),
and
Vision
Computer
(cs.CR),
Cryptography
information
Pattern
area_bigdata
area_responsibleai
Recognition
epFOS:Machinesciences(cs.LG),SecurityLearning(cs.CV),andVisionComputer(cs.CR),CryptographyinformationPatternarea_bigdataarea_responsibleaiRecognition
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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)
6 months ago by @scadsfct
show all tags
MRI;
Neurodegeneration;
learning;
unit_test
Diagnosis;
Volumetry
topic_lifescience
topic_neuroinspired
Dementia;
Machine
MRI;Neurodegeneration;learning;unit_testDiagnosis;Volumetrytopic_lifesciencetopic_neuroinspiredDementia;Machine
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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)
3 months ago by @scadsfct
show all tags
Learning
Capacity
Parking
Estimation
Roadside
Based
Mobile
yaff
Machine
LearningCapacityParkingEstimationRoadsideBasedMobileyaffMachine
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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)
2 months ago by @scadsfct
show all tags
mixed-integer
selection
optimization
meta-learning
machine
learning
Zno
conic
interpretable
cost-sensitive
best
subset
mixed-integerselectionoptimizationmeta-learningmachinelearningZnoconicinterpretablecost-sensitivebestsubset
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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)
6 months ago by @scadsfct
show all tags
problems;
coding
extraction;
feature
sequence;
machine
lncRNA;
learning
classification
problems;codingextraction;featuresequence;machinelncRNA;learningclassification
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1Evaluating 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)
2 months ago by @scadsfct
show all tags
models
Consciousness
Prediction
Syndrome
Predictive
processing
modeling
Theory
algorithms
data
learning
Information
Modeling
Complexity
Processing
Machine
Learning
Training
Neuroscience
Measures
and
Locked-In
Zno
Signal
Computational
modelsConsciousnessPredictionSyndromePredictiveprocessingmodelingTheoryalgorithmsdatalearningInformationModelingComplexityProcessingMachineLearningTrainingNeuroscienceMeasuresandLocked-InZnoSignalComputational
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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)
6 months ago by @scadsfct
show all tags
learning;
single-objective
analysis;
black-box
exploratory
optimization;
continuous
machine
optimization.
Automated
selection;
algorithm
landscape
learning;single-objectiveanalysis;black-boxexploratoryoptimization;continuousmachineoptimization.Automatedselection;algorithmlandscape
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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)
6 months ago by @scadsfct
show all tags
feature-based
learning;
metalearning
data
analysis;
optimisation;
automated
streams.;
exploratory
combinatorial
continuous
machine
approaches;
Automated
selection;
algorithm
configuration;
landscape
feature-basedlearning;metalearningdataanalysis;optimisation;automatedstreams.;exploratorycombinatorialcontinuousmachineapproaches;Automatedselection;algorithmconfiguration;landscape
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publications (hide)33
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1Data-driven determination of plant growth stages for improved weather index insurance design

1CCR2 macrophage response determines the functional outcome following cardiomyocyte transplantation

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

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

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

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

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

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

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

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

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

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

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

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

1Machine Learning Based Mobile Capacity Estimation for Roadside Parking

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

1Common features in lncRNA annotation and classification: A survey

1Evaluating the DoC-Forest tool for Classifying the State of Consciousness in a Completely Locked-In Syndrome Patient

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

1Automated algorithm selection: Survey and perspectives

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