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1Graph Mining for Complex Data Analytics
A. Petermann, M. Junghanns, S. Kemper, K. Gómez, N. Teichmann, and E. Rahm. 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW), page 1316--1319. (December 2016)
a month ago by @scadsfct
show all tags
analysis;Data
algorithms;Conferences;Graph
design
Intelligence
and
Data
mining;Business;Algorithm
Mining;Business
models;Libraries;Partitioning
analysis;Dataalgorithms;Conferences;GraphdesignIntelligenceandDatamining;Business;AlgorithmMining;Businessmodels;Libraries;Partitioning
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1Toward Veracity Assessment in RDF Knowledge Bases: An Exploratory Analysis
D. Esteves, A. Rula, A. Reddy, and J. Lehmann. J. Data and Information Quality, (February 2018)
2 months ago by @scadsfct
show all tags
exploratory
trustworthiness,
benchmark,
fact
DeFacto,
data
checking,
quality,
analysis
data,
linked
exploratorytrustworthiness,benchmark,factDeFacto,datachecking,quality,analysisdata,linked
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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)
26 days 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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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
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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1Integration of mathematical model predictions into routine workflows to support clinical decision making in haematology
K. Hoffmann, K. Cazemier, C. Baldow, S. Schuster, Y. Kheifetz, S. Schirm, M. Horn, T. Ernst, C. Volgmann, C. Thiede and 7 other author(s). BMC Med. Inform. Decis. Mak., 20 (1): 28 (February 2020)
2 months ago by @scadsfct
show all tags
decision-making;
workflow;
Haematology;
Support
Mathematical
therapy
Clinical
Data
Routine
treatment
management;
simulation;
modelling;
system
optimization;
Individual
Computer
planning;
Model-based
decision-making;workflow;Haematology;SupportMathematicaltherapyClinicalDataRoutinetreatmentmanagement;simulation;modelling;systemoptimization;IndividualComputerplanning;Model-based
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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
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
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,transarterialfattymicrovascularconvolutionalinlearning;imaging;Communicationsinvasion;NAFLD,chemoembolisation;hepatocellularReportingsteatohepatitis;TransparentIndividualslidemachinePrognosisArtificialdataforNASH,wholeMedicine;TRIPOD,ofCNN,andImagingcarcinoma;diagnosticsystem;model
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1Multi-source dataset of e-commerce products with attributes for property matching
D. Ayala, I. Hernández, D. Ruiz, and E. Rahm. Data Brief, 41 (107884): 107884 (April 2022)
2 months ago by @scadsfct
show all tags
integration;
engineering;
Data
Ontology;
Property
matching
integration;engineering;DataOntology;Propertymatching
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1Ten topics to get started in medical informatics research
M. Wolfien, N. Ahmadi, K. Fitzer, S. Grummt, K. Heine, I. Jung, D. Krefting, A. Kühn, Y. Peng, I. Reinecke and 7 other author(s). J. Med. Internet Res., (July 2023)
2 months ago by @scadsfct
show all tags
clinical
medical
health;
informatics;
data
communication;
health
digital
data;
interdisciplinary
research
clinicalmedicalhealth;informatics;datacommunication;healthdigitaldata;interdisciplinaryresearch
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1Methods used in the development of common data models for health data: Scoping review
N. Ahmadi, M. Zoch, P. Kelbert, R. Noll, J. Schaaf, M. Wolfien, and M. Sedlmayr. JMIR Med. Inform., (August 2023)
2 months ago by @scadsfct
show all tags
Observational
Data
Healthcare;
Interoperability;
health
Repositories;
Interoperability
Partnership
elements;
elements
Healthcare
model;
;
Process;
Medical
Informatics;
data;
harmonisation;
Suggestive
data
Process
record;
stakeholder
Standardized
Repositories
Outcomes
Partnership;
involvement
common
record
Development
harmonisation
model
Informatics
electronic
ObservationalDataHealthcare;Interoperability;healthRepositories;InteroperabilityPartnershipelements;elementsHealthcaremodel;;Process;MedicalInformatics;data;harmonisation;SuggestivedataProcessrecord;stakeholderStandardizedRepositoriesOutcomesPartnership;involvementcommonrecordDevelopmentharmonisationmodelInformaticselectronic
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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
problems;
visualization
learning;
data
machine
analysis;
AutoML;
classification
problems;visualizationlearning;datamachineanalysis;AutoML;classification
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1ConvGeN: A convex space learning approach for deep-generative oversampling and imbalanced classification of small tabular datasets
K. Schultz, S. Bej, W. Hahn, M. Wolfien, P. Srivastava, and O. Wolkenhauer. Pattern Recognition, (2024)
2 months ago by @scadsfct
show all tags
Tabular
Convex
Imbalanced
learning,
GAN,
data
LoRAS,
space
data,
TabularConvexImbalancedlearning,GAN,dataLoRAS,spacedata,
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publications (hide)11
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1Graph Mining for Complex Data Analytics

1Toward Veracity Assessment in RDF Knowledge Bases: An Exploratory Analysis

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

1Automated algorithm selection: Survey and perspectives

1Integration of mathematical model predictions into routine workflows to support clinical decision making in haematology

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

1Multi-source dataset of e-commerce products with attributes for property matching

1Ten topics to get started in medical informatics research

1Methods used in the development of common data models for health data: Scoping review

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

1ConvGeN: A convex space learning approach for deep-generative oversampling and imbalanced classification of small tabular datasets

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