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2Sleep analysis in a CLIS patient using soft-clustering: a case study
S. Adama, and M. Bogdan. Louvain-la-Neuve (Belgium), Ciaco - i6doc.com, (2023)
a month ago by @scadsfct
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Sleep
Zno
analysis
CLIS
soft-clustering
SleepZnoanalysisCLISsoft-clustering
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2Application of soft-clustering to assess consciousness in a CLIS patient
S. Adama, and M. Bogdan. Brain Sci., 13 (1): 65 (December 2022)
a month ago by @scadsfct
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complexity
electrocorticogram
consciousness
spectral
analysis
extraction
features
syndrome
connectivity
completely
locked-in
zno
soft-clustering
complexityelectrocorticogramconsciousnessspectralanalysisextractionfeaturessyndromeconnectivitycompletelylocked-inznosoft-clustering
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2Assessing consciousness in patients with disorders of consciousness using soft-clustering
S. Adama, and M. Bogdan. Brain Inform., 10 (1): 16 (July 2023)
a month ago by @scadsfct
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Electroencephalogram
Consciousness
consciousness
Disorders
analysis
Complexity
Soft-clustering
interface
Brain-computer
Connectivity
Zno
Spectral
ElectroencephalogramConsciousnessconsciousnessDisordersanalysisComplexitySoft-clusteringinterfaceBrain-computerConnectivityZnoSpectral
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1Extendable 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)
a month ago by @scadsfct
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hybrid
machine
patient
{CLIS}
learning
Zno
states
conscious
hybridmachinepatient{CLIS}learningZnostatesconscious
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1Key Point Analysis via Contrastive Learning and Extractive Argument Summarization
M. Alshomary, T. Gurcke, S. Syed, P. Heinisch, M. Spliethöver, P. Cimiano, M. Potthast, and H. Wachsmuth. 8th Workshop on Argument Mining (ArgMining 2021) at EMNLP, page 184--189. Association for Computational Linguistics, (November 2021)
29 days ago by @scadsfct
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Analysis
Learning
Point
Argument
Extractive
Zno
Contrastive
Key
Summarization
AnalysisLearningPointArgumentExtractiveZnoContrastiveKeySummarization
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2Identifying compound weather drivers of forest biomass loss with generative deep learning
M. Anand, F. Bohn, G. Camps-Valls, R. Fischer, A. Huth, L. Sweet, and J. Zscheischler. Environmental Data Science, (2024)
15 hours ago by @scadsfct
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zno
zno
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1Advancing oncology with federated learning: transcending boundaries in breast, lung, and prostate cancer. A systematic review
A. Ankolekar, S. Boie, M. Abdollahyan, E. Gadaleta, S. Hasheminasab, G. Yang, C. Beauville, N. Dikaios, G. Kastis, M. Bussmann and 4 other author(s). (2024)
28 days ago by @scadsfct
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imported
Zno
importedZno
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1Whitney's connectivity inequalities for directed hypergraphs
A. Arguello, and P. Stadler. The Art of Discrete and Applied Mathematics, 5 (1): P1--01 (2022)
2 months ago by @scadsfct
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imported
zno
importedzno
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2BioDeepfuse: a hybrid deep learning approach with integrated feature extraction techniques for enhanced non-coding RNA classification
A. Avila Santos, B. de Almeida, R. Bonidia, P. Stadler, P. Stefanic, I. Mandic-Mulec, U. Rocha, D. Sanches, and A. de Carvalho. RNA Biology, 21 (1): 410–421 (March 2024)
2 months ago by @scadsfct
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deep
RNA
non-coding
feature
learning
Zno
extraction
BioDeepfuse
classification
deepRNAnon-codingfeaturelearningZnoextractionBioDeepfuseclassification
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1Efficient TBox Reasoning with Value Restrictions Using the ℱℒower Reasoner
F. Baader, P. Koopmann, F. Michel, A. Turhan, and B. Zarriess. DL 2022 - Proceedings of the 35th International Workshop on Description Logics, co-located with Federated Logic Conference, FLoC 2022, volume 3263 of CEUR Workshop Proceedings, CEUR-WS, (2022)35th International Workshop on Description Logics, DL 2022 ; Conference date: 07-08-2022 Through 10-08-2022.
a month ago by @scadsfct
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imported
zno
importedzno
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1Accelerator Programming Using Directives: 8th International Workshop, WACCPD 2021, Virtual Event, November 14, 2021, Proceedings
S. Bhalachandra, C. Daley, and V. Vergara (Eds.) Berlin, Heidelberg, Springer-Verlag, (2021)
28 days ago by @scadsfct
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imported
WACCPD
Programming
2021
Zno
Accelerator
importedWACCPDProgramming2021ZnoAccelerator
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1The Importance of Suppressing Domain Style in Authorship Analysis
S. Bischoff, N. Deckers, M. Schliebs, B. Thies, M. Hagen, E. Stamatatos, B. Stein, and M. Potthast. CoRR, (May 2020)
29 days ago by @scadsfct
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Analysis
Authorship
Importance
Style
Suppressing
Zno
Domain
AnalysisAuthorshipImportanceStyleSuppressingZnoDomain
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2Is Boredom an Indicator on the way to Singularity of Artificial Intelligence? Hypotheses as Thought-Provoking Impulse
M. Bogdan. Louvain-la-Neuve (Belgium), Ciaco - i6doc.com, (2023)
a month ago by @scadsfct
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Singularity
AI
Boredom
Zno
SingularityAIBoredomZno
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1BioAutoML: automated feature engineering and metalearning to predict noncoding RNAs in bacteria
R. Bonidia, A. Santos, B. de Almeida, P. Stadler, U. da Rocha, D. Sanches, and A. de Carvalho. Briefings in Bioinformatics, (June 2022)
2 months ago by @scadsfct
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BioAutoML
noncoding
bacteria
metalearning
predict
engineering
RNAs
Zno
automated
BioAutoMLnoncodingbacteriametalearningpredictengineeringRNAsZnoautomated
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1Periodic system converges and is affected by wars
A. Bran, P. Stadler, J. Jost, and G. Restrepo. (2022)
2 months ago by @scadsfct
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imported
Zno
importedZno
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1The six stages of the convergence of the periodic system to its final structure
A. Bran, P. Stadler, J. Jost, and G. Restrepo. Communications Chemistry, 6 (1): 87 (2023)
2 months ago by @scadsfct
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imported
Zno
importedZno
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1AI-Augmented Facilities: Bridging Experiment and Simulation with ML (Dagstuhl Seminar 23132)
P. Bremer, B. Spears, T. Gibbs, and M. Bussmann. (2023)
28 days ago by @scadsfct
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imported
AI-Augmented
Zno
Facilities
importedAI-AugmentedZnoFacilities
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2From modular decomposition trees to rooted median graphs
C. Bruckmann, P. Stadler, and M. Hellmuth. Discrete Applied Mathematics, (March 2022)
2 months ago by @scadsfct
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modular
graphs
median
rooted
decomposition
zno
trees
modulargraphsmedianrooteddecompositionznotrees
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1Ontology patterns for function modeling with GFO
P. Burek, F. Loebe, and H. Herre. (2019)
a month ago by @scadsfct
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Ontology
patterns
Zno
GFO
OntologypatternsZnoGFO
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2Variational Mode Decomposition Hybridized With Gradient Boost Regression for Seasonal Forecast of Residential Water Demand
T. Carvalho, and F. de Assis de Souza Filho. Water Resources Management, 35 (10): 3431-3445 (2021)
13 hours ago by @scadsfct
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zno
zno
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publications (hide)113
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2Sleep analysis in a CLIS patient using soft-clustering: a case study

2Application of soft-clustering to assess consciousness in a CLIS patient

2Assessing consciousness in patients with disorders of consciousness using soft-clustering

1Extendable hybrid approach to detect conscious states in a CLIS patient using machine learning

1Key Point Analysis via Contrastive Learning and Extractive Argument Summarization

2Identifying compound weather drivers of forest biomass loss with generative deep learning

1Advancing oncology with federated learning: transcending boundaries in breast, lung, and prostate cancer. A systematic review

1Whitney's connectivity inequalities for directed hypergraphs

2BioDeepfuse: a hybrid deep learning approach with integrated feature extraction techniques for enhanced non-coding RNA classification

1Efficient TBox Reasoning with Value Restrictions Using the ℱℒower Reasoner

1Accelerator Programming Using Directives: 8th International Workshop, WACCPD 2021, Virtual Event, November 14, 2021, Proceedings

1The Importance of Suppressing Domain Style in Authorship Analysis

2Is Boredom an Indicator on the way to Singularity of Artificial Intelligence? Hypotheses as Thought-Provoking Impulse

1BioAutoML: automated feature engineering and metalearning to predict noncoding RNAs in bacteria

1Periodic system converges and is affected by wars

1The six stages of the convergence of the periodic system to its final structure

1AI-Augmented Facilities: Bridging Experiment and Simulation with ML (Dagstuhl Seminar 23132)

2From modular decomposition trees to rooted median graphs

1Ontology patterns for function modeling with GFO

2Variational Mode Decomposition Hybridized With Gradient Boost Regression for Seasonal Forecast of Residential Water Demand

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