Hidden Markov Model and multifractal method-based predictive quantization complexity models vis-á-vis the differential prognosis and differentiation of Multiple Sclerosis’ subgroups

Hidden Markov Model (HMM) is a stochastic process where implicit or latent stochastic processes can be inferred indirectly through a sequence of observed states. HMM as a mathematical model for uncertain phenomena is applicable for the description and computation of complex dynamical behaviours enab...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Knowledge-based systems Jg. 246; S. 108694
Hauptverfasser:	Karaca, Yeliz, Baleanu, Dumitru, Karabudak, Rana
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Amsterdam Elsevier B.V 21.06.2022 Elsevier Science Ltd
Schlagworte:	Algorithms Alternative approaches Apoptosis Artificial intelligence Attributes Bayesian analysis Brain Complex Complex systems Complexity Computation Computational dynamic complexity analyses Datasets Decision making Differentiation Dissemination Forward–Backward algorithm Fractals Hidden Markov Model Integrated approach Integrative approach Irregularities Markov analysis Markov chains Mathematical analysis Mathematical models Matrices Matrices (mathematics) Medical diagnosis Medical prognosis Medicine Multifractal analysis Multiple sclerosis Multiple Sclerosis’ subgroups Neurological disorders Nonlinear stochastic processes Physicians Precision medicine Prediction models Predictions Prognosis Self-similarity Singularities Spatial data Stochastic models Stochastic processes Subgroups Time Vector quantization Viterbi algorithm Forward–Backward algorithm Viterbi algorithm Nonlinear stochastic processes Computational dynamic complexity analyses Hidden Markov Model Multiple Sclerosis’ subgroups Multifractal analysis
ISSN:	0950-7051, 1872-7409
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!