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WCDANM 2024

WCDANM 2024

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    • Course: Neural Computing
    • Course: SEM using R and SAS
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Topics

The interest scientific areas of the WCDANM, are:

  • Probability Theory, Distribution Theory, Stochastic Processes, Simulation, Parametric and Nonparametric Inference, Multivariate Analysis, Bayesian Inference, Regression Analysis, Statistical Modeling, Estimation Theory, Decision Theory, Econometrics, Statistical Tomography and Image Processing, Statistical Process Control, Time Series, Structural Equation Modeling, Data Mining, Big Data, Machine Learning, Data Analysis, Sampling Theory, among other areas.
  • Approximation Theory, Combinatorics, Lattices, Algebraic Structures, General Algebraic Systems, Number Theory, Field Theory and Polynomials, Rings and Algebras, Algebraic Geometry, Linear and Multilinear Algebra; Matrix Theory, Category Theory, Homological Algebra, Group Theory and Generalizations, Topological Groups, Real Functions, Measure Theory and Integration, Functions of a Complex Variable, Potential Theory, Ordinary and Partial Differential Equations, Dynamical Systems and Ergodic Theory, Difference and Functional Equations, Fourier Analysis, Integral Equations, Discrete Mathematics, Fuzzy Mathematics, Operator Theory, Coding Theory, Control Theory, Cryptology, Geometry, Information Fusion, Neutrosophic Mathematics, Graph Theory, Functional Analysis, Image Processing, Signal Processing and Tomography, Calculus of Variations, Algebraic Topology, Mathematical Economics and Financial Mathematics, Numerical Analysis, Fluid Mechanics and Solid Mechanics, Statistical Mechanics, Structure Of Matter, Operations Research, Optimization, Game Theory, Mathematical Biology and Other Natural Sciences, Wavelets and Wavelet Transforms, Mathematical Physics.

Sponsors

CIMA is funded by national funds through the FCT - Fundação para a Ciência e a Tecnologia, I.P., under the scope of the project UIDB/04674/2020, DOI 10.54499/UIDB/04674/2020 (https://doi.org/10.54499/UIDB/04674/2020)
Project UIDB/00006/2020: https://doi.org/10.54499/UIDB/00006/2020

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