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Duality at Classical Electrodynamics and its Interpretation through Machine Learning Algorithms
((IJACSA) International Journal of Advanced Computer Science and Applications, 2022)
The aim of this paper is to investigate the hypothetical duality of classical electrodynamics and quantum mechanics
through the usage of Machine Learning principles. Thus, the Mitchell’s criteria are used. Essentially ...
The Machine Learning Principles Based at the Quantum Mechanics Postulates
(Springer Link, 2022)
Quantum mechanics is governed by well-defined postulates by the which one can go through either theory or experimental studies in order to perform measurements of microscopic dynamics of elementary particles, atoms and ...
Efficient Learning Machines: Theories, Concepts, and Applications for Engineers and System Designers
Machine learning techniques provide cost-effective alternatives to traditional methods for extracting underlying relationships between information and data and for predicting future events by processing existing information ...
Theory and Simulation of Electromagnetic Systems Governed by Machine Learning Principles
(IEEE, 2022)
This paper proposes the idea that electromagnetic systems can be formulated through probabilities once the system has been understood by the classical physics. With this, several physical observables are estimated. Also, ...
Machine Learning of a Pair of Charged Electrically Particles Inside a Closed Volume: Electrical Oscillations as Memory and Learning of System
(Springer Link, 2022)
In this paper the problem of two charged particles inside a frustum is faced through the principles of Machine Learning compacted by the criteria of Tom Mitchell. In essence, the relevant equations from the classical ...
Theory of machine learning based on nonrelativistic quantum mechanics
(World ScientificPE, 2021)
The goal of this paper is the presentation of the elementary procedures that normally are done in nonrelativistic Quantum Mechanics in terms of the principles of Machine Learning. In essence, this paper discusses Mitchell's ...
Quantum Displacements Dictated by Machine Learning Principles: Towards Optimization of Quantum Paths
(Springer Link, 2023)
In Physics the energy of any system represents a sensitive variable because of it depends the functionality and evolution of system at time. Thus the deep knowledge of the interactions of system might be a remarkable ...
Quantum Mechanics of Theorem of Bayes Modeled by Machine Learning Principles
(IEEE, 2022)
A theory consisting in quantum mechanics and theorem of Bayes, is presented. In essence, the Bayes probability has been built from two subspaces. While in one some quantum measurements are done, in the another it is seen ...
Using metrics from complex networks to evaluate machine translation
(ELSEVIER SCIENCE BV, 2011)
Establishing metrics to assess machine translation (MT) systems automatically is now crucial owing to the widespread use of MT over the web. In this study we show that such evaluation can be done by modeling text as complex ...
Testing Machine Learning at Classical Electrodynamics
(Institute of Electrical and Electronics EngineersPE, 2021-10-22)
Like physics or another laws-based basic science, machine learning might also be a firm methodology to solve physics problems by the which a kind of optimization and minimization of energy are needed. Expressed at the ...