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Machine Learning to Assess Urbanistic Development in the South Pole of Lima City
(Springer, 2022-01-01)
We employ Machine Learning through the Mitchell’s criteria to carry out an assessment on the potential spatial configurations at the south pole of Lima city, at Perú. Based at both qualitative and quantitative facts, an ...
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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, ...
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Combined Monte Carlo and Machine Learning Algorithms to Predict Horizontal Expansion of Lima City
(IEEE, 2022)
In this paper, the method of Monte Carlo is projected onto the Mitchell criteria inside the framework of Machine Learning. Because the probabilistic character that exhibits the theory of Mitchell, the Monte Carlo technology ...
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Model of Early Intervention Using Machine Learning: Predicting Monkeypox Pandemic
(IEEE, 2022)
This paper presents a model of intervention at the first phases of global pandemic using the criteria of Mitchell that simplifies to some extent the philosophy of Machine Learning. These criteria are projected onto the ...
Acceso restringido
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 ...
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The Criteria of Mitchell to Interpret Classical Radiation as Compton Scattering
(IEEE, 2022)
The principles of Machine Learning through the criteria of Mitchell are used to validate a concrete quantum-mechanics interpretation from a classical radiation scheme inside the framework of linear and nonlinear Compton ...
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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 ...
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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 ...
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Machine Learning and Bayes Probability For Detecting Camouflaged Mini Pandemic at the Waves of Covid-19
(IEEE, 2022)
This paper present a methodology based at Machine Learning and a theory backed by the Bayes probability to identify rare strains that might not be in coherence with the corona virus. By using the criteria of Tom Mitchell ...
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The Approach of Machine Learning to Optimize the Bank-Customer Interaction at Pandemic Epochs
(IEEE, 2022)
Along the pandemic created by the Corona virus 2019 (Covid-19 in shorthand), the global economy was observed to experience various turbulent months that were reflected by the increasing of unemployment and the apparition ...
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