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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 for Management in Software-defined Networks: A Systematic Literature Review
(DBpia, 2022)
Software-Defined Networking (SDN) has emerged as a new paradigm for managing data networks, and Machine Learning (ML) techniques have become relevant in the scientific community to solve management problems. Research on ...
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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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Price Prediction of Agricultural Products: Machine Learning
(Springer, 2022)
Family farming is essentially characterized by the use of family labor force, due to the lack of land, water, and capital resources. An important tool is which allows them to know which products will be the best priced ...
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The Quantum Mechanics Propagator as the Machine Learning Performance in Space-Time Displacements
(Institute of Electrical and Electronics Engineers, 2021-12)
The role of evolution operator is to provide the time displacement of wave function through the Hamiltonian of the system. The usage of coordinates representation gives the well-known propagator that is the Green’s function. ...
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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
Classification of Tweets Related to Natural Disasters Using Machine Learning Algorithms
(International Journal of Interactive Mobile Technologies (iJIM), 2023)
Abstract—In recent years, computer science has advanced exponentially, helping significantly to identify and classify text extracted from social networks, specifically Twitter. This work identifies, classifies, and analyzes ...
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