Results 21 to 30 of about 120,380 (224)
COMPARISON OF K-NN AND NAÏVE BAYES CLASSIFIER FOR ASPHYXIA FACTOR [PDF]
, 2018 Asphyxia is influenced by several factors, including the factors affecting the Immediate Was maternal factors That relates Conditions mother Pregnancy and childbirth such as hypoxia mother, Asphyxia factor data can be modeled using the classification ...
Finki Dona Marleny, Mambang
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Advanced Science, EarlyView.This work introduces a novel photonic Bayesian neural network architecture that utilizes tunable photonic random number generators to independently control weight distribution parameters, significantly improving robustness, scalability, and accuracy while demonstrating practical applications in multimodal data processing and uncertainty‐aware computing.
Yangyang Zhuge +9 more
wiley +1 more source
Identification Types Of Student Learning Modalities In Physics Subjects With Expert Systems Using Bayes Theorem Method [PDF]
, 2023 Learning modality is a person's way of absorbing and processing information effectively and efficiently. This study aims to determine the results of the identification types of student learning modalities in physics subjects with an expert system using ...
Miraswan, Kanda Januar +2 more
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Agribusiness, EarlyView.Abstract Indoor agriculture (IA) presents a pathway to producing leafy greens more sustainably by strictly controlling the growing environment, increasing the efficiency of water and land resources use, reducing pesticide application, and enhancing quality characteristics.
Joseph Seong +3 more
wiley +1 more source
Sharpening the Second Law of Thermodynamics with the Quantum Bayes' Theorem [PDF]
, 2014 We prove a generalization of the classic Groenewold-Lindblad entropy inequality, combining decoherence and the quantum Bayes theorem into a simple unified picture where decoherence increases entropy while observation decreases it.
Gharibyan, Hrant, Tegmark, Max
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Reinforcement learning for optimal control of stochastic nonlinear systems
AIChE Journal, EarlyView.Abstract A reinforcement learning (RL) approach is developed in this work for nonlinear systems under stochastic uncertainty. A stochastic control Lyapunov function (SCLF) candidate is first constructed using neural networks (NNs) as an approximator to the value function, and then a control policy designed using this SCLF is developed to ensure the ...
Xinji Zhu, Yujia Wang, Zhe Wu
wiley +1 more source
Universal efficiency at optimal work with Bayesian statistics
, 2010 If the work per cycle of a quantum heat engine is averaged over an appropriate prior distribution for an external parameter $a$, the work becomes optimal at Curzon-Ahlborn efficiency.
E. T. Jaynes +5 more
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Bayesian Analysis of Many-Pole Fits of Hadron Propagators in Lattice QCD [PDF]
, 1996 We use Bayes' probability theorem to analyze many-pole fits of hadron propagators. An alternative method of estimating values and uncertainties of the fit parameters is offered, which has certain advantages over the conventional methods.
Bretthorst +6 more
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Generative Inverse Design of Metamaterials with Functional Responses by Interpretable Learning
Advanced Intelligent Systems, EarlyView.This work introduces random‐forest‐based interpretable generative inverse design (RIGID), a new single‐shot inverse design method for metamaterials using interpretable machine learning and Markov chain Monte Carlo sampling. Once trained on a small dataset, RIGID can estimate the likelihood of designs achieving target behaviors (e.g., wave‐based ...
Wei (Wayne) Chen +4 more
wiley +1 more source
Informal Logic, 1999 In this paper I identify a fallacy. The fallacy is worth noting for practical and theoretical reasons. First, the rampant occurrences ofthis fallacy-especially at moments calling for careful thought-indicate that it is more pernicious to clear thinking than many of those found in standard logic texts.
openaire +4 more sources