Results 41 to 50 of about 42,250 (218)
Hits-and-misses for the evaluation and combination of forecasts [PDF]
, 2000 Error measures for the evaluation of forecasts are usually based on the size of the forecast errors. Common measures are e.g. the Mean Squared Error (MSE), the Mean Absolute Deviation (MAD) or the Mean Absolute Percentage Error (MAPE).
Wenzel, Thomas
core +1 more source
A Machine Learning Model for Interpretable PECVD Deposition Rate Prediction
Advanced Intelligent Discovery, EarlyView.This study develops six machine learning models (k‐nearest neighbors, support vector regression, decision tree, random forest, CatBoost, and backpropagation neural network) to predict SiNx deposition rates in plasma‐enhanced chemical vapor deposition using hybrid production and simulation data.
Yuxuan Zhai +8 more
wiley +1 more source
Jurnal SisfokomRice is a strategic food commodity in Indonesia, and its price fluctuations significantly impact inflation, economic stability, and poverty levels. Accurate price prediction is, therefore, essential for effective policymaking.
Muhammad Iqbal +2 more
doaj +1 more source
Buana Information Technology and Computer Sciences, 2021 The process of admitting new students is an annual routine activity that occurs in a university. This activity is the starting point of the process of searching for prospective new students who meet the criteria expected by the college.
Tatang Rohana, Bayu Priyatna
doaj +1 more source
A better measure of relative prediction accuracy for model selection and model estimation [PDF]
, 2015 Surveys show that the mean absolute percentage error (MAPE) is the most widely used measure of forecast accuracy in businesses and organizations. It is however, biased: When used to select among competing prediction methods it systematically selects ...
Tofallis, C.
core +1 more source
Advanced Intelligent Discovery, EarlyView.This study introduces FIRE‐GNN, a force‐informed, relaxed equivariant graph neural network for predicting surface work functions and cleavage energies from slab structures. By incorporating surface‐normal symmetry breaking and machine learning interatomic potential‐derived force information, the approach achieves state‐of‐the‐art accuracy and enables ...
Circe Hsu +5 more
wiley +1 more source
Machine Learning Driven Inverse Design of Broadband Acoustic Superscattering
Advanced Intelligent Discovery, EarlyView.Multilayer acoustic superscatterers are designed using machine learning to achieve broadband superscattering and strong sound insulation. By incorporating a weighted mean absolute error into the loss function, the forward and inverse neural networks accurately map structural parameters to spectral responses.
Lijuan Fan, Xiangliang Zhang, Ying Wu
wiley +1 more source
Vertical wind speed extrapolation using statistical approaches [PDF]
FME TransactionsThe wind power industry has experienced a significant increase and popularity in recent times, and the latest statistics indicate that this sector is still thriving.
Nuha Hilal H. +4 more
doaj +1 more source
Predicting Performance of Hall Effect Ion Source Using Machine Learning
Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.This study introduces HallNN, a machine learning tool for predicting Hall effect ion source performance using a neural network ensemble trained on data generated from numerical simulations. HallNN provides faster and more accurate predictions than numerical methods and traditional scaling laws, making it valuable for designing and optimizing Hall ...
Jaehong Park +8 more
wiley +1 more source
Сеченовский вестник, 2017 The article discusses the incidence forecast on the example of respiratory diseases in children in Nizhny Novgorod. The study used the official statistics of the respiratory diseases prevalence in children of 0-14 age group in 2001-2015.
O. V. Zhukova +2 more
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