Hakutulokset - Bayesian optimization algorithm
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An explainable AI-based framework for predicting and optimizing blast-induced ground vibrations in surface mining
Julkaistu 2025-09-01“…This study proposes a novel hybrid artificial intelligence (AI) framework that integrates physics informed neural networks (PINNs) with conventional machine learning (ML) algorithms for the accurate prediction and optimization of BIGV. …”Blast induced ground vibrations (BIGV) pose critical challenges in surface mining, threatening structural integrity, worker safety, and environmental compliance. This study proposes a novel hybrid artificial intelligence (AI) framework that integrates physics informed neural networks (PINNs) with co...
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62
Stacking ensemble learning framework for predicting tribological properties and optimal additive ratios of amide-based greases
Julkaistu 2025-07-01“…Based on the tribological experimental data, the synthetic minority oversampling technique (SMOTE) was utilized for data augmentation, and a stacking ensemble algorithm with Bayesian optimization of hyperparameters was used to construct a predictive model for tribological performance. …”This study employs a stacking ensemble learning framework to establish a regression model for predicting the tribological properties of amide-based lubricating grease and determining the optimal additive ratios. Melamine cyanuric acid (MCA) was selected as the thickener, and three extreme-pressure a...
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63
Analysis of informativeness of features of classification of dangerous weather events based on radar observation results
Julkaistu 2024-07-01Hae kokotekstiOne of the crucial factors affecting the safety and regularity of state and civil aviation flights is the meteorological situation. The European territory of Russia is most characterized by dangerous meteorological phenomena associated with cumulonimbus clouds: shower, thunderstorms, hail, accompani...
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Finite Mixture Model-Based Analysis of Yarn Quality Parameters
Julkaistu 2025-06-01“…Model parameters are estimated using the expectation–maximization (EM) algorithm, and model selection is guided by the Akaike and Bayesian information criteria (AIC and BIC). …”This study investigates the applicability of finite mixture models (FMMs) for accurately modeling yarn quality parameters in 28/1 Ne ring-spun polyester/viscose yarns, focusing on both yarn imperfections and mechanical properties. The research addresses the need for advanced statistical modeling tec...
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65
IT Diagnostics of Parkinson’s Disease Based on the Analysis of Voice Markers and Machine Learning
Julkaistu 2023-06-01“…The Bayesian optimization algorithm and the GridSearch method were used to find the best model hyperparameters. …”The results of studying the parameters of the spectra of speech signals by machine learning with the use of neural networks are presented. This study was carried out in order to confirm experimentally the possibility of performing an assessment of these parameters for the detection of Parkinson’s di...
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66
Machine‐Learning‐Assisted Design and Optimization of Single‐Atom Transition Metal‐Incorporated Carbon Quantum Dot Catalysts for Electrocatalytic Hydrogen Evolution Reaction
Julkaistu 2025-07-01“…Herein, an effective and facile catalyst design strategy is proposed based on machine learning (ML) and its model verification using electrochemical methods accompanied by density functional theory simulations. Based on a Bayesian genetic algorithm ML model, the Ni‐incorporated carbon quantum dots (Ni@CQD) loaded on a three‐dimensional reduced graphene oxide conductor are proposed as the best HER catalyst amongst the various TM‐incorporated CQDs under the optimal conditions of catalyst loading, electrode type, and temperature and pH of electrolyte. …”ABSTRACT Hydrogen evolution reaction (HER) in acidic media has been spotlighted for hydrogen production since it is a favourable kinetics with the supplied protons from a counterpart compared to that within alkaline environment. However, there is no choice but to use a platinum‐based catalyst yet. A...
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Bi-Objective Optimization of Techno-Economic and Environmental Performance of CO<sub>2</sub> Capture Strategy Involving Two-Stage Membrane-Based Separation with Recycling
Julkaistu 2025-06-01“…The implemented algorithm combined experimental design, machine learning, genetic algorithms, and Bayesian optimization. …”To effectively implement complex CO<sub>2</sub> capture, utilization, and storage (CCUS) processes, it is essential to optimize their design by considering various factors. This research bi-objectively optimized a two-stage membrane-based separation process that includes recycling, conce...
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68
Improving the Minimum Free Energy Principle to the Maximum Information Efficiency Principle
Julkaistu 2025-06-01Hae kokotekstiFriston proposed the Minimum Free Energy Principle (FEP) based on the Variational Bayesian (VB) method. This principle emphasizes that the brain and behavior coordinate with the environment, promoting self-organization. However, it has a theoretical flaw, a possibility of being misunderstood, and a...
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Reliability Estimation for the Gull Alpha Power Pareto Model Under Progressive Type-I Censoring
Julkaistu 2025-01-01“…Estimation methods were investigated from an algorithmic perspective: for maximum likelihood estimation, the Newton-Raphson algorithm was adapted, while for Bayesian estimation, Markov Chain Monte Carlo methods were employed, utilizing the Metropolis-Hastings algorithm. …”A new family of Pareto distributions, known as the Gull Alpha Power Pareto (GAPP) model, has been derived and its statistical properties have been examined, followed by an application to life-testing, specifically under Type I progressive censoring schemes (PCS-TI). A comprehensive statistical analy...
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70
Hyperspectral Imaging for Non-Destructive Moisture Prediction in Oat Seeds
Julkaistu 2025-06-01“…Subsequently, a dual-optimized neural network model, termed Bayes-ASFSSA-BP, was developed by incorporating Bayesian optimization and the Adaptive Spiral Flight Sparrow Search Algorithm (ASFSSA). …”Oat is a highly nutritious cereal crop, and the moisture content of its seeds plays a vital role in cultivation management, storage preservation, and quality control. To enable efficient and non-destructive prediction of this key quality parameter, this study presents a modeling framework integratin...
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71
Spatiotemporal pattern analysis of land use in Jiangsu Province based on long-term time series remote sensing images
Julkaistu 2025-06-01“…Principal Component Analysis (PCA) was applied to reduce feature dimensionality, and the Random Forest classification algorithm was optimized with Bayesian Optimization and Tree-structured Parzen Estimators (TPE) for improved performance. …”Studying spatiotemporal patterns of land use is crucial for optimal land resource allocation and sustainable development. This study utilizes the Google Earth Engine (GEE) platform and long-term remote sensing imagery data, selecting Jiangsu Province as a case study area. Principal Component Analysi...
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72
A general two-group constants estimator for 17x17 PWR assembly configurations using artificial neural networks
Julkaistu 2025-06-01“…The number of layers and hyperparameters used in the ANN has been determined using the KerasTuner optimization framework employing the Bayesian optimization algorithm. …”In this study, a preliminary general two-group constants predictor using artificial neural networks (ANNs) for pressurized water reactor (PWR) based assembly designs is established. Users can input arbitrary assembly specifications to the trained ANN, enabling the instant generation of group constan...
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73
An echo state network based on enhanced intersecting cortical model for discrete chaotic system prediction
Julkaistu 2025-07-01“…The model incorporates a neuron model with internal dynamics, including adaptive thresholds and inter-neuron feedback, into the reservoir structure. A Bayesian Optimization algorithm was employed for the selection of hyperparameters. …”IntroductionThe prediction of chaotic time series is a persistent problem in various scientific domains due to system characteristics such as sensitivity to initial conditions and nonlinear dynamics. Deep learning models, while effective, are associated with high computational costs and large data r...
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Novel Conformable Fractional Order Unbiased Kernel Regularized Nonhomogeneous Grey Model and Its Applications in Energy Prediction
Julkaistu 2025-07-01“…The parameter estimation of the CFUKRNGM model requires solving a linear equation with a lower order than the KRNGM model, and is automatically calibrated through the Bayesian optimization algorithm. Experimental results show that the CFUKRNGM model achieves superior prediction accuracy and greater generalization performance compared to both the KRNGM and traditional grey models.…”Grey models have attracted considerable attention as a time series forecasting tool in recent years. Nevertheless, the linear characteristics of the differential equations on which traditional grey models rely frequently result in inadequate predictive accuracy and applicability when addressing intr...
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75
End-of-Line Quality Control Based on Mel-Frequency Spectrogram Analysis and Deep Learning
Julkaistu 2025-07-01“…The neural network was trained on a highly imbalanced dataset, using oversampling and Bayesian hyperparameter optimization. The final classification algorithm achieved classification metrics with high accuracy (99%). …”This study presents a novel approach to the end-of-line (EoL) quality inspection of brushless DC (BLDC) motors by implementing a deep learning model that combines MEL diagrams, convolutional neural networks (CNNs) and bidirectional gated recurrent units (BiGRUs). The suggested system utilizes raw vi...
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76
Prediction of Unconfined Compressive Strength in Cement-Treated Soils: A Machine Learning Approach
Julkaistu 2025-06-01“…Fourteen regression algorithms were initially screened, with the top three performers subsequently evaluated using nested cross-validation and Bayesian hyperparameter optimization via the Optuna framework. …”This study integrates systematic laboratory testing with advanced machine learning techniques to predict the unconfined compressive strength (UCS) of cement-treated clayey silt from northwestern Iași, Romania. Laboratory experiments generated 185 UCS measurements, examining the effects of cement con...
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77
Satellite-Derived Bathymetry Combined With Sentinel-2 and ICESat-2 Datasets Using Deep Learning
Julkaistu 2025-01-01“…This article proposes a convolutional neural network and bidirectional long short-term memory hybrid model based on the Bayesian optimization algorithm (BOA-CNN-BILSTM) to enhance bathymetric inversion accuracy and efficiency. …”Accurate bathymetric data are critical for marine ecological balance and resource management. Deep learning algorithms, known for their capacity to model complex, multivariate, and nonlinear relationships, have been increasingly applied to satellite-derived bathymetry. However, existing deep learnin...
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Stroke Risk Classification Using the Ensemble Learning Method of XGBoost and Random Forest
Julkaistu 2025-06-01“…The dataset was split into 80% training and 20% testing data (hold-out test) to ensure objective evaluation. Hyperparameter optimization was performed using Bayesian optimization, while model evaluation employed stratified K-fold cross-validation to prevent overfitting. …”Stroke is a leading cause of global death and disability. This study proposes a stroke risk classification model using ensemble learning that combines Random Forest and XGBoost algorithms. A Kaggle dataset with 5110 samples (249 stroke, 4861 non-stroke) presented significant class imbalance. To addr...
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79
Surface water quality prediction based on BOA-BiLSTM model(基于BOA-BiLSTM模型的地表水水质预测)
Julkaistu 2025-05-01“…∶为准确评估监测条件有限的平原河网小流域河水水质演变趋势，预知水质变化情况，利用浙江省台州市南官河2021年6月至2023年6月的水质监测数据，基于贝叶斯优化算法（Bayesian optimization algorithm，BOA）和双向长短期记忆神经网络（bi-directional long short-term memory，BiLSTM）建立了地表水水质预测模型。…”∶To accurately assess the water quality evolution trend of small watersheds in plain river networks with limited monitoring conditions and predict the change of water quality in advance, based on the water quality monitoring data of Nanguan River in Taizhou, Zhejiang province from June 2021 to June...
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NEURAL NETWORK FORECASTING OF ENERGY CONSUMPTION OF A METALLURGICAL ENTERPRISE
Julkaistu 2021-03-01“…The best results were shown by the feedforward and backpropagation network, architecture with nonlinear autoregressive and learning algorithms: Levenberg-Marquard nonlinear optimization, Bayesian Regularization method and conjugate gradient method. …”The subject of the research is the methods of constructing and training neural networks as a nonlinear modeling apparatus for solving the problem of predicting the energy consumption of metallurgical enterprises. The purpose of this work is to develop a model for forecasting the consumption of the p...
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