Zoekresultaten - Bayesian optimization algorithm*
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Machine‐Learning‐Assisted Design and Optimization of Single‐Atom Transition Metal‐Incorporated Carbon Quantum Dot Catalysts for Electrocatalytic Hydrogen Evolution Reaction
Gepubliceerd in 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
Gepubliceerd in 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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63
Integrated neural network and evolutionary algorithm approach for liver fibrosis staging: Can artificial intelligence reduce patient costs?
Gepubliceerd in 2024-05-01“…Results We propose a novel algorithm that reduces the number of required patient features to seven inputs. …”Abstract Background and Aim Staging liver fibrosis is important, and liver biopsy is the gold standard diagnostic tool. We aim to design and evaluate an artificial neural network (ANN) method by taking advantage of the Teaching Learning‐Based Optimization (TLBO) algorithm for the prediction of liver...
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64
Substantiation of source data on the parametric algorithms for the classification of weather hazards
Gepubliceerd in 2023-12-01“…Based on the data obtained, it is necessary to build an algorithm to classify the WH “rain shower-thunderstorm-hail”.…”The meteorological situation is one of the decisive factors determining the safety and frequency of civil aviation flights. Weather hazards (WH), associated with cumulonimbus clouds, such as a heavy shower, thunderstorm, hail, combined with high atmosphere turbulence, quite often lead to aviation ev...
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Enhancing LoRaWAN Performance Using Boosting Machine Learning Algorithms Under Environmental Variations
Gepubliceerd in 2025-06-01“…The findings show that boosting algorithms, particularly LightGBM, are highly effective for path loss prediction in LoRaWANs.…”Accurate path loss prediction is essential for optimizing Long-Range Wide-Area Network (LoRaWAN) performance. Previous studies have employed various Machine Learning (ML) models for path loss prediction. However, environmental factors such as temperature, humidity, barometric pressure, and particula...
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66
Comparative Study on Hyperparameter Tuning for Predicting Concrete Compressive Strength
Gepubliceerd in 2025-06-01“…For Dataset 1, the application of search algorithms appeared to improve prediction accuracy to some extent. …”This study assesses the impact of hyperparameter optimization algorithms on the performance of machine learning-based concrete compressive strength prediction models. Three datasets were used to compare the performance of a basic model that had not undergone hyperparameter optimization, with the mod...
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Improving the Minimum Free Energy Principle to the Maximum Information Efficiency Principle
Gepubliceerd in 2025-06-01Volledige tekstFriston 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
Gepubliceerd in 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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69
Finite Mixture Model-Based Analysis of Yarn Quality Parameters
Gepubliceerd in 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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70
Hyperspectral Imaging for Non-Destructive Moisture Prediction in Oat Seeds
Gepubliceerd in 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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Spatiotemporal pattern analysis of land use in Jiangsu Province based on long-term time series remote sensing images
Gepubliceerd in 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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Stroke Risk Classification Using the Ensemble Learning Method of XGBoost and Random Forest
Gepubliceerd in 2025-06-01“…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. …”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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A general two-group constants estimator for 17x17 PWR assembly configurations using artificial neural networks
Gepubliceerd in 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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An echo state network based on enhanced intersecting cortical model for discrete chaotic system prediction
Gepubliceerd in 2025-07-01“…This efficiency gain during optimization is attributed to the model's intrinsic stability, which reduces the number of divergent trials encountered by the search algorithm.DiscussionThe results indicate that the ESN-EICM framework is a viable method for the prediction of the tested chaotic time series. …”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
Gepubliceerd in 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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Recent advances in machine learning applications for MXene materials: Design, synthesis, characterization, and commercialization for energy and environmental applications
Gepubliceerd in 2025-07-01“…Techniques such as genetic algorithms, evolutionary algorithms, and Bayesian optimization accelerate the discovery of novel MXene phases tailored for specific uses. …”MXene-based materials are characterized by excellent superconductivity, superb ion-holding capacity, large surface area, and rapid electrochemical reactions, making them viable options for applications in high-capacity energy storage and conversion systems (ESCS) such as portable digital devices, el...
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77
A Spatial–Temporal Time Series Decomposition for Improving Independent Channel Forecasting
Gepubliceerd in 2025-07-01“…In this paper, we improve the performance of single-channel forecasting algorithms by designing an interpretable front-end that extracts the spatial–temporal components from the input multivariate time series. …”Forecasting multivariate time series is a pivotal task in controlling multi-sensor systems. The joint forecasting of all channels may be too complex, whereas forecasting the channels independently may cause important spatial inter-dependencies to be overlooked. In this paper, we improve the performa...
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78
Generative AI for drug discovery and protein design: the next frontier in AI-driven molecular science
Gepubliceerd in 2025-09-01“…Generative artificial intelligence (AI) has emerged as a disruptive paradigm in molecular science, enabling algorithmic navigation and construction of chemical and proteomic spaces through data-driven modeling. …”Generative artificial intelligence (AI) has emerged as a disruptive paradigm in molecular science, enabling algorithmic navigation and construction of chemical and proteomic spaces through data-driven modeling. This review systematically delineates the theoretical underpinnings, algorithmic architec...
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End-of-Line Quality Control Based on Mel-Frequency Spectrogram Analysis and Deep Learning
Gepubliceerd in 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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Satellite-Derived Bathymetry Combined With Sentinel-2 and ICESat-2 Datasets Using Deep Learning
Gepubliceerd in 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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