Torthaí cuardaigh - Bayesian optimization algorithm*
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Hyperparameter Optimization EM Algorithm via Bayesian Optimization and Relative Entropy
Foilsithe / Cruthaithe 2025-06-01“…The experimental result shows the effectiveness of the EM algorithm of hyperparameter optimization, and the algorithm also has the merit of fast convergence, except that the covariance of the posterior distribution is a singular matrix, which affects the increase in the likelihood.…”Hyperparameter optimization (HPO), which is also called hyperparameter tuning, is a vital component of developing machine learning models. These parameters, which regulate the behavior of the machine learning algorithm and cannot be directly learned from the given training data, can significantly af...
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Bayesian optimization with Optuna for enhanced soil nutrient prediction: a comparative study with genetic algorithm and particle swarm optimization
Foilsithe / Cruthaithe 2025-12-01Faigh an téacs iomlánOptimizing soil nutrient prediction models is important for achieving maximum agricultural output and sustainability while also ensuring effective resource management and environmental protection, as demonstrated by a case study in Johannesburg, South Africa. We implemented machine learning (ML), op...
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Health Risk Classification Using XGBoost with Bayesian Hyperparameter Optimization
Foilsithe / Cruthaithe 2025-06-01“…Hyperparameter Optimization (HO) of XGBoost is critical for maximizing the performance of the XGBoost algorithm. …”Health risk classification is important. However, health risk classification is challenging to address using conventional analytical techniques. The XGBoost algorithm offers many advantages over the traditional methods for risk classification. Hyperparameter Optimization (HO) of XGBoost is critical...
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Bayesian optimization of underground railway tunnels using a surrogate model
Foilsithe / Cruthaithe 2025-01-01“…The results demonstrate that the proposed optimization framework, which combines the Bayesian optimization algorithm with surrogate models, effectively explores trade-offs among multiple design parameters. …”The assessment of soil–structure interaction (SSI) under dynamic loading conditions remains a challenging task due to the complexities of modeling this system and the interplay of SSI effects, which is also characterized by uncertainties across varying loading scenarios. This field of research encom...
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A Bayesian Optimization Approach for Tuning a Grouping Genetic Algorithm for Solving Practically Oriented Pickup and Delivery Problems
Foilsithe / Cruthaithe 2024-02-01“…<i>Methods</i>: To solve the MDPDPTWHV, we present a Grouping Genetic Algorithm (GGA) framework with several operators and population management variants. …”<i>Background</i>: The Multi Depot Pickup and Delivery Problem with Time Windows and Heterogeneous Vehicle Fleets (MDPDPTWHV) is a strongly practically oriented routing problem with many real-world constraints. Due to its complexity, solution approaches with sufficiently good quality ide...
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Personalized Human Thermal Sensation Prediction Based on Bayesian-Optimized Random Forest
Foilsithe / Cruthaithe 2025-07-01“…More accurate personalized thermal sensation prediction models were then constructed using various machine learning algorithms, followed by a comparative analysis of their performance. …”Establishing a predictive model for human thermal sensation serves as the fundamental theoretical basis for intelligent control of building HVAC systems based on thermal comfort. The traditional Predicted Mean Vote (PMV) model exhibits low accuracy in predicting human thermal sensation and is not we...
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Artificial intelligence-based Bayesian optimization and transformer model for tennis motion recognition
Foilsithe / Cruthaithe 2025-06-01“…A human key point recognition algorithm is designed based on Transformer. Then, the optimal pruning rate of each layer of the network is found by using Bayesian optimization algorithm to improve the efficiency and accuracy of subnet search. …”Because the traditional methods are used to analyze human motion behavior, there are large errors and serious over-fitting phenomenon, so a novel tennis motion recognition based on Bayesian optimization and transformer model is proposed in this paper. First, we use an improved generative adversarial...
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Bayesian Denoising Algorithm for Low SNR Photon-Counting Lidar Data via Probabilistic Parameter Optimization Based on Signal and Noise Distribution
Foilsithe / Cruthaithe 2025-06-01“…This study proposes an adaptive Bayesian denoising algorithm integrating minimum spanning tree (MST) -based slope estimation and probabilistic parameter optimization. …”The Ice, Cloud, and land Elevation Satellite-2 has provided unprecedented global surface elevation measurements through photon-counting Lidar (Light detection and ranging), yet its low signal-to-noise ratio (SNR) poses significant challenges for denoising algorithms. Existing methods, relying on fix...
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Comparative study on inversion of the unsaturated hydraulic parameters using optimization and Bayesian estimation methods
Foilsithe / Cruthaithe 2016-09-01Faigh an téacs iomlánThe model of water movement in variably saturated flow is of guiding significance in agricultural production and environmental protection. The accurate acquisition of soil hydraulic parameters is the precondition of reliable prediction. Based on searching for one set of parameters that best fit the...
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Behavioral Study of Bayesian Neural Networks Under a Typical Surrogate Model-Assisted Evolutionary Search Framework
Foilsithe / Cruthaithe 2025-01-01Ábhair: “…surrogate-model-assisted evolutionary algorithm…”The machine learning method for surrogate modeling is a keystone in surrogate model-assisted evolutionary algorithms (SAEAs). The current arguably most widely used surrogate modeling methods in SAEAs are Gaussian process and radial basis function. This paper investigates the behavior of a machine le...
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Bayesian Optimization-Based State-of-Charge Estimation with Temperature Drift Compensation for Lithium-Ion Batteries
Foilsithe / Cruthaithe 2025-06-01“…For this reason, this study proposes an algorithm focusing on Bayesian optimization-based adaptive extended Kalman filter (BO-AEKF) to enhance the numerical accuracy and stability of state-of-charge (SOC) estimation for lithium batteries under various operating conditions. …”With the widespread application of electric vehicles and electrical energy storage systems, the accurate monitoring of lithium battery states has become crucial for ensuring safety and improving efficiency in terms of the applications. For this reason, this study proposes an algorithm focusing on Ba...
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Explainable Artificial Intelligence (XAI) for Flood Susceptibility Assessment in Seoul: Leveraging Evolutionary and Bayesian AutoML Optimization
Foilsithe / Cruthaithe 2025-06-01“…We first employed the Tree-based Pipeline Optimization Tool (TPOT), an evolutionary AutoML algorithm, to construct baseline ensemble models using Gradient Boosting (GB), Random Forest (RF), and XGBoost (XGB). …”This study aims to enhance the accuracy and interpretability of flood susceptibility mapping (FSM) in Seoul, South Korea, by integrating automated machine learning (AutoML) with explainable artificial intelligence (XAI) techniques. Ten topographic and environmental conditioning factors were selected...
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Reinforcement Learning-Based Augmentation of Data Collection for Bayesian Optimization Towards Radiation Survey and Source Localization
Foilsithe / Cruthaithe 2025-04-01“…For radiation survey and source location, the aid of such a machine learning algorithm could significantly cut down on time and health risks required for maintenance and emergency response scenarios. …”Safer and more efficient characterization of radioactive environments requires exploring intelligently, utilizing robotic systems which use smart strategies and physics-based statistical models. Bayesian Optimization (BO) provides one such statistical framework to explainably find the global maximum...
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Machine learning alloying design of biodegradable zinc alloy for bone implants using XGBoost and Bayesian optimization
Foilsithe / Cruthaithe 2025-09-01“…This study proposes a machine learning approach based on Bayesian optimization (BO) and an eXtreme Gradient Boosting (XGBoost) algorithm to design a biodegradable Zinc (Zn) alloy and forecast percentage of elements in the Zn alloy for bone implants. …”Developing implants using biodegradable materials eliminates the need for secondary surgery, improves both mechanical and biological properties, and enhances biocompatibility. This study proposes a machine learning approach based on Bayesian optimization (BO) and an eXtreme Gradient Boosting (XGBoos...
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Achieving Efficient Prompt Engineering in Large Language Models Using a Hybrid and Multi-Objective Optimization Framework
Foilsithe / Cruthaithe 2025-06-01Ábhair: Faigh an téacs iomlánPrompt optimization is crucial for enhancing the performance of large language models. Traditional Bayesian Optimization (BO) methods face challenges such as local refinement limitations, insufficient parameter tuning, and difficulty handling multi-objectives. This study introduces a hybrid multi-ob...
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Automated Class Imbalance Learning via Few-Shot Multi-Objective Bayesian Optimization With Deep Kernel Gaussian Processes
Foilsithe / Cruthaithe 2025-01-01“…Automated Class Imbalance Learning (AutoCIL) is an emerging paradigm that leverages Combined Algorithm Selection and Hyperparameter Optimization (CASH) to automate the configuration of resampling strategies and classifiers for imbalanced classification tasks. …”Automated Class Imbalance Learning (AutoCIL) is an emerging paradigm that leverages Combined Algorithm Selection and Hyperparameter Optimization (CASH) to automate the configuration of resampling strategies and classifiers for imbalanced classification tasks. Existing AutoCIL methods focus solely on...
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A Hybrid Artificial Neural Network and Particle Swarm Optimization algorithm for Detecting COVID-19 Patients
Foilsithe / Cruthaithe 2021-12-01“…Following that, we implemented Neural Network and Particle Swarm Optimization algorithms. We used precision, accuracy score, recall, and F-Measure tests to evaluate the Neural Network with Particle Swarm Optimization algorithms. …”COVID-19, one of the most dangerous pandemics, is currently affecting humanity. COVID-19 is spreading rapidly due to its high reliability transmissibility. Patients who test positive more often have mild to severe symptoms such as a cough, fever, raw throat, and muscle aches. Diseased people experie...
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Statistical bayesian algorithm for processing thermographic images of the cow udder for diagnosing mastitis
Foilsithe / Cruthaithe 2023-08-01Faigh an téacs iomlánThe article presents results of our experiments carried out to study the invariance of the digital description of the imageThere in the paper is formulated a mathematical problem of multi-hypothetical detection of subclinical and clinical mastitis in dairy cows by the maximum values of udder tempera...
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Laser-Induced Breakdown Spectroscopy Quantitative Analysis Using a Bayesian Optimization-Based Tunable Softplus Backpropagation Neural Network
Foilsithe / Cruthaithe 2025-07-01“…Hence chemometrics based on artificial neural network (ANN) algorithms have become increasingly popular in LIBS analysis due to their extraordinary ability in nonlinear feature modeling. …”Laser-induced breakdown spectroscopy (LIBS) has played a critical role in Mars exploration missions, substantially contributing to the geochemical analysis of Martian surface substances. However, the complex nonlinearity of LIBS processes can considerably limit the quantification accuracy of convent...
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ABMF-Net: An Attentive Bayesian Multi-Stage Deep Learning Model for Robust Forecasting of Electricity Price and Demand
Foilsithe / Cruthaithe 2025-01-01“…Furthermore, a multi-objective Salp Swarm Algorithm (MSSA) is used to optimize forecasting accuracy and stability. …”This article presents a novel deep learning model, the Attentive Bayesian Multi-Stage Forecasting Network (ABMF-Net), designed for robust forecasting of electricity price (USD/MWh) and demand (MW). The model incorporates an attention-based data selection mechanism, an encoder-decoder structure with...
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