Результаты поиска - Bayesian optimization algorithm
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Robust Variable Selection via Bayesian LASSO-Composite Quantile Regression with Empirical Likelihood: A Hybrid Sampling Approach
Опубликовано 2025-07-01“...By constructing a hybrid sampling mechanism that incorporates the Expectation–Maximization (EM) algorithm and Metropolis–Hastings (M-H) algorithm within the Gibbs sampling scheme, this approach effectively tackles variable selection in high-dimensional settings with outlier contamination. ...”Since the advent of composite quantile regression (CQR), its inherent robustness has established it as a pivotal methodology for high-dimensional data analysis. High-dimensional outlier contamination refers to data scenarios where the number of observed dimensions (<i>p</i>) is much grea...
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Laser-Induced Breakdown Spectroscopy Quantitative Analysis Using a Bayesian Optimization-Based Tunable Softplus Backpropagation Neural Network
Опубликовано 2025-07-01“...We demonstrate that the Bayesian optimization method surpasses the traditional grid search method regarding both performance and efficiency. ...”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
Опубликовано 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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Bayesian adaptation in Poisson cognitive systems
Опубликовано 2019-09-01“...In particular, to describe the stochastic properties of cognitive systems and the possibility of creating an optimal algorithm, the Bayesian approach recognized in a number of philosophical works is applied.The optimal estimate by the criterion of the minimum standard error is, as is known, a posteriori mathematical expectation of a random estimated value, which is applied in this work. ...”The aim of the study is to investigate the possibility of applying Bayesian adaptation algorithms to cognitive systems that perceive the Poisson process of external events.The method of research is the use of stochastic description and synthesis of cognitive systems, including the theory of doubly s...
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Satellite Image Price Prediction Based on Machine Learning
Опубликовано 2025-06-01“...Two distinct datasets—optical and Synthetic Aperture Radar (SAR) imagery—were assembled, each characterized by nine technical and economic features (e.g., imaging mode, spatial resolution, satellite manufacturing cost, and acquisition timeliness). Bayesian optimization is employed to systematically tune hyperparameters, thereby minimizing overfitting and maximizing generalization. ...”This study develops a comprehensive, data-driven framework for predicting satellite imagery prices using four state-of-the-art ensemble learning algorithms: XGBoost, LightGBM, AdaBoost, and CatBoost. Two distinct datasets—optical and Synthetic Aperture Radar (SAR) imagery—were assembled, each charac...
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Mathematical Formalization and Algorithmization of the Main Modules of Organizational and Technical Systems
Опубликовано 2020-09-01“...The formalization and algorithmization of the organizational and technical systems behavior is undertaken mainly in terms of the Bayesian criterion of optimal statistical estimates. ...”The purpose of the research is to develop a generalized structural scheme of organizational and technical systems based on the general theory of management, which contains the necessary and sufficient number of modules and formalize on this basis the main management tasks that act as goals of the be...
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IT diagnostics of Parkinson's disease based on voice markers and decreased motor activity
Опубликовано 2024-01-01“...A Bayesian optimizer was also used to improve the hyperparameters of the KNN algorithm. ...”The objectives of the article to propose the method for complex recognition of Parkinson's disease using machine learning, based on markers of voice analysis and changes in patient movements on known data sets. The time-frequency function, (the wavelet function) and the Meyer kepstral coefficie...
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A Hierarchical Evolutionary Search Framework with Manifold Learning for Powertrain Optimization of Flying Vehicles
Опубликовано 2025-06-01“...To address this, this paper proposes a hierarchical manifold-enhanced Bayesian evolutionary optimization (HM-BEO) approach for HEVTOL systems. ...”Hybrid electric vertical take-off and landing (HEVTOL) flying vehicles serve as effective platforms for efficient transportation, forming a cornerstone of the emerging low-altitude economy. However, the current lack of co-optimization methods for powertrain component sizing and energy controller des...
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Optimizing Hyperparameters in Meta-Learning for Enhanced Image Classification
Опубликовано 2025-01-01Полный текстThis paper investigates the significance of hyperparameter optimization in meta-learning for image classification tasks. Despite advancements in deep learning, real-time image classification applications often suffer from data inadequacy. Few-shot learning addresses this challenge by enabling learni...
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Bayesian Inference-Based Gaussian Mixture Models With Optimal Components Estimation Towards Large-Scale Synthetic Data Generation for <italic>In Silico</italic> Clinical Trials
Опубликовано 2022-01-01“...<italic>Methods:</italic> We propose the BGMM-OCE, an extension of the conventional BGMM (Bayesian Gaussian Mixture Models) algorithm to provide unbiased estimations regarding the optimal number of Gaussian components and yield high-quality, large-scale synthetic data at reduced computational complexity. ...”<italic>Goal</italic>: To develop a computationally efficient and unbiased synthetic data generator for large-scale <italic>in silico</italic> clinical trials (CTs). <italic>Methods:</italic> We propose the BGMM-OCE, an extension of the conventional BGMM (Bayesian...
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Optimized ensemble learning for non-destructive avocado ripeness classification
Опубликовано 2025-12-01“...Five machine learning models Random Forest, Decision Tree, XGBoost, Gradient Boosting, and Gaussian Mixture Model were trained separately and then merged into an ensemble. Four algorithms were used to optimize the model weight distribution: Bayesian Optimisation, Differential Evolution, Particle Swarm Optimisation, and Grid Search. ...”Classifying avocado ripeness accurately is crucial for enhancing post-harvest management and minimizing waste in agricultural supply chains. This study focuses on creating a strong ensemble classification model using spectral data from 120 kilogrammes of Buccaneer avocados obtained from the Royal Pr...
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AI-aided short-term decision making of rockburst damage scale in underground engineering
Опубликовано 2025-08-01“...This study investigates the effectiveness of ensemble machine learning models optimized through Bayesian optimization (BO) in predicting rockburst damage scales. ...”Rockbursts pose severe risks to underground engineering projects, including mining and tunnelling, where sudden rock failures can lead to substantial infrastructure damage and loss of human lives. An accurate assessment of rockburst damage is essential for safety and effective risk mitigation. This...
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Bayesian subset-based gene selection for biomarker discovery in high-dimensional data
Опубликовано 2025-05-01“... We introduce the Bayesian Subset-based Gene Selection (BSGS) algorithm, a novel framework tailored for high-dimensional gene expression analysis. ...”We introduce the Bayesian Subset-based Gene Selection (BSGS) algorithm, a novel framework tailored for high-dimensional gene expression analysis. BSGS integrates Lasso-based preselection, stochastic subset sampling, and an iterative Bayesian update mechanism to identify a parsimonious yet...
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Machine learning prediction and interpretability analysis of high-risk chest pain: a study from the MIMIC-IV database
Опубликовано 2025-06-01“...To address class imbalance, we implemented feature engineering with SMOTE and under-sampling techniques. Model optimization was performed via Bayesian hyperparameter tuning. ...”BackgroundHigh-risk chest pain is a critical presentation in emergency departments, frequently indicative of life-threatening cardiopulmonary conditions. Rapid and accurate diagnosis is pivotal for improving patient survival rates.MethodsWe developed a machine learning prediction model using the MIM...
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Deconvolution of continuous paleomagnetic data from pass‐through magnetometer: A new algorithm to restore geomagnetic and environmental information based on realistic optimization...
Опубликовано 2014-10-01“...In addition, we present an improved deconvolution algorithm based on Akaike's Bayesian Information Criterion (ABIC) minimization, incorporating new parameters to account for errors in sample measurement position and length. ...”Abstract The development of pass‐through superconducting rock magnetometers (SRM) has greatly promoted collection of paleomagnetic data from continuous long‐core samples. The output of pass‐through measurement is smoothed and distorted due to convolution of magnetization with the magnetometer sensor...
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A Cascade of Encoder–Decoder with Atrous Convolution and Ensemble Deep Convolutional Neural Networks for Tuberculosis Detection
Опубликовано 2025-06-01“...Using the cropped lung images, we trained several pre-trained Deep Convolutional Neural Networks (DCNNs) on the images with hyperparameters optimized by a Bayesian algorithm. Different combinations of trained DCNNs were compared, and the combination with the maximum accuracy was retained as the winning combination. ...”Tuberculosis (TB) is the most serious worldwide infectious disease and the leading cause of death among people with HIV. Early diagnosis and prompt treatment can cut off the rising number of TB deaths, and analysis of chest X-rays is a cost-effective method. We describe a deep learning-based cascade...
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Crash severity prediction using a virtual geometry-group-based deep learning approach with images-based feature representation
Опубликовано 2025-09-01“...To address class imbalance, a combination of the Synthetic Minority over Sampling technique, Edited Nearest Neighbor, and Tomek Link algorithms are applied. A Virtual Geometry Group deep learning model, optimized using Bayesian optimization, is trained to maximize the F1-score and Area Under the Curve, ensuring high prediction performance. ...”Highway traffic accidents pose a significant global challenge, particularly in low- and middle-income countries. Accurate prediction of accident severity is essential for developing effective road safety interventions. While previous research has explored various statistical and machine learning mod...
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A novel method for distracted driving behaviors recognition with hybrid CNN-BiLSTM-AM model
Опубликовано 2025-06-01“...A fully connected neural network layer is applied to establish a nonlinear mapping relationship between the extracted features and driving behavior categories. Bayesian optimization algorithm is adopted to automatically optimize hyperparameters of the network so as to improve training efficiency and performance of the proposed model. ...”Abstract A novel deep learning framework for recognition of distracted driving behavior is proposed in this paper. The proposed framework consists of hybrid convolutional neural network and bidirectional long short term memory network to extract multi-scale spatiotemporal features of high-dimensiona...
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Unmanned aerial vehicle field sampling and antenna pattern reconstruction using Bayesian compressed sensing
Опубликовано 2019-06-01“...It is shown that applying Bayesian CS algorithm to the samples of field intensity gathered by UAV can efficiently reconstruct the pattern. ...”Antenna 3D pattern measurement can be a tedious and time consuming task even for antennas with manageable sizes inside anechoic chambers. Performing onsite measurements by scanning the whole 4π [sr] solid angle around the antenna under test (AUT) is more complicated. In this paper, with the aim of m...
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Enhancements on the Latitudinal and Seasonal Bias Corrections in the SMOS Debiased Non-Bayesian Sea Surface Salinity Retrieval
Опубликовано 2025-01-01“...In this article, we revisit the algorithms to empirically correct the residual latitudinal and seasonal biases seen in the debiased non-Bayesian (DNB) retrieval algorithm. ...”The soil moisture and ocean salinity (SMOS) satellite mission, launched in 2009, provides global measurements of sea surface salinity (SSS) using L-band radiometry. In this article, we revisit the algorithms to empirically correct the residual latitudinal and seasonal biases seen in the debiased non...
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