検索結果 - Bayesian optimization algorithm*
-
21
Bayesian adaptation in Poisson cognitive systems
出版事項 2019-09-01“…An adaptive Bayesian estimation algorithm, also known as the empirical Bayesian approach, is used to overcome this problem. …”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...
全文の入手
論文 -
22
Satellite Image Price Prediction Based on Machine Learning
出版事項 2025-06-01“…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. …”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...
全文の入手
論文 -
23
Explainable artificial intelligence-driven model for ultrafine particle (PM0.1) prediction and explanation using meteorological variables
出版事項 2025-09-01主題: 全文の入手PM0.1, an ultrafine urban air pollutant, poses significant health risks due to its ability to penetrate deep into the lungs, enter the bloodstream, and rapidly circulate throughout the human body, potentially causing severe respiratory diseases. Effective monitoring and explanation of PM0.1 concentr...
論文 -
24
Research on Cultivated Land Quality Assessment at the Farm Scale for Black Soil Region in Northeast China Based on Typical Period Remote Sensing Images from Landsat 9
出版事項 2025-06-01主題: 全文の入手Rapid and efficient evaluation of cultivated land quality in black soil regions at the farm scale using remote sensing techniques is crucial for resource protection. However, current studies face challenges in developing convenient and reliable models that directly leverage raw spectral reflectance....
論文 -
25
A Hierarchical Evolutionary Search Framework with Manifold Learning for Powertrain Optimization of Flying Vehicles
出版事項 2025-06-01“…Subsequently, the approximate Pareto solutions generated by BO are utilized as initial populations for a non-dominated sorting genetic algorithm III (NSGA-III), which performs fine-grained refinement in the original high-dimensional design space. …”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...
全文の入手
論文 -
26
Bayesian Inference-Based Gaussian Mixture Models With Optimal Components Estimation Towards Large-Scale Synthetic Data Generation for <italic>In Silico</italic> Clinical Trials
出版事項 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...
全文の入手
論文 -
27
Robust Variable Selection via Bayesian LASSO-Composite Quantile Regression with Empirical Likelihood: A Hybrid Sampling Approach
出版事項 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...
全文の入手
論文 -
28
AI-aided short-term decision making of rockburst damage scale in underground engineering
出版事項 2025-08-01“…Nine classifier algorithms, including random forest (RF), were evaluated using a dataset of 254 samples. …”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...
全文の入手
論文 -
29
Machine learning prediction and interpretability analysis of high-risk chest pain: a study from the MIMIC-IV database
出版事項 2025-06-01“…Seven algorithms were evaluated: Logistic Regression, Random Forest, SVM, XGBoost, LightGBM, TabTransformer, and TabNet.ResultsThe LightGBM model demonstrated superior performance with accuracy = 0.95, precision = 0.95, recall = 0.95, and F1-score = 0.94. …”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...
全文の入手
論文 -
30
Bayesian subset-based gene selection for biomarker discovery in high-dimensional data
出版事項 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...
全文の入手
論文 -
31
A Cascade of Encoder–Decoder with Atrous Convolution and Ensemble Deep Convolutional Neural Networks for Tuberculosis Detection
出版事項 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...
全文の入手
論文 -
32
Crash severity prediction using a virtual geometry-group-based deep learning approach with images-based feature representation
出版事項 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...
全文の入手
論文 -
33
Optimizing Hyperparameters in Meta-Learning for Enhanced Image Classification
出版事項 2025-01-01全文の入手This 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...
論文 -
34
Unmanned aerial vehicle field sampling and antenna pattern reconstruction using Bayesian compressed sensing
出版事項 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...
全文の入手
論文 -
35
IT diagnostics of Parkinson's disease based on voice markers and decreased motor activity
出版事項 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...
全文の入手
論文 -
36
Optimized ensemble learning for non-destructive avocado ripeness classification
出版事項 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...
全文の入手
論文 -
37
Enhancements on the Latitudinal and Seasonal Bias Corrections in the SMOS Debiased Non-Bayesian Sea Surface Salinity Retrieval
出版事項 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...
全文の入手
論文 -
38
Integrating Bayesian Knowledge Tracing and Human Plausible Reasoning in an Adaptive Augmented Reality System for Spatial Skill Development
出版事項 2025-05-01“…The use of advanced adaptive algorithms in Augmented Reality (AR) systems works to advance spatial skills with valuable relevance in many professional spheres by providing personalized feedback in an immersive environment. …”The use of advanced adaptive algorithms in Augmented Reality (AR) systems works to advance spatial skills with valuable relevance in many professional spheres by providing personalized feedback in an immersive environment. This study combines Bayesian Knowledge Tracing (BKT) and Human Plausible Reas...
全文の入手
論文 -
39
Bayesian Neural Networks With Robust Feature Interpretation for Enhanced Compressive Strength Prediction of Ultra‐High‐Performance Concrete
出版事項 2025-06-01“…Two neural network models were constructed—one optimized using the Levenberg–Marquardt algorithm and the other employing Bayesian regularization—with the Bayesian model demonstrating superior accuracy and generalization. …”ABSTRACT Ultra‐high‐performance concrete (UHPC) has emerged as a revolutionary material in civil engineering due to its superior strength, durability, and longevity. However, accurately predicting its compressive strength remains challenging owing to the complexity and variability inherent in mix de...
全文の入手
論文 -
40
Risk identification and assessment of Internet public opinion on public emergencies based on Bayesian network and association rule mining
出版事項 2025-07-01“…This paper innovatively proposes an Internet public opinion risk identification and assessment method for public emergencies, integrating association rule mining with Bayesian network (BN). The core innovation lies in designing an improved scheme based on the CBA (Classification Based on Associations) algorithm to overcome the limitation of traditional association rule mining in handling non-Boolean data, thereby effectively extracting the correlations among public opinion risk factors to optimize the topological structure of the BN. …”Global public emergencies occur frequently, and the risk of Internet public opinion crises in such contexts is gradually increasing. In the dual context of risk society and network society, effectively identifying and assessing Internet public opinion risks on public emergencies poses challenges to...
全文の入手
論文