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Results for Bayesian hyperparameter optimization

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17 papers · ranked by Valyu relevance

01
PPubMed28 Oct 2025
Credit risk prediction model for listed companies based on improved reinforcement learning and Bayesian optimization hyperband Credit risk prediction model for listed companies
Cai Yuanqing, Zhenming Gao, Zhang Jian, Roohallah Alizadehsani + 2 more
The financial sector has experienced swift growth over recent years, leading to the escalating prominence of credit risk among publicly traded companies. Consequently, forecasting credit risk for these firms has emerged as a critical task for banks, regulatory bodies, and investors. Traditional models include the…
95relevance
02
PPubMed28 Oct 2025
Bayesian-optimized machine learning boosts actual evapotranspiration prediction in water-stressed agricultural regions of China
Ahmed Elbeltagi, Aman Srivastava, Xinchun Cao, Vinay Kumar Gautam + 5 more
The accurate estimation of actual evapotranspiration (AET) is crucial for sustainable water resource management, especially in water-scarce and agriculturally intensive regions like Beijing and Tianjin, China. Traditional methods for AET estimation, whether empirical or physically based, often face limitations due to…
94relevance
03
PPubMed12 Nov 2025
Rolling Bearing Fault Diagnosis via Meta-BOHB Optimized CNN–Transformer Model and Time-Frequency Domain Analysis
Yikang Wang, He Jiang, Baoqi Tong, Shiwei Song + 1 more
Bearing fault diagnosis encounters limitations including insufficient accuracy, elevated model complexity, and demanding hyperparameter optimization. This research introduces a diagnostic framework combining variational mode decomposition (VMD) and fast Fourier transform (FFT) for extracting comprehensive…
93relevance
04
PPubMed17 Oct 2025
Spatial Error Prediction and Compensation of Industrial Robots Based on Extended Joints and BO-XGBoost
Bingran Yang, Xuedong Jing, Shoukun Wang, Zhihua Chen
Robotic positioning accuracy is paramount in complex tasks. This accuracy is influenced by both geometric and non-geometric factors, making error prediction a significant challenge. To address this, this paper introduces two key contributions. First, we propose a novel input feature, the robot’s “extended joint…
92relevance
05
PPubMed12 Jan 2026
Transfer learning with Bayesian optimization for colorectal cancer histopathology classification
Houda Saif ALGhafri, Chia S. Lim
Background Automated colorectal cancer (CRC) histopathology classification remains challenging due to variations in datasets, staining conditions, and tissue morphology across institutions. Many prior studies apply standard CNN architectures with fixed hyperparameters, leaving limited examination of how model choice…
92relevance
06
PPubMed19 Nov 2025
Coronary artery disease prediction using Bayesian-optimized support vector machine with feature selection Baratpur et al. 10.3389/fnetp.2025.1658470
Abdul Zahir Baratpur, Hamed Vahdat-Nejad, Emrah Arslan, Javad Hassannataj Joloudari + 1 more
Introduction Cardiovascular diseases, particularly Coronary Artery Disease (CAD), remain a leading cause of mortality worldwide. Invasive angiography, while accurate, is costly and risky. This study proposes a non-invasive, interpretable CAD prediction framework using the Z-Alizadeh Sani dataset. Methods A hybrid…
91relevance
07
PPubMed18 Nov 2025
Optimizing Fuel Consumption Prediction Model Without an On-Board Diagnostic System in Deep Learning Frameworks
Rıdvan Keskin, Egemen Belge, Senol Hakan Kutoglu, Jongmyon Kim
Real-time prediction of the instantaneous fuel consumption rate (FCR) of any vehicle is the key to improving energy efficiency and reducing emissions. The conventional prediction methods, which include an on-board diagnostic (OBD) system, require the specific vehicle parameters and environmental conditions such as air…
91relevance
08
PPubMed12 Jun 2026
Bayesian hyperparameter optimization improves scGPT fine-tuning for single-cell multi-omics integration
Darren Yu Jun Tay, Nguyen Quoc Khanh Le, Matthew Chin Heng Chua, Pier Luigi Martelli
We developed a hyperparameter optimization framework for scGPT, a large transformer foundation model for single-cell data, by embedding TPE Bayesian optimization loop into the model fine-tuning pipeline (). Our objective was to identify hyperparameter settings that minimize validation loss and improve downstream…
90relevance
09
PPubMed22 Jan 2026
Optimizing Image Segmentation for Microstructure Analysis of High-Strength Steel: Histogram-Based Recognition of Martensite and Bainite
Filip Hallo, Tomasz Jażdżewski, Piotr Bała, Grzegorz Korpała + 2 more
This study systematically compares three unsupervised segmentation algorithms (Simple Linear Iterative Clustering (SLIC), Felzenszwalb’s graph-based method, and the Watershed algorithm) in combination with two classification approaches: Random Forest using histogram-based features and Convolutional Neural Networks…
90relevance
10
PPubMed16 Feb 2026
Facial mark based biometric differentiation of identical twins using dynamic feature enhancement
Khush Jay Brahmbhatt, Krishna Prakasha, Gangothri Sanil
Hyperparameter Optimization represents one of the most critical aspects of machine learning model development, fundamentally determining the difference between mediocre and exceptional model performance48,49. In the context of twin differentiation using facial marks, hyperparameter optimization becomes particularly…
89relevance
11
PPubMed7 Mar 2026
A manta ray-bayesian optimization approach for hyperparameter-tuned convolutional neural networks in lung cancer classification
Sonali Samal, Shyam Sunder, Thippa Reddy Gadekellu, Fatma Hilal Yagin + 2 more
Lung cancer remains a global health challenge that is unavoidable. Despite the advances in lung cancer classification using deep learning models, the performance remains highly dependent on hyperparameter selection, whereas conventional grid or random search methods are often computationally inefficient in…
88relevance
12
PPubMed21 Nov 2025
Random rotational embedding Bayesian optimization for human-in-the-loop personalized music generation Random rotational embedding Bayesian optimization for human-in-the-loop personalized music generation
Miguel Marcos, Lorenzo Mur-Labadia, Ruben Martinez-Cantin, Tara Rajendran
Generative deep learning models, such as those used for music generation, can produce a wide variety of results based on perturbations of random points in their latent space. User preferences can be incorporated in the generative process by replacing this random sampling with a personalized query. Bayesian…
85relevance
13
PPubMed9 Jul 2026
Interpretable machine learning with Bayesian optimization for bond strength prediction of steel reinforcement in geopolymer concrete Bayesian-optimized interpretable ML for bond strength in geopolymer concrete
Viet Hung Tran, Viet Hai Hoang, Quang Minh Tran, André Gustavo de Sousa Galdino
Accurate estimation of bond strength between steel reinforcement and geopolymer concrete is essential for the reliable design of sustainable reinforced concrete structures. However, the highly nonlinear interactions reduce the applicability and accuracy of conventional empirical models. This study proposes a…
85relevance
14
PPubMed16 May 2026
Comprehensive benchmarking and explainable machine learning analysis of EEG imagery activity recognition
Md. Julkar Nain Siam, Tanvir Ahsan Showrov, Md. Sakir Hossain, Najmus Shakif Ayaan + 3 more
Motor imagery (MI)-based brain-computer interfaces (BCIs) enable users to control external devices using EEG signals, offering great potential in assistive and rehabilitation technologies. However, MI recognition remains challenging due to EEG’s low signal-to-noise ratio (SNR), inter-subject variability, and complex…
84relevance
15
PPubMed8 Jan 2026
A Guide to Bayesian Optimization in Bioprocess Engineering
Maximilian Siska, Emma Pajak, Katrin Rosenthal, Antonio del Rio Chanona + 2 more
Bayesian optimization has become widely popular across various experimental sciences due to its favorable attributes: it can handle noisy data, perform well with relatively small data sets, and provide adaptive suggestions for sequential experimentation. While still in its infancy, Bayesian optimization has recently…
79relevance
16
PPubMed14 Nov 2025
HearteXplain: explainable prediction of acute heart failure and identification of hematologic biomarkers using EBMs and Morris sensitivity analysis
Fatma Hilal Yagin, Yasin Görmez, Abdulmohsen Algarni, Fahaid Al-Hashem + 2 more
Hematological biomarkers have emerged as powerful tools in diagnosing Acute Heart Failure (AHF). This study introduces a novel diagnostic framework that integrates Explainable Artificial Intelligence (XAI) with Morris Sensitivity Analysis (MSA) to enhance both the interpretability and performance of machine learning…
79relevance
17
PPubMed11 May 2026
PFTuner: An Efficient and Effective Multi-Objective Configuration Tuning Framework Adaptive to Different Software Systems
Jie Feng, Mingdong He, Lei Jin, Hui Dou + 1 more
Software systems often expose a large number of configurable parameters to satisfy diverse application requirements and deployment scenarios. Given the intricate dependencies between parameters, manually finding a well-performing configuration is a daunting task even for experienced operators. Most existing automatic…
75relevance

Results for Bayesian hyperparameter optimization

Credit risk prediction model for listed companies based on improved reinforcement learning and Bayesian optimization hyperband Credit risk prediction model for listed companies

Bayesian-optimized machine learning boosts actual evapotranspiration prediction in water-stressed agricultural regions of China

Rolling Bearing Fault Diagnosis via Meta-BOHB Optimized CNN–Transformer Model and Time-Frequency Domain Analysis

Spatial Error Prediction and Compensation of Industrial Robots Based on Extended Joints and BO-XGBoost

Transfer learning with Bayesian optimization for colorectal cancer histopathology classification

Coronary artery disease prediction using Bayesian-optimized support vector machine with feature selection Baratpur et al. 10.3389/fnetp.2025.1658470

Optimizing Fuel Consumption Prediction Model Without an On-Board Diagnostic System in Deep Learning Frameworks

Bayesian hyperparameter optimization improves scGPT fine-tuning for single-cell multi-omics integration

Optimizing Image Segmentation for Microstructure Analysis of High-Strength Steel: Histogram-Based Recognition of Martensite and Bainite

Facial mark based biometric differentiation of identical twins using dynamic feature enhancement

A manta ray-bayesian optimization approach for hyperparameter-tuned convolutional neural networks in lung cancer classification

Random rotational embedding Bayesian optimization for human-in-the-loop personalized music generation Random rotational embedding Bayesian optimization for human-in-the-loop personalized music generation

Interpretable machine learning with Bayesian optimization for bond strength prediction of steel reinforcement in geopolymer concrete Bayesian-optimized interpretable ML for bond strength in geopolymer concrete

Comprehensive benchmarking and explainable machine learning analysis of EEG imagery activity recognition

A Guide to Bayesian Optimization in Bioprocess Engineering

HearteXplain: explainable prediction of acute heart failure and identification of hematologic biomarkers using EBMs and Morris sensitivity analysis

PFTuner: An Efficient and Effective Multi-Objective Configuration Tuning Framework Adaptive to Different Software Systems

Credit risk prediction model for listed companies based on improved reinforcement learning and Bayesian optimization hyperband Credit risk prediction model for listed companies

Bayesian-optimized machine learning boosts actual evapotranspiration prediction in water-stressed agricultural regions of China

Rolling Bearing Fault Diagnosis via Meta-BOHB Optimized CNN–Transformer Model and Time-Frequency Domain Analysis

Spatial Error Prediction and Compensation of Industrial Robots Based on Extended Joints and BO-XGBoost

Transfer learning with Bayesian optimization for colorectal cancer histopathology classification

Coronary artery disease prediction using Bayesian-optimized support vector machine with feature selection Baratpur et al. 10.3389/fnetp.2025.1658470

Optimizing Fuel Consumption Prediction Model Without an On-Board Diagnostic System in Deep Learning Frameworks

Bayesian hyperparameter optimization improves scGPT fine-tuning for single-cell multi-omics integration

Optimizing Image Segmentation for Microstructure Analysis of High-Strength Steel: Histogram-Based Recognition of Martensite and Bainite

Facial mark based biometric differentiation of identical twins using dynamic feature enhancement

A manta ray-bayesian optimization approach for hyperparameter-tuned convolutional neural networks in lung cancer classification

Random rotational embedding Bayesian optimization for human-in-the-loop personalized music generation Random rotational embedding Bayesian optimization for human-in-the-loop personalized music generation

Interpretable machine learning with Bayesian optimization for bond strength prediction of steel reinforcement in geopolymer concrete Bayesian-optimized interpretable ML for bond strength in geopolymer concrete

Comprehensive benchmarking and explainable machine learning analysis of EEG imagery activity recognition

A Guide to Bayesian Optimization in Bioprocess Engineering

HearteXplain: explainable prediction of acute heart failure and identification of hematologic biomarkers using EBMs and Morris sensitivity analysis

PFTuner: An Efficient and Effective Multi-Objective Configuration Tuning Framework Adaptive to Different Software Systems