Kernel Ridge Regression Research Articles

Genomic Landscape and Prediction of Udder Traits in Saanen Dairy Goats.

Goats are essential to the dairy industry in Shaanxi, China, with udder traits playing a critical role in determining milk production and economic value for breeding programs. However, the direct measurement of these traits in dairy goats is challenging and resource-intensive. This study leveraged genotyping imputation to explore the genetic parameters and architecture of udder traits and assess the efficiency of genomic prediction methods. Using data from 635 Saanen dairy goats, genotyped for over 14,717,075 SNP markers and phenotyped for three udder traits, heritability was 0.16 for udder width, 0.32 for udder depth, and 0.13 for teat spacing, with genetic correlations of 0.79, 0.70, and 0.45 observed among the traits. Genome-wide association studies (GWAS) revealed four candidate genes with selection signatures linked to udder traits. Predictive models, including GBLUP, kernel ridge regression (KRR), and Adaboost.RT, were evaluated for genomic estimated breeding value (GEBV) prediction. Machine learning models (KRR and Adaboost.RT) outperformed GBLUP by 20% and 11% in predictive accuracy, showing superior stability and reliability. These results underscore the potential of machine learning approaches to enhance genomic prediction accuracy in dairy goats, providing valuable insights that could contribute to improvements in animal health, productivity, and economic outcomes within the dairy goat industry.

First-principles NMR of oxide glasses boosted by machine learning.

Solid-state NMR has established itself as a cutting-edge spectroscopy for elucidating the structure of oxide glasses thanks to several decades of methodological and instrumental progress. First-principles calculations of NMR properties combined with molecular-dynamics (MD) simulations provides a powerful complementary approach for the interpretation of NMR data, although they still suffer from limitations in terms of size, time and high consumption of computational resources. We address this challenge by developing a machine-learning framework to boost predictive modelling of NMR spectra. We use kernel ridge regression techniques (least-squares support vector regression and linear ridge regression) combined with smooth overlap of atomic position (SOAP) atom-centered descriptors to efficiently predict NMR interactions: the isotropic magnetic shielding and the electric field gradient (EFG) tensor. As illustrated in this work, this approach enables the simulation of magic-angle spinning (MAS) and multiple-quantum magic-angle spinning (MQMAS) NMR spectra of very large models (more than 10 000 atoms) and an efficient averaging of NMR properties over MD trajectories of nanoseconds for incorporating finite-temperature effects, at the computational cost of classical MD simulations. We illustrate these advances for sodium silicate glasses (SiO2-Na2O). NMR parameters (isotropic chemical shift and electric field gradient) could be predicted with an accuracy of 1 to 2% in terms of the total span of the NMR parameter values. To include vibrational effects, an approach is proposed of scaling the EFG tensor in NMR simulations with a factor obtained from the time auto-correlation functions computed on MD trajectories.

Development of a novel modeling framework based on weighted kernel extreme learning machine and ridge regression for streamflow forecasting

A precise streamflow forecast is crucial in hydrology for flood alerts, water quantity and quality management, and disaster preparedness. Machine learning (ML) techniques are commonly employed for hydrological prediction; however, they still face certain drawbacks, such as the need to optimize the appropriate predictors, the ability of the models to generalize across different time horizons, and the analysis of high-dimensional time series. This research aims to address these specific drawbacks by developing a novel framework for streamflow forecasting. Specifically, a hybrid ML model, WKELM-R, is developed to predict streamflow based on daily discharge and precipitation. The model combines ridge regression (RR), locally weighted linear regression (LWLR), and kernel extreme learning machine (KELM) to enhance multi-step-ahead predictions by accounting for both linear and nonlinear characteristics. In data preprocessing, this study applies multivariate variational mode decomposition (MVMD) for decomposition to handle non-stationarity and complexity, Boruta-XGBoost for feature selection to select the optimal inputs and decrease the dimension, and gradient-based optimizer (GBO) for adjustment of model parameters to overcome the need to optimize the appropriate predictors. To demonstrate the ability to handle real-world conditions and different time horizons, WKELM-R was applied to a watershed in North Dakota, USA to forecast discharge for three different time horizons. The results were compared with those from the existing standalone and hybrid models by multi-criteria decision-making (MCDM), demonstrating the efficacy and unique capabilities of the new hybrid model in streamflow forecasting (for the testing level at t + 3: R = 0.992, RMSE = 0.426, NSE = 0.983; at t + 7: R = 0.997, RMSE = 0.249, NSE = 0.994; at t + 14: R = 0.996, RMSE = 0.304, NSE = 0.991).

Predicting Subject Traits From Brain Spectral Signatures: An Application to Brain Ageing.

The prediction of subject traits using brain data is an important goal in neuroscience, with relevant applications in clinical research, as well as in the study of differential psychology and cognition. While previous prediction work has predominantly been done on neuroimaging data, our focus is on electroencephalography (EEG), a relatively inexpensive, widely available and non-invasive data modality. However, EEG data is complex and needs some form of feature extraction for subsequent prediction. This process is sometimes done manually, risking biases and suboptimal decisions. Here we investigate the use of data-driven Kernel methods for prediction from single channels using the EEG spectrogram, which reflects macro-scale neural oscillations in the brain. Specifically, we introduce the idea of reinterpreting the spectrogram of each channel as a probability distribution, so that we can leverage advanced machine learning techniques that can handle probability distributions with mathematical rigour and without the need for manual feature extraction. We explore how the resulting technique, Kernel mean embedding regression, compares to a standard application of Kernel ridge regression as well as to a non-Kernelised approach. Overall, we found that the Kernel methods exhibit improved performance thanks to their capacity to handle nonlinearities in the relation between the EEG spectrogram and the trait of interest. We leveraged this method to predict biological age in a multinational EEG data set, HarMNqEEG, showing the method's capacity to generalise across experiments and acquisition setups.

Performance analysis of 500kW solar PV plant based on machine learning algorithms

ABSTRACT This research presents a comprehensive performance analysis of a 500 kW solar photovoltaic (PV) plant, utilizing machine learning (ML) algorithms, installed at PSNA College of Engineering and Technology, Dindigul, India. Real-time experimental data, collected from November 2021 to October 2022, was used to enhance the operational efficiency of the solar PV system. The study focuses on forecasting the output of the 500 kW plant by employing six machine learning algorithms, with a comparative analysis conducted to evaluate the predictive capabilities of each model. Kernel Ridge Regression (KRR) demonstrated superior performance among the algorithms tested, producing results closely aligned with the actual data. Key performance metrics were assessed, including forecasted output power, final yield, system efficiency, performance ratio, and capacity factor. Notably, the system’s efficiency increased from 12% to 13.6%.These findings provide critical insights for optimizing the operational performance of solar PV plants, offering valuable contributions to improving energy management and sustainability.

Nuclear mass predictions of the relativistic continuum Hartree-Bogoliubov theory with the kernel ridge regression. II. Odd-even effects

Nuclear mass predictions of the relativistic continuum Hartree-Bogoliubov theory with the kernel ridge regression. II. Odd-even effects

Support vector regression model for the prediction of buildings’ maximum seismic response based on real monitoring data

Maximum drift ratio (MDR), one of the engineering demand parameters (EDPs), provides fundamental physical value for predicting building damage. Existing machine learning based prediction models mainly rely on numerical simulation data or structural experiments and are not appropriate for prediction of seismic response of real structures. The New Earthquake Data (NDE1.0) is the most comprehensive publicly available dataset of actual structural seismic response observations. Currently the prediction models using NDE1.0 are mainly based on linear or log-linear regression. In this study, based on the NDE1.0 flatfile, we develop a full-feature support vector regression (SVR) based MDR prediction model (SVR-MDR), treating all the available 41 characteristic parameters including structural information as input feature. To improve the model’s efficiency and practical applicability, we also establish a reduced-feature SVR model (RSVR-MDR) by selecting 10 fundamental parameters based on SHapley Additive exPlanations (SHAP) values and the accessibility of features. Our results demonstrate that SVR-MDR model outperform other machine learning models such as kernel ridge regression and decision tree models, and SVR-MDR and RSVR-MDR models outperform conventional loglinear regression and multinomial models, because SVR can map the complex nonlinear function of multiple variables and consider the available information of buildings especially the fundamental frequency. The proposed RSVR-MDR model have promising potential application for post-event seismic damage assessment and post-event emergency response in near real time.

Many-body expansion based machine learning models for octahedral transition metal complexes

Abstract Graph-based machine learning (ML) models for material properties show great potential to accelerate virtual high-throughput screening of large chemical spaces. However, in their simplest forms, graph-based models do not include any 3D information and are unable to distinguish stereoisomers such as those arising from different orderings of ligands around a metal center in coordination complexes. In this work we present a modification to revised autocorrelation descriptors, a molecular graph featurization method, for predicting spin state dependent properties of octahedral transition metal complexes (TMCs). Inspired by analytical semi-empirical models for TMCs, the new modeling strategy is based on the many-body expansion (MBE) and allows one to tune the captured stereoisomer information by changing the truncation order of the MBE. We present the necessary modifications to include this approach in two commonly used ML methods, kernel ridge regression and feed-forward neural networks. On a test set composed of all possible isomers of binary TMCs, the best MBE models achieve mean absolute errors (MAEs) of 2.75 kcal mol−1 on spin-splitting energies and 0.26 eV on frontier orbital energy gaps, a 30%–40% reduction in error compared to models based on our previous approach. We also observe improved generalization to previously unseen ligands where the best-performing models exhibit MAEs of 4.00 kcal mol−1 (i.e. a 0.73 kcal mol−1 reduction) on the spin-splitting energies and 0.53 eV (i.e. a 0.10 eV reduction) on the frontier orbital energy gaps. Because the new approach incorporates insights from electronic structure theory, such as ligand additivity relationships, these models exhibit systematic generalization from homoleptic to heteroleptic complexes, allowing for efficient screening of TMC search spaces.

A Topology Optimization of Electromagnetic Devices Based on Kernel Ridge Regression as a Variant of Gaussian Network-Based Shape Representation

A Topology Optimization of Electromagnetic Devices Based on Kernel Ridge Regression as a Variant of Gaussian Network-Based Shape Representation

Robust prediction of chlorophyll-A from nitrogen and phosphorus content in Philippine and global lakes using fine-tuned, explainable machine learning

Chlorophyll-a (Chl-a) content in waterbodies is a primary indicator of algal biomass and is used to detect impending harmful algal blooms. This paper presents a methodology using 8 popular machine learning (ML) models for estimating Chl-a concentration from nutrient content in lakes. Different from previous works, we introduce 3 novel steps: (i) the use of Bayesian optimization for fine-tuning ML hyper-parameters to improve performance; (ii) the use of explainability methods to understand the most influential inputs to Chl-a prediction; and (iii) the use of robustness analysis to assess how models are affected by measurement noise. Two case studies were used to test our approach: Laguna Lake, Philippines, and various lakes from Japan, the United States of America, Canada, and Uganda. We found that fine-tuned Kernel Ridge Regression and Gaussian Process Regression are consistently the most accurate (>80%) and robust models in both case studies. In Laguna Lake, Shapley explanations revealed that phosphate and nitrate ions are the most important predictors of Chl-a, while total phosphorus is that for global lakes. Hence, these parameters are suggested to be monitored more closely for detecting algal blooms. By making our codes accessible, we hope that our methods can serve as a benchmark for the data-driven modeling of Chl-a content in lakes, and aid in their management through model deployment.

Computational intelligence analysis on drug solubility using thermodynamics and interaction mechanism via models comparison and validation

This study investigates the application of various regression models for predicting drug solubility in polymer and API-polymer interactions in complex datasets. Four models—Gaussian Process Regression (GPR), Support Vector Regression (SVR), Bayesian Ridge Regression (BRR), and Kernel Ridge Regression (KRR)—are evaluated. Preprocessing the dataset using the Z-score approach helped to detect outliers, further improving the accuracy and dependability of the analysis. Also, Fireworks Algorithm (FWA) is employed for hyper-parameter tuning in this work. The GPR model demonstrated superior performance, achieving the lowest MSE and MAE for both drug solubility and gamma predictions, with R2 scores of 0.9980 and 0.9950 for training and test data, respectively. The results of this study show the robustness of GPR in generating reliable and precise forecasts, thus providing a strong method for intricate regression tasks in pharmaceutical and other scientific fields. In addition, the Fireworks Algorithm (FWA) is presented as an optimization method, demonstrating its potential in improving the model’s predictive abilities by effectively exploring and exploiting the search space. The results emphasize the significance of choosing suitable regression models and optimization techniques to attain dependable and superior predictive analytics.

Enhancing Multi-Hole Pressure Probe Data Processing in Turbine Cascade Experiments Using Structural Risk Minimization Principle

Multi-hole pressure probes are crucial for turbomachinery flow measurements, yet conventional data processing methods often lack generalization for complex flows. This study introduces an innovative approach by integrating machine learning techniques with the structural risk minimization (SRM) principle, significantly enhancing the generalization capability of regression models. A comprehensive framework has been developed, combining SRM-based machine learning regression methods, such as ridge regression and kernel ridge regression, with hyperparameter optimization and S-fold cross-validation, to ensure robust model selection and accuracy. Validated using the McCormick function and applied to VKI-RG transonic turbine cascade measurements, the SRM-based methods demonstrated superior performance over traditional empirical risk minimization approaches, with lower error ratios and higher R2 values. Novel insights from SHAP analysis revealed subtle but significant differences in aerodynamic parameters, including a 0.63122° discrepancy in exit flow angle predictions, guiding the probe design and calibration strategies. This study presents a holistic workflow for improving multi-hole pressure probe measurements under high-subsonic conditions, representing a meaningful enhancement over traditional empirical methods and providing valuable references for practical applications.

Optimizing hybrid models for canopy nitrogen mapping from Sentinel-2 in Google Earth Engine

Canopy nitrogen content (CNC) is a crucial variable for plant health, influencing photosynthesis and growth. An optimized, scalable approach for spatially explicit CNC quantification using Sentinel-2 (S2) data is presented, integrating PROSAIL-PRO simulations with Gaussian Process Regression (GPR) and an Active Learning technique, specifically the Euclidean distance-based diversity (EBD) approach for selective sampling. This hybrid method enhances training dataset efficiency and optimizes CNC models for practical applications. Two GPR models based on PROSAIL-PRO variables were evaluated: a protein-based model (Cprot-LAI) and a chlorophyll-based model (Cab-LAI). Both models, implemented in Google Earth Engine (GEE), demonstrated strong performance and outperformed other machine learning methods, including kernel ridge regression, principal component regression, neural network, weighted k-nearest neighbors regression, partial least squares regression and least squares linear regression. Validation results showed moderate to good accuracies: NRMSECprot−LAI = 16.76%, RCprot−LAI2 = 0.47; NRMSECab−LAI = 18.74%, RCab−LAI2 = 0.51. The models revealed high consistency for an independent validation dataset of the Munich-North-Isar (Germany) test site, with R2 values of 0.58 and 0.71 and NRMSEs of 21.47% and 20.17% for the Cprot-LAI model and Cab-LAI model, respectively. The models also demonstrated high consistency across growing seasons, indicating their potential for time series analysis of CNC dynamics. Application of the S2-based mapping workflow across the Iberian Peninsula, with estimates showing relative uncertainty below 30%, highlights the model’s broad applicability and portability. The optimized EBD-GPR-CNC approach within GEE supports scalable CNC estimation and offers a robust tool for monitoring nitrogen dynamics.

Computational analysis of controlled drug release from porous polymeric carrier with the aid of Mass transfer and Artificial Intelligence modeling

Controlled release of a desired drug from porous polymeric biomaterials was analyzed via computational method. The method is based on simulation of mass transfer and utilization of artificial intelligence (AI). This study explores the efficacy of three regression models, i.e., Kernel Ridge Regression (KRR), Gaussian Process Regression (GPR), and Gradient Boosting (GB) in determining the concentration of a chemical substance (C) based on coordinates (r, z). Leveraging Firefly Optimization (FFA) for hyperparameter optimization, the models are fine-tuned to maximize their predictive performance. The findings unveil notable disparities in the performance metrics of the models, with GB showcasing the most impressive R2 score of 0.9977, indicative of a remarkable alignment with the data. GPR closely trails with an R2 score of 0.88754, while KRR falls short with an R2 score of 0.76134. Additionally, GB manifests the most modest Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) among the trio of models, further cementing its supremacy in predictive precision. These outcomes accentuate the significance of judiciously selecting regression methodologies and optimization approaches for adeptly modeling intricate spatial datasets.

Toward Robust Car-Following Dynamics Modeling via Blackbox Models: Methodology, Analysis, and Recommendations

The selection of the target variable is critical for the performance of classical car-following (CF) models. There is a vast body of literature on which target variable is optimal for classical CF models, but there is no study that evaluates the selection of optimal target variables for black-box models prevalent in machine learning, which are increasingly being used to model CF behavior. This paper evaluates different target variables, for example acceleration, velocity, and headway, for three models: Gaussian processes (GP), kernel ridge regression, and long short-term memory (LSTM). These models have different objective functions and work in different vector spaces, for example, GP work in function space, and LSTM in parameter space. Furthermore, this paper evaluates the efficacy of black box and classical CF models across datasets, including diverse traffic scenarios and human versus automated control. Finally, this paper evaluates the coupling of design choices across selection of model type, target variable during model calibration, and dataset characteristics. The experiments show that the optimal target variable recommendations for black-box models differ from classical CF models, depending on the objective function and the vector space. Statistical analysis indicates that selection of models couples greatly with target variables, while recommended target variables do not depend on the datasets used during model calibration. These results provide a path toward more robust CF models, potentially resulting in more accurate and generalizable capacity and/or traffic flow estimates for roadways, even as driving behaviors change with the introduction of new types of automation.

A short trajectory is all you need: A transformer-based model for long-time dissipative quantum dynamics.

In this Communication, we demonstrate that a deep artificial neural network based on a transformer architecture with self-attention layers can predict the long-time population dynamics of a quantum system coupled to a dissipative environment provided that the short-time population dynamics of the system is known. The transformer neural network model developed in this work predicts the long-time dynamics of spin-boson model efficiently and very accurately across different regimes, from weak system-bath coupling to strong coupling non-Markovian regimes. Our model is more accurate than classical forecasting models, such as recurrent neural networks, and is comparable to the state-of-the-art models for simulating the dynamics of quantum dissipative systems based on kernel ridge regression.

Machine learning approaches for modelling of molecular polarizability in gold nanoclusters

The polarizability of molecules describes their response to an external electric field. It quantifies the ability of a system to form an induced dipole moment when subjected to an electric field. In this work, we investigated isotropic polarizability and anisotropy in the polarizability of gold nanoclusters using various machine-learning algorithms. We utilized high-order invariant descriptors based on spherical harmonics, integrated with machine-learning models like artificial neural network, Gaussian process regression, and kernel ridge regression. Our results demonstrate the efficacy of machine-learning in accurately predicting the polarizability of gold nanoclusters. We find that ANN-based model performs better than the other models.

A Bond-Based Machine Learning Model for Molecular Polarizabilities and A Priori Raman Spectra.

The use of machine learning (ML) algorithms in molecular simulations has become commonplace in recent years. There now exists, for instance, a multitude of ML force field algorithms that have enabled simulations approaching ab initio level accuracy at time scales and system sizes that significantly exceed what is otherwise possible with traditional methods. Far fewer algorithms exist for predicting rotationally equivariant, tensorial properties such as the electric polarizability. Here, we introduce a kernel ridge regression algorithm for machine learning of the polarizability tensor. This algorithm is based on the bond polarizability model and allows prediction of the tensor components at the cost similar to that of scalar quantities. We subsequently show the utility of this algorithm by simulating gas phase Raman spectra of biphenyl and malonaldehyde using classical molecular dynamics simulations of these systems performed with the recently developed MACE-OFF23 potential. The calculated spectra are shown to agree very well with the experiments and thus confirm the expediency of our algorithm as well as the accuracy of the used force field. More generally, this work demonstrates the potential of physics-informed approaches to yield simple yet effective machine learning algorithms for molecular properties.

Surfactant-facilitated metabolic induction enhances lipase production from an optimally formulated waste-derived substrate mix using Aspergillus niger: A case of machine learning modeling and metaheuristic optimization

This study introduces a novel method to enhance lipase production by integrating machine learning (ML) models and optimization algorithms. Six ML models, including support vector regression (SVR), kernel ridge regression (KRR), extreme gradient boosting (XGB), extreme learning machine (ELM), random forest (RF), and artificial neural networks (ANN), were employed to predict lipase activity in a multi-substrate system using avocado seed, coconut pulp, and palm oil mill effluent (POME) with Aspergillus niger. SVR proved the most effective (R2 = 0.9738; RMSE = 7.0089). Further optimization using manta ray foraging optimization (MFRO), particle swarm optimization (PSO), and genetic algorithm (GA) identified optimal substrate loadings, achieving a maximum lipase activity of 194.38 U/gds. The addition of a mixture of surfactants (Tween 80, Tween 20, Triton X-100) further increased lipase production to 520.95 U/gds (168.3 % increase). Global sensitivity analysis (GSA) confirmed the important roles of avocado seed, POME, and surfactants in enhancing lipase production. This approach represents a significant advancement in bioprocess scalability.

An investigation of machine learning methods applied to genomic prediction in yellow-feathered broilers

Machine learning (ML) methods have rapidly developed in various theoretical and practical research areas, including predicting genomic breeding values for large livestock animals. However, few studies have investigated the application of ML in broiler breeding. In this study, seven different ML methods—support vector regression (SVR), random forest (RF), gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), kernel ridge regression (KRR) and multilayer perceptron (MLP) were employed to predict the genomic breeding values of laying traits, growth and carcass traits in a yellow-feathered broiler breeding population. The results indicated that classic methods, such as GBLUP and Bayesian, achieved superior prediction accuracy compared to ML methods in five of the eight traits. For half-eviscerated weight (HEW), ML methods showed an average improvement of 54.4% over GBLUP and Bayesian methods. Among the ML methods, SVR, RF, GBDT, and XGBoost exhibited improvements exceeding 60%, with respective values of 61.3%, 61.0%, 60.4%, and 60.7%; while MLP improved by 54.4% and LightGBM by 53.7%, KRR had the lowest improvement at 29.4%. For eviscerated weight (EW), ML methods still outperformed GBLUP and Bayesian methods. MLP gained the largest improvement at 19.0%, while SVR, RF, GBDT, XGBoost, LightGBM, and KRR improved by 15.0%, 16.5%, 9.5%, 7.0%, 1.6%, and 15.9%, respectively. Compared to default hyperparameters, the average improvement of ML methods with tuned hyperparameters was 34.0%, 32.9%, 27.0%, 19.3%, 26.8%, 13.2%, 18.9%, and 46.3%, respectively. The prediction accuracy of above algorithms could be optimized using genome-wide association study (GWAS) to select subsets of significant SNPs. This work provides valuable insights into genomic prediction, aiding genetic breeding in broilers.