Support Vector Machine Regression Model Research Articles

Analysis of Milk Using a Portable Potentiometric Electronic Tongue Based on Five Polymeric Membrane Sensors.

A portable potentiometric electronic tongue (PE-tongue) was developed and applied to evaluate the quality of milk with different fat content (skimmed, semi-skimmed, and whole) and with different nutritional content (classic, calcium-enriched, lactose-free, folic acid–enriched, and enriched in sterols of vegetal origin). The system consisted of a simplified array of five sensors based on PVC membranes, coupled to a data logger. The five sensors were selected from a larger set of 20 sensors by applying the genetic algorithm (GA) to the responses to compounds usually found in milk including salts (KCl, CaCl2, and NaCl), sugars (lactose, glucose, and galactose), and organic acids (citric acid and lactic acid). Principal component analysis (PCA) and support vector machine (SVM) results indicated that the PE-tongue consisting of a five-electrode array could successfully discriminate and classify milk samples according to their nutritional content. The PE-tongue provided similar discrimination capability to that of a more complex system formed by a 20-sensor array. SVM regression models were used to predict the physicochemical parameters classically used in milk quality control (acidity, density, %proteins, %lactose, and %fat). The prediction results were excellent and similar to those obtained with a much more complex array consisting of 20 sensors. Moreover, the SVM method confirmed that spoilage of unsealed milk could be correctly identified with the simplified system and the increase in acidity could be accurately predicted. The results obtained demonstrate the possibility of using the simplified PE-tongue to predict milk quality and provide information on the chemical composition of milk using a simple and portable system.

Use of Fourier Transform Infrared Spectroscopy for Monitoring the Shelf Life and Safety of Yogurts Supplemented With a Lactobacillus plantarum Strain With Probiotic Potential.

The current study aimed to explore the performance of a probiotic Lactobacillus strain as an adjunct culture in yogurt production and to assess Fourier transform infrared spectroscopy as a rapid, noninvasive analytical technique to evaluate the quality and the shelf life of yogurt during storage. In this respect, bovine milk (full-fat) was inoculated with the typical yogurt starter culture without (control case) or with the further addition of Lactobacillus plantarum T571 as an adjunct (probiotic case). The milk was also inoculated with a cocktail mixture of three strains of Listeria monocytogenes in two different initial levels of inoculum, and the fermentation process was followed. Accordingly, yogurt samples were stored at 4 and 12°C, and microbiological, physicochemical, molecular, and sensory analyses were performed during storage. Results showed that the lactic acid bacteria exceeded 7 log CFU/g during storage in all samples, where the probiotic samples displayed higher acidity, lower pH, and reduced counts of Lb. monocytogenes in a shorter period than the control ones at both temperatures. Pulsed-field gel electrophoresis verified the presence of the probiotic strain until the end of storage at both temperatures and in adequate amounts, whereas the survival and the distribution of Listeria strains depended on the case. The sensory evaluation showed that the probiotic samples had desirable organoleptic characteristics, similar to the control. Finally, the spectral data collected from the yogurt samples during storage were correlated with microbiological counts and sensory data. Partial least squares and support vector machine regression and classification models were developed to provide quantitative estimations of yogurt microbiological counts and qualitative estimations of their sensory status, respectively, based on Fourier transform infrared fingerprints. The developed models exhibited satisfactory performance, and the acquired results were promising for the rapid estimation of the microbiological counts and sensory status of yogurt.

Monitoring the Spatiotemporal Trajectory of Urban Area Hotspots Using the SVM Regression Method Based on NPP-VIIRS Imagery

Urban area hotspots are considered to be an ideal proxy for spatial heterogeneity of human activity, which is vulnerable to urban expansion. Nighttime light (NTL) images have been extensively employed in monitoring current urbanization dynamics. However, the existing studies related to NTL images mainly concern detection of urban areas, leaving inner spatial differences in urban NTL luminosity poorly explored. In this study, we propose an innovative approach to explore the spatiotemporal trajectory of urban area hotspots using monthly Visible Infrared Imaging Radiometer Suite (VIIRS) NTL images. Firstly, multi-temporal VIIRS NTL intensity was decomposed by time-series analysis to obtain annual stable components after data preprocessing. Secondly, the support vector machine (SVM) regression model was utilized to identify urban area hotspots. In order to ensure the model accuracy, the grid search and cross-validation method was integrated to achieve the optimized model parameters. Finally, we analyzed the spatiotemporal migration trajectory of urban area hotspots by the center of gravity method (i.e., shift distance and angle of urban area hotspot centroid). The results indicate that our method successfully captured urban area hotspots with a regression coefficient over 0.8. Meanwhile, the findings give an intuitive understanding of coupling interaction between urban area hotspots and socioeconomic indicators. This study provides important insights for further decision-making regarding sustainable urban planning.

SVM, ANN, and PSF modelling approaches for prediction of iron dust minimum ignition temperature (MIT) based on the synergistic effect of dispersion pressure and concentration

Data-driven models for predicting fire and explosion-related properties have been improved greatly in recent years using machine-learning algorithms. However, choosing the best machine learning approach is still a challenging task. Therefore, in this study, the predictability comparisons have been made with the different machine learning methods used to model the MIT for iron dust. The MIT of iron dust was determined using the Godbert-Greenwald furnace for seventy unique combinations of dispersion pressures and dust concentrations. The data has been divided into 'Training Set' and 'Testing Set'. The implementation and efficacy of machine learning and statistical approaches have been demonstrated through real-time experimental results. The support vector machines (SVM) regression models were trained with various kernel functions to enhance the performance of the resultant model. The cubic kernel function was found suitable for training SVMs. Besides, a feed-forward artificial neural network with the backpropagation algorithm and a polynomial surface fit model have also been developed to predict the MIT. For statistical phenomena, such as MIT, predictive modelling based on real-time experimental data is critical. If an accurate estimate of the combustible dust's minimum ignition temperature is confirmed, it is possible to ensure that the temperatures of the surrounding hot surfaces do not rise to that level, preventing an explosion. An overall comparison of predictive models has been given with unseen test data set. All the trained models yielded comparable results with unseen test data set. However, the SVM model with Bayesian optimizer approach can effectively assess the risk of ignition based on dust MIT under the influence of dispersion pressure and dust cloud concentration among all the approaches adopted in this study.

Rapid determination and classification of zeolites based on Si/Al ratio using FTIR spectroscopy and chemometrics

The aim of this study was to develop fast and facile methods for the determination and classification of zeolite samples based on Si/Al ratio by fourier transform infrared spectroscopy (FTIR). The ZSM-5, ZSM-48 and mordenite catalysts spectral data were used for estimation of Si/Al ratio using two approaches according to PLS-R and SVM-R algorithms in whole (600–4000 cm−1) and fingerprint (3000–3800 cm−1) spectral regions. The drift-FTIR spectral data were also analyzed by classification method using the soft independent modelling of class analogies (SIMCA) for mordenite, ZSM-5 and ZSM-48 catalysts classification. Performance of the regression models was adequate with good statistical results. The correlation coefficients (R2) were about 0.99 and 0.96 for SVM and PLS regression models. The results obtained for the SVM model of the MIR spectra was very good and encouraging, since the RMSEP was low (6%) and the quantitative results were in accordance with X-ray fluorescence analysis. Then the samples were classified into three mordenite, ZSM-5 and ZSM-48 classes based on their Si/Al ratio by the SIMCA method using raw and pre-treated spectra. Using SIMCA technique, the 100% of samples were correctly classified in pre-treated spectra. The accurate and stable model was obtained with pre-treated spectra for SIMCA model (100% of sensitivity and 100% of specificity). The results indicated that drift-FTIR spectroscopy in conjunction with multivariate calibration and classification methods is a fast and reliable methodology that can be easily implemented for determination and classification of zeolite catalysts based on their Si/Al ratio.

Analysis on the influencing factors of multiple indicators in the United States based on multiple linear regression model

The US election will have a certain impact on the US economy. In this paper, different index systems are established to analyze the influence of different factors and American economy. After establishing the index system, this paper will use machine learning regression algorithm to fit the data in recent decades. We choose a variety of models, including ordinary linear regression model, Bayesian ridge regression model, elastic network regression model, support vector machine regression model and gradient enhanced regression model. In this paper, a prediction model based on machine learning is established without considering the epidemic situation. First of all, preprocess the data; secondly, determine the index system; then, based on the regression algorithm of machine learning, compare and select the optimal model for data fitting and prediction; finally, use linear regression to predict the indicators of the next four years.

Statistical estimation of next-day nighttime surface urban heat islands

Estimating future temporal patterns of Surface Urban Heat Islands (SUHIs) on multiple time scales is an ongoing research endeavor. Among these time scales, estimation of next-day SUHIs is of special significance to urban residents, yet we currently lack a simple but efficient approach for making such estimations. In the present study, we propose a statistical strategy for estimating next-day nighttime SUHIs, based on incorporating various SUHI controls into a support vector machine regression (SVR) model. The majority of both the surface controls (including factors related to land cover and solar radiation) and meteorological controls (including temperature fluctuations, relative humidity, accumulated precipitation, wind speed, aerosol optical depth, and soil moisture) that have previously been found to account for daily SUHI variations were used as estimators, and we provide estimations for both the overall SUHI intensity (SUHII) and pixel-by-pixel Gaussian-based LSTs over 59 Chinese megacities. For the overall SUHII, the mean absolute error (MAE) is 0.67 K on average, and the mean absolute percentage error (MAPE) is no more than 25% for more than 90% of the cities. For the pixel-by-pixel LSTs, the associated MAE is less than 2.0 K in most scenarios. In addition, the contribution from each selected estimator to SUHII estimation is assessed comprehensively. Among all the estimators, the contribution from relative humidity is the greatest, followed by rural surface temperature and surface air temperature. Moreover, for nearly 78% of the cities, the estimators related to day-to-day SUHI variations make a larger contribution than those related to intra-annual SUHI variations. We conclude that our simple yet effective statistical approach for estimating next-day SUHIs can potentially help urban residents to better adapt to urban heat stress.

Nondestructive simultaneous prediction of internal browning disorder and quality attributes in ‘Rocha’ pear (Pyrus communis L.) using VIS-NIR spectroscopy

This study explores the possibility of predicting the soluble solids content (SSC), firmness and the presence of internal browning disorders in ‘Rocha’ pear (Pyrus communis L.) using a single VIS-NIR spectroscopic measurement in semi-transmittance mode. The spectroscopic measurement setup was developed to mimic real world conditions and takes into account geometry and technical requirements of a commercial fruit sorting optical module. The randomness of the fruit position during the spectra acquisition was simulated by sampling each fruit on four sides. Calibration models for internal quality properties were built using individual and/or average side spectra. The results show that models using the spectrum of each side as an individual sample only under-perform slightly relatively to the models based on spectra averages, which are common in the laboratory but very difficult to implement on an automated grading line. The performance of PLS, SVM and Ridge Regression models was compared for the prediction of SSC and firmness. Multiple types of spectra pre-processing were computed and the best combination of model and pre-processing method identified. The lowest RMSEP results for SSC and firmness were 0.7% (R2 = 0.71) and 7.66 N (R2 = 0.68) respectively, achieved using SVM on data pre-processed with Standard Normal Variate corrected 2nd derivative. For the internal disorder detection (browning), a classification benchmark composed by five different models (PLS-LDA, PCA-Logistic Regression, PCA-Extremely Randomized Trees, Extremely Randomized Trees and SVC) was implemented. PLS-LDA applied to the raw spectra presented the highest sensitivity, 76%. The results confirm that simultaneously achieving viable firmness and SSC predictions and internal disorder detection levels in pears is possible using a single VIS-NIR spectral measurement.

Energy Consumption Estimation of the Electric Bus Based on Grey Wolf Optimization Algorithm and Support Vector Machine Regression

Electric buses have many significant advantages, such as zero emissions and low noise and energy consumption, making them play an important role in saving the operation cost of bus companies and reducing urban traffic pollution emissions. Therefore, in recent years, many cities in the world dedicate to promoting the electrification of public transport vehicles. Whereas due to the limitation of on-board battery capacity, the driving range of electric buses is relatively short. The accurate estimation of energy consumption on the electric bus routes is the premise of conducting bus scheduling and optimizing the layout of charging facilities. This study collected the actual operation data of three electric bus routes in Meihekou City, China, and established the support vector machine regression (SVR) model by taking the state of charge (SOC), trip travel time, mean environment temperature and air-conditioning operation time as the independent variables; while the energy consumptions of the route operations served as the dependent variables. Furthermore, the grey wolf optimization (GWO) algorithm was adopted to select the optimal parameters of the proposed model. Finally, a support vector machine regression model based on the grey wolf optimization algorithm (GWO-SVR) is proposed. Three real bus lines were taken as examples to validate the model. The results show that the mean average percentage error is 14.47% and the mean average error is 0.7776. In addition, the estimation accuracy and training time of the proposed model are superior to the genetic algorithm-back propagation neural network model and grid-search support vector machine regression model.

Identification of Accounting Fraud Based on Support Vector Machine and Logistic Regression Model

The authenticity of the company’s accounting information is an important guarantee for the effective operation of the capital market. Accounting fraud is the tampering and distortion of the company’s public disclosure information. The continuous outbreak of fraud cases has dealt a heavy blow to the confidence of investors, shaken the credit foundation of the capital market, and hindered the healthy and stable development of the capital market. Therefore, it is of great theoretical and practical significance to carry out the research on the identification and governance of accounting fraud. Traditionally, accounting fraud identification is mostly based on linear thinking to build the fraud identification model. However, more and more studies show that fraud has typical nonlinear characteristics, and the multiobjective of fraud means also determines the limitations of using the linear model for identification. Considering that the traditional identification methods may have the defects of model setting error and insufficient information extraction, this paper constructs the support vector machine and logistic regression model to identify accounting fraud. The support vector machine is used to improve the learning ability and generalization ability of unknown phenomena, and the explanatory power of each variable to the whole model is identified by the logistic regression model. This paper breaks through the linear constraint hypothesis and explores the model setting form which is more suitable for the law of corporate fraud behaviour to extract the fraud identification information more fully and provide more powerful support for investors to effectively identify fraud.

Machine-Learning-Based Rehabilitation Prognosis Prediction in Patients with Ischemic Stroke Using Brainstem Auditory Evoked Potential.

To evaluate the feasibility of brainstem auditory evoked potential (BAEP) for rehabilitation prognosis prediction in patients with ischemic stroke, 181 patients were tested using the Korean version of the modified Barthel index (K-MBI) at admission (basal K-MBI) and discharge (follow-up K-MBI). The BAEP measurements were performed within two weeks of admission on average. The criterion between favorable and unfavorable outcomes was defined as a K-MBI score of 75 at discharge, which was the boundary between moderate and mild dependence in daily living activities. The changes in the K-MBI scores (discharge-admission) were analyzed by nonlinear regression models, including the artificial neural network (ANN) and support vector machine (SVM), with the basal K-MBI score, age, and interpeak latencies (IPLs) of the BAEP (waves I, I–III, and III–V). When including the BAEP features, the correlations of the ANN and SVM regression models increased to 0.70 and 0.64, respectively. In the outcome prediction, the ANN model with the basal K-MBI score, age, and BAEP IPLs exhibited a sensitivity of 92% and specificity of 90%. Our results suggest that the BAEP IPLs used with the basal K-MBI score and age can play an adjunctive role in the prediction of patient rehabilitation prognoses.

Rapid and sensitive SERS detection of melamine in milk using Ag nanocube array substrate coupled with multivariate analysis

In this study, a facile Ag nanocube (NC) array substrate was fabricated for rapid SERS detection of melamine in milk. This easily-prepared substrate exhibited high Raman enhancement factor (~1.02 × 105) and good reproducibility with ~10.75% spot-to-spot variation in Raman intensity. Our proposed method can detect melamine as low as 0.01 ppm in standard solutions and 0.5 ppm in real milk samples after a simple one-step solvent extraction. Two multivariate analysis tools including partial least squares and support vector machines (SVM) were explored to develop reliable regression models for quantitative SERS analysis of melamine. By comparison, SVM regression models exhibited better predictive performance, especially in liquid milk, with root mean square error (RMSE) of calibration = 5.5783, coefficient of determination (R2) of calibration = 0.9807, RMSE of prediction = 1.9636, and R2 of prediction = 0.9736. Hence, this study offers a rapid and sensitive detection of adulterant melamine in milk samples.

Improving the accuracy of soil organic carbon content prediction based on visible and near-infrared spectroscopy and machine learning

Choosing appropriate multivariate calibration and preprocessing transformation techniques is important in the determination of soil organic carbon (SOC) content based on visible and near-infrared (Vis–NIR) spectroscopy. The performance levels of partial least-squares regression (PLSR), support vector machine regression (SVMR), and wavelet neural network (WNN) calibration methods coupled with different preprocessing approaches were compared using three kinds of criteria, including the coefficient of determination (R2), root mean square error (RMSE), and residual prediction deviation (RPD). A total of 328 soil samples collected from the south bank of Hangzhou Bay were used as the dataset for the calibration–validation procedure and SOC content inversion. The effects of spectra preprocessing transformation methods were evaluated for raw spectra, Savitzky–Golay smoothing with the first derivatives of reflectance (FDR) and Savitzky–Golay smoothing with logarithm of reciprocal of the reflectance (log R−1). The results indicate that the SVMR is superior to the PLSR, and WNN models for SOC content prediction. The combination of the SVMR model with FDR provided the best prediction results for the SOC content, with R2p = 0.92, RPDP = 2.82, RMSEP = 0.36%, and a kappa correlation coefficient of interpolation as high as 0.97. The FDR of Vis–NIR spectroscopy combined with the SVMR model is recommended over the PLSR and WNN modeling techniques for the high-accuracy determination of the SOC content.

Prediction of surface tension of solution in the presence of hydrophilic silica nanoparticle and anionic surfactant by ATR-FTIR spectroscopy and chemometric methods

In the current research, an analytical method was proposed for the quantitative determination of surface tension of anionic surfactant solutions in the presence of hydrophilic silica nanoparticles using attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy and chemometric methods. The surface tension behavior of anionic surfactant solutions considerably changes by the addition of silica nanoparticles with different particle size. The spectral data of solutions were used for prediction of surface tension using two calibration methods based on support vector machine regression (SVM-R) as a non-linear algorithm and partial least squares regression (PLS-R) as a linear algorithm. For preprocessing of data, baseline correction and standard normal variate (SNV) were also applied. Root mean square error of prediction (RMSEP) in SVM-R and PLS-R methods were 4.203 and 4.507, respectively. Considering the complexity of the samples, the SVM-R model was found to be reliable. The proposed method is fast and easy for measurement of the surface tension of surfactant solutions without any sample preparation step in chemical enhanced oil recovery (C-EOR).

Brain Development From Newborn to Adolescence: Evaluation by Neurite Orientation Dispersion and Density Imaging.

Neurite orientation dispersion and density imaging (NODDI) is a diffusion model specifically designed for brain magnetic resonance imaging. Despite recent studies suggesting that NODDI modeling might be more sensitive to brain development than diffusion tensor imaging (DTI), these studies were limited to a relatively small age range and mainly based on the manually operated region of interest analysis. Therefore, this study applied NODDI to investigate brain development in a large sample size of 214 subjects ranging in ages from 0 to 14. The whole brain was automatically segmented into 122 regions. The maturation trajectory of each region was characterized by the time course of diffusion metrics and further quantified using nonlinear regression. The NODDI-derived metrics, neurite density index (NDI) and orientation dispersion index (ODI), increased with age. And these two metrics were superior to the DTI-derived metrics in SVM regression models of age. The NDI in white matter exhibited a more rapid growth than that in gray matter (including the cortex and deep nucleus). These diffusion indicators experienced conspicuous increases during early childhood and the growth speed slowed down in adolescence. Region-specific maturation patterns were described throughout the brain, including white matter, cortical and deep gray matter. These development patterns were evaluated and discussed on the basis of NODDI’s model assumptions. To summarize, this study verified the high sensitivity of NODDI to age over a crucial developmental period from newborn to adolescence. Moreover, the existing knowledge of brain development has been complemented, suggesting that NODDI has a potential capability in the investigation of brain development.

Predicting the Severity of Parkinson's Disease Dementia by Assessing the Neuropsychiatric Symptoms with an SVM Regression Model.

In this study, we measured the convergence rate using the mean-squared error (MSE) of the standardized neuropsychological test to determine the severity of Parkinson’s disease dementia (PDD), which is based on support vector machine (SVM) regression (SVR) and present baseline data in order to develop a model to predict the severity of PDD. We analyzed 328 individuals with PDD who were 60 years or older. To identify the SVR with the best prediction power, we compared the classification performance (convergence rate) of eight SVR models (Eps-SVR and Nu-SVR with four kernel functions (a radial basis function (RBF), linear algorithm, polynomial algorithm, and sigmoid)). Among the eight models, the MSE of Nu-SVR-RBF was the lowest (0.078), with the highest convergence rate, whereas the MSE of Eps-SVR-sigmoid was 0.110, with the lowest convergence rate. The results of this study imply that this approach could be useful for measuring the severity of dementia by comprehensively examining axial atypical features, the Korean instrumental activities of daily living (K-IADL), changes in rapid eye movement sleep behavior disorder (RBD), etc. for optimal intervention and caring of the elderly living alone or patients with PDD residing in medically vulnerable areas.

Fast discrimination and quantification analysis of Curcumae Radix from four botanical origins using NIR spectroscopy coupled with chemometrics tools

Curcumae Radix (Yujin) is a multi-origin herbal medicine with excellent clinical efficacy. For fast discrimination and quantification analysis of Yujin from four botanical origins (Guiyujin, Huangyujin, Lvyujin and Wenyujin), near infrared (NIR) spectroscopy combined with chemometrics tools was employed in this study. Based on NIR data, principal component analysis (PCA) could only realize the separation between Guiyujin and Wenyujin samples, and the partial least squares-discrimination analysis (PLS-DA), support vector machine (SVM) and k-nearest neighbors (KNN) models achieved the complete discrimination of the four species of Yujin with 100% accuracy. Moreover, the method for the simultaneous determination of six bioactive compounds in Yujin was developed by HPLC. Germacrone, curdione and curcumenol could be found in all samples, and curcumin, demethoxycurcumin and bisdemethoxycurcumin were only observed in Huangyujin samples. Then, the support vector machine regression (SVMR) model for the prediction of germacrone content was successfully constructed. And the coefficients of determination were 0.88 and 0.89 for calibration and validation sets, respectively. The present work proposes a quick, economic and reliable method for the discrimination of Yujin from four botanical origins and the prediction of germacrone content, which will contribute to its quality control researches.

Robotic Process Automation for Stock Selection Process and Price Prediction Model using Machine Learning Techniques

The usage of AI in the financial industry has increased the reliance on stochastic models for forecasting market behaviour. Quantitative analysts are constantly working to increase the precision with which machine learning models predict stock returns. Regression models using Support Vector Machine (SVM) and Random Forest are well renowned for their ability to predict closing prices with high accuracy. This study suggests a method for analysing and forecasting stock prices using an ensemble of these algorithms.Using basic market price data from India's National Stock Exchange (NSE), datasets are used that have been preprocessed to add common technical indicators as features. The size of the training dataset is decreased by using feature selection techniques to rank the features according to their impact on the final closing price. Sentiment analysis is also used in the study to examine the effect of investor sentiment on stock prices. Twitter postings with a specific corporate hashtag are rated as good or bad using a trained Word2Vec model. The ensemble model is then trained on a fresh dataset made up of counts of both positive and negative tweets across time as well as technical indicator data.This paper makes a contribution to the area by offering an ensemble model for stock price prediction that blends SVM and Random Forest regression models. The study illustrates the significance of feature selection in lowering dataset size as well as the limited influence of aggregated sentiment analysis from Twitter data on the performance of the model as a whole. For researchers and quantitative analysts looking to improve the precision of stock price prediction models in the financial industry, these findings offer invaluable insights.

Support Vector Machines Model of the Nonlinear Hydrodynamics of Fixed Cylinders

Abstract Data-driven modeling is considered as a prospective approach for many conventional physical problems including ocean applications. Among various machine learning techniques, support vector machine stands out as one of the most widely used algorithms to establish models connecting pertinent features to physical quantities of interest. This paper takes the experimental data for a fixed cylinder in shallow water as the baseline data set and explores the modeling of nonlinear wave loads by the support vector machine (SVM) regression method. Different feature and target selections are studied in this paper to establish the nonlinear mapping relations from ambient wave elevations and kinematics to nonlinear wave loads. The performance of the SVM regression model is discussed and compared with nonlinear potential flow theory focusing on the overall statistics (standard deviation and kurtosis), which is critical for fatigue and extreme statistics analysis.

Machine learning for knowledge transfer across multiple metals additive manufacturing printers

Adopting new metals 3D printers introduces time and cost obstacles to printing parts with the same quality as was attained on existing printers. A large number of trial-and-error experiments or computationally intense simulations for property prediction and process optimization are often required. However, machine learning (ML) promises the ability to accelerate the adoption of new printing technologies. Specifically, it should enable transfer to the new printer prior knowledge built up from existing data, resulting in a data-informed starting point that is closer than would be possible without the statistics-based model. To enable the reuse of previously acquired knowledge, this study proposes a data-mining-assisted ML knowledge transfer framework. Bayesian models are found to more effectively model process-property relations and outperform support vector machine and logistic regression models. This framework is verified through 3 “industry-use” inspired scenarios for laser powder bed fusion of Ti-6Al-4V: 1) adopting a new model of printer from the same manufacturer that uses similar technology, 2) adopting a new model of printer from a different manufacturer that uses similar technology, and 3) adopting a new model of printer from a new manufacturer that uses different technology. The multi-property optimization experiments demonstrate the feasibility of cross-machine knowledge transfer to accelerate the adoption of new metals AM printing technologies.