Learning Regression Algorithms Research Articles

Uncertainty evaluation in density and viscosity of nanofluids at different temperatures using Gaussian process regression-based Monte-Carlo simulations

Uncertainty in experimental measurements is likely to be incorporated due to various limitations in measurement instruments, experimental procedures, quantity of chemicals, etc. Obtaining the true properties of nanofluids is a difficult task. Hence, the evaluation of the dynamic viscosity and density is strenuous. The present work aims to study the influence of uncertainties in temperature and concentration on density and dynamic viscosity of Graphene nanoparticle/distilled water, multiwall carbon nanotube/distilled water, and alumina/distilled water nanofluids using Gaussian Process Regression (GPR) learning algorithm guided Monte-Carlo simulations. The GPR algorithm is employed to predict the properties of the nanofluids which are then fed to Monte-Carlo simulations to predict the uncertainty. The noise and uncertainties are introduced in the mole fraction and temperature artificially and are validated using the Kolmogorov-Smirnov test and Quantile-Quantile plot. The influence of different noise levels in the data is also carried out.

Distributed statistical learning algorithm for nonlinear regression with autoregressive errors

The growing size of modern data brings challenges to statistical learning, and substantial distributed algorithms have been proposed. However, most of them need the homogeneity assumption that the distribution of the local data is the same as that of the global data. This is seldom in practice, and the learning performance deteriorates seriously if this assumption is not satisfied. Moreover, they are only for independent data, and cannot incorporate the serial correlations between data. To solve these issues, we propose a novel distributed statistical learning framework for the nonlinear regression with autoregressive errors, which realizes communication-efficient distributed optimization, and overcomes the homogeneity assumption. The theoretical results also guarantee that the new distributed framework is equivalent to the global one. Numerical experiments also illustrate the good performance of the new method.

Approximation of functions from Korobov spaces by shallow neural networks

In this paper, we consider the problem of approximating functions from a Korobov space on [−1,1]d by ReLU shallow neural networks and present a rate O(m−25(1+2d)log⁡m) of uniform approximation by networks of m hidden neurons. This is achieved by combining a novel Fourier analysis approach and a probability argument. We apply our approximation theory to a learning algorithm for regression based on ReLU shallow neural networks and derive learning rates of order O(N−4(d+2)9d+8log⁡N) for the excess generalization error with the sample size N when the regression function lies in the Korobov space.

Machine learning regression algorithms for generating chemical element maps from X-ray fluorescence data of paintings

Generating chemical element maps of paintings from X-ray fluorescence (XRF) data is a very valuable tool for the scientific community of conservators and art historians. Hand-held XRF scanners are cheap and easily portable but their use provides scans with a few data, so additional analytical tools are needed to obtain reliable chemical element maps from them. Recently, the software tool SmART_Scan was released, which uses an algorithm based on the minimum hypercube distance (MHD) to compute this kind of maps. In this paper, we propose a new methodology to address this problem by using machine learning algorithms for regression as alternative and more accurate techniques than MHD. We tested MHD versus eight machine learning regression algorithms on two paintings with different features. Our results showed that machine learning algorithms Random Forest and kNN significantly outperformed MHD in Mean Squared Error (MSE) and coefficient of determination (R2) for all the experiments. When using experts’ data and a hold-out validation, kNN was the best-ranked algorithm. Random Forest was the best-ranked algorithm when cross-validation was used. We did not find significant differences in average MSE nor in R2 between kNN and Random Forest, so we can conclude that Random Forest is the best-suited algorithm for computing chemical element maps of paintings from XRF data.

Environmental causality calibration: Advancing WLAN RF fingerprinting for precise indoor localization.

In recent years, considerable and valuable research progress has been made in indoor positioning technologies based on WLAN Radio Frequency (RF) fingerprinting, identifying it as one of the most promising positioning technologies with substantial potential for wider adoption. However, indoor environmental factors significantly influence the propagation of wireless RF signals, resulting in a considerable decrease in positioning accuracy as the indoor environmental conditions vary. Thus, effectively mitigating the impact of indoor environmental factors on WLAN RF fingerprinting-based positioning systems has become a crucial research problem. Currently, there is a dearth of comprehensive research on the influence of indoor climatic factors, particularly the variations in relative humidity, on the propagation of WLAN RF signals within indoor spaces and its consequential impact on positioning accuracy. To address the aforementioned issues, this paper proposes an Adaptive expansion fingerprint database (AeFd) model based on a regression learning algorithm. The AeFd, through the design of a relationship model describing the interaction between fingerprint databases under varying relative humidity, allows the fingerprint database expanded by AeFd to dynamically adapt to the changes in indoor relative humidity. Our experiments show that using the AeFd model with the KNN algorithm, a 5% performance improvement was observed over 10 days and an 8% improvement over 10 months. According to experimental test results, the fingerprint database expansion model AeFd proposed in this paper can effectively expand the fingerprint database under different relative humidity levels, thereby significantly enhancing the positioning performance of the system and improving its stability.

A Generalised Method for Friction Optimisation of Surface Textured Seals by Machine Learning

Friction behaviour is an important characteristic of dynamic seals. Surface texturing is an effective method to control the friction level without the need to change materials or lubricants. However, it is difficult to put the manual prediction of optimal friction reducing textures as a function of operating conditions into practice. Therefore, in this paper, we use machine learning techniques for the prediction of optimal texture parameters for friction optimisation. The application of pneumatic piston seals serves as an illustrative example to demonstrate the machine learning method and results. The analyses of this work are based on experimentally determined data of surface texture parameters, defined by the dimple diameter, distance, and depth. Furthermore friction data between the seal and the pneumatic cylinder are measured in different friction regimes from boundary over mixed up to hydrodynamic lubrication. A particular innovation of this work is the definition of a generalised method that guides the entire machine learning process from raw data acquisition to model prediction, without committing to only a few learning algorithms. A large number of 26 regression learning algorithms are used to build machine learning models through supervised learning to evaluate the suitability of different models in the specific application context. In order to select the best model, mathematical metrics and tribological relationships, like Stribeck curves, are applied and compared with each other. The resulting model is utilised in the subsequent friction optimisation step, in which optimal surface texture parameter combinations with the lowest friction coefficients are predicted over a defined interval of relative velocities. Finally, the friction behaviour is evaluated in the context of the model and optimal value combinations of the surface texture parameters are identified for different lubrication conditions.

Theoretical Models Constructed by Artificial Intelligence Algorithms for Enhanced Lipid Production: Decision Support Tools

Theoretical models that predict the lipid content of microalgae are an important tool for increasing lipid productivity. In this study, response surface methodology (RSM), RSM combined with artificial neural network (ANN), and RSM combined with ensemble learning algorithms (ELA) for regression were used to calculate the maximum lipid percentage (%) from Chlorella minutissima (C. minutissima). We defined one set of rules to achieve the highest lipid content and used trees.RandomTree (tRT) to simulate the process parameters under various conditions. Among the various models, results showed the optimum values of the root mean squared error (0.2156), mean absolute error (0.1167), and correlation coefficient (0.9961) in the tRT model. RSM combined with tRT estimated that the lipid percentage was 30.3% in wastewater (< 35%), lysozyme (≥ 3.5 U/mL), and chitinase (< 15 U/mL) concentrations, achieving the best model based on experimental data. The optimal values of wastewater concentration, chitinase, and lysozyme were 20% (v/v), 5 U/mL, and 10 U/mL, respectively. Also, the if-then rules obtained from tRT were also used to test the process parameters. The tRT model served as a powerful tool to obtain maximum lipid content. The final rankings of the performance of various algorithms were determined. Furthermore, the models developed can be used by the fuel industry to achieve cost-effective, large-scale production of lipid content and biodiesel.

A comprehensive analysis for classification and regression of surface points based on geodesics and machine learning algorithms

PurposeFeature extraction from 3D datasets is a current problem. Machine learning is an important tool for classification of complex 3D datasets. Machine learning classification techniques are widely used in various fields, such as text classification, pattern recognition, medical disease analysis, etc. The aim of this study is to apply the most popular classification and regression methods to determine the best classification and regression method based on the geodesics.Design/methodology/approachThe feature vector is determined by the unit normal vector and the unit principal vector at each point of the 3D surface along with the point coordinates themselves. Moreover, different examples are compared according to the classification methods in terms of accuracy and the regression algorithms in terms of R-squared value.FindingsSeveral surface examples are analyzed for the feature vector using classification (31 methods) and regression (23 methods) machine learning algorithms. In addition, two ensemble methods XGBoost and LightGBM are used for classification and regression. Also, the scores for each surface example are compared.Originality/valueTo the best of the author’s knowledge, this is the first study to analyze datasets based on geodesics using machine learning algorithms for classification and regression.

Companies’ E-waste Estimation Based on General Equilibrium Theory Context and Random Forest Regression Algorithm in Cameroon: Case Study of SMEs Implementing ISO 14001:2015

Given the challenge of estimating or calculating quantities of waste electrical and electronic equipment (WEEE) in developing countries, this article focuses on predicting the WEEE generated by Cameroonian small and medium enterprises (SMEs) that are engaged in ISO 14001:2015 initiatives and consume electrical and electronic equipment (EEE) to enhance their performance and profitability. The methodology employed an exploratory approach involving the application of general equilibrium theory (GET) to contextualize the study and generate relevant parameters for deploying the random forest regression learning algorithm for predictions. Machine learning was applied to 80% of the samples for training, while simulation was conducted on the remaining 20% of samples based on quantities of EEE utilized over a specific period, utilization rates, repair rates, and average lifespans. The results demonstrate that the model’s predicted values are significantly close to the actual quantities of generated WEEE, and the model’s performance was evaluated using the mean squared error (MSE) and yielding satisfactory results. Based on this model, both companies and stakeholders can set realistic objectives for managing companies’ WEEE, fostering sustainable socio-environmental practices.

A multimodal sensing ring for quantification of scratch intensity

BackgroundAn objective measurement of chronic itch is necessary for improvements in patient care for numerous medical conditions. While wearables have shown promise for scratch detection, they are currently unable to estimate scratch intensity, preventing a comprehensive understanding of the effect of itch on an individual.MethodsIn this work, we present a framework for the estimation of scratch intensity in addition to the detection of scratch. This is accomplished with a multimodal ring device, consisting of an accelerometer and a contact microphone, a pressure-sensitive tablet for capturing ground truth intensity values, and machine learning algorithms for regression of scratch intensity on a 0–600 milliwatts (mW) power scale that can be mapped to a 0–10 continuous scale.ResultsWe evaluate the performance of our algorithms on 20 individuals using leave one subject out cross-validation and using data from 14 additional participants, we show that our algorithms achieve clinically-relevant discrimination of scratching intensity levels. By doing so, our device enables the quantification of the substantial variations in the interpretation of the 0–10 scale frequently utilized in patient self-reported clinical assessments.ConclusionsThis work demonstrates that a finger-worn device can provide multidimensional, objective, real-time measures for the action of scratching.

Fingerprint-Based mmWave Positioning System Aided by Reconfigurable Intelligent Surface

Reconfigurable intelligent surface (RIS) is a promising technique for millimeter wave (mmWave) positioning systems. In this paper, we consider multiple mobile users (MUs) positioning problem in the multiple-input multiple-output (MIMO) time-division duplex (TDD) mmWave systems aided by the RIS. We derive the expression for the space-time channel response vector (STCRV) as a novel type of fingerprint. The STCRV fingerprint comprises the channel characteristics of the cascaded channel, such as the angle of arrival (AOA) at the RIS and the time delay from the MU to the RIS, which are indicative of the position of the MU. To process the STCRV with more features, we propose a novel residual convolution network regression (RCNR) learning algorithm to output the estimated three-dimensional (3D) position of the MU with higher accuracy. Specifically, the RCNR learninng algorithm includes a data processing block to process the input STCRV, a normal convolution block to extract the features of STCRV, four residual convolution blocks to further extract the features and protect the integrity of the features, and a regression block to estimate the 3D position. Extensive simulation results are also presented to demonstrate that the proposed RCNR learning algorithm outperforms the traditional convolution neural network (CNN).

Experimental Study on Remaining Useful Life Prediction of Lithium-Ion Batteries Based on Three Regression Models for Electric Vehicle Application

This paper presents three regression models that predict the lithium-ion battery life for electric cars based on a supervised machine learning regression algorithm. The linear regression, bagging regressor, and random forest regressor models will be compared for the capacity prediction of lithium-ion batteries based on voltage-dependent per-cell modeling. When sufficient test data are available, three linear regression learning algorithms will train this model to give a promising battery capacity prediction result. The effectiveness of the three linear regression models will be demonstrated experimentally. The experiment table system is built with an NVIDIA Jetson Nano 4 GB Developer Kit B01, a battery, an Arduino, and a voltage sensor. The random forest regressor model has evaluated the model’s accuracy based on the average of the square of the difference between the initial value and the predicted value in the data set (MSE (mean square error)) and RMSE (root mean squared error), which is smaller than the linear regression model and bagging regressor model (MSE is 516.332762; RMSE is 22.722957). The linear regression model with MSE and RMSE is the biggest (MSE is 22060.500669; RMSE is 148.527777). This result allows the random forest regressor model to remain helpful in predicting the life of lithium-ion batteries. Moreover, this result allows rapid identification of battery manufacturing processes and will enable users to decide to replace defective batteries when deterioration in battery performance and lifespan is identified.

Adaptive First-Crossing Approach for Life-Cycle Reliability Analysis

Abstract Life-cycle reliability analysis can effectively estimate and present the changes in the state of safety for structures under dynamic uncertainties during their lifecycle. The first-crossing approach is an efficient way to evaluate time-variant reliability-based on the probabilistic characteristics of the first-crossing time point (FCTP). However, the FCTP model has a number of critical challenges, such as computational accuracy. This paper proposes an adaptive first-crossing approach for the time-varying reliability of structures over their whole lifecycle, which can provide a tool for cycle-life reliability analysis and design. The response surface of FCTP regarding input variables is first estimated by performing support vector regression. Furthermore, the adaptive learning algorithm for training support vector regression is developed by integrating the uniform design and the central moments of the surrogate model. Then, the convergence condition, which combines the raw moments and entropy of the first-crossing probability distribution function (PDF), is constructed to build the optimal first-crossing surrogate model. Finally, the first-crossing PDF is solved using the adaptive kernel density estimation to obtain the time-variant reliability trend during the whole lifecycle. Examples are demonstrated to specify the proposed method in applications.

Comparison of probabilistic jet fuel property models for the fuel screening and design

Model-based property prediction is considered a key enabler in the screening and design of new jet fuel candidates. Combined with two-dimensional Gas Chromatography (GCxGC) for compositional analytics, they allow the prediction of fuel properties critical for the assessment of fuel candidates already from small volumes (5 ml). As safety relevant use case, the assessment of new aviation fuel candidates makes the consideration of uncertainties due to unidentifiable isomers, measurement and model uncertainty necessary. Recent developments yield several probabilistic modeling approaches that can estimate uncertainties as part of the property prediction. In this work, we present three of those approaches: Direct correlation (DC), Mean Quantitative Structure-Property Relationship Modeling (M-QSPR) and Quantitative Structure-Property Relationship Modeling (QSPR) with sampling. The approaches use Monte-Carlo Neural Networks (MCNN), a Machine Learning algorithm for the underling regression of the properties from the GCxGC measurements. We assess their predictive capabilities for the modeling of eight safety-relevant properties like the freezing point and the cetane number for conventional and synthetic jet fuels. The predictive capability assessment is based on metrics that quantify the accuracy, reliability and precision of the models. Identifying the strengths and limitations of the different approaches, we provide recommendations for the practical application of the different models and the use of model-based property prediction for the support of jet fuel assessments in general.

TPOT-MTR: A Multiple Target Regression Based on Genetic Algorithm of Automated Machine Learning Systems

The concept that a cross correlation might improve prediction error underpins machine learning algorithms for multi-target regression (MTR). Numerous MTR approaches have been created in recent years, however there are still uncertainties concerning how their performances are impacted by dataset properties such as linearity, number of targets, and cross correlational complexity. In order to contribute to a better understanding of the relationship between dataset properties and MTR methods, authors proposed a new model of TPOT-MTR, which its result will be compared to previously generated 33 synthetic datasets with controlled characteristics and tested their performance against other two MTR methods, Random Forest and SVM. The results demonstrated that TPOT-MTR approaches could enhance performance even in datasets with non-linearly correlated targets, although the prediction improvement varies depending on the method and regressor combinations used.

Use of machine learning to improve the estimation of conductivity and permittivity based on longitudinal relaxation time T1 in magnetic resonance at 7 T

Electrical property tomography (EPT) is a noninvasive method that uses magnetic resonance imaging (MRI) to estimate the conductivity and permittivity of tissues, and hence, can be used as a biomarker. One branch of EPT is based on the correlation of water and relaxation time T1 with the conductivity and permittivity of tissues. This correlation was applied to a curve-fitting function to estimate electrical properties, it was found to have a high correlation between permittivity and T1 however the computation of conductivity based on T1 requires to estimate the water content. In this study, we developed multiple phantoms with several ingredients that modify the conductivity and permittivity and explored the use of machine learning algorithms to have a direct estimation of conductivity and permittivity based on MR images and the relaxation time T1. To train the algorithms, each phantom was measured using a dielectric measurement device to acquire the true conductivity and permittivity. MR images were taken for each phantom, and the T1 values were measured. Then, the acquired data were tested using curve fitting, regression learning, and neural fit models to estimate the conductivity and permittivity values based on the T1 values. In particular, the regression learning algorithm based on Gaussian process regression showed high accuracy with a coefficient of determination R2 of 0.96 and 0.99 for permittivity and conductivity, respectively. The estimation of permittivity using regression learning demonstrated a lower mean error of 0.66% compared to the curve fitting method, which resulted in a mean error of 3.6%. The estimation of conductivity also showed that the regression learning approach had a lower mean error of 0.49%, whereas the curve fitting method resulted in a mean error of 6%. The findings suggest that utilizing regression learning models, specifically Gaussian process regression, can result in more accurate estimations for both permittivity and conductivity compared to other methods.

Protein Prediction using Dictionary Based Regression Learning

Research Objectives: Molecular genetic data is managed by the information technology known as bioinformatics. Major concept involved in bioinformatics is a protein sequence. Amino acids bonded with peptide bond constitute the sequence of Protein and it is very essential to lead life. To predict sequence of amino acid, primary sequence obtains amino sequence folding and structures prediction. Research Novelty: In this manuscript, dictionary based regression learning and fuzzy genetic algorithm is proposed for protein prediction from structural analysis (DRL-FGA-PD-SA). In this input data are taken from Kaggle domain dataset. The extraction of protein features from given data is made through Kernel Matrix (KM) which extracts composition of amino acids, composition of dipeptide, composition of pseudo-amino-acid, composition of functional domain and distance-based features. Then fuzzy based genetic algorithm (FGA) update the selected features for classification of protein and the features are clustered. Finally, dictionary based regression learning (DRL) predicts the class of protein with conversion of values either 0’s or 1’s. Research Conclusions: The proposed method is executed on MATLAB. Here evaluation metrics as sensitivity, precision, f-measure, specificity, accuracy and error rate are outlined. Then the performance of the proposed DRL-FGA-PD-SA method provides 22.08%, 24.03%, 34.76% higher accuracy, 23.34%, 26.45%, 34.44% higher precision, compared with the existing systems such assdeep learning methods in protein structure prediction (FFNN-RNN-PD-SA), deep learning technique for protein structure prediction and protein design (DNN-PD-SA) and improved protein structure prediction using potentials from deep learning (DNN-SGDA-PD-SA) respectively.

Efficient Vertical Federated Learning Method for Ridge Regression of Large-Scale Samples

Integrating data from multiple parties to achieve cross-institutional machine learning is an important trend in Industry 4.0 era. However, the privacy risks from sharing data pose a significant challenge to data integration. To integrate data without sharing data and meet large-scale samples' modeling needs, we propose two vertical federation learning algorithms for ridge regression via least-squares solution for two-party and multi-party scenarios, respectively. Compared with the state-of-the-art algorithms, our algorithms only need one round of calculation for the optimization instead of iteration. Furthermore, our algorithms can effectively handle large-scale samples due to the number of cryptographic operations in our algorithms being independent of the number of samples. Through our proposed the matrix secure agent computing theory and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\delta$</tex-math></inline-formula> -data indistinguishability theory, we provide quantitative theoretical guarantees for the security of our algorithms. Our algorithms satisfy complete data indistinguishability under the “semi-honest” assumption and the quantitative security under the “malicious” assumption. The experiments show that our proposed algorithm takes only about 400 seconds to handle up to 9.6 million large-scale samples, while the state-of-the-art algorithms take close to 1000 seconds to handle every 1000 samples, which embodies the advantage of our algorithms in handling large-scale samples.

A Machine Learning Method for Predicting Corrosion Weight Gain of Uranium and Uranium Alloys.

As an irreplaceable structural and functional material in strategic equipment, uranium and uranium alloys are generally susceptible to corrosion reactions during service, and predicting corrosion behavior has important research significance. There have been substantial studies conducted on metal corrosion research. Accelerated experiments can shorten the test time, but there are still differences in real corrosion processes. Numerical simulation methods can avoid radioactive experiments, but it is difficult to fully simulate a real corrosion environment. The modeling of real corrosion data using machine learning methods allows for effective corrosion prediction. This research used machine learning methods to study the corrosion of uranium and uranium alloys in air and established a corrosion weight gain prediction model. Eleven classic machine learning algorithms for regression were compared and a ten-fold cross validation method was used to choose the highest accuracy algorithm, which was the extra trees algorithm. Feature selection methods, including the extra trees and Pearson correlation analysis methods, were used to select the most important four factors in corrosion weight gain. As a result, the prediction accuracy of the corrosion weight gain prediction model was 96.8%, which could determine a good prediction of corrosion for uranium and uranium alloys.

Predictive Modeling of Movie Merchandise Market: A Machine Learning Approach

The mature development and the extension of the industry chain make the income structure of the film industry. The income of the traditional film industry depends on the box office and also includes movie merchandise, advertisement, home entertainment, book sales etc. Movie merchandise can even become more profitable than the box office. Therefore, market analysis and forecasting methods for multi-feature merchandise of multi-type films are particularly important. Traditional market research is time-consuming and labour-intensive, and its practical value is restricted. Due to the limited research method, more effective predictive analysis technology needs to be formed. With the rapid development of machine learning and big data, a large number of machine learning algorithms for predictive regression and classification recognition have been proposed and widely used in product design and industry analysis. This paper proposes a high-precision movie merchandise prediction model based on machine learning technology: WE model. This model integrates three machine learning algorithms to accurately predict the movie merchandise market. The WE model learns the relationship between the movie merchandise market and movie features by analyzing the main feature information of movies. After testing, the accuracy rate of prediction and evaluation in the merchandise market reaches 72.5%, and it has achieved a strong market control effect.