Ridge Regression Technique Research Articles

A Deep Neural Network Approach towards Performance Prediction of Bituminous Mixtures Produced Using Secondary Raw Materials

With the progressive reduction in virgin material availability and the growing global concern for sustainability, civil engineering researchers worldwide are shifting their attention toward exploring alternative and mechanically sound technological solutions. The feasibility of preparing both cold and hot asphalt mixtures (AMs) for road pavement binder layers with construction and demolition wastes (C&DWs) and reclaimed asphalt pavement (RAP) partially replacing virgin materials like limestone aggregates and filler has already been proven. The technical suitability and compliance with technical specifications for road paving materials involved the evaluation of mechanical and volumetric aspects by means of indirect tensile strength tests and saturated surface dry voids, respectively. Thus, the main goal of the present study is to train, validate, and test selected machine learning algorithms based on data obtained from the previous experimental campaign with the aim of predicting the volumetric properties and the mechanical performance of the investigated mixtures. A comparison between the predictions made by ridge and lasso regression techniques and both shallow (SNN) and deep neural network (DNN) models showed that the latter achieved better predictive capabilities, highlighted by fully satisfactory performance metrics. DNN performance can be summarized by R2 values equal to 0.8990 in terms of saturated surface dry void predictions, as well as 0.9954 in terms of indirect tensile strength predictions. Predicted observations can be thus implemented within the traditional mix design software. This would reduce the need to carry out additional expensive and time-consuming experimental campaigns.

Comparing the performance of Ridge Regression and Lasso techniques for modelling daily maximum temperatures in Utraradit Province of Thailand

Comparing the performance of Ridge Regression and Lasso techniques for modelling daily maximum temperatures in Utraradit Province of Thailand

Hydrodesulfurization of Dibenzothiophene: A Machine Learning Approach.

The hydrodesulfurization (HDS) process is widely used in the industry to eliminate sulfur compounds from fuels. However, removing dibenzothiophene (DBT) and its derivatives is a challenge. Here, the key aspects that affect the efficiency of catalysts in the HDS of DBT were investigated using machine learning (ML) algorithms. The conversion of DBT and selectivity was estimated by applying Lasso, Ridge, and Random Forest regression techniques. For the estimation of conversion of DBT, Random Forest and Lasso offer adequate predictions. At the same time, regularized regressions have similar outcomes, which are suitable for selectivity estimations. According to the regression coefficient, the structural parameters are essential predictors for selectivity, highlighting the pore size, and slab length. These properties can connect with aspects like the availability of active sites. The insights gained through ML techniques about the HDS catalysts agree with the interpretations of previous experimental reports.

Ground-Motion Model for Small-to-Moderate Potentially Induced Earthquakes Using an Ensemble Machine Learning Approach for CENA

ABSTRACT Ground-motion models (GMMs) are vital in assessing probabilistic seismic hazards and uncertainties. This study develops new GMMs benefiting from nonparametric machine learning algorithms, including artificial neural networks, kernel ridge, random forest, and gradient boosting regression techniques for small-to-moderate potentially induced earthquakes in central and eastern North America (CENA). As part of this study, we evaluate the performance of different machine learning models in estimating peak ground acceleration (PGA) and 17 spectral accelerations based on the moment magnitude (Mw), hypocentral distance (Rhypo), and the timed-average shear-wave velocity of the upper 30 m of soil (VS30). To train the algorithms, we have utilized a database of nearly 31,000 ground motions with small and moderate moment magnitudes ranging from 3.0 to 5.8, recorded within a hypocentral distance of less than 200 km in CENA. Typically, for GMM development, analysts employ linear regression-based models with predefined functional forms. The requirement for predefined functional forms can restrict the use of complicated and nonlinear equations to improve performance. Although the conventional regression model is more interpretable, machine learning can achieve a better result given sufficient training data. The results of error metrics reveal that gradient-boosting regression provides a better performance. Furthermore, a machine learning ensemble method is used to combine the regression results of four machine learning algorithms. The ensemble method improves the GMM performance and provides smoother results.

Evaluation of data-driven NARX model based compensation for multi-axial real-time hybrid simulation benchmark study

Actuator control takes a pivotal role in achieving stability and accuracy, particularly in the context of multi-axial real-time hybrid simulation (maRTHS). In maRTHS, multiple hydraulic actuators are necessitated to apply precise motions to experimental substructures thus necessitating the application of multiple-input multiple-output (MIMO)control strategies. This study evaluates the data-driven nonlinear autoregressive with external input (NARX) based compensation for the servo-hydraulic dynamics within the maRTHS benchmark model. Different from previous study, nonlinear terms are incorporated into the NARX model. Online least square and ridge regression techniques are utilized to estimate the model coefficients to achieve optimal compensation. The influence of various model order and window length is assessed for the NARX model-based compensation. The findings of this research demonstrate that NARX-based compensation has significant potential not only in facilitating precise actuator control for maRTHS but also in enabling robust control in the presence of unknown uncertainties inherent to the servo-hydraulic system.

Assessing the Impact of Infrastructure on Tax Structure and Improving Economic Performance: The Case of Nigeria

The relevance of this study is to establish the interactive role of infrastructure on tax composition in stimulating long-term economic performance in Nigeria. However, empirical evidence showed that greater emphasis was placed on studies relating to the impact of taxation on the performance of the economy. The purpose of this study is to examine the impact of infrastructure on tax structure and improving economic performance in Nigeria. The hypothesis - the interactive role of infrastructure on tax composition and the economic performance nexus in Nigeria - was estimated with the Ridge Regression (RR) technique to take care of the incidence of multicollinearity that existed among the study variables using the dataset covering 1993-2022. Also, the study utilized the Barro growth model, which emphasized investment in infrastructure enhanced through taxes as the core endogenous factor that drives growth. The findings revealed that with the presence of infrastructure; value added tax, company tax, customs and excise duties and petroleum profit tax has a positive and significant relationship with economic performance while personal income tax has a positive but insignificant relationship with economic performance in Nigeria within the study period. The study thus concluded that with the presence of infrastructure, tax composition significantly contributed to economic performance in Nigeria within the study period. Based on this finding, this study recommends that policymakers should focus on measures such as tax education and enlightenment to encourage voluntary tax compliance to generate substantial revenue to fund infrastructural development, thus, improving the level of economic performance in Nigeria.

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.

DZ-SMS: An Authentic Corpus of Algerian SMS

In this article, a complete methodology of a corpus realization of authentic Short Message Service (SMS) from Algerian dialect and which are transcribed in Latin characters or symbols is presented. A linguistic material constituted by 6,000 SMS coming from the different geographical regions of Algeria (Middle, East, and West) corresponding to 42 administrative and geographical departments, have been collected. The coexistence of several dialects through these three regions simultaneously has obliged us to consider and operate a classification of the data for each dialect. This data classification has yielded three extracted regional dialectic corpora, each of them covering a specific number of administrative departments. These treatments are based on the so-called Data-n-gram tokenization targeting the suppression of the stop words, the stemming and the imbalance of the classes linked to the nature of the SMS. Consequently, three text classifiers based on three linear classifiers, namely, Stochastic Gradient Descent (SGD), The Ridge Regression (RDG), and Linear Support Vector Machines, to find out the number of significant corpora to extract from the collected data. A deep analysis of the results has shown that the 5-grams data representation is more representative whereas the stop-words removal and stemming process has generated an information loss that has subsequently inferred an alteration of the recognition rate of about 2%. The emerging problem of classes imbalance has been treated by using three techniques: Random Oversampling, Synthetic Minorities Oversampling Technique (SMOTE), and Adaptive Synthetic (ADASYN). This treatment produced interesting results and enhancements; particularly, the classification by region with the oversampling process SMOTE by using the RDG technique has reached a better percentage of 55.93% whereas the classification by department with the oversampling process ADASYN associated with the SGD has only yielded a maximum score of about 17.11%. The results, which undoubtedly are in favor of the classification by region, have compelled us to create three Subdialectal regional corpora, each, covering a certain number of Algerian departments.

Forecasting Future Sales of Bigmarts

Abstract: In this contemporary era, supermarket and general stores have been scrutinizing the sales record for knowing the demands of customers and to find the straggles in general trend. So, as the data is available a predictive model has been built using various algorithms such as Polynomial regression, XGBoost, Linear regression, and Ridge regression techniques for forecasting the sales of a business in advance. The prediction is based on sales of supermarket for various outlets to calibrate the business model to expected outcomes. With the results and analysis provided by the model retailers can know the sales volume in advance

Nature-inspired stochastic hybrid technique for joint and individual inversion of DC and MT data

Here, a new naturally-inspired stochastic nonlinear joint and individual inversion technique for integrating direct current (DC) and magnetotelluric (MT) data interpretation-based simulation of a swarm intelligence combo with specific capabilities for exploitation of the variable weight particle swarm optimizer (vPSO) and exploration of the grey wolf optimizer (GWO), vPSOGWO, is used. They are particularly notable for their capacity for information exchange while hunting for food. Through synthetic MT and DC data contaminated with various sets of random noise, the applicability of the anticipated vPSOGWO algorithm based joint and individual inversion algorithm was assessed. The field examples, collected from diversified different geological terrains, including Digha (West Bengal), India; Sundar Pahari (Jharkhand), India; Puga Valley (Ladakh), India; New Brunswick, Canada; and South Central Australia, have shown the practical application of the proposed algorithm. Further, a Bayesian probability density function (bpdf) for estimating a mean global model and uncertainty assessment in posterior; and a histogram for model resolution assessment have also been created using 1000 inverted models. We examined the inverted outcomes and compared them with results from other cutting-edge methodologies, including the GWO, PSO, genetic algorithm (GA), Levenberg–Marquardt (LM), and ridge-regression (RR). Our findings showed that the current methodology is more effective than the GWO, PSO, GA, LM, and RR techniques at consistently improving the convergence of the global minimum. In contrast to earlier approaches, the current cutting-edge strategy vPSOGWO offers an improved resolution of an additional significant crustal thickness of about 65.68 ± 1.96 km over the Puga Valley, in which the inverted crustal thickness determined by vPSOGWO agrees well with the published crustal thickness over the Puga Valley. The new technology brings simulations closer to genuine models by significantly reducing uncertainty and enhancing model resolution.

Comparative Analysis of Ridge, Bridge and Lasso Regression Models In the Presence of Multicollinearity

Aims: This research work investigated the best regression technique in handling multicollinearity using the Ridge, Least Absolute Shrinkage and Selection Operator (LASSO) and Bridge regression models in comparison to Analysis and Prediction. Study Design: Two sets of secondary data on Body Size and Heart Rate gotten from the Lulu Briggs Health Center, University of Port Harcourt were used for comparison for model fit and in handling multicollinearity between the regression techniques. Tables were used to present Comparisons made using MSE, RMSE, VIF, AIC and BIC for efficiency. Scatter plots were employed to show fitted regression models. R Software was used to perform data analysis. Methodology: The data were tested for the presence of Multicollinearity using VIF respectively, before proceeding to apply Ridge, LASSO and Bridge regression techniques to solve the problem of multicollinearity. Then comparison was made in analysis and prediction between the regression techniques. Results: The results from the study show that, for analysis on body size, we found that none of the Regression Techniques handled the problem of multicollinearity, even though the degree of multicollinearity present in the data set reduces, with VIF values of 11.36762 for Ridge, 10.8042 for LASSO, and Bridge which are 10.95578, 11.24945, 12.22628 and 12.14645 respectively. For Heart Rate analysis, we see that all the regularized regression techniques handled the problem of multicollinearity. The results show that the Bridge regression technique performed better with a VIF of 1.744461 when

ESTIMATION OF EFFECT ON GROSS DOMESTIC PRODUCT OF PRODUCTION FACTORS USING CES AND TRANSLOG PRODUCTION FUNCTIONS: AN APPLICATION TO CHINA ECONOMY

In this study, the effects on the economic growth (GDP) of capital, labor and energy input factors for the Chinese economy are investigated with the help of the CES and Translog production functions. According to the empirical findings of the study, it can be said that the GDP data obtained using the CES production function are less efficient than the estimates obtained using the Translog production function. The Ridge regression technique was used for parameter estimation of Translog production model since there is multicollinearity between the variables in the model. The output elasticities and the substitution elasticities between each input factor are then dynamically estimated, based on the appropriate Translog production model that includes the capital, labor and energy input factors. In addition, the inputs of the Translog production model were estimated using the Holt-Winter's method to predict the future economic growth of the Chinese economy. Consequently, output elasticities of all input factors are positive, and we can rank the input factors as labor, capital and energy according to their degree of impact on GDP, respectively. This shows that the Chinese economy is labor and capital intensive.

A Holistic Strategy for Classification of Sleep Stages with EEG.

Manual sleep stage scoring is usually implemented with the help of sleep specialists by means of visual inspection of the neurophysiological signals of the patient. As it is a very hectic task to perform, automated sleep stage classification systems were developed in the past, and advancements are being made consistently by researchers. The various stages of sleep are identified by these automated sleep stage classification systems, and it is quite an important step to assist doctors for the diagnosis of sleep-related disorders. In this work, a holistic strategy named as clustering and dimensionality reduction with feature extraction cum selection for classification along with deep learning (CDFCD) is proposed for the classification of sleep stages with EEG signals. Though the methodology follows a similar structural flow as proposed in the past works, many advanced and novel techniques are proposed under each category in this work flow. Initially, clustering is applied with the help of hierarchical clustering, spectral clustering, and the proposed principal component analysis (PCA)-based subspace clustering. Then the dimensionality of it is reduced with the help of the proposed singular value decomposition (SVD)-based spectral algorithm and the standard variational Bayesian matrix factorization (VBMF) technique. Then the features are extracted and selected with the two novel proposed techniques, such as the sparse group lasso technique with dual-level implementation (SGL-DLI) and the ridge regression technique with limiting weight scheme (RR-LWS). Finally, the classification happens with the less explored multiclass Gaussian process classification (MGC), the proposed random arbitrary collective classification (RACC), and the deep learning technique using long short-term memory (LSTM) along with other conventional machine learning techniques. This methodology is validated on the sleep EDF database, and the results obtained with this methodology have surpassed the results of the previous studies in terms of the obtained classification accuracy reporting a high accuracy of 93.51% even for the six-classes classification problem.

The Solution of Multicollinearity Problem via Biased Regression Analysis in Southern Anatolian Red Cattle

The aim of this study is to investigate the effectiveness of biased estimation methods, principal component regression (PC) and ridge regression (RR) methods, according to unbiased the least squares (LS) method, against the multiple linearity problem (multicollinearity) encountered in regression methods. For this purpose to fit a model on account to predict body weight from some body measurements of 32 South Anatolian Red Kilis (SAR) cattle of different ages. R2, RMSE, MSE, and CV were used as the goodness of fit criteria for the performance of the models. According to these criteria respectively, 0.9970, 0.0224, 0.0005, 0.0099 for LS; 0.9970, 0.0224, 0.0005, 0.0099 for PC; and 0.9876, 0.0455, 0.0021, 0.0201 of k=0.02 for RR gave the best fit values. According to these results, LR and PC showed the best fit. But RR and PC techniques from biased prediction techniques provided more consistent, valid, stable, and theoretical expectations than LS technique, since LR did not provide the necessary assumptions.

Rate-Constrained Trellis-Coded Quantization for Large-Scale Noisy Graph Signals

We consider the issue of compressing large-scale noise-corrupted graph signals under a rate constraint, to tackle the communication resource limitations, from rate-distortion perspective. To guarantee the fidelity of the overall compression system for noisy graph signals, we adopt the technique of kernel ridge regression on graphs for preprocessing. We show that, as a compression component for the output of the kernel ridge regression, trellis-coded quantization has superior distortion performance in comparison to other feasible quantization methods for long source blocks, and hence is suitable for large-scale graph signals. Targeting at decreasing further distortion in the multiple trellis-coded quantizer system under rate restriction, we design a novel rate allocation scheme based on the intrinsic topology of graph signals and the rate-distortion characteristics of trellis-coded quantization. We perform sufficient simulation with in-depth analysis, which demonstrates both the effectiveness of the proposed trellis-coded quantization for large-scale noisy graph signals and the advantage of the proposed rate allocation scheme over the existing rate allocation schemes in the common distortion measure under communication resource constraint.

ASSESSMENT OF THE IMPACTS OF OIL PRICES ON NIGERIA ECONOMY USING COBB-DOUGLAS PRODUCTION FUNCTION

Fluctuations in oil prices have been a global issue over the years. Although many studies have been carried out the majority of those studies relating to oil prices focused more on its effects on oil-consuming nations than oil-producing ones. This study, however, examines the vulnerability of the economy of the oil-producing country to oil price changes using Nigeria being an OPEC member as a case study. The Cobb-Douglas production function was used to formulate the appropriate model that relates oil prices with the economy of Nigeria. However, the close to close (standard deviation) volatility method was used to measure the amount of variability in oil prices. Nevertheless, the perpetual inventory method was used to estimate the accumulated physical capital of Nigeria and the problems of multicollinearity inherent in the data were attenuated using ridge regression techniques as capital cannot be left out while dealing with production.

An Efficient Estimation Technique for investigating Economic Growth and its Determinants for Nigeria in the Presence of Multicollinearity

This study aimed to investigate economic growth (RGDP) and its determinants such as internal debt (INDT), external debt (EXDT), interest rate (RINR), exchange rate (REXR) and trade openness (OPEN). Quarterly data from 1986-2021 used for this investigation were extracted from Central Bank of Nigeria. Exploratory data analysis (EDA) revealed linear dependence of RGDP and the aforementioned determinants. The EDA and the variance inflation factor revealed the presence of multicollinearity caused by the INDT. As a result of this, the ridge regression method was used and it was found that the multicollinearity problem was addressed with appropriate ridge constant K= 0.29. Hence, ridge regression technique with constant K= 0.29 was a robust method to predict economic growth in Nigeria using the explanatory variables under consideration. Thus, served as a great benefit to the policy makers as the study provided a better understanding of the economic growth and the aforementioned determinants in terms of relationship and in particular the set back caused by EXDT on the economy of Nigeria. Also, the researchers benefited from this study as it engendered the understanding of the appropriate estimation technique to be used when the presence of multicollinearity was established in fitting a linear model.

A cross-country analysis of the role of service sector in the relationship between CO<SUB align="right">2 emissions and economic growth using machine learning techniques

The study aims to explore the relationship between CO2 emissions per capita, service sector share in GDP and GDP per capita using decision tree, and multiple ridge and lasso regression techniques on cross-sectional data of 175 countries. GDP per capita is a better determinant of the CO2 emissions of a country than the share of services in GDP. The fit between emissions and income improves on account of service sector share in GDP. The study finds that an increase in service sector share in high income countries leads to decrease in emissions while in low income countries it leads to an increase in emissions. An N-shaped relationship is found between CO2 emissions and income across the countries. Service sector share acts as a moderator in this relationship.

Towards Improving Predictive Statistical Learning Model Accuracy by Enhancing Learning Technique

The accuracy of the statistical learning model depends on the learning technique used which in turn depends on the dataset’s values. In most research studies, the existence of missing values (MVs) is a vital problem. In addition, any dataset with MVs cannot be used for further analysis or with any data driven tool especially when the percentage of MVs are high. In this paper, the authors propose a novel algorithm for dealing with MVs depending on the feature selection (FS) of similarity classifier with fuzzy entropy measure. The proposed algorithm imputes MVs in cumulative order. The candidate feature to be manipulated is selected using similarity classifier with Parkash’s fuzzy entropy measure. The predictive model to predict MVs within the candidate feature is the Bayesian Ridge Regression (BRR) technique. Furthermore, any imputed features will be incorporated within the BRR equation to impute the MVs in the next chosen incomplete feature. The proposed algorithm was compared against some practical state-of-the-art imputation methods by conducting an experiment on four medical datasets which were gathered from several databases repository with MVs generated from the three missingness mechanisms. The evaluation metrics of mean absolute error (MAE), root mean square error (RMSE) and coefficient of determination (R2 score) were used to measure the performance. The results exhibited that performance vary depending on the size of the dataset, amount of MVs and the missingness mechanism type. Moreover, compared to other methods, the results showed that the proposed method gives better accuracy and less error in most cases.

Regularization algorithm and its implementation in general systems theory and its implications in physics

In recent years a new physics known as complexity has emerged, whose fundamental structure is based on the general theory of systems or the so-called mathematical biology. There, problems arise with a large number of variables on which decisions must be made, and it is important to have selection techniques that are robust and efficient, In this research work, a couple of techniques are presented that will serve the aforementioned purpose; these are: the Lasso regularized linear regression technique and the Ridge regression technique, the latter with the slight difference in the penalty, since it makes use of a different rule than the Euclidean one, which implies very different consequences. One of these techniques was applied in a clinical problem and corresponds to the study of patients with diabetes. With this objective, the algorithm of descent of coordinates was implemented for the type of regularization loop and the analytical solution of regression of ridges. In addition, Kernels was used for the implementation of the regularized vector machine. The above algorithms were compared with the Ridge regularization and the Euclidean regularized vector machine. These types of techniques are common in physics, and it is vitally important to be able to implement other types of rules for a problem in which the choice of them can be simplified.