Linear and support vector regressions based on geometrical correlation of data

The present study quantitative structure-property relationship (QSPR) model developed for the adsorption efficiency (AE) of 70 pesticides in water sample on MWCNT/PbO2 solid phase extraction cartridge. Stepwise-multiple linear regression (SW-MLR) method employed for selection of descriptors. The selected descriptors are MATS7v, MATS6c, GATS3s, ATSC6i, C040, SpMin8_Bhi, E2v, JGI1 and Mor08u. Curiosity at the effective descriptors indicates electronic, topological and geometrical characteristics of studied pesticides are the most effective parameters on their AE on MWCNT/PbO2 nanocomposite adsorbent. Symbolic regression via genetic programming (SR-GP) utilized to offer the symbolic regression QSPR model. The accuracy and predictive power of the SR-GP model compared with traditional linear and nonlinear regression models contain multiple linear regression (MLR) and support vector regression (SVR). Inspection the fitness parameters confirmed the superiority of SR-GP model over MLR, and SVR models. In SR-GP model, the correlation coefficient (R) was 0.930 and 0.890, and the root mean square errors (RMSE) were 0.04 and 0.05 for the training and test sets, respectively. These results can be used to predict the AE for other pesticides by MWCNT/PbO2 adsorbent and designing a more efficient nano cartridge for SPE.

The absorption of capillary water is one of the most crucial factors in the flow of groundwater in rocks (CWA). Although meticulous experimental studies are needed to determine a rock’s CWA, predictive techniques might cut down on the expense and effort. There are various data mining methods for this purpose, but the considered algorithms in this study were not proposed so far for predicting the CWA. Different rock samples were taken for this purpose from various locations, yielding diverse rocks. For the prediction procedures, four support vector regression (SVR) models were created: a traditional SVR, two ensembled models, and a hybrid SVR model using the whale optimization technique (WOA - SVR). Results show that all models have acceptable performance in predicting the CWA with R2 larger than 0.797 and 0.806 for the training and testing data, respectively, representing the acceptable correlation between observed and predicted values. Regarding developed models, the conventional SVR model has the worst performance of all models. All statistical evaluation criteria were improved by assembling models, which present the ability of additive regression and bagging predictions in improving prediction processes. The hybrid WOA - SVR model has the best performance considering all indices. This hybrid model could also gain the lowest values of error indices between all SVR models, which leads to outperforming the WOA - SVR model compared to other methods.

Globally, buildings are responsible for an estimated 40% of energy consumption and 33% of CO2 emissions. In a bid to reduce CO2 emissions and hence, global warming, it has become necessary to ensure the energy efficient construction and operation of buildings. Understanding how a building utilises energy is a critical step to increase its efficiency. In this study, we leverage on an open-source data obtained from UCI data repository. Exploratory data analysis and feature engineering were used to eliminate non-contributing features while identifying key attributes of the data for model training. Linear Regression (LR) and Support Vector Regression (SVR) were employed as the machine learning techniques for the study. The models were trained using a repeated cross-validation technique. The models’ performance was evaluated on an independent data set segregated for testing. The LR model was trained with nine out of thirty-three features, while the Support Vector Regression (SVR) model used twenty-eight features for its training. The SVR model had a higher variance (0.48), accuracy (92.41%), and lower Mean Absolute Percentage Error (MAPE) of 7.59% compared to the LR model's variance of 0.26, accuracy of 91.87%, and MAPE of 8.13%. The SVR model was more accurate in predicting energy consumption, as it showed better accuracy on the test set with lower MAPE and higher R-squared value. Both models outperformed a relatively complex and computationally expensive model in a previous study. It also identified areas with high energy consumption which could be used to inform the building's energy management strategy.

Permeability prediction of isolated channel sands using machine learning

Modelling and investigating the impacts of climatic variables on ozone concentration in Malaysia using correlation analysis with random forest, decision tree regression, linear regression, and support vector regression

Augmented regression models using neurochaos learning

Processing experimental data with uncertainty is a huge challenge for robust regression modeling and high-accuracy data forecasting. The objective of the present study is the development of a robust support vector regression model for processing observed data of independent variables containing polyhedral perturbation and achievement of high forecasting precision. Firstly, the conception of the data collection of polyhedral perturbation is given and analyzed. Secondly, a novel robust support vector regression model is constructed by replacing the original independent variables in a linear support vector regression model with the independent variables containing polyhedral perturbation. Thirdly, the novel robust linear support vector regression method is also expanded to the non-linear regression model. Both the two models are validated by the linear and nonlinear numerical regression experiments. Comparison of the experimental results show that the proposed method provide more accurate predictions than the traditional regression method for processing data with polyhedral perturbation.

European Exchange Trading Funds Trading with Locally Weighted Support Vector Regression

Mental health disorders constitute a major global public health concern, affecting millions of individuals across diverse socioeconomic and cultural contexts. Accurate prediction of mental health outcomes at the population level remains challenging due to the complex and non-linear relationships among co-occurring disorders. Previous studies relying on traditional statistical approaches, particularly linear regression, have reported limited predictive performance, with an R² of approximately 0.7175. This limitation highlights the need for more advanced analytical frameworks capable of capturing comorbidity patterns and non-linear interactions among mental health conditions. This study proposes and evaluates a novel multi-modal ensemble machine learning framework to improve the prediction accuracy of eating disorder prevalence using global mental health data. The analysis utilizes country-level prevalence data for schizophrenia, depression, anxiety, bipolar disorder, and eating disorders across multiple countries and years. Eating disorder prevalence is modeled as the primary target variable, while other mental health disorders are incorporated as predictive features to represent clinically established comorbidity relationships. To enhance the representational capacity of the data, an extensive feature engineering strategy was applied, generating 19 additional features through polynomial transformations, interaction terms, ratio-based indicators, and aggregate burden measures. Unsupervised clustering techniques, including K-Means, DBSCAN, and hierarchical clustering, were employed to identify natural groupings of countries based on their mental health profiles. Furthermore, ten machine learning algorithms were systematically evaluated, including linear models, tree-based methods, neural networks, and support vector regression. The best-performing models were subsequently integrated into a stacking ensemble architecture. Experimental results demonstrate that the proposed stacking ensemble achieved a test R² score of 0.9955, corresponding to a 42.2% improvement over the baseline linear regression model. These results indicate that multi-modal ensemble approaches substantially enhance predictive accuracy and provide valuable insights to support evidence-based global mental health policy and targeted intervention planning.

Next generation cognitive radar/radio systems rely on dynamic spectrum access (DSA) to adaptively and ef- ficiently utilize the radio frequency (RF) spectrum. Such technology must detect, predict, and avoid channels occupied by RF interference. Conventional spectrum sensing methods may fail to determine signal occupancy states during transition periods. Predicting RF activity reduces the probability of interference during such transition periods and improves the overall efficiency of DSA schemes. This work employs a one-step ahead prediction approach to determine future busy or idle states through linear support vector regression (SVR). Supervised learning forecasts future signal energy which then acts as a decision statistic to determine occupancy in a sub-band of interest. The scheme’s prediction accuracy is evaluated with respect to input signal-to-noise ratio (SNR) and RF activity as a function of mean busy/idle time. Generalizing RF activity as an alternating renewal process allows exponential random variables to generate simulated data for SVR training and testing. The results show that this approach predicts RF activity with high accuracy over various signal traffic statistics and SNRs. Prediction accuracy is also evaluated with respect to the expected busy/idle transitions given activity statistics.

A hypothesis-driven method based on machine learning for neuroimaging data analysis

Electricity has become a basic need for some human beings because all activities are almost related to electricity. Indonesia has several power plant projects and the largest power plant is generated from PLTU which can have an impact that we feel is greenhouse gas emissions and bad air pollution and also relies heavily on coal while the natural resources are not renewable with this fact if we reduce the use of coal it will be a boomerang for Indonesia itself. This research aims to predict areas that can potentially become Solar Power Plants with a machine learning regression model approach. So hopefully this research can be a reference in the development of Solar Power Plants in Indonesia. The methods used are Linear Regression (LR), Lasso Regression (LR), Ridge Regression (RR), and Support Vector Regression (SVR). The R2 coefficients for solar radiation were 0.924; 0.910; 0.917; 0.949; and 0.987, respectively.

Linear support vector regression and partial least squares chemometric models for determination of Hydrochlorothiazide and Benazepril hydrochloride in presence of related impurities: A comparative study

The purpose of this study is to experimentally design the drying, calcination, and sintering processes of artificial lightweight aggregates through the orthogonal array, to expand the data using the results, and to model the manufacturing process of lightweight aggregates through machine-learning techniques. The experimental design of the process consisted of L18(3661), which means that 36 × 61 data can be obtained in 18 experiments using an orthogonal array design. After the experiment, the data were expanded to 486 instances and trained by several machine-learning techniques such as linear regression, random forest, and support vector regression (SVR). We evaluated the predictive performance of machine-learning models by comparing predicted and actual values. As a result, the SVR showed the best performance for predicting measured values. This model also worked well for predictions of untested cases.

Though India is a global leader in milk production, on the flip side, about 80% cattle belonging to indigenous and non-descript breeds are low yielders whose productivity needs to be improved by adopting appropriate breeding techniques and Artificial Insemination (AI) comes to this rescue. AI plays a vital role in improving the productivity of bovines by upgrading their genetic potential thereby enhancing the milk production and productivity in the country. Though milk production is influenced by a number of factors, the authors analyzed one of the revolutionary innovations in Indian dairy sector, the artificial insemination (AI) in bovines which was introduced in India in 1951-56. Hence a statistical approach to inspect the influence of artificial insemination as a factor behind the growth in milk production in India was undertaken. In this study, Linear Regression (LR) and Support Vector Regression (SVR) were utilized. LR was used to establish the linear relationship between variables and determine the role of AI in that relation. SVR is an eminent machine learning technique which works on the structural risk minimization principle to minimize the generalization error which leads to better prediction accuracy, whereas LR provides the value by which one can estimate that to what extent AI can show it's impact on milk yield. Empirical results noticeably reveal the positive impact of AI on milk yield using LR and better prediction accuracy of SVR as compared to LR for both in training and testing dataset.