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The analysis of the high-dimensional dataset when the number of explanatory variables is greater than the observations using classical regression approaches is not applicable and the results may be misleading. In this research, we proposed to analyze such data by introducing modern and up-to-date techniques such as support vector regression, symmet...
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Power systems faces significant uncertainty during operation owing to the increased integration of renewable energy into power grids and the expansion of the scale of power systems, these factors lead to higher load-loss risks; therefore, realization of a fast online load-loss risk assessment is crucial to ensuring the operational safety and reliab...
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... An overall analysis of trade-offs between various algorithms, such as SVR, Lasso Regression, RR, LR, AdaBoost, GB, DT, RF, and XGBoost, based on complexity, feature handling, and interpretability is presented in Table 2 [27][28][29] . Simpler models can be more transparent, and require less processing power, but capture less intricate patterns in the data, whereas more complicated ensemble approaches capture more intricate patterns in the data at the expense of interpretability. ...
This research conducts a comparative analysis of nine Machine Learning (ML) models for temperature and humidity prediction in Photovoltaic (PV) environments. Using a dataset of 5,000 samples (80% for training, 20% for testing), the models—Support Vector Regression (SVR), Lasso Regression, Ridge Regression (RR), Linear Regression (LR), AdaBoost, Gradient Boosting (GB), Decision Tree (DT), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost)—were evaluated based on Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the Coefficient of Determination (R²). For temperature prediction, XGBoost demonstrated the best performance, achieving the lowest MAE of 1.544, the lowest RMSE of 1.242, and the highest R² of 0.947, indicating strong predictive accuracy. Conversely, SVR had the weakest performance with an MAE of 4.558 and an R² of 0.674. Similarly, for humidity prediction, XGBoost outperformed other models, achieving an MAE of 3.550, RMSE of 1.884, and R² of 0.744, while SVR exhibited the lowest predictive power with an R² of 0.253. This comprehensive study serves as a benchmark for the application of ML models to environmental prediction in PV systems, a research area that is relatively important. Notably, the results underscore the performance advantage of ensemble-based approaches, especially for XGBoost and RF compared to simpler, linear-based methods such as LR and SVR, when it comes to well-dispersed environmental interactions. The proposed machine-learning based power generation analysis approach shows significant improvements in predictive analytics capabilities for renewable energy systems, as well as a means for real-time monitoring and maintenance practices to improve PV performance and reliability.
... The SVR model demonstrates a poor fit, with data points widely scattered and an R² of just 0.085, indicating minimal predictive power. This poor performance may be due to suboptimal hyperparameter tuning or insufficient data, as SVR typically requires careful calibration to function effectively in high-variance datasets [18]. ...
Blasting operations in quarrying and mining require precise control over fragmentation to optimize downstream processes. Traditional fragmentation analysis methods often fail to capture all the variables impacting fragmentation. This study presents a soft-computing approach integrating Support Vector Regression (SVR), Random Forest (RF), Artificial Neural Networks (ANN), Linear Regression and the traditional Kuz-Ram models to predict characteristic particle size at D63, N, and fragmentation size-uniformity Index, Xc. Rock properties such as Uniaxial Compressive Strength (UCS), Young's Modulus, and Poisson's Ratio were utilized alongside blast design parameters like spacing, burden, drillhole diameter and drillhole length. Python, with libraries such as Pandas, Scikit-learn, Keras, and Numpy were used for the modelling, Model performances were evaluated using RMSE, RAE, and R², with RF demonstrating superior predictive capability in predicting both fragmentation index and characteristic particle sizes with R 2 score of 0.6 and 0.7, respectively. Whereas ANN performed the worst with R 2 score of-1549 and 142 for characteristic particle size and uniformity index, respectively, due to small dataset. The results highlight the potential of AI-driven models in optimizing blasting efficiency. I. INTRODUCTION In mining and quarrying activities, blasting operations play a pivotal role. It serves as the primary method for rock fragmentation. The efficiency of these operations influences downstream processes, including loading, hauling, and crushing, thereby affecting overall productivity and operational costs. A critical aspect of evaluating blasting performance is the fragmentation size-uniformity index, which quantifies the distribution uniformity of fragmented rock sizes [1]. Achieving optimal fragmentation enhances operational efficiency, while poor fragmentation can lead to increased costs and reduced productivity [2]. Traditionally, empirical models such as the Kuz-Ram model have been employed to predict rock fragmentation outcomes. The Kuz-Ram model integrates explosive properties, rock characteristics, and blast design parameters to estimate mean fragment size and size distribution [3]. However, this model has limitations, particularly in accounting for the inherent variability and complexity of geological formations, which can lead to inaccuracies in fragmentation predictions. In recent years, advancements in soft computing techniques have provided alternative approaches to modelling complex, nonlinear systems like rock blasting. Soft computing encompasses methodologies such as artificial neural networks (ANNs), support vector machines (SVMs), and gene expression programming (GEP), which can handle uncertainties and learning from data patterns [4]. These techniques have been applied to predict rock fragmentation, offering improved accuracy over traditional empirical models. For instance, [5] developed various novel soft computing models based on metaheuristic algorithms to predict rock size distribution in mine blasting. Their study demonstrated that these models could effectively capture the complex relationships between blasting parameters and fragmentation outcomes, leading to more reliable predictions. Similarly, [2] explored the development of soft computing-based mathematical models for predicting mean fragment size, coupled with Monte Carlo simulations. Their findings indicated that ANN models exhibited high predictive accuracy, underscoring the potential of soft computing techniques in enhancing fragmentation prediction.
... SVR is highlighted for its adeptness in handling complex relationships and competing effectively with other models [142]. Its proficiency in managing high-dimensional data and capturing intricate patterns contributes to accurate RUL predictions [156]. However, challenges, such as hyperparameter tuning and sensitivity to kernel function choices, prompt the need for careful consideration and further exploration [157][158][159][160]. ...
Lithium-ion batteries are central to contemporary energy storage systems, yet the precise estimation of critical states—state of charge (SOC), state of health (SOH), and remaining useful life (RUL)—remains a complex challenge under dynamic and varied conditions. Conventional methodologies often fail to meet the required adaptability and precision, leading to a growing emphasis on the application of machine learning (ML) techniques to enhance battery management systems (BMS). This review examines a decade of progress (2013–2024) in ML-based state estimation, meticulously analysing 58 pivotal publications selected from an initial corpus of 2414 studies. Unlike existing reviews, this work uniquely emphasizes the integration of novel frameworks such as Tiny Machine Learning (TinyML) and Scientific Machine Learning (SciML), which address critical limitations by offering resource-efficient and interpretable solutions. Through detailed comparative analyses, the review explores the strengths, weaknesses, and practical considerations of various ML methodologies, focusing on trade-offs in computational complexity, real-time implementation, and generalization across diverse datasets. Persistent barriers, including the absence of standardized datasets, stagnation in innovation, and scalability constraints, are identified alongside targeted recommendations. By synthesizing past advancements and proposing forward-thinking approaches, this review provides valuable insights and actionable strategies to drive the development of robust, scalable, and efficient energy storage technologies.
... Liu [12] combined the Stein estimator with the conventional ordinary ridge regression estimator to derive the Liu estimator, as described in [13][14][15]. Other alternative approaches to addressing the issue of multicollinearity can be found in the research papers [16][17][18][19]. ...
Outliers are a common problem in applied statistics, together with multicollinearity. In this paper, robust Liu estimators are introduced into a partially linear model to combat the presence of multicollinearity and outlier challenges when the error terms are not independent and some linear constraints are assumed to hold in the parameter space. The Liu estimator is used to address the multicollinearity, while robust methods are used to handle the outlier problem. In the literature on the Liu methodology, obtaining the best value for the biased parameter plays an important role in model prediction and is still an unsolved problem. In this regard, some robust estimators of the biased parameter are proposed based on the least trimmed squares (LTS) technique and its extensions using a semidefinite programming approach. Based on a set of observations with a sample size of n, and the integer trimming parameter h ≤ n, the LTS estimator computes the hyperplane that minimizes the sum of the lowest h squared residuals. Even though the LTS estimator is statistically more effective than the widely used least median squares (LMS) estimate, it is less complicated computationally than LMS. It is shown that the proposed robust extended Liu estimators perform better than classical estimators. As part of our proposal, using Monte Carlo simulation schemes and a real data example, the performance of robust Liu estimators is compared with that of classical ones in restricted partially linear models.
... Liu [16] combined the Stein type estimator with the conventional ordinary ridge regression estimator to derive the Liu estimator, as described in [17,18]. Other alternative approaches to addressing the issue of multicollinearity can be found in [19][20][21]. ...
Regression analysis frequently encounters two issues: multicollinearity among the explanatory variables, and the existence of outliers in the data set. Multicollinearity in the semiparametric regression model causes the variance of the ordinary least-squares estimator to become inflated. Furthermore, the existence of multicollinearity may lead to wide confidence intervals for the individual parameters and even produce estimates with wrong signs. On the other hand, as is often known, the ordinary least-squares estimator is extremely sensitive to outliers, and it may be completely corrupted by the existence of even a single outlier in the data. Due to such drawbacks of the least-squares method, a robust Liu estimator based on the least trimmed squares (LTS) method for the regression parameters is introduced under some linear restrictions on the whole parameter space of the linear part in a semiparametric model. Considering that the covariance matrix of the error terms is usually unknown in practice, the feasible forms of the proposed estimators are substituted, and their asymptotic distributional properties are derived. Moreover, necessary and sufficient conditions for the superiority of the Liu type estimators over their counterparts for choosing the biasing Liu parameter d are extracted. The performance of the feasible type of robust Liu estimators is compared with the classical ones in constrained semiparametric regression models using extensive Monte-Carlo simulation experiments and a real data example.
This review highlights the critical role of software sensors in advancing biosystem monitoring and control by addressing the unique challenges biological systems pose. Biosystems—from cellular interactions to ecological dynamics—are characterized by intrinsic nonlinearity, temporal variability, and uncertainty, posing significant challenges for traditional monitoring approaches. A critical challenge highlighted is that what is typically measurable may not align with what needs to be monitored. Software sensors offer a transformative approach by integrating hardware sensor data with advanced computational models, enabling the indirect estimation of hard-to-measure variables, such as stress indicators, health metrics in animals and humans, and key soil properties. This article outlines advancements in sensor technologies and their integration into model-based monitoring and control systems, leveraging the capabilities of Internet of Things (IoT) devices, wearables, remote sensing, and smart sensors. It provides an overview of common methodologies for designing software sensors, focusing on the modelling process. The discussion contrasts hypothetico-deductive (mechanistic) models with inductive (data-driven) models, illustrating the trade-offs between model accuracy and interpretability. Specific case studies are presented, showcasing software sensor applications such as the use of a Kalman filter in greenhouse control, the remote detection of soil organic matter, and sound recognition algorithms for the early detection of respiratory infections in animals. Key challenges in designing software sensors, including the complexity of biological systems, inherent temporal and individual variabilities, and the trade-offs between model simplicity and predictive performance, are also discussed. This review emphasizes the potential of software sensors to enhance decision-making and promote sustainability in agriculture, healthcare, and environmental monitoring.
