Optimized Support Vector Regression Research Articles

Secure Aware Optimized Support Vector Regression Models Based Host Overload Detection in Cloud

Secure Aware Optimized Support Vector Regression Models Based Host Overload Detection in Cloud

A framework for estimating the matric suction in unsaturated soils using multiple artificial intelligence techniques

AbstractImplementation of the state‐of‐the‐art understanding of the mechanics of unsaturated soils into geotechnical engineering practice is partly limited due to the lack of quick, reliable, and economical techniques for matric suction measurement. Matric suction is one of the key stress state variables that significantly influences the hydro‐mechanical behavior of unsaturated soils. In this paper, to address this objective, two artificial intelligence (AI) models were developed for estimating matric suction in unsaturated soils based on the particle swarm optimization support vector regression (PSO‐SVR) and multivariate adaptive regression spline (MARS) algorithms. The results suggest that both these models can reasonably estimate matric suction. Compared to the MARS model, the PSO‐SVR model can achieve higher accuracy. Nonetheless, the MARS model facilitates the sensitivity analysis and the selection of essential inputs. A novel integrated framework is proposed and validated, leveraging the strengths, and alleviating the limitations of the PSO‐SVR and MARS algorithms for reliable and rapid estimation of matric suction in the range of 0–1500 kPa for low plastic soils (0 < Ip ≤ 7). Six inputs are required to use this model successfully; some can be measured using conventional laboratory tests, and others can be calculated from mass‐volume relationships.

A Crude Oil Spot Price Forecasting Method Incorporating Quadratic Decomposition and Residual Forecasting

The world economy is affected by fluctuations in the price of crude oil, making precise and effective forecasting of crude oil prices essential. In this study, we propose a combined forecasting scheme, which combines a quadratic decomposition and optimized support vector regression (SVR). In the decomposition part, the original crude oil price series are first decomposed using empirical modal decomposition (CEEMDAN), and then the residuals of the first decomposition (RES) are decomposed using variational modal decomposition (VMD). Additionally, this work proposes to optimize the support vector regression model (SVR) by the seagull optimization algorithm (SOA). Ultimately, the empirical investigation created the feature-variable system and predicted the filtered features. By computing evaluation indices like MAE, MSE, R2, and MAPE and validating using Brent and WTI crude oil spot, the prediction errors of the CEEMDAN -RES.-VMD -SOA-SVR combination prediction model presented in this paper are assessed and compared with those of the other twelve comparative models. The empirical evidence shows that the combination model being proposed in this paper outperforms the other related comparative models and improves the accuracy of the crude oil price forecasting model.

Optimal weight support vector regression ensemble with cluster-based subsampling for electricity price forecasting

Accurate prediction of short-term electricity price is the key to obtain economic benefit and also an important index of power system planning and management. Support vector regression (SVR) based ensemble works have gained remarkable achievements in terms of high accuracy and steady performance, but they are highly dependent on data representativeness and have a high computational complexity O (k * N 3) of data samples and parameter selection. To further improve the data representativeness and reduce its computational complexity, this paper develops a new approach to forecast electricity price via optimal weighted ensemble. In the model, the cluster-based subsampling algorithm is proposed to categorize the inputs being seasonally decomposed into several groups, and representative data are drawn from each group in a certain proportion to ensure that each subset trained with SVR has the same representativeness and features. Moreover, the optimal weighted combination method is presented to assign weights to the sub-SVRs to obtain the optimal support vector regression ensemble model (OWSSVRE). The experimental results show that the improved support vector regression ensemble model with the same features and representativeness of the subset has better performance in electricity price forecasting. As a result, it is suitable to support decision making in the energy and other sectors.

Abstract 4892: MicroRNA signature and telomere genes in kidney cancer survival

Abstract Introduction: Kidney cancer ranks among the top 10 most prevalent cancers in the United States, clear cell renal cell carcinoma (ccRCC) has the worst disease specific survival histology. MicroRNAs (miRNAs) have emerged as potential prognostic indicators in various cancers, including kidney cancers. Telomerase, a biomarker associated with uncontrolled cancer cell growth, plays a crucial role in the human kidney cortex's telomere shortening over time. However, the link between miRNAs and telomeres in kidney cancer survival remains underexplored. Our study aims to uncover a miRNA signature capable of predicting 5-year survival in ccRCC patients and to investigate the biological relevance of this signature, particularly its shared pathway enrichment mechanisms with telomere genes. Methods: We extracted miRNA expression profiles from The Cancer Genome Atlas database, comprising 166 patients with ccRCC. To identify a robust miRNA signature predicting 5-year survival in ccRCC, we established an Evolutionary Kidney Survival Estimator (EKSE) using an optimal feature selection algorithm and support vector regression. Additionally, we conducted bioinformatics analyses to uncover common biological pathways between miRNAs and telomere genes using Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) categories, encompassing biological processes, molecular functions, and cellular components. Results: EKSE identified a robust signature consisting of 37 miRNAs, achieving a mean correlation coefficient and mean absolute error of 0.82±0.01 and 0.65±0.18, respectively between actual and estimated survival times. The top 10 ranked miRNAs in the signature, including hsa-miR-26a-1-3p, hsa-miR-28-5p, hsa-miR-3913-5p, hsa-miR-3170, hsa-miR-148a-5p, hsa-miR-671-3p, hsa-miR-224-3p, hsa-miR-10a-5p, hsa-miR-29b-1-5p, and hsa-miR-106b-5p, also showed promise in diagnosing ccRCC with AUC values ranging from 0.56 to 0.91. Furthermore, bioinformatics analysis revealed that the miRNA signature targeted well known telomere-associated genes such as TERT, DKC1, CTC1, and RTEL1. Notably, miRNA signature and telomere genes shared several common pathways, including cellular senescence (hsa04218) and proteoglycans in cancer (hsa05205). They also had overlapping GO-Molecular functions, such as DNA-binding transcription factor activity (GO:0003700), histone deacetylase binding (GO:0042826), and promoter-specific chromatin binding (GO:1990841). Conclusions: Our findings suggest an association between microRNAs and telomere genes, potentially playing significant roles in the survival of patients with ccRCC. The identified miRNA signature robustly predicts 5-year survival in ccRCC. The shared biological pathways between these miRNAs and telomere genes offer new insights into the mechanisms driving ccRCC, signaling potential for therapeutic targets and improved prognostic markers in kidney cancer. Citation Format: Srinivasulu Yerukala Sathipati, Rohit Sharma, Scott Hebbring. MicroRNA signature and telomere genes in kidney cancer survival [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4892.

Investigation of electrical resistivity for fiber-reinforced coral aggregate concrete

The application of fiber-reinforced coral aggregate concrete (FRCAC) holds importance in alleviating material scarcity for island and reef construction and in enhancing structural performance. Electrical resistivity, a nondestructive metric, can be employed to assess the comprehensive performance of FRCAC. Therefore, FRCAC was prepared by incorporating basalt fibers (BF), basalt fibers (MBF), and steel fibers (SF) into coral aggregate concrete in this study. The influence of fiber content, fiber addition form, curing time, and erosion time on the electrical resistivity of FRCAC (FRCAC-ER) was analyzed. Mechanistic analysis of the differences in FRCAC-ER was conducted by mercury intrusion porosimetry and scanning electron microscopy. A predictive model for FRCAC-ER was established using the multi-objective slime mold optimization support vector regression (MOSMA-SVR) algorithm. Results indicate that the optimal fiber content for single fiber incorporation is 0.05% for BF, 1.5% for MBF, and 1.5% for SF. The optimal content decreases to 1% when hybrid fibers (MBF+BF or SF+BF) are used. The microscopic analysis reveals that excessive fiber content increases porosity, reduces the proportion of harmless and less-harmful pores, and increases the proportion of more-harmful pores. Extending the curing time improves FRCAC-ER. At curing times of 3 and 7 days, FRCAC-ER reaches 70–75% and 80–85% of the 28-day resistivity, respectively. Seawater erosion reduces the erosion coefficient of FRCAC-ER, with a more pronounced decrease as the exposure time lengthens. The MOSMA-SVR predictive model for FRCAC-ER demonstrates superior predictive accuracy, model error distribution, and performance evaluation indicators compared with other models. The SF proportion and the water-binder ratio emerge as the most crucial ingredient parameters, exhibiting a negative correlation. A graphical user interface for FRCAC-ER has been developed to assist researchers in predicting and quantifying FRCAC-ER effectively. This research employs resistivity as a metric to gauge FRCAC performance and enhance its overall capabilities, thereby establishing a foundational framework for reef construction.

Bayesian optimization algorithm based support vector regression analysis for estimation of resilient modulus of crushed rock materials for pavement design

The precise determination of the resilient modulus (MR) of the base and sub-base materials is a major preoccupation and a key criterion in the flexible pavement design process. The experimental determination of MR implies a challenging process which requires usually very difficult test procedures and extreme precautions and manpower. This is why soft computing techniques are increasingly popular and of growing importance. Many prediction techniques based primarily on linear and non-linear regression could not provide flexible use and consistent prediction of MR for practical engineering. This article introduces a hybrid of the Bayesian optimization algorithm (BOA) and support vector regression (SVR) as a new modelling tool for the MR prediction of crushed stone materials used as base and sub-base layers for pavement design. For this purpose, an experimental database was utilized to generate the hybrid BOA-SVR model of indirect estimation of the resilient modulus based on material type, basic engineering characteristics and loading conditions. The database consists of 260 experimental datasets obtained from repeated loading triaxial tests performed by the laboratory of the Central Transportation Agency located in Algiers, Algeria. To develop the model, all hyperparameters were optimised using the BOA technique. It was found that the average, median, standard deviation, minimum, maximum and interquartile range of the expected values of the developed hybrid model are very close to the experimental results. Results revealed that the hybrid BOA-SVR model predict the MR of the crushed stone materials with a coefficient of determination of 99.91% and root mean squared error of 3.55. Comparisons with traditional and other Artificial intelligence models showed that BOASVR hybrid model predictions are more accurate and robust than those of other models.

Prediction of Ground Vibration Induced by Rock Blasting Based on Optimized Support Vector Regression Models

Prediction of Ground Vibration Induced by Rock Blasting Based on Optimized Support Vector Regression Models

Enhancing short-term wind power forecasting accuracy for reliable and safe integration into power systems: A gray relational analysis and optimized support vector regression machine approach

The reliability and safety of power systems heavily depend on accurate forecasting of new energy generation. However, the non-stationarity and randomness of new energy generation power increase forecasting difficulty. This paper aims to propose a short-term wind power forecasting method with strong characterization ability to accurately understand future new energy generation conditions so as to ensure power systems' reliability and safety. The required input variables for wind power forecasting are determined by the gray relational analysis method. An advanced marine predators algorithm is proposed by improving the marine predators algorithm to enhance convergence ability and probability of escaping local optimal solutions. The advanced marine predators algorithm optimizes support vector regression machine to address the issue of insufficient utilization of its forecasting performance due to the selection of parameter values based on personal experience in traditional methods. Finally, different wind power generation scenarios verify its effectiveness and universality. This study promotes the application of artificial intelligence technology for improving short-term wind power forecasting accuracy, thereby enhancing the reliability and safety level of power systems.

A method based on improved ant colony algorithm feature selection combined with GWO-SVR model for predicting chlorophyll-a concentration in Wuliangsu Lake.

Chlorophyll-a (Chl-a) is an important parameter in water bodies. Due to the complexity of optics in water bodies, it is difficult to accurately predict Chl-a concentrations in water bodies by current traditional methods. In this paper, using Sentinel-2 remote sensing images as the data source combined with measured data, taking Wuliangsu Lake as the study area, a new intelligent algorithm is proposed for prediction of Chl-a concentration, which uses the adaptive ant colony exhaustive optimization algorithm (A-ACEO) for feature selection and the gray wolf optimization algorithm (GWO) to optimize support vector regression (SVR) to achieve Chl-a concentration prediction. The ant colony optimization algorithm is improved to select remote sensing feature bands for Chl-a concentration by introducing relevant optimization strategies. The GWO-SVR model is built by optimizing SVR using GWO with the selected feature bands as input and comparing it with the traditional SVR model. The results show that the usage of feature bands selected by the presented A-ACEO algorithm as inputs can effectively reduce complexity and improve the prediction performance of the model, under the condition of the same model, which can provide valuable references for monitoring the Chl-a concentration in Wuliangsu Lake.

Remaining useful life prediction of lithium-ion batteries using EM-PF-SSA-SVR with gamma stochastic process

Due to the complex changes in physicochemical properties of lithium-ion batteries during the process from degradation to failure, it is difficult for methods based on physical or data-driven models to fully characterize this nonlinear process, and existing methods that hybridize physical and data-driven models suffer from ambiguous hybridization, which results in the vast majority of existing methods for predicting the remaining useful life (RUL) of lithium-ion batteries suffering from a lack of accuracy and robustness. In this study, a novel hybrid approach based on empirical modeling and data-driven techniques is proposed for predicting the RUL of lithium-ion batteries. To better capture its complexity, stochasticity, and state transition, and improve the modeling accuracy and RUL prediction precision, Gamma stochasticity and state-space modeling are used to empirically model the complex Li-ion battery degradation process. Moreover, the expectation maximization (EM) method of particle filtering (PF) was used to estimate the hidden parameters of the empirical model, and the estimated parameters were corrected using an optimized support vector regression (SVR) method to enhance the generalization performance and robustness of the data-driven model. The results show that the gamma state-space model is effective in capturing the inherent stochastic properties of the battery degradation and the proposed hybrid method outperforms the existing prediction methods in RUL prediction. The experiments show that the sparrow search algorithm (SSA) optimized SVR is considered to be the most effective correction method for the estimated parameters, while the new EM-PF-SSA-SVR hybrid method provides better performance for state assessment and RUL prediction of lithium-ion batteries. It is indicated that the proposed EM-PF-SSA-SVR method with Gamma stochastic process has hybrid validity and superior performance with equal performance and less parameter computation relative to the existing state-of-the-art deep learning RUL prediction methods.

A New Approach for Seepage Parameters Inversion Analysis Using Improved Whale Optimization Algorithm and Support Vector Regression

Seepage is the primary cause of dam failures. Conducting regular seepage analysis for dams can effectively prevent accidents from occurring. Accurate and rapid determination of seepage parameters is a prerequisite for seepage calculation in hydraulic engineering. The Whale Optimization Algorithm (WOA) was combined with Support Vector Regression (SVR) to invert the hydraulic conductivity. The good point set initialization method, a cosine-based nonlinear convergence factor, the Levy flight strategy, and the Quasi-oppositional learning strategy were employed to improve WOA. The effectiveness and practicality of Improved Whale Optimization Algorithm (IWOA) were evaluated via numerical experiments. As a case study, the seepage parameters of the Dono Dam located on the Baishui River in China were inversed, adopting the proposed inversion model. The calculated seepage field was reasonable, and the relative error between the simulated head and the measured value at each monitoring point was within 2%. This new inversion method is more feasible and accurate than the existing hydraulic conductivity estimation methods.

Leaf Area Index Inversion of Spartina alterniflora Using UAV Hyperspectral Data Based on Multiple Optimized Machine Learning Algorithms

The leaf area index (LAI) is an essential biophysical parameter for describing the vegetation canopy structure and predicting its growth and productivity. Using unmanned aerial vehicle (UAV) hyperspectral imagery to accurately estimate the LAI is of great significance for Spartina alterniflora (S. alterniflora) growth status monitoring. In this study, UAV hyperspectral imagery and the LAI of S. alterniflora during the flourishing growth period were acquired. The hyperspectral data were preprocessed with Savitzky–Golay (SG) smoothing, and the first derivative (FD) and the second derivative (SD) spectral transformations of the data were then carried out. Then, using the band combination index (BCI) method, the characteristic bands related to the LAI were extracted from the hyperspectral image data obtained with the UAV, and spectral indices (SIs) were constructed through the characteristic bands. Finally, three machine learning (ML) regression methods—optimized support vector regression (OSVR), optimized random forest regression (ORFR), and optimized extreme gradient boosting regression (OXGBoostR)—were used to establish LAI estimation models. The results showed the following: (1) the three ML methods accurately predicted the LAI, and the optimal model was provided by the ORFR method, with an R2 of 0.85, an RMSE of 0.19, and an RPD of 4.33; (2) the combination of FD SIs improved the model accuracy, with the R2 value improving by 41.7%; (3) the band combinations screened using the BCI method were mainly concentrated in the red and near-infrared bands; (4) the higher LAI was distributed on the seaward side of the study area, while the lower LAI was located at the junction between the S. alterniflora and the tidal flat. This study serves as both theoretical and technological support for research on the LAI of S. alterniflora and as a solid foundation for the use of UAV remote sensing technologies in the supervisory control of S. alterniflora.

An interval-valued carbon price forecasting method based on web search data and social media sentiment.

Accurate carbon price prediction is a crucial task for the carbon trading market. Previous studies have ignored the impact of online data and are limited to point predictions, which brings challenges to the accurate forecasting of carbon prices. To address those issues, this paper proposes an interval-valued carbon price forecasting method based on web search data and social media sentiment. First, we collect web search data and social media sentiment to improve prediction performance by synthesizing multiple types of data information. Second, we employ principal component analysis (PCA) to preprocess high-dimensional web search data, and utilize BosonNLP for quantifying social media information, thereby enhancing the predictability of the dataset. Subsequently, a variational mode decomposition (VMD) is applied to the carbon price and online data, followed by utilizing particle swarm optimization support vector regression (PSO-SVR) to predict each sub-modes and summing them up to obtain the ultimate forecasting outcome. Finally, using carbon prices in Guangdong and Hubei provinces as case studies, the experimental results demonstrate that web search data and social media sentiment significantly enhance the predictive accuracy of interval-valued carbon prices. Furthermore, the proposed VMD-PSO-SVR outperforms other comparative models in the accuracy and reliability of interval-valued forecasting.

Urban river ammonia nitrogen prediction model based on improved whale optimization support vector regression mixed synchronous compression wavelet transform

Accurate prediction of ammonia nitrogen (NH3–N) in water was crucial to efficient and reasonable judgment of water quality and environmental conditions. A model named SCWT-IWOA-SVR was proposed in this study by combining synchronous compressed wavelet transform (SCWT), improved whale optimization algorithm (IWOA) and support vector regression (SVR). Firstly, the SCWT algorithm was used to de-noise the original data, overcome the nonlinearity of the data, and explore the variation rule of the water quality index time series. Statistical methods and Pearson correlation analysis were then used to select features. IWOA was introduced to optimize the hyperparameters in SVR kernel function, tent mapping was used to randomly initialize the location of the whales, making them more evenly distributed, combined wheel selection mechanism was used to select more representative prey, which leading to improve the accuracy of forecasts results. The results indicated that the SCWT-IWOA-SVR model, which incorporates synchronous compression wavelet transform and the improved whale optimization algorithm, exhibit a better prediction effect compared with SVR, WOA-SVR, WOA-SVR, and Random Forest (RF) models. The results indicated that the model has a good prediction performance of low error and high generalization (RMSE = 0.055, MAE = 0.015, R2 = 0.989), the RMSE <0.2 and R2 > 0.915 of the proposed SCWT-IWOA-SVR model in three days advance predicted. SCWT-IWOA-SVR model can provide a scientific basis for the comprehensive treatment and decision-making of water pollutants in the watershed.

Support Vector Regression Model for Determining Optimal Parameters of HfAlO-Based Charge Trapping Memory Devices

The production and optimization of HfAlO-based charge trapping memory devices is central to our research. Current optimization methods, based largely on experimental experience, are tedious and time-consuming. We examine various fabrication parameters and use the resulting memory window data to train machine learning algorithms. An optimized Support Vector Regression model, processed using the Swarm algorithm, is applied for data prediction and process optimization. Our model achieves a MSE of 0.47, an R2 of 0.98856, and a recognition accuracy of 90.3% under cross-validation. The findings underscore the effectiveness of machine learning algorithms in non-volatile memory fabrication process optimization, enabling efficient parameter selection or outcome prediction.

Prediction using multi-objective slime mould algorithm optimized support vector regression model

Different from the traditional neural network models based on empirical risk minimization principle, support vector regression (SVR) minimizes the upper limit of generalization error by the structural risk minimization principle. SVR is suitable for solving nonlinear, small-sample, high-dimensional modelling, and pattern recognition problems. However, the selection of hyperparameters of SVR can significantly affect the prediction accuracy and computational time. This paper proposes a method to optimize the SVR hyperparameters through an intelligent optimization algorithm, specifically multi-objective slime mould algorithm (MOSMA). MOSMA is a new multi-objective optimization algorithm with strong global search ability and fast convergence. The principle and calculation process of MOSMA are first described in detail. The analytical method for transforming the SVR hyperparameter optimization problem into a multi-objective problem is then presented. The process of optimizing two SVR hyperparameters (the penalty coefficient and radial basis function kernel parameter) based on MOSMA is described in steps. Meanwhile, the procedures of algorithm parameter setting, fitness function design, and population update are given. To investigate the characteristics and performance of MOSMA-SVR, two datasets are used to predict the vibration trend of the spindle of CNC milling machine in one and the maximum bending normal stress in the cutting process in the other. The performance of MOSMA-SVR is evaluated by multiple statistical indexes and compared with seven other prediction models (GA-SVR, PSO-SVR, ABC-SVR, GWO-SVR, SSA-SVR, SMA-SVR and MOWOA-SVR).

Prediction of chloride ion concentration distribution in basalt-polypropylene fiber reinforced concrete based on optimized machine learning algorithm

The distribution characteristics of chloride ion concentration in basalt-polypropylene fiber reinforced concrete (BPFRC-CC) are important for assessing the durability of concrete structures under marine environment. This study collected the distribution data of BPFRC-CC in three different environments: tidal, submersible, and salt spray. Several optimized machine learning models were developed to predict the distribution of BPFRC-CC. The performance of these models was evaluated based on a comparison between actual and predicted values, residual distribution histograms, and performance indicators. The research results of this article showed that the particle swarm optimization support vector regression (PSO-SVR) model exhibits significant accuracy and generalization ability, with closer proximity to the actual values in both the training and testing sets. The residual distribution histogram of the PSO-SVR model approximated a normal distribution with a smaller mean and standard deviation. Evaluation indicators, including R2, MSE, RMSE, MAE, and MAPE, for the PSO-SVR model were 0.994, 6.92e-5, 0.0083, 0.0062, and 0.0453, respectively. The SHAP values revealed that depth and age were the most critical influencing factors. The decrease in age, water-cement ratio, and water-binder ratio, the addition of basalt fibers and polypropylene fibers, and the increase in depth reduced the BPFRC-CC. In addition, BPFRC-CC in tidal, salt spray, and submersible areas decreased in that order. Finally, a graphical user interface for BPFRC-CC was developed, providing researchers and production personnel with an intuitive, efficient, and visual production experience. This study has important implications for the optimization of BPFRC formulations and the prediction and assessment of the life of BPFRC structures.

Optimized support vector regression predicting treatment duration among tuberculosis patients in Malaysia

Optimized support vector regression predicting treatment duration among tuberculosis patients in Malaysia

Hybrid prediction model for cold load in large public buildings based on mean residual feedback and improved SVR

In order to control the energy efficiency of air conditioning systems and design energy management strategies, accurate and effective prediction of building cooling demands is a crucial step. We propose a time-granular residual feedback-based and improved sparrow algorithm for optimizing support vector regression models (TGRF-ISSA-SVR). To begin, select the input features using Random forest. Second, a method (TGRF) is proposed to calculate and feedback the mean residual with time granularities of hour, day, and month. Then, three strategies are used to improve the Sparrow Algorithm (ISSA) and apply it to SVR parameter optimization. Finally, the measured data of a large commercial building in Xi'an are used for experimental verification. The RMSE of TGRF-ISSA-SVR is 4.33 and the MAPE is 0.66, indicating that the model has low error. The RMSE and MAPE of TGRF-ISSA-SVR are 75% and 74% lower than those of TGRF-SVR. The RMSE and MAPE of TGRF-SVR are 37% and 38% lower than those of SVR, demonstrating that the improved model has accuracy improvement. In addition, compared with models such as LSTM and GRNN, the TGRF-ISSA-SVR model has higher prediction accuracy and shorter prediction time, so TGRF-ISSA-SVR can provide effective support for load prediction of large public buildings.