Particle Swarm Optimization Support Vector Research Articles

Research on Sustainable Form Design of NEV Vehicle Based on Particle Swarm Algorithm Optimized Support Vector Regression

With the growing emphasis on eco-friendly and sustainable development concepts, new energy vehicles (NEVs) have emerged as a popular alternative to traditional fuel vehicles (FVs). Due to the absence of an internal combustion engine, electric vehicles (EVs) do not require a front air intake grille, allowing for a more minimalist and flexible design. Consequently, aligning EV styling with users’ visual cognition and emotional perception is a critical objective for automakers and designers. In this study, we establish the mapping relationship between users’ emotional cognition and NEV styling design based on experimental data. We introduce Particle Swarm Optimization Support Vector Regression (PSO-SVR) into the perceptual engineering (KE) research process to predict user emotions using Support Vector Regression (SVR). To optimize the three hyperparameters (penalty coefficient C, RBF kernel function parameter γ, and insensitivity loss coefficient ε) of the SVR model, we utilize the Particle Swarm Optimization (PSO) algorithm. The results indicate that the proposed PSO-SVR model outperforms traditional SVR and BPNN models in predicting NEV user emotions. This model effectively captures the nonlinear relationship between battery electric vehicle (BEV) morphological features and users’ emotional cognition, providing a novel method for enhancing NEV design. The results of this research are expected to drive design innovation and technological advancement in the new energy vehicle industry, contributing to the achievement of the ambitious goal of global eco-friendliness and sustainable development.

Study on the Determination of Flavor Value of Rice Based on Grid Iterative Search Swarm Optimization Support Vector Machine Model and Hyperspectral Imaging.

In the field of rice processing and cultivation, it is crucial to adopt efficient, rapid and user-friendly techniques to detect the flavor values of various rice varieties. The conventional methods for flavor value assessment mainly rely on chemical analysis and technical evaluation, which not only deplete the rice resources but also incur significant time and labor costs. In this study, hyperspectral imaging technology was utilized in combination with an improved Particle Swarm Optimization Support Vector Machine (PSO-SVM) algorithm, i.e., the Grid Iterative Search Particle Swarm Optimization Support Vector Machine (GISPSO-SVM) algorithm, introducing a new non-destructive technique to determine the flavor value of rice. The method captures the hyperspectral feature data of different rice varieties through image acquisition, preprocessing and feature extraction, and then uses these features to train a model using an optimized machine learning algorithm. The results show that the introduction of GIS algorithms in a PSO-optimized SVM is very effective and can improve the parameter finding ability. In terms of flavor value prediction accuracy, the Principal Component Analysis (PCA) combined with the GISPSO-SVM algorithm achieved 96% accuracy, which was higher than the 93% of the Competitive Adaptive Weighted Sampling (CARS) algorithm. And the introduction of the GIS algorithm in different feature selection can improve the accuracy to different degrees. This novel approach helps to evaluate the flavor values of new rice varieties non-destructively and provides a new perspective for future rice flavor value detection methods.

Prediction of Deformation in Expansive Soil Landslides Utilizing AMPSO-SVR

A non-periodic “step-like” variation in displacement is exhibited owing to the repeated instability of expansive soil landslides. The dynamic prediction of deformation for expansive soil landslides has become a challenge in actual engineering for disaster prevention and mitigation. Therefore, a support vector regression prediction (AMPSO-SVR) model based on adaptive mutation particle swarm optimization is proposed, which is suitable for small samples of data. The shallow displacement is decomposed into a trend component and fluctuating component by complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and the trend displacement is predicted by cubic polynomial fitting. In this paper, the multiple disaster-inducing factors of expansive landslides and the time hysteresis effect between displacement and its influencing factors are fully considered, and the crucial influencing factors which eliminate the time lag effect and state factors are input into the model to predict the fluctuation displacement. Monitoring data in the Ningming area of China are employed for the model validation. The predicted results are compared with those of the traditional model. The model performance is evaluated through indicators such as the goodness of fit R2 and root mean square error RMSE. The results show that the prediction RMSE of the new model for three monitoring stations can reach 2.6 mm, 6.6 mm, and 2.5 mm, respectively. Compared with the common Grid search support vector regression (GS-SVR), the Particle Swarm Optimization Support Vector Regression (PSO-SVR) and Back Propagation Neural Network (BPNN) models have average improvements of 58.4%, 38.1%, and 25.2% respectively. The goodness of fit R2 is superior to 0.99 in the new method. The proposed model can effectively be deployed for the displacement prediction of non-periodic stepped expansive soil landslides driven by multiple influencing factors, providing a reference idea for the deformation prediction of expansive soil landslides.

Rapid and Non-Destructive Determination of the Protein and Fat Contents in Wheat by Hyperspectral Imaging Combined with AdaBoost-SVR Modeling

Wheat protein and fat contents directly affect the flavor and yield of liquor. This study utilized hyperspectral imaging (HSI) technology along with an integrated learning model to rapidly and non-destructively detect protein and fat contents in wheat. The hyperspectral data of the wheat grains was preprocessed using various methods. The competitive adaptive reweighted sampling (CARS) algorithm, the successive projections algorithm (SPA), and the combination of the two algorithms (CARS-SPA) were used to extract the characteristic wavelengths. Three support vector regression (SVR) models were created to forecast wheat protein and fat contents using full-band spectral data and characteristic wavelengths. These models are gray wolf optimized support vector regression (GWO-SVR), particle swarm optimized support vector regression (PSO-SVR), and support vector regression-based integrated learning (AdaBoost-SVR). Comparing the models showed that the AdaBoost-SVR model, utilizing the Eigen wavelengths extracted through the CARS-SPA and CARS algorithms, stood out as the most accurate in predicting wheat protein and fat contents, with Rp 2 values of 0.9789 and 0.9651, respectively. The study findings indicate that combining HSI with AdaBoost-SVR integrated learning modeling enables rapid, non-destructive determination of wheat protein and fat contents with greater accuracy than using a single machine learning (ML) model.

Simultaneous measurement of NH3 and NO by mid-infrared tunable diode laser absorption spectroscopy based on machine-learning algorithms

Accurate measurements of NH3 and NO are essential for controlling NOx emissions from coal-fired power plants, reducing ammonia slip, and improving the efficiency of selective catalytic reduction (SCR) operation. We propose a method for measuring NH3 and NO concentrations based on machine learning algorithms to address the challenges posed by overlapping interference of CO2 and H2O spectral lines in flue gas. Our approach introduces a novel method for acquiring mixture spectra for the model. Initially, we measure the spectra of individual components under different concentrations and temperatures. Subsequently, mixed spectral samples are generated by combining the measured spectra of the individual components. This approach simplifies the spectral measurement process while preserving accuracy. The particle swarm optimization support vector machine (PSO-SVM) algorithm is leveraged, providing a reliable foundation for the continuous and synchronous measurement of NH3 and NO. Upon testing, the PSO-SVM demonstrates average relative errors of 4.0 % and 0.8 % for NH3 and NO concentrations, respectively. The corresponding measurement precision is 0.05 ppm for NH3 and 0.42 ppm for NO, better than the conventional integral absorbance method. The minimum detection limit (MDL) for NH3 is 16.1 ppb at an average time of 50 s, while for NO, it is 38.4 ppb at an average time of 71 s. The methodology of this paper is expected to play an important role in reducing the influence of interfering components and improving the accuracy of field measurements.

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.

Acoustic tunnel lining cavity detection using cepstral coefficients with optimized filter bank

Tunnels are an essential component of modern transportation infrastructure, and their structural health is critical to traffic safety, which can be seriously affected by tunnel lining cavities. In this paper, an acoustic-based detection approach for assessing the integrity of tunnel linings is studied. By tapping the tunnel lining surface, acoustic signals are sampled and analyzed using a novel feature parameter extraction algorithm-the energy-frequency cepstral coefficient, which uses wavelet packet decomposition to obtain energy distribution statistics in the frequency domain of the signal, and constructs a signal-dependent filter bank to achieve the cepstral coefficient extraction. Compared with the traditional Mel filter bank, this method can adaptively adjust the resolution of the filter bank according to the frequency characteristics of the classified samples. This allows for higher frequency resolution in regions where the energy distribution is concentrated. As a result, the extracted feature parameters achieve both dimensional compression and superior information retention. Experimental results show that the proposed energy-frequency cepstral coefficient feature outperforms the traditional Mel-frequency cepstral coefficient feature, resulting in a higher accuracy of tunnel lining detection. The convolutional neural network model achieves an accuracy of 99.2%, with a 78.9% reduction in error rate compared with the traditional Mel-frequency cepstral coefficient feature parameters. Additionally, a particle swarm optimization support vector machine model is trained to achieve an accuracy rate of 99.6% and an error rate reduction of 76.5%.

Near-Infrared Off-Axis Cavity-Enhanced Optical Frequency Comb Spectroscopy for CO2/CO Dual-Gas Detection Assisted by Machine Learning.

Cavity-enhanced direct frequency comb spectroscopy (CE-DFCS) is widely used as a highly sensitive gas sensing technology in various gas detection fields. For the on-axis coupling incidence scheme, the detection accuracy and stability are seriously affected by the cavity-mode noise, and therefore, stable operation inevitably requires external electronic mode-locking and sweeping devices, substantially increasing system complexity. To address this issue, we propose off-axis cavity-enhanced optical frequency comb spectroscopy from both theoretical and experimental aspects, which is applied to the detection of single- and dual-gas of carbon monoxide (CO) and carbon dioxide (CO2) in the near-infrared. An erbium-doped fiber frequency comb with a repetition frequency of ∼41.709 MHz is coupled into a resonant cavity with a length of ∼360 mm in an off-axis manner, exciting numerous high-order modes to effectively suppress cavity-mode noise. The performance of multiple machine learning models is compared for the inversion of a single/dual gas concentration. A few absorbance spectra are collected to build a sample data set, which is then utilized for model training and learning. The results demonstrate that the Particle Swarm Optimization Support Vector Machine (PSO-SVM) model achieves the highest predictive accuracy for gas concentration and is ultimately applied to the detection system. Based on Allan deviation, the detection limit for CO in single-gas detection can reach 8.247 parts per million by volume (ppmv) by averaging 87 spectra. Meanwhile, for simultaneous CO2/CO measurement with highly overlapping absorbance spectra, the LoD can be reduced to 13.196 and 4.658 ppmv, respectively. The proposed optical gas sensing technique indicates the potential for the development of a field-deployable and intelligent sensor system capable of simultaneous detection of multiple gases.

Particle swarm optimization support vector machine-based coal and rock cutting tool load spectrum identification method

The goal of this research is to achieve safe and efficient excavation of coal and rock tunnels with complex geological structures, and to enhance the self-sensing ability of coal and rock cutting equipment and tools. Particle swarm optimization support vector machine is used to identify the cutting state of disc cutting tools. EDEM finite element analysis software is used to analyze cutting process characteristics of the disc cutting tool when used to cut through coal and rock with different compressive strengths. Empirical mode decomposition is used to decompose the load spectrum characteristics; for this purpose, the first-order and seventh-order intrinsic mode functions containing all the feature information of the original signal of the load spectrum are selected. The sample entropy is calculated as the feature input vector. The extracted feature vector is input into the trained support vector machine model and the particle swarm optimization support vector machine model. By extracting the sample entropy of the load spectrum of the disc cutter as the feature vector, the particle swarm optimization support vector model is used to identify the cutting state of the coal and rock. The recognition accuracy of the support vector machine model before and after the improvement is compared and analyzed. The results show that compared to the unoptimized support vector machine, the support vector machine optimized by particle swarm optimization can identify the load spectrum of the coal more quickly and accurately. The recognition accuracy is 96,82%, which verifies the effectiveness of the particle swarm optimization support vector machine model in identifying the load spectrum of the coal and rock disc cutter.

Coal structure identification based on geophysical logging data: Insights from Wavelet Transform (WT) and Particle Swarm Optimization Support Vector Machine (PSO-SVM) algorithms

Coal structure is closely related to microscopic and macroscopic properties of coal, and its accurate identification is of great significance to coalbed methane (CBM) reservoir evaluation, hydraulic fracturing performance and production efficiency prediction. The identification of coal structure based on geophysical logging data has become a popular topic as well as a challenge. The emerging Machine Learning (ML) provides convenience for this issue. Under this background, in the case of the Shanxi Formation No. 3 coal seam and the Taiyuan Formation No. 15 coal seam in Mabidong Block, Qinshui Basin, China, this paper proposed a new coal structure identification method based on geophysical logging data using Wavelet Transform (WT) and Particle Swarm Optimization Support Vector Machine (PSO-SVM) algorithms. Our results showed that the vertical resolution of logging data can be effectively improved when sym5 wavelet basis and third level decomposition were selected in wavelet decomposition, and 4.5 was selected as the weighting coefficient in wavelet reconstruction. The coal structure prediction model based on the logging data processed by WT was established by PSO-SVM algorithm, where PSO was used for parameter optimization (optimal penalty factor C and width parameter σ) and SVM with Radial Basis Function (RBF) kernel was used for model establishment. The Hold-out Cross Validation (HO CV) method was used to test the generalization ability of the prediction model, and the accuracy (ACC) of coal structure prediction in the training set and testing set was 94.26% and 88.46%, respectively. The prediction model was applied to identify the coal structure of two coring wells and the predicted coal structure class was consistent with the true coal structure class, confirming the validity of the model. The coal structure prediction results in the whole study area showed that the tectonic conditions control the coal structure. This work provides new insights for coal structure identification.

Forecast earthquake precursor in the Flores Sea

<span>Artificial intelligence (AI) can use seismic training data to discover relationships between inputs and outcomes in real-world applications. Despite this, particularly when using geographical data, it has not been used to predict earthquakes in the Flores Sea. The algorithm will read the seismic data as a pattern of iterations throughout the operation. The output data is created by grouping based on clusters using the most effective WCSS analysis, while the input features are derived from the original international resource information system (IRIS) web service data. Given that earthquake prediction is an effort to reduce seismic disasters, this research is essential. By generating predictions, it can reduce the devastation caused by earthquakes. Using the support vector machine (SVM), hyperparameter support vector machine (HP-SVM), and particle swarm optimization support vector machine (PSO-SVM) algorithms, this study seeks to forecast the Flores Sea earthquake. According to the estimation results, the SVM algorithm’s evaluation value is less precise than that of the HP-SVM, especially the linear HP-SVM and HP-SVM Polynomial models. However, the HP-SVM RBF model’s accuracy rating is identical to that of the traditional SVM model. <br /> The improvement of the PSO-SVM model, which has the finest gamma position and a value of 9.</span>

Penerapan Fitur Seleksi dan Particle Swarm Optimization pada Algoritma Support Vector Machine untuk Analisis Credit Scoring

After the Covid-19 pandemic, the banking sector faced significant challenges in contributing to achieving national goals in terms of increasing living standards and equalizing the regional economy. Hundreds of millions of low-income people have no credit or bank accounts because they do not have sufficient credit history to warrant the credit scores assigned to them. An estimated 1.7 billion people (31% of the adult population) do not have an account with a financial institution. People today are usually concentrated in developing countries, especially in China 204 million, India 357 million and Indonesia 102 million people. Because it is very difficult to make accurate predictions in determining credit worthiness for low-income people. Cooperatives are financial institutions that have a crucial role in channeling financing to members and the community to develop their businesses. An inappropriate credit distribution process can have a negative effect on KSP, resulting in frequent losses. This risk is known as problem loans, the cause is the KSP's failure to analyze the credit of prospective debtors. Therefore, calculations are needed to detect opportunities for credit risk default by prospective debtors objectively and precisely so that loan problems do not occur. Credit scoring is a method used to evaluate credit risk in terms of loan applications from consumers [4]. In this research we will provide a solution using classification techniques with feature selection methods in the Particle Swarm Optimization (PSO) Algorithm and Support Vector Machine (SVM) to predict the credit risk of prospective debtors failing to make loan payments. The application of the SVM algorithm in credit scoring research is because SVM is good at data classification. However, the standard SVM model still does not produce optimal results due to the difficulty of determining the best parameters, therefore researchers will optimize it with the Feature Selection and PSO algorithms to determine the best parameters. The results from the combination of several parameters using PSO-SVM obtained an accuracy of 87.23%, therefore the application of this method was proven to improve the performance of the SVM algorithm to increase its accuracy results in predicting the feasibility of granting credit.

Feature recognition of complex systems using cumulative residual Tsallis signal entropy and grey wolf optimized support vector machine

In this paper, the cumulative residual Tsallis singular entropy (CRTSE) is introduced to measure the complex characteristics of nonlinear signals. Firstly, we do singular value decomposition on time series, which can reduce the interference of noise on information extraction, and the singular values represent the information characteristics of the signal. Then the statistical distribution of the signal is described by the cumulative residual function of singular values, and the Tsallis entropy is calculated to quantify the complexity. We verify the effectiveness and robustness of CRTSE through simulation experiments. Finally, we propose a grey wolf optimized support vector machine based on CRTSE called CRTSE-GWOSVM to intelligently diagnose complex systems. The results show that CRTSE can effectively measure the complex characteristics of time series, GWOSVM is superior to SVM and particle swarm optimized support vector machine (PSOSVM) in data identification, and CRTSE-GWOSVM model can identify complex systems more effectively and accurately.

Landslide Susceptibility Evaluation of Bayesian Optimized CNN Gengma Seismic Zone Considering InSAR Deformation

Landslides are one of the most common geological disasters in China, characterized by suddenness and uncertainty. Traditional methods are not sufficient for the accurate identification, early warning, and forecasting of landslide disasters. As high-resolution remote sensing satellites and interferometric synthetic aperture radar (InSAR) surface deformation monitoring technology have been leaping forward, the traditional methods of landslide monitoring data sources are limited, and there have been few effective methods to excavate the characteristics of the spatial distribution of landslide hazards and their triggering factors, etc. In this study, an area extending 10 km from the VII isobar of the Gengma earthquake was taken as the study area, and 13 evaluation factors were screened out by integrating the factors of InSAR surface deformation, topography, and geological environment. Landslide susceptibility was evaluated through the Bayesian optimized convolutional neural network (BO-CNN), and the Bayesian optimized random forests (BO-RF) and particle swarm optimization support vector machines (PSO-SVM) models were selected for comparative analyses. The accuracy of the model was evaluated by using three indices, including the ROC curve, the AUC value, and the FR value. Specifically, the ROC curves of PSO-SVM, BO-RF, and BO-CNN were close to the upper-left corner, indicating excellent model performance. Moreover, the AUC values were computed as 0.9388, 0.9529, and 0.9535, respectively, and the FR value of landslides in the high susceptibility area of BO-CNN reached up to 14.9 and exceeded those of PSO-SVM and BO-RF, respectively. Furthermore, the mentioned values of the SVM and BO-RF models were 4.55 and 3.69 higher. The experimental results indicated that, compared with other models, the BO-CNN model used in this study had a better effect on landslide susceptibility evaluation, and the research results are of great significance to the disaster prevention and mitigation measures of local governments.

Distributed photovoltaic short‐term power forecasting using hybrid competitive particle swarm optimization support vector machines based on spatial correlation analysis

AbstractIn order to further improve the accuracy of distributed photovoltaic (DPV) power prediction, this paper proposes a support vector machine (SVM) model based on hybrid competitive particle swarm optimization (HCPSO) with consideration of spatial correlation (SC), for realizing short‐term PV power prediction tasks. Firstly, the spatial correlation analysis is conducted on the distributed PV stations. The k‐means clustering method based on morphological similarity distance improvement and mutual information function is used to select the best reference station and the best delay, which generates strongly correlated PV sequences. Then, a hybrid algorithm of particle swarm optimization (PSO) and sine cosine algorithm (SCA) in a competitive framework (HCPSO) is proposed, aiming to fuse the fast convergence capability of PSO algorithm with the global search capability of SCA algorithm, while enabling the algorithm to effectively handle high‐dimensional optimization problems based on a competitive mechanism. Finally, the HCPSO algorithm is combined with SVM algorithm, which expands the applicable scenarios of the SVM model and effectively improves the accuracy of PV short‐term prediction.

Working condition perception for froth flotation based on NSCT multiscale features

Visual features at the froth surface of a flotation cell are closely related to the process condition and performance. Accurate identification of flotation conditions is crucial. However, the multi-scale properties of froth images are not all extracted based on existing perception methods. Considering that multi-scale frequency domain analysis can obtain more informative statistical froth features, a new working condition recognition based on nonsubsampled contourlet transform (NSCT) multiscale features is proposed and successfully validated by industrial froth images taken under different process conditions. NSCT was used to enhance the froth image, including the decomposition and reconstruction, contrast-limited adaptive histogram equalization (CLAHE), and multi-scale features of the enhanced image are extracted and analyzed. The fuzzy binarization method is adopted for extracting the morphological feature. Texture feature parameters are extracted from other high-frequency images. The particle swarm optimization support vector machine is used to recognize the working condition. The results indicate that the proposed algorithms, in particular the multiscale features classification based on NSCT, have the highest classification accuracy (93.33 %), and can accurately and reliably identify the working condition in the actual froth images.

Application of graphene gas sensor technological convergence PSO-SVM in distribution transformer insulation condition monitoring and fault diagnosis

This study aims to improve the real-time monitoring and fault diagnosis of distribution transformers by utilizing a combination of five thin film gas detectors, these detectors include metal-modified graphene composite films and SnO2/RGO humidity sensors, which were prepared using the hydrothermal method. The experiment focused on investigating humidity and main fault characteristic gases that can reflect the insulation status of transformers. Additionally, a gas sensor array was constructed using a deep confidence neural network model. Based on the analysis of dissolved gas in transformer oil, the study extensively discusses the insulation fault diagnosis model and constructs the transformer fault diagnosis model using various methods including TRM, Particle swarm optimization support vector machine. The results demonstrated that the SnO2/RGO thin film humidity sensor exhibited high humidity sensitivity, and the other thin film gas sensors also exhibited good sensitivity. The average accuracy of the three classification methods mentioned is 80%, 92%, and 96%, respectively. These findings highlighted that the vector machine model not only improved the fault diagnosis accuracy but also possessed the characteristics of fewer parameters and a fast rate of convergence. Consequently, it effectively addressed the issue of early diagnosis of potential transformer faults. This study was of significant practical importance for ensuring the secure operation of the power grid.

Establishing a Berry Sensory Evaluation Model Based on Machine Learning.

In recent years, people's quality of life has increased, and the requirements for fruits have also become higher; blueberries are particularly popular because of their rich nutrients. In the blueberry industry chain, sensory evaluation is an important link in determining the quality of blueberries. Therefore, to make a more objective scientific evaluation of blueberry quality and reduce the influence of human factors, on the basis of traditional sensory evaluation methods, machine learning is introduced to establish a support vector regression prediction model optimized by the particle swarm algorithm. Ten physical and chemical flavor indices of blueberries (such as catalase, flavonoids, and soluble solids) were used as input data, and sensory evaluation scores were used as output data. Three different predictive models were applied and compared: a particle swarm optimization support vector machine, a convolutional neural network, and a long short-term memory network model. To ensure reliability, the experiments with each of the three models were repeated 20 times, and the mean of each index was calculated. The experimental results showed that the root mean square error and mean absolute error of the particle swarm optimization support vector machine were 0.45 and 0.40, respectively; these values were lower than those of the convolutional neural network (0.96 and 0.78, respectively) and the long short-term memory network (1.22 and 0.97, respectively). Hence, these results highlighted the superiority of the proposed model when sample data are limited.

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.

New cardiovascular disease prediction approach using support vector machine and quantum-behaved particle swarm optimization

Cardiovascular disease (CVD) is one of the leading causes of death worldwide. Early detection of CVD reduces the risk of a heart attack and increases the chance of recovery. The use of angiography to detect CVD is expensive and has negative side effects. In addition, existing CVD diagnostic methods usually achieve low detection rates and reach the best decision after many iterations with low convergence speeds. Therefore, a novel heart disease detection model based on the quantum-behaved particle swarm optimization (QPSO) algorithm and support vector machine (SVM) classification model, namely, QPSO-SVM, was proposed to analyze and predict heart disease risk. First, the data preprocessing was performed by transforming nominal data into numerical data and applying effective scaling techniques. Next, the SVM fitness equation is expressed as an optimization problem and solved using the QPSO to determine the optimal features. Finally, a self-adaptive threshold method for tuning the QPSO-SVM parameters is proposed, which permits it to drop into local minima, and balances between exploration and exploitation in the solution search space. The proposed model is applied to the Cleveland heart disease dataset and compared with state-of-the-art models. The experimental results show that the proposed QPSO-SVM model achieved the best heart-disease-prediction accuracies of 96.31% on the Cleveland heart data set. Furthermore, QPSO-SVM outperforms other state-of-the-art prediction models considered in this research in terms of sensitivity (96.13%), specificity (93.56%), precision (94.23%), and F1 score (0.95%).