Optimized Support Vector Machine Research Articles

Functional regionalization of land resources considering eco-efficiency in Nanjing Metropolitan Area, China

The rapid urbanization in the region has caused a shift in land-use structure and, as a result, the degrading of ecosystems. Elucidating the spatial–temporal evolution of ecosystem services (ESs) and ecological function zoning, it was beneficial to develop land use optimization strategy structures for different regions to support regional sustainable development. Taking the Nanjing Metropolitan Area (NMA), China, as a research area, we investigated the spatial–temporal evolution of land use and ESs in 2000, 2005, 2010, 2015, and 2020 by using biophysical models, quantified the trade-offs/ synergies and constraint effects among ESs using Spearman coefficients and constraint lines, and subsequently realized ecological function zoning through the Self-Organizing Feature Map (SOM) and Support Vector Machine (SVM) models on this basis. The results indicated that: (1) constructed land was the greatest area expansion type (an increase of 2,864.46 km2), while cropland was the primary source of its increase (89.69% of the total transfer of constructed land); (2) Habitat Quality (HQ), Landscape Aesthetics (LA), Net Primary Production (NPP), Soil Retention (SR), Nitrogen Retention (NR), and Water Yield (WY) showed a general spatial pattern of “high in the southern mountains and low in the northern plains”, whereas Crop Production (CP) was more susceptible to create trade-offs with other ESs and has four types of constraint effects; (3) to ensure the maximal spatial variation in geography condition, we combined the SOM-SVM to divide the NMA area into 6 ecosystem service zones and advised land development and utilization by zoning characteristics. In addition, we compared the outcomes of zoning before and after SVM optimization to demonstrate the suitability of combining the SOM-SVM approach in zoning. Our research can provide a reference for the sustainable management of regional land resources and landscape planning.

A machine-learning method to accurately recognize the leakage pressure-drop signals in trunk natural gas pipelines

A block valve installed along a trunk natural gas pipeline can automatically shut down when it detects a pressure-drop rate that is over a threshold value for a specified duration, indicating that leakage accidents have occurred. However, current methods have difficulties on identifying the differences among the pressure-drop rate signals caused by small-hole leakage, compressor startup, and block valve emergency shut-down conditions. This usually leads to improperly shutting down the block valve. Based on an optimized support vector machine, a machine-learning method is proposed to recognize the pressure-drop rate signals through the eigenvalue of the pressure-drop curve instead of only using the pressure-drop signal and its duration. Firstly, the singular decomposition method and the support vector machine method are applied to initially classify the pressure-drop rate signals of three operating conditions: the gas leakage, compressor startup, and block valve emergency shut-down conditions. Secondly, a teaching-learning-based optimization method is applied to optimize the penalty factor and kernel function parameter in the support vector machine model. Particularly, the tent chaotic maps and adaptive inertia weight methods are applied to improve the teaching-learning-based optimization method to balance its local and global search performance. Thirdly, an improved teaching-learning-based support vector machine method is established. Finally, 960 sets of simulated pressure-drop signals taken from three trunk natural gas pipelines and pressure-drop rate signals collected from an actual pipeline are applied to verify the accuracy of the proposed model. The results show that the improved teaching-learning-based support vector machine model achieved high classification and recognition accuracy. Specifically, it achieved accuracy rates of 99.4%, 100%, 98.3%, and 100% for the pressure-drop rate signals generated by pipelines A, B, C, and Cangzhou branch in the presence of pipeline leakage (the leak hole diameter is from 25 to 125 mm). Additionally, it demonstrated an average recognition accuracy of 97.42% for the pressure-drop rate signals generated by pipelines under other operating conditions. Through cooperating with Supervisory Control And Data Acquisition system, this method provides a more relevant approach to determine the shutdown conditions of a block valve while preventing mistaken actions.

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.

Swarm-intelligent elephant herding optimized support vector machine for predicting heart disease

Considering the significant worldwide mortality rate of cardiac disease, early and precise prognosis is essential to enhancing patient outcomes. The accuracy, effectiveness, and dependability of the heart disease prediction techniques currently in use, however, continue to encounter difficulties. Therefore, it is essential to create novel strategies that can get beyond these constraints and offer a better way to forecast cardiac disease. A novel advanced elephant herding optimised support vector machine (AEHO-SVM) technique utilized for the study in suggest method for predicting cardiovascular disease. A heart disease dataset is used to assess the efficacy of the suggested AEHO-SVM algorithm. To deal with missing values, normalise the data, and minimise dimensionality, the dataset is pre-processed. The AEHO-SVM approach aims to maximise classification accuracy while lowering the possibility of misdiagnosis by optimising the hyperplane separation and changing the support vectors iteratively. A number of metrics, like sensitivity, accuracy, f-measure, and specificity are used for assess model's performance. The experiment show that the AEHO-SVM technique works better than conventional methods and achieves good predictive accuracy for the diagnosis of heart disease. A suggested AEHO-SVM technique makes use of the collective thinking of elephant herding and its built-in optimisation skills to provide a more effective and trustworthy prediction method.

Bio-inspired swarm-intelligent with machine learning framework for prediction and classification of lung cancer

Early detection of lung cancer nodules, which is often dependent on a tomography scan filmic examination, greatly increases the probability of survival. Earlier tumor detection reduces lung cancer mortality by increasing the likelihood of successful therapy. Traditionally, radiologists have analyzed medical pictures for signs of lung cancer using a laborious and inaccurate systematic approach. The confidentiality and integrity of medical data have become a serious challenge for healthcare applications as a result of the tremendous improvement in the transmission of medical data in the healthcare sector. This research makes use of computational intelligence methods. In this research, a brand-new Enhanced vortex search algorithm optimized Support Vector Machine (EVSAO-SVM) is developed for detection and classification. Preprocessing using the Gaussian Filter, segmentation with Otsu thresholding, feature extraction with local binary patterns (LPB), and classification and prediction with the EVSAO-SVM are the processes that are simulated. Regarding performance criteria including accuracy, sensitivity, specificity, and F1-measure, this study indicates the suggested framework's superiority over the traditional approaches. The results of the tests conducted demonstrate that the suggested model can reach up to 95.42 percent sensitivity, 96.24 percent accuracy, 98.92 percent specificity, and 94.26 percent F1 measure.

Copy-Move Forgery Verification in Images Using Local Feature Extractors and Optimized Classifiers

Passive image forgery detection methods that identify forgeries without prior knowledge have become a key research focus. In copy-move forgery, the assailant intends to hide a portion of an image by pasting other portions of the same image. The detection of such manipulations in images has great demand in legal evidence, forensic investigation, and many other fields. The paper aims to present copy-move forgery detection algorithms with the help of advanced feature descriptors, such as local ternary pattern, local phase quantization, local Gabor binary pattern histogram sequence, Weber local descriptor, and local monotonic pattern, and classifiers such as optimized support vector machine and optimized NBC. The proposed algorithms can classify an image efficiently as either copy-move forged or authenticated, even if the test image is subjected to attacks such as JPEG compression, scaling, rotation, and brightness variation. CoMoFoD, CASIA, and MICC datasets and a combination of CoMoFoD and CASIA datasets images are used to quantify the performance of the proposed algorithms. The proposed algorithms are more efficient than state-of-the-art algorithms even though the suspected image is post-processed.

Multi-objective optimization of phase change cooling battery module based on optimal support vector machineoptimal support Vector Machine

An optimization model based on Particle Swarm Optimization-Support Vector Machine(PSO-SVM) and improved NSGA-II algorithm is established in this paper to balance the heat dissipation performance and lightweight of battery. First, the data set is obtained by orthogonal experiments, and then the PSO-SVM model for predicting the battery heat dissipation performance (maximum temperature and maximum temperature difference) is acquired by optimizing the parameters of the support vector machine through particle swarm optimization. With the goal of improving the battery heat dissipation performance and reducing the system mass, the improved NSGA-II algorithm is used for multi-objective optimization to obtain the Pareto solution set. Results show that the R2 (goodness of fit) of the maximum temperature and maximum temperature difference prediction models are 0.99478 and 0.9787, respectively, which verifies the accuracy of the PSO-SVM model. The maximum temperature, the maximum temperature difference, and the mass of the phase change material are in conflict. The temperature rise and temperature difference of the battery can be effectively controlled by sacrificing the mass of the phase change material. Four optimal solutions were selected by using LINMAP and TOPSIS. Compared with the initial model, the mass of phase change materials was reduced by 34.5 %, 29.5 %, 55.3 %, and 2.5 %. On the premise of meeting the requirements of thermal management performance, the entire system becomes lighter. Based on the four optimal solutions, the decision maker can choose from the Pareto optimal solution set according to different requirements for heat dissipation performance and lightweight.

Hybrid Feature-Learning-Based PSO-PCA Feature Engineering Approach for Blood Cancer Classification.

Acute lymphoblastic leukemia (ALL) is a lethal blood cancer that is characterized by an abnormal increased number of immature lymphocytes in the blood or bone marrow. For effective treatment of ALL, early assessment of the disease is essential. Manual examination of stained blood smear images is current practice for initially screening ALL. This practice is time-consuming and error-prone. In order to effectively diagnose ALL, numerous deep-learning-based computer vision systems have been developed for detecting ALL in blood peripheral images (BPIs). Such systems extract a huge number of image features and use them to perform the classification task. The extracted features may contain irrelevant or redundant features that could reduce classification accuracy and increase the running time of the classifier. Feature selection is considered an effective tool to mitigate the curse of the dimensionality problem and alleviate its corresponding shortcomings. One of the most effective dimensionality-reduction tools is principal component analysis (PCA), which maps input features into an orthogonal space and extracts the features that convey the highest variability from the data. Other feature selection approaches utilize evolutionary computation (EC) to search the feature space and localize optimal features. To profit from both feature selection approaches in improving the classification performance of ALL, in this study, a new hybrid deep-learning-based feature engineering approach is proposed. The introduced approach integrates the powerful capability of PCA and particle swarm optimization (PSO) approaches in selecting informative features from BPI mages with the power of pre-trained CNNs of feature extraction. Image features are first extracted through the feature-transfer capability of the GoogleNet convolutional neural network (CNN). PCA is utilized to generate a feature set of the principal components that covers 95% of the variability in the data. In parallel, bio-inspired particle swarm optimization is used to search for the optimal image features. The PCA and PSO-derived feature sets are then integrated to develop a hybrid set of features that are then used to train a Bayesian-based optimized support vector machine (SVM) and subspace discriminant ensemble-learning (SDEL) classifiers. The obtained results show improved classification performance for the ML classifiers trained by the proposed hybrid feature set over the original PCA, PSO, and all extracted feature sets for ALL multi-class classification. The Bayesian-optimized SVM trained with the proposed hybrid PCA-PSO feature set achieves the highest classification accuracy of 97.4%. The classification performance of the proposed feature engineering approach competes with the state of the art.

A novel interval decomposition correlation particle swarm optimization-extreme learning machine model for short-term and long-term water quality prediction

Water quality prediction plays a crucial role in pollution treatment. However, inaccurate long-term prediction resulting from complex information patterns and insufficient feature extraction may lead to unnecessary environmental costs. In this study, a novel Interval Decomposition Correlation Particle Swarm Optimization-Extreme Learning Machine (IDCPSO-ELM) model is proposed to improve long-term water quality prediction ability. We employ Multivariate Variational Mode Decomposition (MVMD), Variational Mode Decomposition (VMD), Sliding Correlation and Permutation Entropy to reconstruct features to reduce complexity. Seasonal and Trend decomposition using Loess-Empirical Wavelet Transform decomposition (STL-EWT) is applied to target variables to improve feature extraction ability. Particle Swarm Optimization-Extreme Learning Machine (PSO-ELM) is used to predict high-frequency components based on the reconstructive features, and Back Propagation Neural Network-Extreme Learning Machine (BPNN-ELM) is employed to predict the trend and lowest-frequency components. Grey Wolf Optimization Algorithm (GWO) is used to combine these results. Six stations with high potential pollution threats in the Haihe River basin of Beijing from 2021 to 2022 are taken into model competition. The results show that: (1) In short-term prediction, average MAPE, RMSE and NSE of IDCPSO-ELM model reach 0.0934, 0.0410 and 0.8061, respectively, which are better than Genetic Algorithm-Elman Network (GA-ELMAN), Cuckoo Search Algorithm-Back Propagation Neural Network (CS-BPNN), PSO-ELM and Sparrow Search Algorithm-Kernel Extreme Learning Machine (SSA-KELM) short-term prediction models. (2) In long-term prediction, average MAPE, RMSE and NSE of IDCPSO-ELM model reach 0.1114, 0.0420 and 0.8268, respectively, which are better than the Radial Basis Function Neural Network (RBFNN), Grey Wolf Optimization-Support Vector Machine Regression (GWO-SVR), Whale Optimization Algorithm-Deep Extreme Learning Machine (WOA-DELM), Wavelet Transform decomposition-GWO-SVR-BPNN (WT-GWO-SVR-BPNN) and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise-PSO-ELM-Long Short-Term Memory (CEEMDAN-PSO-ELM-LSTM) long-term prediction models. IDCPSO-ELM model is considered competitive and promising for long-term water quality prediction, particularly in areas with high potential pollution threats.

Implementation of novel pattern recognition approach using data mining for medical applications

The capacity of pattern recognition algorithms to analyze complicated data and retrieve useful information has attracted a lot of interest in the world of medical solutions. Data mining methods have become effective tools for pattern detection in the medical field, allowing for the extraction of insightful knowledge from huge and complex datasets. In this study, a diversified convergent squirrel search optimization algorithm and support vector machine (DCSSO-SVM) are combined to provide a unique pattern recognition method for medical applications. The DCSSO algorithm draws its inspiration from squirrels' efficient search-space exploration and exploitation during foraging. The suggested method integrates this algorithm with SVM to enhance feature selection, speed up convergence, and increase classification performance. To assess the effectiveness of the suggested DCSSO-SVM strategy, we assembled a dataset of medical images for this study. Other cutting-edge methods are contrasted with the suggested methodology. The experimental findings show that, regarding the accuracy, precision, f1-score, and recall metrics, the suggested DCSSO-SVM technique performs better than the current methods. It also has great toughness and support, making it appropriate for a variety of medicinal applications.

Comfort Study of General Aviation Pilot Seats Based on Improved Particle Swam Algorithm (IPSO) and Support Vector Machine Regression (SVR)

Little work has been carried out to predict the comfort of aircraft seats, a component in close contact with the human body during travel. In order to more accurately predict the nonlinear and complex relationship between subjective and objective evaluations of comfort, this paper proposes a prediction method based on the Improved Particle Swarm Algorithm (IPSO) and optimized Support Vector Machine Regression (SVR). Focusing on the problems of the too-fast convergence and low accuracy of the traditional particle swarm algorithm (PSO), the improved particle swarm algorithm (IPSO) is obtained by linearly decreasing the dynamic adjustments of inertia weight ω, self-learning factor c1, and social factor c2; then, the penalty parameter C and kernel function parameter σ of SVR are optimized by the IPSO algorithm, and the comfort prediction of IPSO-SVR is established. The prediction accuracy of IPSO-SVR was 94.00%, the root mean square error RMSE was 0.37, the mean absolute value error MAE was 0.32, and the goodness of fit R2 was 0.92. The results show that the optimized IPSO-SVR prediction model can more accurately predict seat comfort under different angles and backrest tilt angles and can provide reference and research value for related industries. The results show that the optimized nonlinear prediction model of IPSO-SVR has higher accuracy, and its prediction method is feasible and generalizable, meaning it can provide a reliable basis for the prediction of seat comfort under different angles and backrest inclinations, as well as providing reference and research value for related industries.

Spatiotemporal Characteristics Prediction and Driving Factors Analysis of NPP in Shanxi Province Covering the Period 2001–2020

The advent of a range of high-precision NPP products, including MODIS NPP, MOD17 NPP, and GIMMS NPP, has sparked growing interest in the study of Earth’s ecosystems. In order to enhance comprehension of ecosystem health, in order to facilitate the development of rational resource management and environmental conservation policies, this investigation employs the MOD17A3 dataset to analyze historical variations in Net Primary Productivity (NPP) within Shanxi Province from 2001 to 2020, while also exploring future trends. The Theil–Sen median trend analysis and Mann–Kendall test are commonly used methods for analyzing time series data, employed to study the spatiotemporal trends and variations in NPP. The Grey Wolf Optimization–Support Vector Machine (GWO–SVM) model combines optimization algorithms and machine learning methods, enhancing the predictive capacity of the model for future NPP time series changes. Conversely, the Hurst exponent utilizes historical NPP trends to assess the persistence characteristics of NPP and predict future spatial variations in NPP. This study additionally investigates the natural driving factors of NPP using the Geographic Detector approach. The key findings of this study are as follows. (1) Overall, NPP in Shanxi Province exhibits a fluctuating upward trend from 2001 to 2020, with an average value of 206.278 gCm−2a−1. Spatially, NPP exhibits a northwest–low and southeast–high pattern, with significant spatial heterogeneity and considerable variability. (2) The average Hurst exponent is 0.86, indicating a characteristic of strong persistence in growth in future NPP. Regions with strong or higher persistent growth account for 95.54% of the total area. (3) According to the CMIP6 climate scenarios, NPP is projected to gradually increase from 2025 to 2030. (4) The interactive effects between natural factors contribute more to NPP variations than individual factors, with the rainfall–elevation interaction having the highest contribution percentage.

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%).

Spatial Flood Forecasting Modeling under Lack of Data Using RS and Optimized Support Vector Machine: A Case Study of the Zahedan Watershed

This research was conducted with the aim of producing flood susceptible area maps of the Zahedan catchment in Iran during lack of data using remote sensing. To determine the factors to be included in the model, factors identified in previous studies were prepared using remote sensing and were evaluated by the information gain ratio (IGR) method and the multicollinearity diagnostic test, and ultimately 10 factors with the highest IGR values were chosen as the most effective factors in the region. The flood inventory map was produced by processing the Sentinel-1 satellite data. The gathered data were used to map flood susceptibility maps with the SVM model optimized with IWO and ACO. The prediction accuracy of the models was evaluated in terms of root mean square error (RMSE), mean absolute error (MAE), and the area under the receiver operating characteristic (ROC) curve (AUC). While both optimization algorithms are effective in improving the performance of SVM, the hybrid of SVM with IWO shows the best performance in terms of statistical measures and Friedman test results. The results of the study confirm the good performance of the proposed models in spatial prediction of flood susceptible areas. Since over half of the urban lands of the city of Zahedan are at moderate to very high risk of flooding, this area needs more attention in terms of flood prevention and control measures.

Prediction of fractional flow reserve with enhanced ant lion optimized support vector machine

The evaluation of coronary morphology provides important guidance for the treatment of coronary heart disease (CHD). A chaotic Gaussian mutation antlion optimizer algorithm (CGALO) is proposed in the paper, and it is combined with SVM to construct a classification prediction model for Fractional flow reserve (FFR). To overcome the limitations of the original antlion optimizer (ALO) algorithm, the chaotic Gaussian mutation strategy is introduced, which leads to an improvement in its convergence speed and accuracy. To evaluate the proposed algorithm's performance, comparative experiments were conducted on 23 benchmark functions alongside 12 other cutting-edge optimization algorithms. The experimental outcomes demonstrate that the proposed algorithm achieves superior convergence accuracy and speed compared to the alternative comparison algorithms. Additionally, it is combined with SVM and FS to construct a hierarchical FFR classification model, which is utilized to make effective predictions for 84 patients at the affiliated hospital of medical school, Ningbo university. The experimental results demonstrate that the proposed model achieves an average accuracy of 92%. Moreover, it concludes that smoking history, number of lesion vessels, lesion location, diffuse lesions and ST segment changes, and other factors are the most critical indicators for FFR. Therefore, the model that has been established is a new FFR intelligent classification prediction technology that can effectively assist doctors in making corresponding decisions and evaluation plans.

Future Climate Prediction Based on Support Vector Machine Optimization in Tianjin, China

Climate is closely related to human life, food security and ecosystems. Forecasting future climate provides important information for agricultural production, water resources management and so on. In this paper, historical climate data from 1962–2001 was used at three sites in Tianjin Baodi, Tianjin and Tanggu districts as baseline and the model parameters were calibrated by the Long Ashton Research Station Weather Generator (LARS-WG). 2m-temperatures in 2011–2020 were verified under two scenarios, representative concentration pathway (RCP) 4.5 and RCP8.5 in different atmospheric circulation models with optimal minimum 2m-temperatures at the three sites. From 2031–2050, Tianjin will be using more moderate minimum 2m-temperatures in future simulations. Support vector machines (SVM) were used to optimize the simulated data to obtain more accurate future maximum and minimum 2m-temperatures for the three sites. The results showed that the determinant coefficient of LARS-WG simulation was 0.8 and SVM optimized determinant coefficient was 0.9 which greatly improved the prediction accuracy. The minimum and maximum future 2m-temperatures optimized under European Community Earth System Model (EC-EARTH) were relatively low and the same future 2m-temperatures optimized under Hadley Centre Global Environment Model Earth System (Had-GEM2-ES4) were high especially in the RCP8.5 scenario which simulated 2051–2070 climate. The SVM optimization showed that the maximum and minimum 2m-temperatures were in general agreement with the original simulation values.

Prediction of bond strength of reinforced concrete structures based on feature selection and GWO-SVR model

Bond strength, as a mechanical property of reinforced concrete (RC) structures, is a crucial factor affecting the force characteristics of RC structures. In order to assess the load-bearing and deformation capacity of RC structures, it is essential to develop a model that can accurately predict the bond strength of RC structures. Therefore, a prediction model based on Random Forest (RF) feature selection and Grey Wolf algorithm optimized (GWO) support vector regression (GWO-SVR) is synthesized in this paper. An extensive database containing 1008 bonded slip test samples was first collected, and various feature parameters were finally preprocessed to filter 935 sets of test data. Next, the optimized support vector machine was trained using the training set data using the GWO-optimized SVR method. Meanwhile, the prediction accuracy of the model was evaluated using the integrated absolute error (IAE), the coefficient of determination (R2), and the mean absolute error (MAE). The model with a good prediction effect can predict the bond strength of unknown tests. When the sample size is small and the number of features is extensive, redundant features must be filtered out before the model prediction. The model prediction after feature selection is significantly better than that before training. The results show that the GWO-SVR model proposed in this study has a higher prediction accuracy than the existing bond strength model. The prediction results of the GWO-SVR model (Test set: R2 = 0.9547, MAE = 1.2487 MPa, IAE = 12.761%) outperform benchmark models such as the multilayer perceptron (MLP) and linear regression (LR). According to the training accuracy of the regression model, it can be found that the GWO-SVR model has good generalization performance (Training set: R2 = 0.9562, MAE = 1.1203 MPa, IAE = 12.182%). In addition, different optimization-seeking algorithms, kernel function types, and machine learning models have significant effects on the prediction results of the ultimate bond strength. In addition, the compressive strength of concrete was verified to be the most sensitive variable for the bond strength of RC structures using RF and Pearson correlation coefficients. The model presented in this study can also be extended to other regression issues.

Quantitative analysis of aflatoxin B1 of peanut by optimized support vector machine models based on near-infrared spectral features

This study designs a chemometric framework for quantitatively evaluating aflatoxin B1 (AFB1) in peanuts based on near-infrared (NIR) spectroscopy technique. The NIR spectra of peanut samples exhibiting diverse fungal contamination levels were acquired using a portable NIR spectrometer. Subsequently, appropriate pre-processing techniques were employed for data refinement. To streamline the analysis, the iterative variable subset optimization (IVSO) technique was employed to conduct an initial screening of the pre-processed NIR spectra, eliminating numerous irrelevant variables. Building upon this screening process, the beluga whale optimization (BWO) algorithm was utilized to optimize the selected feature variables further. Subsequently, support vector machine (SVM) models were developed using the refined near-infrared spectral features to test AFB1 in peanuts quantitatively. The results indicate that the SVM model significantly improves detection performance and generalization proficiency, particularly after secondary optimization using BWO-IVSO. Among the different models considered, the SVM model established after BWO-IVSO optimization exhibited the most extraordinary level of generalization, with a root mean square error of prediction of 24.6322 μg∙kg−1, a correlation coefficient of 0.9761, and a relative percent deviation of 4.6999. Overall, this investigation highlights the effectiveness of the proposed NIR spectroscopy model based on BWO-IVSO-SVM for quantitatively analyzing AFB1 in peanuts. The study contributes valuable technical and methodological insights that can serve as a reference for rapidly determining mycotoxins in cereal crops.

Multi-Classification of Electroencephalogram Epileptic Seizures Based on Robust Hybrid Feature Extraction Technique and Optimized Support Vector Machine Classifier

Multi-Classification of Electroencephalogram Epileptic Seizures Based on Robust Hybrid Feature Extraction Technique and Optimized Support Vector Machine Classifier

Analyst and machine learning opinions in fire debris analysis

The principles of subjective logic are applied to the competing propositions that ignitable liquid residue (ILR) is present, or is not present, in a fire debris sample. Analysts’ estimates of the strength of evidence coupled with their perceived levels of uncertainty combine to define a “fuzzy category” that is mapped to an opinion triangle. The opinion is expressed as a tuple consisting of the belief mass, disbelief mass, uncertainty and base rate. A workflow is introduced to guide the analyst through the fuzzy category formulation. Opinion tuples are also generated from a set of machine learning (ML) models trained on an ensemble of data sets. A set of 20 single-blind fire debris samples were analyzed by each of the authors, and by an ensemble of optimized support vector machine models. The opinions of each analyst and the ML ensemble were compared and combined to obtain an opinion representing a consensus of each analyst and the ML. The opinions of the analysts and ML were projected onto the zero-uncertainty axis and the projected opinion probabilities were used as scores to construct an receiver operating characteristic (ROC) curve. The area under the ROC curves for each analyst were greater than or equal to 0.90 and the area under the ML ROC curve was 0.96. The methodology is widely applicable to forensic problems that can be represented as a pair of mutually exclusive and exhaustive hypotheses.