Automated scaffolding safety analysis: strain feature investigation using support vector machines

Cancer is a threatening disease for the human race affecting most people around the world. The topmost reason for cancer demise across the globe is lung cancer and therefore there were many algorithms applied in predicting the survival rate of lung cancer patients. As a result of which the survival rate of lung cancer patients is increasing gradually. Support Vector Machine (SVM) technique have high accuracy than other technique in prediction. The performance of the SVM algorithm depends on the kernel function. In this paper, a comparison of three different kernel functions predicting the survival rate of a lung cancer patient with an efficient normalization technique is studied. Experiments are conducted in the dataset obtained from Cancer Imaging Archive (TCIA). Along with SVM kernel functions, five machine learning techniques were also used in predicting the survival rate of lung cancer. RBF_SVM with normalized data produced high accuracy of 97.72% compared to another algorithm. Various performance metrics such as accuracy, precision, recall, F1 score are used to evaluate the performance of the SVM kernel function.

The incorporation of prior knowledge into the Support Vector Machine (SVM) architecture is a problem which if solved can lead to much more accurate classifiers in the near future. This result could be particularly effective in the classification of remote sensing imagery, where an abundance of information is available prior to classification. The most evident method to feed prior knowledge into the SVM algorithm is through the SVM kernel function. This paper proposes several composite kernel functions designed specifically for land cover classification of remote sensing imagery. These kernels make use of the spectral signature information, inherently available in remote sensing imagery. The results achieved from these kernels are very much satisfactory and surpass all previous results produced by classical kernels.

Health monitoring and prognostics of axial piston pump is very helpful for the safety of aerial hydraulic system. Directing to the difficulties of measuring the wear loss between the valve plate and cylinder barrel, this paper presents a hybrid prediction method based on EMD (Empirical Mode Decomposition) and SVM (Support Vector Machine), in which EMD is used to get the pump's health state and PSO (Particle Swarm Optimization)-SVM is used to make a prediction for the pump's remaining useful lifetime. Oil-return flow was selected to indicate the wear condition of pump with several IMFs. SVM was trained by these IMFs, and then be used to predict the oil-return flow one-step ahead or multi-step ahead. In the SVM's training process, PSO was used to search the optimal parameter of SVM's kernel function. Applications show that hybrid prediction method has higher prediction precision and could be applied for the remaining useful lifetime prognostics.

The recognition of coal and gangue is the premise and foundation of coal gangue intelligent sorting. Adaptive boosting (AdaBoost) algorithm-based coal gangue identification has not been studied in depth. This paper proposed a coal gangue image recognition algorithm and a strong classifier based on the AdaBoost algorithm with a genetic algorithm (GA)-optimized support vector machine (SVM). One thousand coal gangue images were collected on-site and expanded to five thousand via rotation and exposure adjustment. The 12 gray-level gradient co-occurrence matrix texture features of the images were extracted to construct a feature vector, establishing the training dataset and test dataset. Selection of the SVM kernel function, the GA optimization parameter setting, and the base classifier number was discussed. The coal gangue image recognition effects of the AdaB-GA-SVM classifier and the other strong classifiers with different base SVM classifiers were investigated. The results indicated that the recognition accuracy of GA-SVM was the best when the kernel function of SVM was RBF and the population number, crossover probability, and mutation probability were 80, 0.9, and 0.005, respectively. The AdaB-GA-SVM classifier has excellent identification and effective classification performance with the highest accuracy of 95%, a precision rate of 92.8%, recall rate of 97.3%, and KS values of 0.79.

The excellent generalisation ability of the Support Vector Machine (SVM) algorithm has made it one of the most popular statistical learning theories in supervised machine learning. The classification accuracy and effectiveness of SVM is highly sensitive to the kernel function used during the training process. This paper compares linear, polynomial, and Gaussian kernel functions for evaluating their contribution to SVM for accurately and effectively classifying healthy and faulty status of rotating machinery. A three-phase induction motor and a four-stroke diesel engine were considered as the machinery for this study. Acoustic signals coming from these machines were collected using microphones and Fast Fourier Transform (FFT) was used to extract the magnitudes of the dominant frequency components of the signals. The extracted ominant frequency components are considered as acoustic signatures and their variations are taken as condition monitoring parameters. The results show that with the second-order polynomial kernel function, SVM achieved an accuracy of at least 2.4% greater than the other kernel functions with 1.2% less training time. Furthermore, the third-order polynomial kernel function found to be the second best choice.

Breast cancer is a highly prevalent malignant tumor among women worldwide, and early accurate diagnosis is crucial for improving prognosis. Traditional diagnostic methods have limitations such as high rates of misdiagnosis and missed diagnosis, while existing machine learning studies lack targeted strategies for screening nuclear morphological features. This study is the first to adopt Pearson correlation coefficient for targeted screening of breast cancer nuclear morphological features, retaining 25 features with an absolute correlation coefficient greater than 0.2 with diagnostic results; these features achieve a cumulative explained variance of 92.5% and accurately match the core pathological indicator of "nuclear pleomorphism". To address the class imbalance in the original dataset (569 cases, including 357 benign and 212 malignant cases), 143 synthetic samples were generated using the SMOTE algorithm, constructing a balanced dataset of 712 cases. After dividing the training and testing sets via 7:3 stratified sampling, a diagnostic model was built using a radial basis function (RBF) kernel support vector machine (SVM) and compared with a random forest model. Results showed that the SVM achieved an accuracy of 97.2%, precision of 98.1%, and recall of 96.3% on the testing set, with a 10-fold cross-validation average accuracy of 0.968 (with a fluctuation of 0.026); in contrast, the random forest model yielded corresponding indicators of 95.3%, 96.2%, 94.4%, and a 10-fold cross-validation average accuracy of 0.934 (with a fluctuation of 0.022), demonstrating that SVM significantly outperformed random forest. Additionally, the SVM achieved an AUC value of 0.968 on the ROC curve, showing excellent performance in the low false-positive range. Studies have shown that the SVM model based on features selected via the Pearson correlation coefficient exhibits significant advantages in breast cancer diagnosis. Compared with traditional diagnostic methods, such as mammography which has a misdiagnosis rate of 10%-15%, this model can reduce the risk of misdiagnosis by approximately 70%. Meanwhile, the model can effectively capture the clinical significance of microscopic nuclear features, not only providing a reliable tool for accurate breast cancer diagnosis but also promoting the in-depth integration of machine learning and pathological diagnosis.

Fast discrimination of rice varieties plays a key role in the rice processing industry and benefits the management of rice in the supermarket. In order to discriminate rice varieties in a fast and nondestructive way, hyperspectral technology and several classification algorithms were used in this study. The hyperspectral data of 250 rice samples of 5 varieties were obtained using FieldSpec®3 spectrometer. Multiplication Scatter Correction (MSC) was used to preprocess the raw spectra. Principal Component Analysis (PCA) was used to reduce the dimension of raw spectra. To investigate the influence of different linear and non-linear classification algorithms on the discrimination results, K-Nearest Neighbors (KNN), Support Vector Machine (SVM) and Least Square Support Vector Machine (LS-SVM) were used to develop the discrimination models respectively. Then the performances of these three multivariate classification methods were compared according to the discrimination accuracy. The number of Principal Components (PCs) and K parameter of KNN, kernel function of SVM or LS-SVM, were optimized by cross-validation in corresponding models. One hundred and twenty five rice samples (25 of each variety) were chosen as calibration set and the remaining 125 rice samples were prediction set. The experiment results showed that, the optimal PCs was 8 and the cross-validation accuracy of KNN (K = 2), SVM, LS-SVM were 94.4, 96.8 and 100%, respectively, while the prediction accuracy of KNN (K = 2), SVM, LS-SVM were 89.6, 93.6 and 100%, respectively. The results indicated that LS-SVM performed the best in the discrimination of rice varieties.

In the classification of hyperspectral images with supervised methods, acquisition of ground-truth information for a hyperspectral image is a challenging process in terms of time and cost. Besides, amount of the labeled data also affects the performance of classifiers. In this study, as a solution to this problem, a hyperspectral image classifier is proposed with semi-supervised learning, support vector machine classifier and deep learning. In the first phase to improve the classification performance, limited number of training data is increased by semisupervised learning methodology. Then, the classification process is performed with support vector machines and convolutional neural networks. According to the acquired classification results, a close classification performance is obtained by the system with small number of training data to the supervised classification. Furthermore, deep neural network has reached more successful results than support vector machines.

In this study, a novel modular-type Support Vector Machine (SVM) is presented to simulate rainfall prediction. First of all, a bagging sampling technique is used to generate different training sets. Secondly, different kernel function of SVM with different parameters, i.e., base models, are then trained to formulate different regression based on the different training sets. Thirdly, the Partial Least Square (PLS) technology is used to select choose the appropriate number of SVR combination members. Finally, a v-SVM can be produced by learning from all base models. The technique will be implemented to forecast monthly rainfall in the Guangxi, China. Empirical results show that the prediction by using the SVM combination model is generally better than those obtained using other models presented in this study in terms of the same evaluation measurements. Our findings reveal that the nonlinear ensemble model proposed here can be used as an alternative forecasting tool for a Meteorological application in achieving greater forecasting accuracy and improving prediction quality further.

Optical image provides a unique spectral and textural opportunity to acquire data free of charge in cloud-free weather conditions. However, textural information from recent Sentinel-2A data has not been sufficiently recognized. Several researches have shown that texture features are essential for extracting crop information. This study presents a new scheme that integrates spectral bands and textural features for mapping maize fields and other land cover types (i.e., build-up, trees, others, and water) and evaluates the effectiveness of random forest (RF) and support vector machine (SVM) classifiers with different kernel functions based on Sentinel 2A (S-2A) data. The textural features were derived from multiple bands of S-2A image using a grey-level co-occurrence matrix (GLCM) algorithm. To normalize the analysis of these features, Random Forest feature selection was employed. The outcomes show that S-2A images are appropriate for mapping maize fields and other land covers when the image is accessible during the growing season. The SVM classifier with a radial basis function (RBF) kernel is superior to a polynomial (P), linear (L), and sigmoid (S) kernel functions, and the RF classifiers. For the SVM_RBF, the highest overall accuracy was obtained from the integration of spectral bands and textural features at scenario 2 SVM_RBF (87.40%), followed by the combination of spectral and textural features at scenario 2 SVM_P kernel (86.56%). However, scenario 5 SVM_P kernel (85.54%), scenario 4 SVM_P kernel function (85.30%), the spectral features alone, and the SVM_P kernel function, have the highest O/A (83.90%) than that of the SVM_RBF (83.66%). The inclusion of textural features contributed significantly in mapping the smallholder’s maize field in the study area. This demonstrated the value of S-2A in agricultural research.

In order to predict the anti-trypanosome effect of carbazole-derived compounds by quantitative structure-activity relationship, five models were established by the linear method, random forest, radial basis kernel function support vector machine, linear combination mix-kernel function support vector machine, and nonlinear combination mix-kernel function support vector machine (NLMIX-SVM). The heuristic method and optimized CatBoost were used to select two different key descriptor sets for building linear and nonlinear models, respectively. Hyperparameters in all nonlinear models were optimized by comprehensive learning particle swarm optimization with low complexity and fast convergence. Furthermore, the models' robustness and reliability underwent rigorous assessment using fivefold and leave-one-out cross-validation, y-randomization, and statistics including concordance correlation coefficient (CCC), [Formula: see text] , [Formula: see text] , and [Formula: see text] . Among all the models, the NLMIX-SVM model, which was established by support vector regression using a nonlinear combination of radial basis kernel function, sigmoid kernel function, and linear kernel function as a new kernel function, demonstrated excellent learning and generalization abilities as well as robustness: [Formula: see text] = 0.9581, mean square error (MSE) = 0.0199 for the training set and [Formula: see text] = 0.9528, MSE = 0.0174 for the test set. [Formula: see text] , [Formula: see text] , CCC, [Formula: see text] , [Formula: see text], and [Formula: see text] are 0.9539, 0.8908, 0.9752, 0.9529, 0.9528, and 0.9633, respectively. The NLMIX-SVM method proved to be a promising way in quantitative structure-activity relationship research. In addition, molecular docking experiments were conducted to analyze the properties of new derivatives, and a new potential candidate drug molecule was ultimately found. In summary, this study will provide help for the design and screening of novel anti-trypanosome drugs.

The types of kernel function and relevant parameters’ selection in support vector machine (SVM) have a major impact on the performance of the classifier. In order to improve the accuracy and generalization ability of the model, we used mixed kernel function SVM classification algorithm based on the information entropy particle swarm optimization (PSO): on the one hand, the generalization ability of classifier is effectively enhanced by constructing a mixed kernel function with global kernel function and local kernel function; on the other hand, the accuracy of classification is improved through optimization for related kernel parameters based on information entropy PSO. Compared with PSO-RBF kernel and PSO-mixed kernel, the improved PSO-mixed kernel SVM can effectively improve the classification accuracy through the classification experiment on biomedical datasets, which would not only prove the efficiency of this algorithm, but also show that the algorithm has good practical application value in biomedicine prediction.

Short-Term Load Forecasting Based on Gaussian Wavelet SVM

This study deals with the linear, polynomial, and radial basis function (RBF) kernel-based support vector machines (SVM) applied to the classification of breast cancer. It checks the efficiency of different kernel-based techniques for distinguishing benign and malignant tumors against medical datasets. This analysis is conducted on the public Wisconsin Breast Cancer Dataset borrowed from the UCI Machine Learning Repository, which consists of 569 cases, 357 benign and 212 malignant. It aims to develop the assessment necessary for each Kernel based on its accuracy, precision, recall, computational efficiency, F1 score, and area under the curve (AUC). The outcome reveals that, in general, the polynomial Kernel will provide top-ranking results across the various features, thereby enhancing accuracy (96.84%), specificity (97.76%), and AUC (0.9966) compared to the linear and RBF kernels for use in disease classifications of cancers. These findings indicate that the polynomial kernel adeptly captures complex, non-linear data relationships, making it a strong candidate for developing more accurate SVM-based breast cancer detection models.

The problem with development of the support vector machine (SVM) classifiers using modified particle swarm optimization (PSO) algorithm and their ensembles has been considered. Solving this problem would allow fulfilling the high-precision data classification, especially Big Data classification, with the acceptable time expenditures. The modified PSO algorithm conducts a simultaneous search of the type of kernel functions, the parameters of the kernel function and the value of the regularization parameter for the SVM classifier. The idea of particles' «regeneration» served as the basis for the modified PSO algorithm. In the implementation of this algorithm, some particles change the type of their kernel function to the one which corresponds to the particle with the best value of the classification accuracy. The offered PSO algorithm allows reducing the time expenditures for the developed SVM classifiers, which is very important for Big Data classification problem. In most cases such SVM classifier provides the high quality of data classification. In exceptional cases the SVM ensembles based on the decorrelation maximization algorithm for the different strategies of the decision-making on the data classification and the majority vote rule can be used. Also, the two-level SVM classifier has been offered. This classifier works as the group of the SVM classifiers at the first level and as the SVM classifier on the base of the modified PSO algorithm at the second level. The results of experimental studies confirm the efficiency of the offered approaches for Big Data classification.