Onboard classifiers for science event detection on a remote sensing spacecraft

In this Letter, the authors present a unified framework for fall event detection and classification using the cumulants extracted from the acceleration (ACC) signals acquired using a single waist-mounted triaxial accelerometer. The main objective of this Letter is to find suitable representative cumulants and classifiers in effectively detecting and classifying different types of fall and non-fall events. It was discovered that the first level of the proposed hierarchical decision tree algorithm implements fall detection using fifth-order cumulants and support vector machine (SVM) classifier. In the second level, the fall event classification algorithm uses the fifth-order cumulants and SVM. Finally, human activity classification is performed using the second-order cumulants and SVM. The detection and classification results are compared with those of the decision tree, naive Bayes, multilayer perceptron and SVM classifiers with different types of time-domain features including the second-, third-, fourth- and fifth-order cumulants and the signal magnitude vector and signal magnitude area. The experimental results demonstrate that the second- and fifth-order cumulant features and SVM classifier can achieve optimal detection and classification rates of above 95%, as well as the lowest false alarm rate of 1.03%.

Background and purposeRenal cell carcinoma (RCC) is a heterogeneous group of cancers. The collagen fiber content in the tumor microenvironment of renal cancer has an important role in tumor progression and prognosis. A radiomics model was developed from dual-energy CT iodine maps to assess collagen fiber content in the tumor microenvironment of ccRCC.MethodsA total of 87 patients with ccRCC admitted to our hospital were included in this retrospective study. Among them, 59 cases contained large amounts of collagen fibers and 28 cases contained a small amount of collagen fibers. We established a radiomics model using preoperative dual-energy CT scan Iodine map (IV) imaging to distinguish patients with multiple collagen fibers from those with few collagen fibers in the tumor microenvironment of ccRCC. We extracted features from dual-energy CT Iodine map images to evaluate the effects of six classifiers, namely k-nearest neighbor (KNN), support vector machine (SVM), extreme gradient boosting (XGBoost), random forest (RF), logistic regression (LR), and decision tree (DT). The effects of the models built based on the dynamic and venous phases are also compared. Model performance was evaluated using quintuple cross-validation and area under the receiver operating characteristic curve (AUC). In addition, a clinical model was developed to assess the clinical factors affecting collagen fiber content.ResultsCompared to KNN, SVM, and LR classifiers, RF, DT, and XGBoost classifiers trained with higher AUC values, with training sets of 0.997, 1.0, and 1.0, respectively. In the validation set, the highest AUC was found in the SVM classifier with a size of 0.722. In the comparative test of the active and intravenous phase models, the SVM classifier had the best effect with its validation set AUC of 0.698 and 0.741. In addition, there was a statistically significant effect of patient age and maximum tumor diameter on the collagen fiber content in the tumor microenvironment of kidney cancer.ConclusionRadionics features based on preoperative dual-energy CT IV can be used to predict the amount of collagen fibers in the tumor microenvironment of renal cancer. This study better informs clinical prognosis and patient management. Iodograms may add additional value to dual-energy CTs.

Recently the usage of voltage-sensitive devices around the world is growing rapidly. These devices are not ideal and may get affected by Power Quality (PQ) events such as Sag, Swell, Harmonics, and Interruptions, etc. In IoT applications, failure of these sensitive devices may cause serious damages. Existing methods classify the disturbances 90–97% accurately with a large feature set. To avoid complexity, this paper deals with the classification of PQ events with a set of wavelet decomposed & direct signal features such as Shannon entropy, mean energy, and total harmonic distortion (THD), etc. Machine Learning (ML) approaches such as Decision Tree (DT) and Support Vector Machine (SVM) classifiers are used for classification. By considering the different training rates, the performance analysis is carried out. Simulation results indicate the supremacy of the proposed DT & SVM classifiers when compared with the already existing methods.KeywordsPQ eventsWTDecision treesSVM classifiers

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.

Aim: The aim of the work is to evaluate the accuracy and precision in predicting heart disease using Support Vector Machine (SVM), Random forest (RF), Logistic Regression (LR), Decision Tree (DT) Classification algorithms. Materials and Methods: Classification algorithm is appealed on a heart dataset which consists of 180 records. A framework for heart disease prediction in the medical sector comparing Random forest, Logistic Regression, Decision Tree and SVM classifiers has been proposed and developed. The sample size was calculated as 55 in each group using G power 80%. Sample size was calculated using clincalc analysis, with alpha and beta values 0.05 and 0.5, 95% confidence, pretest power 80% and enrolment ratio 1. Results: The Novel Random Forest Algorithm (92.13%), Support Vector Machine (62.51%), Logistic Regression (84.89%), Decision Tree (86.25%) classifiers produce respectively. SVM, RF exists a statistically significant difference between the two groups (p=0.001,p=.004;p<0.05).LR, RF exists a statistically insignificant difference between the two groups (p=.103, P=.080;p>0.05) both with confidence interval 95%. Hence Random forest is better than SVM, RF, DT classifiers. Conclusion: The results show that the performance of RF is better when compared with SVM, LR and DT in terms of both precision and accuracy.

Using information technology such as search engine for collecting and monitoring of network public opinion is a practical and effective method. This paper puts forward an improved algorithm of SVM classifier based on incremental learning online, and then implements a topic crawler system for network public opinion and to grab the public opinion.

In order to improve the performance of Support Vector Machine (SVM) classifier for imbalanced data, this paper proposes dynamic cost-sensitive SVM classifier based on chaos particle swarm optimization (CPDC_SVM). Firstly, this paper introduces dynamic cost-sensitive thought to SVM classifier, and gives the method for structuring dynamic cost and cost-sensitive SVM model. Secondly, we propose the evaluation methodology performance for classifier, and adopts decimal base to code the particles. At last, chaos thought is introduced in particle swarm optimization algorithm, and the Algorithm of the dynamic cost-sensitive SVM classifier is given, which improves convergent speed and accuracy of particle swarm optimization, and can optimize dynamic cost-sensitive SVM well, so CPDC_SVM adds effectively the convergence speed and accuracy for the particle swarm optimization algorithm. Experimental results show CPDC_SVM has higher precision than traditional SVM classifier, and dynamic cost and chaos particle swarm optimization can improve the performance for classifier.

A boosted SVM classifier trained by incremental learning and decremental unlearning approach

BackgroundThis study aimed to screen the feature modules and characteristic genes related to ulcerative colitis (UC) and construct a support vector machine (SVM) classifier to distinguish UC patients.MethodsFour datasets that contained UC and control samples were obtained from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) with consistency were screened via the MetaDE method. The weighted gene coexpression network (WGCNA) was used to distinguish significant modules based on the four datasets. The protein–protein interaction network was established based on intersection genes. Enrichment analysis of Gene Ontology (GO) biological processes (BPs) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were established based on DAVID. An SVM combined with recursive feature elimination was also applied to construct a disease classifier for the disease diagnosis of UC patients. The efficacy of the SVM classifier was evaluated through receiver operating characteristic curves.ResultsTwelve highly preserved modules were obtained using the WGCNA, and 2009 DEGs with significant consistency were selected using the MetaDE method. Sixteen significantly related GO BPs and 12 KEGG pathways were obtained, such as cytokine-cytokine receptor interaction, cell adhesion molecules, and leukocyte transendothelial migration. Subsequently, 41 genes were used to construct an SVM classifier, such as CXCL1, CCR2, IL1B, and IL1A. The area under the curve (AUC) was 0.999 in the training dataset, whereas the AUC was 0.886, 0.790, and 0.819 in the validation set (GSE65114, GSE37283, and GSE36807, respectively).ConclusionsAn SVM classifier based on feature genes might correctly identify healthy people or UC patients.

The conventional method used in forest inventory is time-consuming, especially in field surveying and requires an expert’s knowledge and observation to do species identification. This standard method can be facilitated by advances in remote sensing technology. An airborne hyperspectral image mosaic was utilised in the research to evaluate the ability of this high spectral fidelity dataset that was incorporated into a feature extraction Support Vector Machine (SVM) classifier to successfully detect and map a tree species of interest (Shorea kunstleri King) within the mixed dipterocarp forest stand of the Lambir Hills National Park. In this study, two types of feature extraction Principal Component Analysis (PCA) and Spectral Derivative (SD) were applied to classifiers SVM. To demonstrate the effect, classification of the hyperspectral image with no feature extraction was done. The accuracies achieved from different feature extraction were compared statistically. Based on the results, Shorea kunstleri King was able to be discriminated against within 53 species of Shorea. SVM classifier is known for its ability to generalise well even with limited training samples and is commonly used in image classification. The Kernel parameter in SVM classifier alongside feature extraction significantly affects the classification accuracy. Therefore, feature extraction does affect the classification accuracy, but it depends on the morphology of the study area. It is concluded that the SVM method with SD feature extraction could detect tree crowns of Shorea kunstleri with a Kappa coefficient of 0.8126 which showed a great agreement with the observed samples and overall accuracy of 89.89% that shows the accuracy of the final map.

Introduction/Background*The most important prognostic factor for the survival of advanced-stage ovarian cancer is the completeness of cytoreductive surgery (CRS). Therefore, an intraoperative technique to detect microscopic tumours would be of...

This study investigated the effect of a new approach, the support vector machine, as a classifier tool to identify the weeds in corn fields at early growth stage. Image segmentation was done by transforming original color images to gray level images according to the statistical values of red, green, blue components. The Gray Level Co-occurrence Matrix (GLCM) and statistical properties of the histogram from the gray level images were further used to obtain the texture features of the weeds and corn seedlings. These texture features were used in the classification procedure. Principle component analysis was used to select the texture features according to their better contributions to reduce space dimensions. A Support Vector Machine (SVM) classifier was employed to recognize the weeds and the corn seedlings. The results indicated that the SVM classifiers with different feature selections could identify successfully weed-corn with a higher accuracy ranged from 92.31 to 100%. A comparison study of the recognition capabilities of SVM and back-propagation (BP) neural-network classifier using the same data set was conducted. It was found that the SVM classifier provided the best recognition performance with an accuracy of 100%, which exceeded the accuracy of 80% given by the BP classifier. Key words: Corn seedling, weeds, texture, support vector machine, recognize.

An improved SVM classifier based on double chains quantum genetic algorithm and its application in analogue circuit diagnosis

We perceive the digital watermark detection as classification problem in image processing. We classify watermarked images as positive class whilst unwatermarked images as negative class. Support Vector Machine (SVM) is used as classifier of the watermarked and unwatermarked digital images. Two watermarking schemes i.e. Cox's spread spectrum (SS) and Single Value Decomposition (SVD) are used to embed watermark into digital images. These algorithms are selected based on their different level of robustness to Stirmark attacks. The payload of the watermark used for both algorithms is consistent at certain number of bits. SVM is trained with both the watermarked and unwatermarked images. Receiver Operating Characteristics (ROC) graphs are plotted to assess the statistical detection behavior of both the correlation detector and SVM classifier. We found that straight forward application of SVM leads to generalization problem. We suggest remedies to preprocess the training data in order to achieve substantially better performance from SVM classifier than those resulting from the straightforward application of SVM. Both watermarked and unwatermarked images are attacked under Stirmark and are then tested with the correlation detectors and SVM classifier. A comparison of the ROC of the correlation detectors and SVM classifier is performed to assess the accuracy of SVM classifier relative to correlation detectors. We found that SVM classifier has higher robustness to Stirmark attacks.

Performance analysis of SAR filtering techniques using SVM and Wishart Classifier