Evaluation Of Support Vector Machine Research Articles

Evaluation of Support Vector Machine, Naive Bayes, Decision Tree, and Gradient Boosting Algorithms for Sentiment Analysis on ChatGPT Twitter Dataset

Evaluation of Support Vector Machine, Naive Bayes, Decision Tree, and Gradient Boosting Algorithms for Sentiment Analysis on ChatGPT Twitter Dataset

Different slope units division-based geohazard susceptibility evaluation of support vector machine optimized by sparrow search algorithm

Different slope units division-based geohazard susceptibility evaluation of support vector machine optimized by sparrow search algorithm

Securing healthcare data management using machine learning and blockchain technology: A comparative performance evaluation of support vector machine and conventional classifiers

As access to healthcare has become more central to people’s daily lives, the amount of medical big data has grown exponentially. Wearable Internet of Things (IoT)-reliant technology is gaining popularity in the medical field as a means to improve patient care and reduce wait times. In recent years, billions of sensors, devices, and machines have been hooked up to the web. One such technology, remote patient monitoring, is increasingly used in modern patient care and treatment. In addition, these developments pose substantial security issues about the recording of transaction data and the transmission of information itself, and pose considerable dangers to users’ privacy. Concerns about the privacy of a patient’s medical records have the ability to discontinue treatment, putting the patient’s life in threat. Thus, a system is proposed using machine learning in combination with blockchain technology to allow secure management as well as analysis of large amounts of healthcare data. With the help of machine learning, it is feasible to sort through all of the data and extract out only the most pertinent information. This is accomplished with the help of trained methodologies. After this data has been saved, the next issue will be the exchange of data and ensuring its trustworthiness. The concept of blockchain is introduced at this point. The Blockchain technology relies on consensus to ensure that all data is accurate and that all transactions are conducted in a safe manner. The management of healthcare is one area where blockchain technology offers the ability to have a huge impact by placing patients at the center of the system and improving the privacy and portability of health records. This study is primarily concerned with finding solutions to issues relating to the administration of healthcare data by utilizing Blockchain technology and incorporating some crucial characteristics developed with Machine Learning. The performance evaluation of proposed Support Vector Machine is compared with the other conventional machine learning classifiers. It is observed from the experimental finding that performance accuracy of Support vector machine is 98% which is better as compared to other traditional machine learning classifiers.

Path planning of patrol robot based on improved discrete electrostatic discharge algorithm

Safety is the premise of the stable and sustainable development of the chemical industry, safety accidents will not only cause casualties and economic losses, but also cause panic among workers and nearby residents. Robot safety inspection based on the fire risk level in a chemical industrial park can effectively reduce process accident losses and can even prevent accidents. The optimal inspection path is an important support for patrol efficiency, therefore, in this study, the fire risk level of each location to be inspected, which is obtained by the electrostatic discharge algorithm (ESDA)–nonparallel support vector machine evaluation model, is combined with the optimisation of the inspection path; that is, the fire risk level is used to guide the inspection path planning. The inspection path planning problem is a typical travelling salesman problem (TSP). The discrete ESDA (DESDA), based on the ESDA, is proposed. In view of the shortcomings of the long convergence time and ease of falling into the local optimum of the DESDA, further improvements are proposed in the form of the IDESDA, in which the greedy algorithm is used for the initial population, the 2-opt algorithm is applied to generate new solutions, and the elite set is joined to provide the best segment for jumping out of the local optimum. In the experiments, 11 public calculation examples were used to verify the algorithm performance. The IDESDA exhibited higher accuracy and better stability when solving the TSP. Its application to chemical industrial parks can effectively solve the path optimisation problem of patrol robots.

Performance Evaluation of Support Vector Machine: Before and After Image Data Augmentation

Performance Evaluation of Support Vector Machine: Before and After Image Data Augmentation

Performance Evaluation of Support Vector Machines (SVM) and Convolution Neural Networks (CNN) for Video Tampering Classification

Performance Evaluation of Support Vector Machines (SVM) and Convolution Neural Networks (CNN) for Video Tampering Classification

Comparison and Evaluation of Support Vector Machine and Gene Programming in River Suspended Sediment Estimation (Case Study: Kashkan River)

Comparison and Evaluation of Support Vector Machine and Gene Programming in River Suspended Sediment Estimation (Case Study: Kashkan River)

Real-Time Replication of Arm Movements Using Surface EMG Signals

In this paper, a real time application to replicate nine arm movements is proposed. The two important joints that are controlled are wrist and elbow. Electromyogram signals are recorded for four wrist positions and five elbow positions. These signals are enhanced and features pertaining to muscle movements are extracted. Dimension of these feature sets is reduced to obtain the optimal set of features. These feature sets are given as input to the classifier. Performance evaluation of Support Vector Machine (SVM), K-Nearest Neighbors, Random Forest and Relevant Vector Machine (RVM) classifiers, in recognizing different wrist and elbow positions, is discussed. As per the results, the best overall accuracy of 93.3% was obtained from SVM with radial basis function (RBF) kernel, in classifying both the wrist and elbow positions. Although, RVM as a classifier yielded the same accuracy in recognizing wrist positions, it resulted in the lowest accuracy of 88.67% in recognizing elbow positions. Therefore, SVM-RBF fared better in identifying the arm movements. Furthermore, these arm movements are used to control the actuators.

Evaluation of Support Vector Machine and Decision Tree for Emotion Recognition of Malay Folklores

In this paper, the performance of Support Vector Machine (SVM) and Decision Tree (DT) in classifying emotions from Malay folklores is presented. This work is the continuation of our storytelling speech synthesis work to add emotions for a more natural storytelling. A total of 100 documents from children short stories are collected and used as the datasets of the text-based emotion recognition experiment. Term Frequency-Inverse Document Frequency (TF-IDF) is extracted from the text documents and classified using SVM and DT. Four types of common emotions, which are happy, angry, fearful and sad are classified using the two classifiers. Results showed that DT outperformed SVM by more than 22.2% accuracy rate. However, the overall emotion recognition is only at moderate rate suggesting an improvement is needed in future work. The accuracy of the emotion recognition should be improved in future studies by using semantic feature extractors or by incorporating deep learning for classification.

Performance evaluation of support vector machine and convolutional neural network algorithms in real-time vehicle type and color classification

In order for traffic management and information systems to provide proper traffic flow, it is necessary to obtain information about traffic with the help of various sensors. In this context, in recent years the use of video cameras in traffic observation and control has become very widespread and actively used. Numerous studies such as license plate recognition, vehicle number finding, traffic intensity determination, vehicle speed calculation, band violation and vehicle classification can be done with the help of video processing based video monitoring systems. Traffic surveillance videos are very actively used for this purpose. In this paper, we have developed a system that classifies vehicles according to their type. Firstly we create a vehicle dataset from an uncalibrated camera. Then, we test Tiny-YOLO real-time object detection and classification system and support vector machine (SVM) classifier model on our dataset and well-known public BIT-Vehicle dataset in terms of recall, precision, and intersection over union performance metrics. Experimental results show that two methods can be used to classify real-time streaming traffic video data.

Evaluation of support vector machine and artificial neural networks in weed detection using shape features

Weed detection is still a challenging problem for robotic weed removal. Small tolerance between the cutting tine and main crop position requires highly precise discrimination of the weed against the main crop. Close similarities between the shape features of sugar beet and common weeds make it impossible to define an exclusive feature to be able to efficiently detect all the weeds with acceptable accuracy. Therefore in this study, it was tried to integrate several shape features to establish a pattern for each variety of the plants. To enable the vision system in the detection of the weeds based on their pattern, support vector machine and artificial neural networks were employed. Four species of common weeds in sugar beet fields were studied. Shape feature sets included Fourier descriptors and moment invariant features. Results showed that the overall classification accuracy of ANN was 92.92%, where 92.50% of weeds were correctly classified. Higher accuracies were obtained when the SVM was used as the classifier with an overall accuracy of 95.00% whereas 93.33% of weeds were correctly classified. Also, 93.33% and 96.67% of sugar beet plants were correctly classified by ANN and SVM respectively.

Performance evaluation of support vector machine (SVM)-based predictors in genomic selection

The aim was to compare predictive performance of SVM-based predictors constructed using different kernel functions (radial, sigmoid, linear and polynomial) in different genetic architectures of a trait (number of QTL, distribution of QTL effects) and heritability levels. To this end, a genome comprised of five chromosomes, oneMorgan each, was simulated on which 10,000 bi-allelic single nucleotide polymorphisms (SNP) were distributed.Cross validation employing a grid search was used to tune the meta-parameters of each kernel function. Pearson’scorrelation between the true and predicted genomic breeding values (rp,t) and mean squared error of predictedgenomic breeding values (MSEp) were used, respectively, as measures of the predictive accuracy and the overallfit. Meta-parameter optimization had a significant effect on predictive performance of SVM-based predictors insuch a way that by using improper meta-parameters, the predictive power of models decreased significantly. In allmodels, the accuracy of prediction increased following increase in heritability and decrease in the number ofQTLs. In most of scenarios, radial- and sigmoid-based SVM predictors outperformed polynomial and linear models.The linear-and polynomial-based SVM had lower rp,t and higher MSEp and, therefore, were not recommended forgenomic selection. The prediction accuracy of radial and sigmoid models was approximately the same in most ofthe studied scenarios; however, considering all pros and cons of radial and sigmoid kernels, radial kernel wasrecommended as the best kernel function for constructing SVM. All of studied SVM-based predictors were efficientusers of time and memory.

Optimizing explicit feature maps on intervals

Approximating non-linear kernels by finite-dimensional feature maps is a popular approach for accelerating training and evaluation of support vector machines or to encode information into efficient match kernels. We propose a novel method of data independent construction of low-dimensional feature maps. The problem is formulated as a linear program that jointly considers two competing objectives: the quality of the approximation and the dimensionality of the feature map.For both shift-invariant and homogeneous kernels the proposed method achieves better approximation at the same dimensionality or comparable approximations at lower dimensionality of the feature map compared with state-of-the-art methods.

A Comprehensive Evaluation of Support Vector Machine in Hand Movement Classification Using Surface Electromyography

A Comprehensive Evaluation of Support Vector Machine in Hand Movement Classification Using Surface Electromyography

Comparative evaluation of support vector machines for computer aided diagnosis of lung cancer in CT based on a multi-dimensional data set

Lung cancer is one of the most common forms of cancer resulting in over a million deaths per year worldwide. In this paper, the usage of support vector machine (SVM) classification for lung cancer is investigated, presenting a systematic quantitative evaluation against Boosting, Decision trees, k-nearest neighbor, LASSO regressions, neural networks and random forests. A large database of 5984 regions of interest (ROIs) and 488 input features (including textural features, patient characteristics, and morphological features) were used to train the classifiers and evaluate for their performance. The evaluation for classifiers’ performance was based on a tenfold cross validation framework, receiver operating characteristic curve (ROC), and Matthews correlation coefficient. Area under curve (AUC) of SVM, Boosting, Decision trees, k-nearest neighbor, LASSO, neural networks, random forests were 0.94, 0.86, 0.73, 0.72, 0.91, 0.92, and 0.85, respectively. It was proved that SVM classification offered significantly increased classification performance compared to the reference methods. This scheme may be used as an auxiliary tool to differentiate between benign and malignant SPNs of CT images in future

Evaluation of support vector machine for estimation of solar radiation from measured meteorological variables

Solar radiation is an essential and important variable to many models. However, it is measured at a very limited number of meteorological stations in the world. Developing method for accurate estimation of solar radiation from measured meteorological variables has been a focus and challenging task. This paper presents the method of solar radiation estimation using support vector machine (SVM). The main objective of this work is to examine the feasibility of SVM and explore its potential in solar radiation estimation. A total of 20 SVM models using different combinations of sunshine ratio, maximum and minimum air temperature, relative humidity, and atmospheric water vapor pressure as input attributes are explored using meteorological data at 15 stations in China. These models significantly outperform the empirical models with an average 14 % higher accuracy. When sunshine duration data are available, model SVM2 using sunshine ratio and air temperature range is proposed. It significantly outperforms the empirical models with an average 26 % higher accuracy. When sunshine duration data are not available, model SVM19 using maximum temperature, minimum temperature and atmospheric water vapor pressure is proposed. It significantly outperforms the temperature-based empirical models with an average of 18 % higher accuracy. The remarkable improvement indicates that the SVM method would be a promising alternative over traditional approaches for estimation of solar radiation at any locations.

The application of support vector regression for prediction of the antiallodynic effect of drug combinations in the mouse model of streptozocin-induced diabetic neuropathy

Drug interactions are an important issue of efficacious and safe pharmacotherapy. Although the use of drug combinations carries the potential risk of enhanced toxicity, when carefully introduced it enables to optimize the therapy and achieve pharmacological effects at doses lower than those of single agents. In view of the development of novel analgesic compounds for the neuropathic pain treatment little is known about their influence on the efficacy of currently used analgesic drugs.Below we describe the preliminary evaluation of support vector machine in the regression mode (SVR) application for the prediction of maximal antiallodynic effect of a new derivative of dihydrofuran-2-one (LPP1) used in combination with pregabalin (PGB) in the streptozocin-induced neuropathic pain model in mice. Based on SVR the most effective doses of co-administered LPP1 (4mg/kg) and PGB (1mg/kg) were predicted to cause the paw withdrawal threshold at 6.7g in the von Frey test. In vivo for the same combination of doses the paw withdrawal was observed at 6.5g, which confirms good predictive properties of SVR.

A new classification for power quality events in distribution systems

This paper presents the performance evaluation of support vector machine (SVM) with one against all (OAA) and different classification methods for power quality monitoring. The first aim of this study is to investigate EEMD (ensemble empirical mode decomposition) performance and to compare it with classical EMD (empirical mode decomposition) for feature vector extraction and selection of power quality disturbances. Feature vectors are extracted from the sampled power signals with the Hilbert Huang Transform (HHT) technique. HHT is a combination of EEMD and Hilbert transform (HT). The outputs of HHT are intrinsic mode functions (IMFs), instantaneous frequency (IF), and instantaneous amplitude (IA). Characteristic features are obtained from first IMFs, IF, and IA. The ten features—i.e., the mean, standard deviation, singular values, maxima and minima—of both IF and IA are then calculated. These features are normalized along with the inputs of SVM and other classifiers.

Evaluation of Urban Public Transportation System Based on Support Vector Machine

Evaluation of urban public transportation system plays an important role in developing urban public transportation system. When evaluating urban public transportation system, Support vector machine method can overcome the subjective problem of setting target weight, which is difficult to be solved by fuzzy comprehensive evaluation. Evaluation index system can be established according to the procurability and the effectiveness of the index. By studying several bus systems, the effectiveness of support vector machine evaluation could be proved, which shows that support vector machine model is applicable for the evaluation of urban public transportation system.

Feature Selection and Performance Evaluation of Support Vector Machine (SVM)-Based Classifier for Differentiating Benign and Malignant Pulmonary Nodules by Computed Tomography

There are lots of work being done to develop computer-assisted diagnosis and detection (CAD) technologies and systems to improve the diagnostic quality for pulmonary nodules. Another way to improve accuracy of diagnosis on new images is to recall or find images with similar features from archived historical images which already have confirmed diagnostic results, and the content-based image retrieval (CBIR) technology has been proposed for this purpose. In this paper, we present a method to find and select texture features of solitary pulmonary nodules (SPNs) detected by computed tomography (CT) and evaluate the performance of support vector machine (SVM)-based classifiers in differentiating benign from malignant SPNs. Seventy-seven biopsy-confirmed CT cases of SPNs were included in this study. A total of 67 features were extracted by a feature extraction procedure, and around 25 features were finally selected after 300 genetic generations. We constructed the SVM-based classifier with the selected features and evaluated the performance of the classifier by comparing the classification results of the SVM-based classifier with six senior radiologists' observations. The evaluation results not only showed that most of the selected features are characteristics frequently considered by radiologists and used in CAD analyses previously reported in classifying SPNs, but also indicated that some newly found features have important contribution in differentiating benign from malignant SPNs in SVM-based feature space. The results of this research can be used to build the highly efficient feature index of a CBIR system for CT images with pulmonary nodules.