Kernel Support Vector Machine Research Articles

Non-linear Kernel Optimisation of Support Vector Machine Algorithm for Online Marketplace Sentiment Analysis

Twitter is a social media platform that is very important in the digital world. Fast communication and interaction make Twitter a vital information center in sentiment analysis. The purpose of this research is to classify public opinion about the presence of marketplaces in Indonesia, both positive and negative sentiments, using a Non-linear SVM algorithm based on 1276 tweets. This research involves the stages of data pre-processing, labeling, feature extraction using TF-IDF, and data division into three scenarios: 80% training data and 20% test data, 50% training data and 50% test data scenario, and 20% training data and 80% test data scenario. The last process, GridSearchCV, combines cross-validation and non-linear SVM parameters for model evaluation using a confusion matrix. The best SVM model resulting from the scenario was 80% training and 20% test data, with hyperparameters Gamma = 100 and C = 0.01, achieving 89% accuracy. When tested on never-before-seen data, the accuracy increased to 90%, with an f1-score of 91%, precision of 88%, and recall of 95% on negative sentiments. In conclusion, evaluating the performance of non-linear SVM kernels with a combination of hyperparameter values can improve accuracy, especially on public response information about online marketplaces and public sentiment.

Machine Learning Insights: Exploring Key Factors Influencing Sale-to-List Ratio—Insights from SVM Classification and Recursive Feature Selection in the US Real Estate Market

The US real estate market is a complex ecosystem influenced by multiple factors, making it critical for stakeholders to understand its dynamics. This study uses Zillow Econ (monthly) data from January 2018 to October 2023 across 100 major regions gathered through Metropolitan Statistical Area (MSA) and advanced machine learning techniques, including radial kernel Support Vector Machines (SVMs), used to predict the sale-to-list ratio, a key metric that indicates the market health and competitiveness of the US real estate. Recursive Feature Elimination (RFE) is used to identify influential variables that provide insight into market dynamics. Results show that SVM achieves approximately 85% accuracy, with temporal indicators such as Days to Pending and Days to Close, pricing dynamics such as Listing Price Cut and Share of Listings with Price Cut, and rental market conditions captured by the Zillow Observed Rent Index (ZORI) emerging as critical factors influencing the sale-to-list ratio. The comparison between SVM alphas and RFE highlights the importance of time, price, and rental market indicators in understanding market trends. This study underscores the interplay between these variables and provides actionable insights for stakeholders. By contextualizing the findings within the existing literature, this study emphasizes the importance of considering multiple factors in housing market analysis. Recommendations include using pricing dynamics and rental market conditions to inform pricing strategies and negotiation tactics. This study adds to the body of knowledge in real estate research and provides a foundation for informed decision-making in the ever-evolving real estate landscape.

Detection of Exploit Websites Using Machine Learning and Data Analysis

Abstract: With the growing integration of internet integration into daily life, the prevalence of security threats such as phishing attacks has become a significant concern. Phishing websites aim to illicitly acquire sensitive user information, posing serious risks to online security. In response, machine learning methods have been investigated for the detection of such fraudulent websites. concentrates on developing a robust phishing website detection system, emphasizing efficiency, accuracy, and costeffectiveness. Four supervised classification models, namely K-Nearest Neighbour, Kernel Support Vector Machine, Decision Tree, and Random Forest Classifier, are employed and compared. Among these models, the Random Forest classifier demonstrates superior performance, achieving an impressive accuracy score of 96.82% on the selected dataset

Explaining the predictions of kernel SVM models for neuroimaging data analysis

Machine learning methods have shown great performance in many areas, including neuroimaging data analysis. However, model performance is only one objective in neuroimaging analysis. Gaining insight from the data is also critical in this field, such as identifying regions where detected signals are relevant to cognitive and diagnostic tasks. To fulfill this need, enabling the explainability of a model’s decision-making process is critical. Predictions of complex machine learning models are notoriously difficult to explain. This limits the use of complex models like kernel support vector machines (SVM) in neuroimaging analysis. Recently, several permutation-based methods have been developed to explain these complex models. However, the explanation results are affected by class-irrelevant features like suppressor variables and high background noise variables. This problem may also happen when explaining linear models. One possible reason is that the permutation process will produce unrealistic data instances when features are not independent, e.g. correlated. These unrealistic data instances will influence the explanation results. In neuroimaging analysis, the activation pattern, the estimated weight of the assumed generative model corresponding to the current classifier, is used to deal with this problem for linear models. This method does not rely on a permutation process but rather on the available data information. In this paper, we propose a novel method of Explanation through Activation Pattern (EAP) to explain the SVM models with different types of kernels for neuroimaging data analysis. Our method can generate a global feature importance score by estimating the activation pattern of kernel SVM models. We evaluate our method against three popular methods on both simulation datasets and a publicly available EEG/MEG dataset on visual tasks. The experimental results demonstrate that the proposed EAP method can provide explanations with low computational cost and is less affected by class-irrelevant features than the other three methods. In the experiment using the MEG/EEG dataset of visual tasks, the proposed EAP method provides agreement results with the brain’s electrical activity patterns reported in the literature on the visual tasks EEG/MEG data and is significantly faster than the other explanation methods.

Ice-Core Micro-CT Image Segmentation with Dual Stream Spectrum Deconvolution Neural Network and Gaussian Mixture Model

Polar ice sheets, or ice cores, are among the most well-known natural archives that might provide crucial historical details about our planet's previous environment. An important factor in establishing the fundamental characteristics of ice, likes pore close-off, albedo, melt events, is the ice-core microstructure. To engulf these complications Ice-Core Micro-CT Image Segmentation with Dual Stream Spectrum Deconvolution Neural Network and Gaussian Mixture Model (ICMCTS-WSOA-DSSDNN-GMM)is proposed. Initially, micro scale CT images are collect from Alfred Wegener Institute ice-core storage as input. Then,data’s are given to pre-processing. In pre-processing, it enhance image brightness, remove salt pepper noise, crop outer ring (carbon fiber casing) to have only ice particles in image utilizing Federated Neural Collaborative Filtering (FNCF).The pre-processing output is given to segmentation. Here, micro scale CT image is utilized for segmenting high-resolution scans using Gaussian Mixture Model (GMM). After that, the segmented images are given to Dual Stream Spectrum Deconvolution Neural Network optimized with Water Strider Optimization Algorithm for classifying micro scale CT images as sintered snow, compacted firn and bubbly ice. The proposed ICMCTS-WSOA-DSSDNN-GMM method is executed in python. The performance of ICMCTS-WSOA-DSSDNN-GMM approach attains 16.24%, 17.90% and 27.7% high accuracy, 14.04%, 25.51% and 19.31% higher precision and 14.36%, 12.65%, 14.51% higher recall analyzed with existing techniques likes Ice-Core Micro-CT Image Segmentation With Deep Learning and Gaussian Mixture Method (ICMCTS-U-net-GMM), Computer Aided Detection of COVID 19 from CT Images Depend on Gaussian Mixture Method with Kernel Support Vector Machines Classifer (ICMCTS-KNN-GMM) and the Gmmseg: Gaussian mixture based generative semantic segmentation methods(ICMCTS-FCN-GMM) respectively.

A new COVID-19 classification approach based on Bayesian optimization SVM kernel using chest X-ray datasets

A new COVID-19 classification approach based on Bayesian optimization SVM kernel using chest X-ray datasets

Integration of graph network with kernel SVM and logistic regression for identification of biomarkers in SCA12 and its diagnosis.

Spinocerebellar ataxia type 12 is a hereditary and neurodegenerative illness commonly found in India. However, there is no established noninvasive automatic diagnostic system for its diagnosis and identification of imaging biomarkers. This work proposes a novel four-phase machine learning-based diagnostic framework to find spinocerebellar ataxia type 12 disease-specific atrophic-brain regions and distinguish spinocerebellar ataxia type 12 from healthy using a real structural magnetic resonance imaging dataset. Firstly, each brain region is represented in terms of statistics of coefficients obtained using 3D-discrete wavelet transform. Secondly, a set of relevant regions are selected using a graph network-based method. Thirdly, a kernel support vector machine is used to capture nonlinear relationships among the voxels of a brain region. Finally, the linear relationship among the brain regions is captured to build a decision model to distinguish spinocerebellar ataxia type 12 from healthy by using the regularized logistic regression method. A classification accuracy of 95% and a harmonic mean of precision and recall, i.e. F1-score of 94.92%, is achieved. The proposed framework provides relevant regions responsible for the atrophy. The importance of each region is captured using Shapley Additive exPlanations values. We also performed a statistical analysis to find volumetric changes in spinocerebellar ataxia type 12 group compared to healthy. The promising result of the proposed framework shows that clinicians can use it for early and timely diagnosis of spinocerebellar ataxia type 12.

Hybrid Conditional Kernel SVM for Wire Rope Defect Recognition

Hybrid Conditional Kernel SVM for Wire Rope Defect Recognition

Brain Tumor Detection and Classification Via Kernel Support Vector Machine Using MRI Images

Abstract: The MR images of the brain requires automatic and accurate classification for medical analysis and interpretation nowadays. Numerous methods have been declared already in the previous years. In this paper we have presented a method which classifies the brain image of MRI into normal and abnormal brain tumor images. This method uses wavelet transform that extract features from images. The next step involves principle component analysis (PCA) that reduces the feature dimensions. These reduced features are then employed to a kernel support vector machine (KSVM). 180 images of the brain were collected from the diseased which contained 130 abnormal brain and 50 normal brain images. Four kernels of different types were performed.

Brain tumor MRI identification and classification using DWT, PCA and kernel support vector machine

Classification, segmentation, and the identification of the infection region in MRI images of brain tumors are labor-intensive and iterative processes. Numerous anatomical structures of the human body may be envisioned using an image processing theory. With basic imaging methods, it is challenging to see the aberrant human brain's structure. The neurological structure of the human brain may be distinguished and made clearer using the magnetic resonance imaging technique. The MRI approach uses a number of imaging techniques to evaluate and record the human brain’s interior features. In this study, we focused on strategies for noise removal, gray-level co-occurrence matrix (GLCM) extraction of features, and segmentation of brain tumor regions based on Discrete Wavelet Transform (DWT) to minimize complexity and enhance performance. In turn, this reduces any noise that could have been left over after segmentation due to morphological filtering. Brain MRI scans were utilized to test the accuracy of the classification and the location of the tumor using probabilistic neural network classifiers. The classifier's accuracy and position detection were tested using MRI brain imaging. The efficiency of the suggested approach is demonstrated by experimental findings, which showed that normal and diseased tissues could be distinguished from one another from brain MRI scans with about 100% accuracy.

Adaptive Arctan kernel: a generalized kernel for support vector machine

Adaptive Arctan kernel: a generalized kernel for support vector machine

Enhancing Lung Cancer Classification Effectiveness Through Hyperparameter-Tuned Support Vector Machine

This research aims to improve the effectiveness of lung cancer classification performance using Support Vector Machines (SVM) with hyperparameter tuning. Using Radial Basis Function (RBF) kernels in SVM helps deal with non-linear problems. At the same time, hyperparameter tuning is done through Random Grid Search to find the best combination of parameters. Where the best parameter settings are C = 10, Gamma = 10, Probability = True. Test results show that the tuned SVM improves accuracy, precision, specificity, and F1 score significantly. However, there was a slight decrease in recall, namely 0.02. Even though recall is one of the most important measuring tools in disease classification, especially in imbalanced datasets, specificity also plays a vital role in avoiding misidentifying negative cases. Without hyperparameter tuning, the specificity results are so poor that considering both becomes very important. Overall, the best performance obtained by the proposed method is 0.99 for accuracy, 1.00 for precision, 0.98 for recall, 0.99 for f1-score, and 1.00 for specificity. This research confirms the potential of tuned SVMs in addressing complex data classification challenges and offers important insights for medical diagnostic applications.

Preliminary Study on the Identification of Aerobic Vaginitis by Artificial Intelligence Analysis System

To develop an artificial intelligence vaginal secretion analysis system based on deep learning and to evaluate the accuracy of automated microscopy in the clinical diagnosis of aerobic vaginitis (AV). In this study, the vaginal secretion samples of 3769 patients receiving treatment at the Department of Obstetrics and Gynecology, West China Second Hospital, Sichuan University between January 2020 and December 2021 were selected. Using the results of manual microscopy as the control, we developed the linear kernel SVM algorithm, an artificial intelligence (AI) automated analysis software, with Python Scikit-learn script. The AI automated analysis software could identify leucocytes with toxic appearance and parabasal epitheliocytes (PBC). The bacterial grading parameters were reset using standard strains of lactobacillus and AV common isolates. The receiver operating characteristic (ROC) curve analysis was used to determine the cut-off value of AV evaluation results for different scoring items were obtained by using the results of manual microscopy as the control. Then, the parameters of automatic AV identification were determined and the automatic AV analysis scoring method was initially established. A total of 3769 vaginal secretion samples were collected. The AI automated analysis system incorporated five parameters and each parameter incorporated three severity scoring levels. We selected 1.5 μm as the cut-off value for the diameter between Lactobacillus and common AV bacterial isolates. The automated identification parameter of Lactobacillus was the ratio of bacteria ≥1.5 μm to those <1.5 μm. The cut-off scores were 2.5 and 0.5, In the parameter of white blood cells (WBC), the cut-off value of the absolute number of WBC was 103 μL－1 and the cut-off value of WBC-to-epithelial cell ratio was 10. The automated identification parameter of toxic WBC was the ratio of toxic WBC toWBC and the cut-off values were 1% and 15%. The parameter of background flora was bacteria<1.5 μm and the cut-off values were 5×103 μL－1 and 3×104 μL－1. The parameter of the parabasal epitheliocytes was the ratio of PBC to epithelial cells and the cut-off values were 1% and 10%. The agreement rate between the results of automated microscopy and those of manual microscopy was 92.5%. Out of 200 samples, automated microscopy and manual microscopy produced consistent scores for 185 samples, while the results for 15 samples were inconsistent. We developed an AI recognition software for AV and established an automated vaginal secretion microscopy scoring system for AV. There was good overall concordance between automated microscopy and manual microscopy. The AI identification software for AV can complete clinical lab examination with rather high objectivity, sensitivity, and efficiency, markedly reducing the workload of manual microscopy.

Identifying Flare-indicative Photospheric Magnetic Field Parameters from Multivariate Time-series Data of Solar Active Regions

Photospheric magnetic field parameters are frequently used to analyze and predict solar events. Observation of these parameters over time, i.e., representing solar events by multivariate time-series (MVTS) data, can determine relationships between magnetic field states in active regions and extreme solar events, e.g., solar flares. We can improve our understanding of these events by selecting the most relevant parameters that give the highest predictive performance. In this study, we propose a two-step incremental feature selection method for MVTS data using a deep-learning model based on long short-term memory (LSTM) networks. First, each MVTS feature (magnetic field parameter) is evaluated individually by a univariate sequence classifier utilizing an LSTM network. Then, the top performing features are combined to produce input for an LSTM-based multivariate sequence classifier. Finally, we tested the discrimination ability of the selected features by training downstream classifiers, e.g., Minimally Random Convolutional Kernel Transform and support vector machine. We performed our experiments using a benchmark data set for flare prediction known as Space Weather Analytics for Solar Flares. We compared our proposed method with three other baseline feature selection methods and demonstrated that our method selects more discriminatory features compared to other methods. Due to the imbalanced nature of the data, primarily caused by the rarity of minority flare classes (e.g., the X and M classes), we used the true skill statistic as the evaluation metric. Finally, we reported the set of photospheric magnetic field parameters that give the highest discrimination performance in predicting flare classes.

Multi-dimensional deep dense residual networks and multiple kernel learning for hyperspectral image classification

To address the issues of inadequate feature expression capacity and poor adaptability of feature fusion in traditional hyperspectral image classification methods, a new approach to classification utilizing a multi-dimensional deep dense residual network and multiple kernel learning is suggested. First, we construct a deep dense residual structure to obtain the spectral, spatial and spectral-spatial features of the image, respectively. Then, the multiple kernel learning is employed to map the three extracted features into the high-dimensional space to achieve the adaptive fusion between features. Finally, multiple kernel support vector machine is adopted to accurately classify the multiple kernel fused features. By randomly selecting 10%, 1% and 1% of samples in each ground object category as training samples, our proposed method demonstrated an enhanced overall classification accuracy of 99.58%, 99.52% and 99.66% according to the experimental results conducted on three typical hyperspectral images. Comparing with some multiple kernel learning based methods, our method can obtain higher classification accuracy. So our method has better recognition ability for hyperspectral image features, and has strong adaptability and robustness.

Automatic Speckle Noise Reduction in SAR Imagery Using Optimized Kernel Support Vector Machine

Speckle noise (SN) is one of the major types of noise that frequently occurs in different coherent imaging systems such as medical imaging, Synthetic Aperture Radar (SAR) and active Radar images. SAR is a powerful imaging technology that generates fine-resolution images and monitors the earth’s surface in order to identify its physical properties. The satellite images captured by SAR are mainly affected by SN, which reduces the quality of images and complicates the image representation. Therefore, removing SN from SAR images is one of the major challenges and needs significant attention. The proposed study introduces an optimal Machine Learning (ML) classifier named Kernel Support Vector Machine-Improved Aquila Optimization (KSVM-IAO) for reducing SN in SAR images. This study uses a two-step process called filtering and enhanced despeckling to minimize the consequence of speckle suppression. In the first step, different imaging filters, namely Improved Lee Filter (ILF), Improved Frost Filter (IFF), Improved Kuan Filter (IKF) and Improved Boxcar Filter (IBF), are utilized to remove the SN in SAR images. Next, the denoised image is fed to the second stage, which makes use of an optimized KSVM-IAO classifier to obtain an enhanced despeckle image. The hyperparameters of KSVM are tuned using the IAO algorithm, which reduces overfitting issues and increases accuracy. MATLAB is the simulation tool used for the analysis of SAR images. The simulation outcomes reveal that the proposed KSVM-IAO method obtained excellent SN removal while preserving the edges and fine details with low computational complexity.

Wavelet Transform and SVM Based Heart Disease Monitoring for Flexible Wearable Devices

INTRODUCTION: Heart disease has been a major health challenge globally, therefore the development of reliable and real-time heart disease monitoring methods is crucial for the prevention and management of heart health. The aim of this study is to explore a flexible wearable device approach based on wavelet transform and support vector machine (SVM) to improve the accuracy and portability of heart disease monitoring. OBJECTIVES: The main objective of this study is to develop a wearable device that combines wavelet transform and SVM techniques to achieve accurate monitoring of physiological signals of heart diseases. METHODS: An integrated method for heart disease monitoring was constructed using flexible sensor technology combined with a wavelet transform and support vector machine. The Marr wavelet transform was applied to the ECG signals, and the feature vectors were constructed by feature parameter extraction. Then, the radial basis kernel SVM was utilized to identify the three ECG signals. The performance of the algorithm was optimized by adjusting the SVM parameters to improve the accurate monitoring of heart diseases. RESULTS: The experimental results show that the proposed wavelet transform and SVM-based approach for flexible wearable devices achieves satisfactory results in heart disease monitoring. In particular, the algorithm successfully extracted feature vectors and accurately classified different ECG signals by skillfully combining the wavelet transform and SVM techniques for the processing of premature beat signals. CONCLUSION: The potential application value of the wavelet transform and SVM-based flexible wearable device approach in heart disease monitoring is emphasized. By efficiently processing ECG signals, the method provides an innovative and comfortable solution for real-time monitoring of cardiac diseases.

A Trustworthy and Explainable Framework for Benchmarking Hybrid Deep Learning Models Based on Chest X-Ray Analysis in CAD Systems

Evaluating the trustworthiness of deep learning-based computer-aided diagnosis (CAD) systems is challenging. There is a need to optimize trust and performance in model selection. A wide range of models based on evaluation metrics can make it challenging to choose the best one, especially for complex multi-criteria decision-making problems. In the case of COVID-19 diagnosis, using both physicians’ evaluation and AI techniques to establish trust is essential. In this study, 1551 chest X-rays were analyzed using deep transfer learning (DTL) with six models and four SVM kernels. This resulted in 24 hybrid DTL–SVM models. Seven metrics were evaluated using fuzzy multiple-criteria decision-making (MCDM), which uses a dynamic decision matrix to select the best model. This matrix incorporates fuzzy-weighted zero-inconsistency (FWZIC) for weight coefficients and Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR) for benchmarking. The Grad-CAM technique compared the best model with 16 images, ensuring explainability. Top-performing models were identified, including SqueezeNet-SVM linear, VGG19-SVM linear, and VGG19-SVM. Sensitivity analysis was used to quantify the impact of changing weighted criteria values. A physician expert validated fuzzy MCDM through Grad-CAM analysis, a new aspect of this study. The framework presented in this study was benchmarked against seven other studies and achieved a perfect score in four crucial areas. Trustworthiness is essential for CAD systems, and this study effectively addresses trust and performance challenges in AI model-based CAD systems. The study systematically evaluated the requirements for trustworthiness, including accountability, fairness, robustness, accuracy, and reproducibility, and the results supported this.

Using mixed kernel support vector machine to improve the predictive accuracy of genome selection1

The advantages of genome selection (GS) in animal and plant breeding are self-evident. Traditional parametric models have disadvantage in better fit the increasingly large sequencing data and capture complex effects accurately. Machine learning models have demonstrated remarkable potential in addressing these challenges. In this study, we introduced the concept of mixed kernel functions to explore the performance of support vector machine regression (SVR) in GS. Six single kernel functions (SVR_L, SVR_C, SVR_G, SVR_P, SVR_S, SVR_L) and four mixed kernel functions (SVR_GS, SVR_GP, SVR_LS, SVR_LP) were used to predict genome breeding values. The prediction accuracy, mean squared error (MSE) and mean absolute error (MAE) were used as evaluation indicators to compare with two traditional parametric models (GBLUP, BayesB) and two popular machine learning models (RF, KcRR). The results indicate that in most cases, the performance of the mixed kernel function model significantly outperforms that of GBLUP, BayesB and single kernel function. For instance, for T1 in the pig dataset, the predictive accuracy of SVR_GS is improved by 10% compared to GBLUP, and by approximately 4.4 and 18.6% compared to SVR_G and SVR_S respectively. For E1 in the wheat dataset, SVR_GS achieves 13.3% higher prediction accuracy than GBLUP. Among single kernel functions, the Laplacian and Gaussian kernel functions yield similar results, with the Gaussian kernel function performing better. The mixed kernel function notably reduces the MSE and MAE when compared to all single kernel functions. Furthermore, regarding runtime, SVR_GS and SVR_GP mixed kernel functions run approximately three times faster than GBLUP in the pig dataset, with only a slight increase in runtime compared to the single kernel function model. In summary, the mixed kernel function model of SVR demonstrates speed and accuracy competitiveness, and the model such as SVR_GS has important application potential for GS.

Comparison of Discriminant Analysis and Support Vector Machine on Mixed Categorical and Continuous Independent Variables for COVID-19 Patients Data

Purpose: Numerous factors can affect the duration of COVID-19 recovery. One method involves utilizing natural herbal medication. This study seeks to determine the variables influencing the duration of COVID-19 recovery and to compare discriminant analysis and support vector machine models using COVID-19 patient data from West Sumatra.Methods: Two data mining methods, Discriminant Analysis and Support Vector Machine with different types of kernels (linear, polynomial, and radial basis function), were employed to categorize the time of COVID-19 recovery in this work. The study utilized 428 data points, with 75% allocated for training data and 25% for testing data. The independent factors were evaluated by determining the selection variables' information value (IV) to gauge their influence on the dependent variable. Data resampling techniques were employed to tackle the problem of data imbalance. This study employs data resampling techniques, including undersampling, oversampling, and SMOTE. The balancing accuracy of Discriminant Analysis and Support Vector Machine was examined.Result: The Discriminant Analysis with SMOTE achieved a balanced accuracy of 66.50%, outperforming the linear kernel Support Vector Machine with SMOTE, which had a balanced accuracy of 63.20% in this dataset.Novelty: This study assessed the novelty, originality, and value by comparing Discriminant Analysis and SVM algorithms with categorical and continuous independent variables. This research explores techniques for managing imbalanced data using undersampling, oversampling, and SMOTE, with variable selection based on information value assessment.