Kernel Support Vector Machine Research Articles

A stacked multiple kernel support vector machine for blast induced flyrock prediction

As a widely used rock excavation method in civil and mining construction works, the blasting operations and the induced side effects are always investigated by the existing studies. The occurrence of flyrock is regarded as one of the most important issues induced by blasting operations, since the accurate prediction of which is crucial for delineating safety zone. For this purpose, this study developed a flyrock prediction model based on 234 sets of blasting data collected from Sugun Copper Mine site. A stacked multiple kernel support vector machine (stacked MK-SVM) model was proposed for flyrock prediction. The proposed stacked structure can effectively improve the model performance by addressing the importance level of different features. For comparison purpose, 6 other machine learning models were developed, including SVM, MK-SVM, Lagragian Twin SVM (LTSVM), Artificial Neural Network (ANN), Random Forest (RF) and M5 Tree. This study implemented a 5-fold cross validation process for hyperparameters tuning purpose. According to the evaluation results, the proposed stacked MK-SVM model achieved the best overall performance, with RMSE of 1.73 and 1.74, MAE of 0.58 and 1.08, VAF of 98.95 and 99.25 in training and testing phase, respectively.

Digital image analysis and machine learning-assisted prediction of neoadjuvant chemotherapy response in triple-negative breast cancer

BackgroundPathological complete response (pCR) is associated with favorable prognosis in patients with triple-negative breast cancer (TNBC). However, only 30–40% of TNBC patients treated with neoadjuvant chemotherapy (NAC) show pCR, while the remaining 60–70% show residual disease (RD). The role of the tumor microenvironment in NAC response in patients with TNBC remains unclear. In this study, we developed a machine learning-based two-step pipeline to distinguish between various histological components in hematoxylin and eosin (H&E)-stained whole slide images (WSIs) of TNBC tissue biopsies and to identify histological features that can predict NAC response.MethodsH&E-stained WSIs of treatment-naïve biopsies from 85 patients (51 with pCR and 34 with RD) of the model development cohort and 79 patients (41 with pCR and 38 with RD) of the validation cohort were separated through a stratified eightfold cross-validation strategy for the first step and leave-one-out cross-validation strategy for the second step. A tile-level histology label prediction pipeline and four machine-learning classifiers were used to analyze 468,043 tiles of WSIs. The best-trained classifier used 55 texture features from each tile to produce a probability profile during testing. The predicted histology classes were used to generate a histology classification map of the spatial distributions of different tissue regions. A patient-level NAC response prediction pipeline was trained with features derived from paired histology classification maps. The top graph-based features capturing the relevant spatial information across the different histological classes were provided to the radial basis function kernel support vector machine (rbfSVM) classifier for NAC treatment response prediction.ResultsThe tile-level prediction pipeline achieved 86.72% accuracy for histology class classification, while the patient-level pipeline achieved 83.53% NAC response (pCR vs. RD) prediction accuracy of the model development cohort. The model was validated with an independent cohort with tile histology validation accuracy of 83.59% and NAC prediction accuracy of 81.01%. The histological class pairs with the strongest NAC response predictive ability were tumor and tumor tumor-infiltrating lymphocytes for pCR and microvessel density and polyploid giant cancer cells for RD.ConclusionOur machine learning pipeline can robustly identify clinically relevant histological classes that predict NAC response in TNBC patients and may help guide patient selection for NAC treatment.

Optimizing feature subset for schizophrenia detection using multichannel EEG signals and rough set theory.

Schizophrenia (SZ) is a mental disorder that causes lifelong disorders based on delusions, cognitive deficits, and hallucinations. By visual assessment, SZ diagnosis is time-consuming and complicated, because brain states are more effectively revealed by electroencephalogram (EEG) signals, which are effectively used in SZ diagnosis. The application of existing deep learning methods in SZ detection is effective in the classification of 2-dimensional images, and these methods require more computational resources. Therefore, dimensionality reduction is necessary for SZ diagnosis using EEG signals. To reduce the dimensionality of the data, an improved CAO (ICAO) dimensionality reduction method is proposed, which integrates horizontal and vertical crossover approaches with AOA. The optimal feature subset is achieved by satisfying the ICAO conditions, and a fitness function is evaluated based on rough sets for improved accuracy in feature selection. Therefore a Crossover-boosted Archimedes optimization algorithm (AOA) with rough sets for Schizophrenia detection (CAORS-SD) was proposed using multichannel EEG signals from both SZ and normal patients. The signals are decomposed using multivariate empirical mode decomposition into multivariate intrinsic mode functions (MIMFs). Entropy metrics such as spectral entropy, permutation entropy, approximate entropy, sample entropy, and SVD entropy are evaluated on the MIMF domain to detect SZ. The processing time of the kernel support vector machine classifier is minimized with fewer features, reducing the risk Fof overfitting. Accuracy, sensitivity, specificity, precision, and F1-score of the CAORS-SD model should be conducted to diagnose SZ. Therefore, the proposed CAORS-SD method achieves the higher performance of accuracy, sensitivity, specificity, precision, and F1-score values of 96.34, 98.95, 96.86, 98.52, and 96.74% respectively. Also, the CAORS-SD method minimizes the error rate and significantly reduces the execution time.

A machine learning-based model for "In-time" prediction of periprosthetic joint infection.

Previous criteria had limited value in early diagnosis of periprosthetic joint infection (PJI). Here, we constructed a novel machine learning (ML)-derived, "in-time" diagnostic system for PJI and proved its validity. We filtered "in-time" diagnostic indicators reported in the literature based on our continuous retrospective cohort of PJI and aseptic prosthetic loosening patients. With the indicators, we developed a two-level ML model with six base learners including Elastic Net, Linear Support Vector Machine, Kernel Support Vector Machine, Extra Trees, Light Gradient Boosting Machine and Multilayer Perceptron), and one meta-learner, Ensemble Learning of Weighted Voting. The prediction performance of this model was compared with those of previous diagnostic criteria (International Consensus Meeting in 2018 (ICM 2018), etc.). Another prospective cohort was used for internal validation. Based on our ML model, a user-friendly web tool was developed for swift PJI diagnosis in clinical practice. A total of 254 patients (199 for development and 55 for validation cohort) were included in this study with 38.2% of them diagnosed as PJI. We included 21 widely accessible features including imaging indicators (X-ray and CT) in the model. The sensitivity and accuracy of our ML model were significantly higher than ICM 2018 in development cohort (90.6% vs. 76.1%, P = 0.032; 94.5% vs. 86.7%, P = 0.020), which was supported by internal validation cohort (84.2% vs. 78.6%; 94.6% vs. 81.8%). Our novel ML-derived PJI "in-time" diagnostic system demonstrated significantly improved diagnostic potency for surgical decision-making compared with the commonly used criteria. Moreover, our web-based tool greatly assisted surgeons in distinguishing PJI patients comprehensively. Diagnostic Level III.

A Memory-Efficient Federated Kernel Support Vector Machine for Edge Devices.

A federated learning (FL) scheme (denoted as Fed-KSVM) is designed to train kernel support vector machines (SVMs) over multiple edge devices with low memory consumption. To decompose the training process of kernel SVM, each edge device first constructs high-dimensional random feature vectors of its local data, and then trains a local SVM model over the random feature vectors. To reduce the memory consumption on each edge device, the optimization problem of the local model is divided into several subproblems. Each subproblem only optimizes a subset of the model parameters over a block of random feature vectors with a low dimension. To achieve the same optimal solution to the original optimization problem, an incremental learning algorithm called block boosting is designed to solve these subproblems sequentially. After training of the local models, the central server constructs a global SVM model by averaging the model parameters of these local models. Fed-KSVM only increases the iterations of training the local SVM models to save the memory consumption, while the communication rounds between the edge devices and the central server are not affected. Theoretical analysis shows that the kernel SVM model trained by Fed-KSVM converges to the optimal model with a linear convergence rate. Because of such a fast convergence rate, Fed-KSVM reduces the communication cost during training by up to 99% compared with the centralized training method. The experimental results also show that Fed-KSVM reduces the memory consumption on the edge devices by nearly 90% while achieving the highest test accuracy, compared with the state-of-the-art schemes.

Classification of delivery type of pregnant women using support vector machine

One of the ways to reduce maternal mortality is by diagnosing childbirth to find out whether a mother will give birth normally or not so that appropriate treatment can be done. This study aims to improve maternal safety and health by classifying delivery type of pregnant women, either Caesarean or normal types, using the Support Vector Machine method. The dataset used in this study was taken from a hospital in 2020. It consists of 25 attributes and 302 records that include information about the health conditions of pregnant women and babies. Several experiments were performed towards the dataset with and without balancing. Three types of SVM kernels, namely Linear, RBF, and Polynomial kernels, were then implemented to classify the dataset using several variations of parameters of C, gamma, and degree. The validation was performed using several k-fold cross validations. The results of this study show that the highest accuracy is 92.98% at 5-fold cross validation using the RBF kernel with parameters C = 10 and gamma = 1. The performances of the three SVM kernels varied depending on the type of data used.

MedOptNet: Meta-Learning Framework for Few-Shot Medical Image Classification.

In the medical research domain, limited data and high annotation costs have made efficient classification under few-shot conditions a popular research area. This paper proposes a meta-learning framework, termed MedOptNet, for few-shot medical image classification. The framework enables the use of various high-performance convex optimization models as classifiers, such as multi-class kernel support vector machines, ridge regression, and other models. End-to-end training is then implemented using dual problems and differentiation in the paper. Additionally, various regularization techniques are employed to enhance the model's generalization capabilities. Experiments on the BreakHis, ISIC2018, and Pap smear medical few-shot datasets demonstrate that the MedOptNet framework outperforms benchmark models. Moreover, the model training time is also compared to prove its effectiveness in the paper, and an ablation study is conducted to validate the effectiveness of each module.

Dual Kernel Support Vector-based Crossover Red Fox Algorithm: Advancements in Assistive Technology for Hearing-impaired Individuals

Individuals with hearing impairment face several challenges, including difficulties in communication, social interactions, and accessibility to information on various auditory abilities. Innovations range from hearing aids to advanced communication devices and mobile apps. Designing solutions that prioritize user feedback ensures a more inclusive and empowering experience for people with hearing impairment. Assistive technology (AT) endeavors to enhance the daily lives of individuals, fostering greater connectivity, and also plays a crucial role in addressing these challenges. Therefore, an attention dual kernel support vector-based crossover red fox (ADKS-CRF) algorithm is developed for superior performance. This research study proposes a model combining a dual kernel support vector machine with an attention mechanism to implicitly operate in a high-dimensional feature space without computing the transformed vector features. The crossover strategy is incorporated with the red fox optimization algorithm, and the integrated formation of CRF fine-tunes the parameters of the ADKS model, removing the complexity of local optima. This work conducted experiments using raw data from an effective 3D ear acquisition system dataset. Experimental validation is conducted using various evaluation measures to assess effectiveness. The proposed hybrid approach achieves a sensitivity of 97.8%, an F1-score of 97.1%, a specificity of 96.3%, an accuracy of 98.4%, a false alarm rate of 90.8%, and a Matthews correlation coefficient of 97.3%. The comparative analysis evaluates the efficacy of the ADKS-CRF method with various baseline approaches for the development of ATs for hearing-impaired people.

Improvement of Kernel SVM to Enhance Accuracy in Chronic Kidney Disease

Chronic Kidney Disease (CKD) is a highly serious health issue, affecting millions of people worldwide. Early diagnosis and accurate prediction of chronic kidney disease are key factors in successful treatment. One of the approaches used for diagnosing this disease is through machine learning algorithms, specifically the Support Vector Machine (SVM) method. By collecting CKD data that includes various clinical parameters, initial kernel selection as well as various kernels are tested. However, the accuracy of the SVM method can be further improved for better diagnosis. The objective of this research is to enhance accuracy, optimize parameters, and improve the SVM kernel by incorporating the Particle Swarm Optimization (PSO) algorithm. The results of this study indicate that the use of PSO method to improve SVM kernels can significantly enhance accuracy in CKD diagnosis compared to conventional SVM approaches, potentially aiding medical practitioners in early disease diagnosis and better CKD management, which in turn can improve patient prognosis and quality of life

Prediction of Student Performance Based on Behavior using E-Learning During the Covid-19 Pandemic using Support Vector Machine

The COVID-19 crisis has profoundly impacted many sectors globally, including education, necessitating the shift from traditional in-person learning to independent or online learning through various digital platforms. The integrity of e-learning can be ensured by leveraging e-learning behavioral data. The objective of this research is to develop a novel data model to navigate the educational challenges of the COVID-19 era. Previous studies employed the Support Vector Machine (SVM) technique to predict student performance in an e-learning setting, yet they failed to contrast different SVM kernels and their outcomes. In contrast, this study uses SVM and compares three types of kernels: Radial, Polynomial, and Linear. The dataset used for this research was procured from X-API-Edu-Data. The SVM technique was utilized in a unique way to process the data, which comprised 17 variables and 40 observations. Notably, all 17 variables were character variables, with only four being numeric. Two variables, Raisedhands and Discussion, were selected for analysis due to their key role in effective learning and their association with student performance in an e-learning environment. The evaluation of the model was performed using the Topic variable, which represents the subjects in the dataset. The research findings revealed a marked improvement in accuracy compared to earlier studies. Among the three SVM kernels tested - Radial, Polynomial, and Linear, the Polynomial kernel demonstrated superior accuracy with a score of 0.9979. Therefore, the Polynomial model was deemed most appropriate for analyzing the Topic variable. In conclusion, the study indicates that the application of the e-learning method, specifically during the COVID-19 pandemic, proved highly effective in forecasting student performance.

Solving diabetes diagnosis problems using machine learning

This research is devoted to the study of the use of machine learning methods to solve the problem of diagnosing diabetes. The results of using machine learning in the context of diabetes are varied and depend on the methods of data analysis, the models used and the quality of the data provided. Experiments on the Diabetes dataset were conducted in the study using a Naive Bayes classifier model and a linear kernel SVM for a binary classification problem. Models are trained on the training dataset, standardizing features, and evaluated on the test set using confusion, precision, recall, F1-measure, and AUC-ROC metrics. The results obtained confirm that machine learning can improve the accuracy of diagnosing diabetes and classifying its type. This allows for customized treatment plans to be developed, considering the unique characteristics of each patient. Machine learning models are also successful in predicting the likelihood of complications, allowing for preventative measures to be taken. Their use facilitates the integration of data from various sources, enriching patient information. In conclusion, machine learning-based decision support systems assist physicians and patients in making informed decisions.

Comparison of SVM kernels in Credit Card Fraud Detection using GANs

Comparison of SVM kernels in Credit Card Fraud Detection using GANs

An Extensive Analysis of Machine Learning Techniques With Hyper-Parameter Tuning by Bayesian Optimized SVM Kernel for the Detection of Human Lung Disease

An Extensive Analysis of Machine Learning Techniques With Hyper-Parameter Tuning by Bayesian Optimized SVM Kernel for the Detection of Human Lung Disease

Securing Cyberspace: Exploring the Efficacy of SVM (Poly, Sigmoid) and ANN in Malware Analysis

This study presents a comprehensive exploration and comparative analysis of three prominent classification algorithms—Support Vector Machine (SVM) with polynomial and sigmoid kernels, and Artificial Neural Network (ANN)—in the context of malware classification. Leveraging a dataset comprising 5184 samples, including both malware and benign instances, the research systematically evaluates the performance of these algorithms using key metrics such as accuracy, precision, recall, F1 score, and AUC-ROC. The SVM classifier with a polynomial kernel emerges as the top performer, achieving remarkable accuracy (98.08%), precision (98.56%), and recall (97.85%). Its capacity to minimize false positives while maintaining a high true positive rate positions it as a robust tool for accurate malware identification. The sigmoid kernel SVM demonstrates a well-balanced performance, suitable for scenarios requiring a nuanced trade-off between false positives and false negatives. The ANN model, while exhibiting a lower overall accuracy (89.00%), excels in recall (92.61%), showcasing its proficiency in capturing instances of malware. The study underscores the significance of selecting an algorithm aligned with specific application requirements, whether prioritizing precision, recall, or a balanced approach. Furthermore, the research acknowledges the dataset's limitations and calls for future exploration with diverse datasets and additional preprocessing techniques. As cybersecurity threats evolve, the insights provided by this study contribute to the ongoing discourse on developing robust tools for effective malware detection. The findings empower cybersecurity professionals and researchers with valuable considerations for selecting the most suitable classification algorithm in the dynamic landscape of digital security.

Comparison of Machine Learning Algorithms on Classification of Covid-19 Cough Sounds Using MFCC Extraction

Early detection for COVID-19 has now been widely developed. One of the methods used is cough audio detection. This research aims to classify cough audio. Audio feature extraction is performed using MFCC to obtain numerical features. Feature classification is done using SVM, Random Forest, and Naive Bayes methods. Evaluation is done to find the best classification method. The evaluation results in this study show that SVM Kernel RBF produces the best evaluation value with an AUC value of 0.657715.

A machine learning-based universal outbreak risk prediction tool

In order to prevent and control the increasing number of serious epidemics, the ability to predict the risk caused by emerging outbreaks is essential. However, most current risk prediction tools, except EPIRISK, are limited by being designed for targeting only one specific disease and one country. Differences between countries and diseases (e.g., different economic conditions, different modes of transmission, etc.) pose challenges for building models with cross-country and cross-disease prediction capabilities. The limitation of universality affects domestic and international efforts to control and prevent pandemic outbreaks. To address this problem, we used outbreak data from 43 diseases in 206 countries to develop a universal risk prediction system that can be used across countries and diseases. This system used five machine learning models (including Neural Network XGBoost, Logistic Boost, Random Forest and Kernel SVM) to predict and vote together to make ensemble predictions. It can make predictions with around 80%–90 % accuracy from economic, cultural, social, and epidemiological factors. Three different datasets were designed to test the performance of ML models under different realistic situations. This prediction system has strong predictive ability, adaptability, and generality. It can give universal outbreak risk assessment that are not limited by border or disease type, facilitate rapid response to pandemic outbreaks, government decision-making and international cooperation.

Cognizance detection during mental arithmetic task using statistical approach

The handheld diagnosis and analysis are highly dependent on the physiological data in the clinical sector. Detection of the defect in the neuronal-assisted activity raises the challenge to the prevailing treatment that benefits from machine learning approaches. The congregated EEG data is then utilized in design of learning applications to develop a model that classifies intricate EEG patterns into active and inactive segments. During arithmetic problem-solving EEG signal acquired from frontal lobe contributes for intelligence detection. The low intricate statistical parameters help in understanding the objective. The mean of the segmented samples and standard deviation are the features extracted for model building. The feature selection is handled using correlation and Fisher score between {Fp1 and F8} and priority ranking of the regions with enhanced activity are selected for the classifier models to the training net. The R-studio platform is used to classify the data based on active and inactive liability. The radial basis function kernel for support vector machine (SVM) is deployed to substantiate the proposed methodology. The vulnerable regions F1 and F8 for arithmetic activity can be visualized from the correlation fit performed between regions. Using SVM classifier sensitivity of 92.5% is obtained for the selected features. A wide range of clinical problems can be diagnosed using this model and used for brain-computer interface.

N-gram and Kernel Performance Using Support Vector Machine Algorithm for Fake News Detection System

The modern technological advancements have made it simpler for fake news to circulate online. The researchers have developed several strategies to overcome this obstacle, including text classification, distribution network analysis, and human-machine hybrid methods. The most common method is text categorization, and many researchers offer deep learning and machine learning models as remedies. An Indonesian language fake news detection system based on news headlines was developed in this work using the Support Vector Machine (SVM) kernel and n-gram. The objective of this research is to identify the model that produces the best performance outcomes. The system deployment on the web will employ the model that produces the greatest outcomes. According to the research findings, the linear kernel SVM algorithm produces the best results, with an accuracy value of 0.974. Furthermore, the bigram feature used in the development of a classification model does not increase the precision of fake news identification in Indonesian. Utilizing the unigram function yields the most accurate results.

Multi-biometric fusion for enhanced human authentication in information security

In the digital age, information security has taken on greater importance, necessitating the development of strong and trustworthy human authentication techniques. Traditional single-factor authentication schemes, such passwords or PINs, are prone to a number of security flaws. Multi-biometric fusion approaches have become a possible remedy to these restrictions. In comparison to other fusion techniques, feature level fusion is a very popular technique. Features are taken from every biometric attribute in this fusion. Then, the retrieved features are concatenated to create a high dimension final feature vector. In this study, we present a novel method for feature-level fusion employing optimal feature level fusion. The essential features are chosen and identified using a Binary chimp optimized adaptive kernel support vector machine (BCO-AKSVM). According to f1-score (99 %), recall (98 %), accuracy (97 %), precision (88 %), and time computation (1000 ms), the experimental results of the suggested method are analyzed. When compared to current methods, it can show that the proposed BCO-AKSVM achieves the highest performance of information security in human biometric authentication. Multi-biometric fusion is anticipated to play a significant role as technology develops in maintaining secure access control and safeguarding sensitive information in numerous sectors.

Predicting positron emission tomography brain amyloid positivity using interpretable machine learning models with wearable sensor data and lifestyle factors

BackgroundDeveloping a screening method for identifying individuals at higher risk of elevated brain amyloid burden is important to reduce costs and burden to patients in clinical trials on Alzheimer’s disease or the clinical setting. We developed machine learning models using objectively measured lifestyle factors to predict elevated brain amyloid burden on positron emission tomography.MethodsOur prospective cohort study of non-demented, community-dwelling older adults aged ≥ 65 years was conducted from August 2015 to September 2019 in Usuki, Oita Prefecture, Japan. One hundred and twenty-two individuals with mild cognitive impairment or subjective memory complaints (54 men and 68 women, median age: 75.50 years) wore wearable sensors and completed self-reported questionnaires, cognitive test, and positron emission tomography imaging at baseline. Moreover, 99 individuals in the second year and 61 individuals in the third year were followed up. In total, 282 eligible records with valid wearable sensors, cognitive test results, and amyloid imaging and data on demographic characteristics, living environments, and health behaviors were used in the machine learning models. Amyloid positivity was defined as a standardized uptake value ratio of ≥ 1.4. Models were constructed using kernel support vector machine, Elastic Net, and logistic regression for predicting amyloid positivity. The mean score among 10 times fivefold cross-validation repeats was utilized for evaluation.ResultsIn Elastic Net, the mean area under the receiver operating characteristic curve of the model using objectively measured lifestyle factors alone was 0.70, whereas that of the models using wearable sensors in combination with demographic characteristics and health and life environment questionnaires was 0.79. Moreover, 22 variables were common to all machine learning models.ConclusionOur machine learning models are useful for predicting elevated brain amyloid burden using readily-available and noninvasive variables without the need to visit a hospital.Trial registrationThis prospective study was conducted in accordance with the Declaration of Helsinki and was approved by the local ethics committee of Oita University Hospital (UMIN000017442). A written informed consent was obtained from all participants. This research was performed based on the Strengthening the Reporting of Observational Studies in Epidemiology reporting guideline.