Gaussian Kernel Support Vector Machine Research Articles

EEG-based detection of adverse mental state under multi-dimensional unsafe psychology for construction workers at height

Working at height in construction sites is universal but dangerous, which can directly or indirectly lead to numerous injuries and fatalities. Meanwhile, workers' adverse mental state exerts a significant influence on the occurrence of safety accidents. Recent attempts have been made to precisely detect workers' unsafe psychology using electroencephalogram (EEG) technology. Unfortunately, unidimensional psychological factors considered in previous studies cannot represent complicated mental state. To fill this major knowledge gap, this study proposed a framework for comprehensively considering the effects of multi-dimensional critical unsafe psychology (i.e., fear of height, distraction, and mental fatigue) on workers’ adverse mental state at height. Results show that the four support vector machines (SVMs) achieved excellent performance with 96.33%, 96.75%, 95.50%, and 96.50% accuracy, respectively, when inputting the critical EEG features for adverse mental state assessment, verifying the effectiveness of the proposed framework. In addition, the Gaussian kernel SVM achieved 96.50% accuracy and balanced classification performance, making it most applicable to the development of adverse mental state assessment approach. The framework proposed reveals the complex interactions between unsafe psychology and adverse mental states, enriching the theoretical models of occupational safety and mental health. It provides a more comprehensive perspective on the factors influencing unsafe environments at high altitudes. This offers the possibility for the automatic detection of adverse mental states, contributing to a more proactive approach to safety management in high-altitude operations.

A novel machine learning model for efficacy prediction of immunotherapy-chemotherapy in NSCLC based on CT radiomics

Lung cancer is categorized into two main types: non-small cell lung cancer (NSCLC) and small cell lung cancer. Of these, NSCLC accounts for approximately 85% of all cases and encompasses varieties such as squamous cell carcinoma and adenocarcinoma. For patients with advanced NSCLC that do not have oncogene addiction, the preferred treatment approach is a combination of immunotherapy and chemotherapy. However, the progression-free survival (PFS) typically ranges only from about 6 to 8 months, accompanied by certain adverse events. In order to carry out individualized treatment more effectively, it is urgent to accurately screen patients with PFS for more than 12 months under this treatment regimen. Therefore, this study undertook a retrospective collection of pulmonary CT images from 60 patients diagnosed with NSCLC treated at the First Affiliated Hospital of Wenzhou Medical University. It developed a machine learning model, designated as bSGSRIME-SVM, which integrates the rime optimization algorithm with self-adaptive Gaussian kernel probability search (SGSRIME) and support vector machine (SVM) classifier. Specifically, the model initiates its process by employing the SGSRIME algorithm to identify pivotal image features. Subsequently, it utilizes an SVM classifier to assess these features, aiming to enhance the model's predictive accuracy. Initially, the superior optimization capability and robustness of SGSRIME in IEEE CEC 2017 benchmark functions were validated. Subsequently, employing color moments and gray-level co-occurrence matrix methods, image features were extracted from images of 60 NSCLC patients undergoing immunotherapy combined with chemotherapy. The developed model was then utilized for analysis. The results indicate a significant advantage of the model in predicting the efficacy of immunotherapy combined with chemotherapy for NSCLC, with an accuracy of 92.381% and a specificity of 96.667%. This lays the foundation for more accurate PFS predictions and personalized treatment plans.

SGS: SqueezeNet-guided Gaussian-kernel SVM for COVID-19 Diagnosis

AbstractThe ongoing global pandemic has underscored the importance of rapid and reliable identification of COVID-19 cases to enable effective disease management and control. Traditional diagnostic methods, while valuable, often have limitations in terms of time, resources, and accuracy. The approach involved combining the SqueezeNet deep neural network with the Gaussian kernel in support vector machines (SVMs). The model was trained and evaluated on a dataset of CT images, leveraging SqueezeNet for feature extraction and the Gaussian kernel for non-linear classification. The SN-guided Gaussian-Kernel SVM (SGS) model achieved high accuracy and sensitivity in diagnosing COVID-19. It outperformed other models with an impressive accuracy of 96.15% and exhibited robust diagnostic capabilities. The SGS model presents a promising approach for accurate COVID-19 diagnosis. Integrating SqueezeNet and the Gaussian kernel enhances its ability to capture complex relationships and classify COVID-19 cases effectively.

Ischemic stroke prediction using machine learning in elderly Chinese population: The Rugao Longitudinal Ageing Study.

Compared logistic regression (LR) with machine learning (ML) models, to predict the risk of ischemic stroke in an elderly population in China. We applied 2208 records from the Rugao Longitudinal Ageing Study (RLAS) for ischemic stroke risk prediction assessment. Input variables included 103 phenotypes. For 3-year ischemic stroke risk prediction, we compared the discrimination and calibration of LR model and ML methods, where ML methods include Random Forest (RF), Gaussian kernel Support Vector Machines (SVM), Multilayer perceptron (MLP), K-Nearest Neighbors Algorithm (KNN), and Gradient Boosting Decision Tree (GBDT) to develop an ischemic stroke risk prediction model. Age, pulse, waist circumference, education level, β2-microglobulin, homocysteine, cystatin C, folate, free triiodothyronine, platelet distribution width, QT interval, and QTc interval were significant induced predictors of ischemic stroke. For ischemic stroke prediction, the ML approach was able to tap more biochemical and ECG-related multidimensional phenotypic indicators compared to the LR model, which placed more importance on general demographic indicators. Compared to the LR model, SVM provided the best discrimination and calibration (C-index: 0.79vs. 0.71, 11.27% improvement in model utility), with the best performance in both validation and test data. In a comparison of LR with five ML models, the accuracy of ischemic stroke prediction was higher by combining ML with multiple phenotypes. Combined with other studies based on elderly populations in China, ML techniques, especially SVM, have shown good long-term predictive performance, inspiring the potential value of ML use in clinical practice.

Classification of Alzheimer’s Disease Based on White Matter Connectivity Network

Alzheimer’s disease (AD) is one of the most common irreversible brain diseases in the elderly. Mild cognitive impairment (MCI) is an early symptom of AD, and the early intervention of MCI may slow down the progress of AD. However, due to the subtle neuroimaging differences between MCI and normal control (NC), the clinical diagnosis is subjective and easy to misdiagnose. Machine learning can extract depth features from neural images, and analyze and label them to assist the diagnosis of diseases. This paper combines diffusion tensor imaging (DTI) and support vector machine (SVM) to classify AD, MCI, and NC. First, the white matter connectivity network was constructed based on DTI. Second, the nodes with significant differences between groups were screened out by the two-sample t-test. Third, the optimal feature subset was selected as the classification feature by recursive feature elimination (RFE). Finally, the Gaussian kernel support vector machine was used for classification. The experiment tested and verified the data downloaded from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, and the area under the curve (AUC) of AD/MCI and MCI/NC are 0.94 and 0.95, respectively, which have certain competitive advantages compared with other methods.

Improved HHT-microstate analysis of EEG in nicotine addicts.

Substance addiction is a chronic disease which causes great harm to modern society and individuals. At present, many studies have applied EEG analysis methods to the substance addiction detection and treatment. As a tool to describe the spatio-temporal dynamic characteristics of large-scale electrophysiological data, EEG microstate analysis has been widely used, which is an effective method to study the relationship between EEG electrodynamics and cognition or disease. To study the difference of EEG microstate parameters of nicotine addicts at each frequency band, we combine an improved Hilbert Huang Transformation (HHT) decomposition with microstate analysis, which is applied to the EEG of nicotine addicts. After using improved HHT-Microstate method, we notice that there is significant difference in EEG microstates of nicotine addicts between viewing smoke pictures group (smoke) and viewing neutral pictures group (neutral). Firstly, there is a significant difference in EEG microstates at full-frequency band between smoke and neutral group. Compared with the FIR-Microstate method, the similarity index of microstate topographic maps at alpha and beta bands had significant differences between smoke and neutral group. Secondly, we find significant class × group interactions for microstate parameters at delta, alpha and beta bands. Finally, the microstate parameters at delta, alpha and beta bands obtained by the improved HHT-microstate analysis method are selected as features for classification and detection under the Gaussian kernel support vector machine. The highest accuracy is 92% sensitivity is 94% and specificity is 91%, which can more effectively detect and identify addiction diseases than FIR-Microstate and FIR-Riemann methods. Thus, the improved HHT-Microstate analysis method can effectively identify substance addiction diseases and provide new ideas and insights for the brain research of nicotine addiction.

Depthwise convolution based pyramid ResNet model for accurate detection of COVID-19 from chest X-Ray images

ABSTRACT The global pandemic of coronavirus disease 2019 (COVID-19) causes severe respiratory problems in humans. The Chest X-ray (CXR) imaging technique majorly assists in detecting abnormalities in the chest and lung areas caused by COVID-19. Hence, developing an automatic system for CXR-based COVID-19 detection is vital for disease diagnosis. To accomplish this requirement, an enhanced Residual Network (ResNet) model is proposed in this paper for accurate COVID-19 detection. The proposed model combines the Depthwise Separable Convolutional ResNet and Pyramid dilated module(DSC-ResNet-PDM) for deep feature extraction. Employing the DSC layer minimizes the number of parameters to mitigate the overfitting issue. Further, the pyramid dilated module is used for extracting multi-scale features. The extracted features are finally fed into the optimized Medium Gaussian kernel Support Vector Machine classifier (MGKSVM) for COVID-19 detection. The proposed model attained an accuracy of 99.5%, which is comparatively higher than the standard ResNet50 and ResNet101 models.

Decoding transient sEMG data for intent motion recognition in transhumeral amputees

The use of surface electromyographic (sEMG) signals, alongside pattern recognition (PR) systems, is fundamental in the design and control of assistive technologies. Transient sEMG signal epochs at the early beginning of the movement provide important information for upper-limb intent of motion recognition. However, only few studies investigated the role of transient sEMG for myoelectric control architectures. Therefore, in this work, focus was given to transient sEMG signals of intact-limb (IL) subjects and transhumeral amputees (AMP), who performed a series of shoulder movements. The role of the window length for feature extraction was investigated by sub-windowing the transient epochs at 200, 150, 100, and 50 ms window length (WL). Gaussian kernel discriminant analysis (SRKDA) and support vector machine (SVM) were used for recognizing seven classes of motion at different hold-out percentage of training/testing data, i.e. 70%–30%, 60%–40% and 50%–50%. In all the latter conditions, the median classification accuracy and F1 score were greater than 80% for both IL and AMP groups when using SRKDA. Wilcoxon rank sum test was employed to verify possible differences between WL conditions. Although the latter did not show significant differences, 100 ms WL showed the best classification performances for both groups (classification accuracy greater than 90%, near that of a usable PR system). Results demonstrated that a reliable motion intent recognition of shoulder joint in transhumeral amputee patients can be obtained employing transient sEMG epochs. This can be used in a better design of myoelectric control architectures of assistive technologies, involving the upper-limb for clinical use.

A rule-based deep fuzzy system with nonlinear fuzzy feature transform for data classification

In today's fuzzy community, the blending of fuzzy model and deep learning has become one hot topic for the development of more sophisticated and high-powered fuzzy systems. In this study, a rule-based deep fuzzy system with nonlinear fuzzy feature transform for data classification named by NFFT-DFSC is proposed, borrowing the “hierarchically stacked thought” originated from deep learning. Firstly, two fuzzy models with nonlinear fuzzy feature transform function and with decision-making function, and their neuro-fuzzy network implementations are proposed. Subsequently, we stack multiple fuzzy models with nonlinear fuzzy feature transform function and the fuzzy model with decision-making function together in a cascade way, which results in the formation of NFFT-DFSC. Here the stacked fuzzy models with nonlinear fuzzy feature transform function snoops and transforms features of raw data continuously to finally obtain the high-level latent fuzzy features, while the fuzzy model with decision-making function completes classification by exploring the multiple prototypes used to represent the high-level latent fuzzy features of each class. Besides, batch normalization operation and mini-batch gradient descent optimization rooted from the training of deep neural networks are also used to enhance the generalization of NFFT-DFSC and learn its parameters in end-to-end manner, respectively. Experiments between NFFT-DFSCs with three nonlinear mapping functions and 15 benchmark classifiers involving gaussian kernel support vector machine (GSVM), Takagi-Sugeno-Kang classifier (TSK), deep neural network (DNN) and state-of-the-art deep TSK ones on synthetic datasets and nineteen diverse real-world datasets demonstrate that the proposed NFFT-DFSC is a more competitive classifier on classification accuracy.

Machine learning model predicts the occurrence of acute kidney injury after open surgery for abdominal aortic aneurysm repair.

Abdominal aortic aneurysm is a pathological condition in which the abdominal aorta is dilated beyond 3.0 cm. The surgical options include open surgical repair (OSR) and endovascular aneurysm repair (EVAR). Prediction of acute kidney injury (AKI) after OSR is helpful for decision-making during the postoperative phase. To find a more efficient method for making a prediction, this study aims to perform tests on the efficacy of different machine learning models. Perioperative data of 80 OSR patients were retrospectively collected from January 2009 to December 2021 at Xiangya Hospital, Central South University. The vascular surgeon performed the surgical operation. Four commonly used machine learning classification models (logistic regression, linear kernel support vector machine, Gaussian kernel support vector machine, and random forest) were chosen to predict AKI. The efficacy of the models was validated by five-fold cross-validation. AKI was identified in 33 patients. Five-fold cross-validation showed that among the 4 classification models, random forest was the most precise model for predicting AKI, with an area under the curve of 0.90±0.12. Machine learning models can precisely predict AKI during early stages after surgery, which allows vascular surgeons to address complications earlier and may help improve the clinical outcomes of OSR.

Roughness detection method based on image multi-features

Roughness was one of the most visual manifestations of the surface quality of metal parts. It affected the performance and life of the parts. Accurate and efficient roughness grade detection technology was of great significance to smart manufacturing. Traditional machine shops often used roughness comparison sample blocks and stylus profilers to check roughness. However, there were disadvantages such as slow detection speed and high influence by human factors. As a non-destructive testing technique, optical imaging gad already demonstrated to be an effective roughness inspection method. In this paper, a roughness detection approach based on image multi-features was proposed, using part surface images as the research object. First, gray level co-occurrence matrix (GLCM), Gabor transform, and local binary patterns (LBP) were used for the extraction of image texture features. After using principal components analysis to reduce the dimensionality of texture features, multiple texture features were concatenated to form a multi-feature vector. Finally, the multi-feature vectors were input into the Gaussian radial basis kernel support vector machine to classify the part surface images and thus completed the detection of roughness grade.

Damage detection of in-service steel railway bridges using a fine k-nearest neighbor machine learning classifier

Minor areas of surface corrosion in steel railway bridges can grow progressively andlead to localized section losses and structural failure over time. This paper proposes a novel combined damage detection approach for the classification of various extents and degrees of cross section losses due to damages like corrosion using a k-Nearest Neighbor (kNN) machine learning classifier. A Finite Element (FE) model of an in-service railway bridge is developed and validated using vibration data from field testing and these combined FE-field data are trained and tested to classify various corrosion cases following the Australian Standard AS7636. The results show that the proposed technique is practical and highly accurate in classifying damages in steel railway bridges, even if a minor level of steel corrosion is intended to be classified. Furthermore, a comparison between the accuracies of kNN classifier and the Radial Basis Function (RBF) Gaussian kernel Support Vector Machine (SVM) is presented.

Prediction Method of Beijing Electric-Energy Substitution Potential Based on a Grid-Search Support Vector Machine

Recently, “power cuts” and “coal price surges” have been significant concerns of individuals and societies. The main reasons for a power cut are a recent rapid increase in power consumption, shortage of thermal coal or the large shutdown capacity of thermal power units, resulting in a tight power supply in the power grid. In recent years, the shortage of fossil resources has led to frequent energy crises. In the context of carbon peaks and carbon neutralization, how to better develop electric-energy substitution and eliminate the dependence on fossil energy has become a problem that needs to be solved at present. In this paper, the influencing factors of electric-energy substitution in Beijing are analyzed, and the indexes affecting the electric-energy substitution are outlined. By constructing various machine-learning models, the prediction is performed. The results show that the Gaussian kernel support vector machine model based on a grid search has a good prediction effect on the electric-energy substitution potential in Beijing, which has certain guiding significance for electric-energy substitution potential analysis.

Learning-based fault diagnosis of air brake system using wheel speed data

Faults in the air brake system used in Heavy Commercial Road Vehicles (HCRVs) would adversely affect the vehicle’s dynamic performance, and hence their prompt detection is critical for vehicle safety. This paper first investigates the effect of air brake system faults through extensive hardware-in-loop experiments. These faults were observed to degrade the braking response, yaw stability, and vehicle braking distance. In many countries, an antilock brake system is mandatory in HCRVs, and wheel speed data are readily available. Inspired by this, the feasibility of using wheel speed data to detect faults is investigated in this study. As an initial step of predictive maintenance, a fault diagnostic scheme based on a supervised learning algorithm, Support Vector Machine (SVM) that uses only wheel speed data has been developed. The SVM algorithm’s efficacy was tested for 1937 test cases that encompassed a wide range of operating conditions. It was found that a Gaussian kernel SVM (G-SVM) provided a good classification accuracy of 96.54%, demonstrating its ability to predict a faulty condition accurately. The standard deviation of G-SVM’s prediction accuracy for five groups of data sets with 100 instances was found to be 1.57%, which shows that the model is more precise to predict the fault/no-fault condition of the air brake system.

Design of thermal imaging-based health condition monitoring and early fault detection technique for porcelain insulators using Machine learning

The inspection of insulator faults is an important task to prevent catastrophic failures in the operation of an electric substation. Manual inspection of overhead power line insulators can be very dangerous owning to the presence of high voltage in power sub-stations. Hence, in this paper, we present an infrared thermal (IRT) camera based non-invasive computer vision system for automatic monitoring and visual inspection of overhead on line power insulators. In the proposed work, initially, an optimal threshold method is applied to segment the region of interest (ROI) in IRT images. Subsequently, various geometrical, morphological, intensity and statistical features are computed from the segmented ROI, which are eventually utilized as an input to Gaussian kernel support vector machine to classify the different type of faults in insulator images. Computer vision based automatic inspection of insulators can play an important role from environment as well as human safety point of view. Timely inspection of insulators can ensure the environmental safety through prevention of fire that may be caused due to the insulator failures leading to the sudden breakdown of high-power lines. The proposed system achieved the true positive rate (TPR), and false negative rate (FNR) of 97.3%, and 2.66%, respectively. Whereas, the system obtained the Positive Predictive Value (PPV) and False Discovery Rates (FDR) of about 97% and 3%, respectively with the accuracy of 0.97 on the receiver operating characteristics (ROC) curve. The performance of the proposed method is compared with other existing state of the art methods and found that our method outperformed over them. Hence, we recommend proposed system to detect the severity of faults in IRT images long before any catastrophic failures take place at power sub-stations.

Computer-Aided Detection of COVID-19 from CT Images Based on Gaussian Mixture Model and Kernel Support Vector Machines Classifier.

COVID-19 is a virus that has been declared an epidemic by the world health organization and causes more than 2 million deaths in the world. To achieve this, computer-aided automatic diagnosis systems are created on medical images. In this study, an image processing and machine learning-based method is proposed that enables segmenting of CT images taken from COVID-19 patients and automatic detection of the virus through the segmented images. The main purpose of the study is to automatically diagnose the COVID-19 virus. The study consists of three basic steps: preprocessing, segmentation and classification. Image resizing, image sharpening, noise removal, contrast stretching processes are included in the preprocessing phase and segmentation of images with Expectation–Maximization-based Gaussian Mixture Model in the segmentation phase. In the classification stage, COVID-19 is classified as positive and negative by using kNN, decision tree, and two different ensemble methods together with the kernel support vector machines method. In the study, two different CT datasets that are open to the public and a mixed dataset created by combining these datasets were used. The best accuracy values for Dataset-1, Dataset-2 and Mixed Dataset are 98.5%, 86.3%, 94.5%, respectively. The achieved results prove that the proposed approach advances state-of-the-art performance. Within the scope of the study, a GUI that can automatically detect COVID-19 has been created.

Individual Tree Crown Delineation for the Species Classification and Assessment of Vital Status of Forest Stands from UAV Images

Monitoring the structure parameters and damage to trees plays an important role in forest management. Remote-sensing data collected by an unmanned aerial vehicle (UAV) provides valuable resources to improve the efficiency of decision making. In this work, we propose an approach to enhance algorithms for species classification and assessment of the vital status of forest stands by using automated individual tree crowns delineation (ITCD). The approach can be potentially used for inventory and identifying the health status of trees in regional-scale forest areas. The proposed ITCD algorithm goes through three stages: preprocessing (contrast enhancement), crown segmentation based on wavelet transformation and morphological operations, and boundaries detection. The performance of the ITCD algorithm was demonstrated for different test plots containing homogeneous and complex structured forest stands. For typical scenes, the crown contouring accuracy is about 95%. The pixel-by-pixel classification is based on the ensemble supervised classification method error correcting output codes with the Gaussian kernel support vector machine chosen as a binary learner. We demonstrated that pixel-by-pixel species classification of multi-spectral images can be performed with a total error of about 1%, which is significantly less than by processing RGB images. The advantage of the proposed approach lies in the combined processing of multispectral and RGB photo images.

Automatic Detection of Short-Term Atrial Fibrillation Segments Based on Frequency Slice Wavelet Transform and Machine Learning Techniques.

Atrial fibrillation (AF) is the most frequently encountered cardiac arrhythmia and is often associated with other cardiovascular and cerebrovascular diseases, such as ischemic heart disease, chronic heart failure, and stroke. Automatic detection of AF by analyzing electrocardiogram (ECG) signals has an important application value. Using the contaminated and actual ECG signals, it is not enough to only analyze the atrial activity of disappeared P wave and appeared F wave in the TQ segment. Moreover, the best analysis method is to combine nonlinear features analyzing ventricular activity based on the detection of R peak. In this paper, to utilize the information of the P-QRS-T waveform generated by atrial and ventricular activity, frequency slice wavelet transform (FSWT) is adopted to conduct time-frequency analysis on short-term ECG segments from the MIT-BIH Atrial Fibrillation Database. The two-dimensional time-frequency matrices are obtained. Furthermore, an average sliding window is used to convert the two-dimensional time-frequency matrices to the one-dimensional feature vectors, which are classified using five machine learning (ML) techniques. The experimental results show that the classification performance of the Gaussian-kernel support vector machine (GKSVM) based on the Bayesian optimizer is better. The accuracy of the training set and validation set are 100% and 93.4%. The accuracy, sensitivity, and specificity of the test set without training are 98.15%, 96.43%, and 100%, respectively. Compared with previous research results, our proposed FSWT-GKSVM model shows stability and robustness, and it could achieve the purpose of automatic detection of AF.

Fast Gaussian kernel support vector machine recursive feature elimination algorithm

Gaussian kernel support vector machine recursive feature elimination (GKSVM-RFE) is a method for feature ranking in a nonlinear way. However, GKSVM-RFE suffers from the issue of high computational complexity, which hinders its applications. This paper investigates the issue of computational complexity in GKSVM-RFE, and proposes two fast versions for GKSVM-RFE, called fast GKSVM-RFE (FGKSVM-RFE), to speed up the procedure of recursive feature elimination in GKSVM-RFE. For this purpose, we design two kinds of ranking scores based on the first-order and second-order approximate schemes by introducing approximate Gaussian kernels. In iterations, FGKSVM-RFE fast calculates approximate ranking scores according to approximate schemes and ranks features based on approximate ranking scores. Experimental results reveal that our proposed methods can faster perform feature ranking than GKSVM-RFE and have compared performance to GKSVM-RFE.

Stability analysis of distributed smart grid based on machine learning

Decentralized Smart Grid (DSG) is a new technology proposed to power networks with elastic nodes. It can realize dynamic electricity price demand response without large-scale transformation of the infrastructure.In order to analyze the system stability of DSG, six representative machine learning classification models were applied to analyze the stability data of 10,000 samples of the 4-node system. Combined with the requirements of power system security, stability and economic performance, the effect of each classification model on the stability prediction of DSG system was tested.The test results showed that the model with a Gaussian kernel basis function kernel support Vector machine (RBF SVM) was suitable for data analysis, with the accuracy up to 97.10% and the F metric up to 0.977.CART decision tree model is suitable for real-time forecasting of power system. Under the requirement of ensuring real-time forecasting, its accuracy can reach 84.90% and F metric can reach 0.882. Its modeling and prediction calculation requirements are only 0.98% and 1.59% of that of RBF SVM model respectively.