Traditional Support Vector Research Articles

Prediction of CO<sub>2</sub>, CH<sub>4</sub> diffusion fluxes at the water-air interface and analysis on its influencing factors using machine learning algorithms in the Three Gorges Reservoir

传统的水-气界面温室气体通量的监测方法具有诸多局限,对其影响因素的分析也大多基于数学统计层面。对此,本研究提供了一种较为新颖的研究和分析方法——基于机器学习的数据预测和分析。本研究采用2种经典机器学习算法——随机森林(RF)和支持向量机(SVM)和2种深度学习算法——卷积神经网络(CNN)和长短时记忆神经网络(LSTM),通过环境因素预测水库水-气界面CO₂和CH₄扩散通量。此外,采用RF中的特征重要性评估和经典算法决策树(DT),对环境因素和水库温室气体扩散通量的关系进行了全新角度的数据挖掘和分析。结果表明:深度学习算法的预测效果均较好,经典机器学习算法中RF预测效果显著优于SVM。LSTM和RF分别产生了最优的CO₂扩散通量和CH₄扩散通量的预测精度,均方根误差(RMSE)分别为0.424 mmol/(m²·h)和0.140 μmol/(m²·h),预测值与实测值的R²分别为0.960和0.758。RF的特征重要性评估表明沉积物因子和营养因子均为影响CO₂和CH₄扩散通量的关键因子,气候因子和水环境因子相较次之。采用决策树描绘决定CO₂扩散通量源和汇的环境因子的极限阈值,决策树对所有样本的分类准确性高达100%,且其结果还表明低浓度的溶解无机碳和碱性条件有利于水体成为CO₂汇。因此,使用机器学习算法预测和分析水库水-气界面温室气体通量的潜力巨大。;Traditional methods for monitoring greenhouse gas fluxes at the water-air interface in reservoirs have many limitations. The analysis on its influencing factors is also mainly based on mathematical statistics. This study provides an innovative approach by using machine learning algorithms. In this study, two traditional machine learning algorithms (Random forests (RF) and Support vector machine (SVM)) and two deep learning algorithms (Convolutional neural network (CNN) and long and short term memory neural network (LSTM)) were applied to predict CO₂ and CH₄ diffusion fluxes. In addition, the feature importance assessment in RF and the decision tree (DT) are used to analyze the relationship between environmental factors and GHG diffusion fluxes in reservoirs from a new perspective. The results showed that deep learning produced excellent prediction accuracy, whereas prediction accuracy of RF was significantly better than SVM in traditional machine learning. LSTM and RF yielded optimal accuracy in predicting CO₂ flux and CH₄ flux, respectively. The root mean square error (RMSE) was 0.424 mmol/(m²·h) and 0.140 μmol/(m²·h) and R² of the predicted and measured values were 0.960 and 0.758, respectively. RF identified sediment and nutrient as critical environmental factors to GHG fluxes, followed by climate factors and water environment factors. Lastly, a decision tree was used innovatively to depict the limiting threshold of environmental factors that determines the source or sink of CO₂. The classification accuracy of this decision tree is as high as 100% in this study. The results of decision tree also showed that low dissolved inorganic carbon concentration and alkaline conditions are favorable for water to absorb atmospheric CO₂. These results demonstrate the great potential of using machine learning algorithms to predict and analyze GHG fluxes at the water-air interface in reservoirs.

Just-In-Time Learning Based Functional Spectral Data Modeling for In-Situ Measurement of Slurry Component Concentrations via Infrared Spectroscopy

For using the infrared spectroscopy to conduct in-situ measurement of slurry component concentrations during batch crystallization or fermentation process in engineering practice, a novel just-in-time learning (JITL) based functional modelling method is proposed for spectral data analysis in this paper to improve on-line measurement accuracy, by addressing engineering issues of very limited assay samples, spectral absorption nonlinearity and high-dimensional spectral variables. The traditional univariate or multivariate partial least-squares (PLS) model is subtly extended to the corresponding functional form for describing the relationship between the measured spectra and slurry component concentrations, such that the salient problem of only a small number of slurry component concentration samples available in practice for spectral data modeling could be effectively solved. The wavelet functions are adopted to properly approximate the measured spectral curves owing to their multi-scale and orthogonal properties. Accordingly, a functional spectral data model is established by wavelet functional PLS for on-line measurement of single and multiple component concentrations, respectively. Meanwhile, a JITL strategy is introduced to select the most representative samples for such modeling, which could efficiently handle the disparity issue involved with real-time samples and guarantee the model validity. Experimental results show that root mean square error of model prediction on the solution concentration during L-glutamic acid cooling crystallization process and multiple componts of ethanol fermentation process, could be evidently reduced over 40% and 50%, respectively, compared with the traditional PLS and support vector regression methods.

A Deep Learning Method for Facies Recognition from Core Images and Its Application: A Case Study of Mackay River Oil Sands Reservoir

There is a large amount of drilling core data in the Mackay River oil sands block in Canada, and the accurate identification of facies from the cores is important and necessary for the understanding of the subsurface reservoir. The traditional recognition method of facies from cores is by human work and is very time consuming. Furthermore, the results are different according to different geologists because of the subjective judgment criterion. An efficient and objective method is important to solve the above problem. In this paper, the deep learning image-recognition algorithm is used to automatically and intelligently recognize the facies type from the core image. Through a series of high-reliability preprocessing operations, such as cropping, segmentation, rotation transformation, and noise removal of the original core image, that have been manually identified, the key feature information in the images is extracted based on the ResNet50 convolutional neural network. On the dataset of about 200 core images from 13 facies, an intelligent identification system of facies from core images is constructed, which realizes automatic facies identification from core images. Comparing this method with traditional convolutional neural networks and support vector machines (SVM), the results show that the recognition accuracy of this model is as high as 91.12%, which is higher than the other two models. It is also shown that for a relatively special dataset, such as core images, it is necessary to rely on their global features in order to classify them, and, with a large similarity between some of the categories, it is extremely difficult to classify them. The selection of a suitable neural network model can have a great impact on the accuracy of recognition results. Then, the recognized facies are input as hard data to construct the three-dimensional facies model, which reveals the complex heterogeneity and distribution of the subsurface reservoir for further exploration and development.

Precise Multi-Class Classification of Brain Tumor via Optimization Based Relevance Vector Machine

The objective of this research is to examine the use of feature selection and classification methods for distinguishing different types of brain tumors. The brain tumor is characterized by an anomalous proliferation of brain cells that can either be benign or malignant. Most tumors are misdiagnosed due to the variability and complexity of lesions, which reduces the survival rate in patients. Diagnosis of brain tumors via computer vision algorithms is a challenging task. Segmentation and classification of brain tumors are currently one of the most essential surgical and pharmaceutical procedures. Traditional brain tumor identification techniques require manual segmentation or handcrafted feature extraction that is error-prone and time-consuming. Hence the proposed research work is mainly focused on medical image processing, which takes Magnetic Resonance Imaging (MRI) images as input and performs preprocessing, segmentation, feature extraction, feature selection, similarity measurement, and classification steps for identifying brain tumors. Initially, the median filter is practically applied to the input image to reduce the noise. The graph-cut segmentation technique is used to segment the tumor region. The texture feature is extracted from the output of the segmented image. The extracted feature is selected by using the Ant Colony Optimization (ACO) algorithm to improve the performance of the classifier. This probabilistic approach is used to solve computing issues. The Euclidean distance is used to calculate the degree of similarity for each extracted feature. The selected feature value is given to the Relevance Vector Machine (RVM) which is a multi-class classification technique. Finally, the tumor is classified as abnormal or normal. The experimental result reveals that the proposed RVM technique gives a better accuracy range of 98.87% when compared to the traditional Support Vector Machine (SVM) technique.

Recognition of Corrosion State of Water Pipe Inner Wall Based on SMA-SVM under RF Feature Selection

To solve the problem of low detection accuracy of water supply pipeline internal wall damage, a random forest algorithm with simplified features and a slime mold optimization support vector machine detection method was proposed. Firstly, the color statistical characteristics, gray level co-occurrence matrix, and gray level run length matrix features of the pipeline image are extracted for multi-feature fusion. The contribution of the fused features is analyzed using the feature simplified random forest algorithm, and the feature set with the strongest feature expression ability is selected for classification and recognition. The global search ability of the slime mold optimization algorithm is used to find the optimal kernel function parameters and penalty factors of the support vector machine model. Finally, the optimal parameters are applied to support the vector machine model for classification prediction. The experimental results show that the recognition accuracy of the classification model proposed in this paper reaches 94.710% on the data sets of different corrosion forms on the inner wall of the pipeline. Compared with the traditional Support Vector Machines (SVM) classification model, the SVM model based on differential pollination optimization, the SVM model based on particle swarm optimization, and the back propagation (BP) neural network classification model, it is improved by 4.786%, 3.023%, 4.030%, and 0.503% respectively.

Improved Support Vector Machine Personal Credit Rating Model

Credit consumption has become the choice of more and more people. While the expansion of credit consumption brings convenience, the consequent problem of discredit brings incalculable loss to trust institutions, such as intentional arrears, malicious overdraft consumption, etc. to the trust in the operation of the huge losses caused. On the one hand, our country is unable to establish perfect personal credit record, customer credit information is difficult to share, access to relevant data is limited, personal credit system is not perfect, therefore, it is necessary to establish a perfect and automatic personal credit evaluation system, identify the personal credit risk scientifically, and realize the maximization of bank credit income. In the bank risk management, the main problem is how to measure and avoid the bank's financial risk, and personal credit assessment is the most difficult and important part. In this paper, we use the XGBOOST algorithm to sort the importance of features, and select the number of features and the specific features according to the relationship between the model accuracy and the number of features, then 80% of the data was taken into the traditional Support vector machine model as a training set and tested with the remaining data with low accuracy, so that, in terms of individual credit risk assessment, this paper introduces the quadric surface into the traditional Support vector machine and builds the kernel-free quadric surface Support vector machine model, which improves the accuracy by 9.8 percentage points and has a guiding significance for the bank reference risk control.

A kernel-free Laplacian quadratic surface optimal margin distribution machine with application to credit risk assessment

This paper proposes a novel binary classification approach named Laplacian quadratic surface optimal margin distribution machine (LapQSODM), for semisupervised learning. This new model exploits the geometric information embedded in unlabeled samples through a manifold regularizer to overcome the problem of insufficient labeled samples. Different from the traditional support vector machines (SVMs) based on the largest minimum margin idea and using the kernel technique to address the nonlinearity in the datasets, our proposed model optimizes the margin distribution and directly generates a quadratic surface to perform the classification. This new model not only improves the generalization performance but also avoids the difficulty of searching for an appropriate kernel function and tuning its parameters. For the regular-scale datasets, we extend the classical conjugate gradient algorithm for the proposed method and design an easy iterative method to calculate its exact step size; for the large-scale datasets, we develop an unbiased estimation for the LapQSODM gradient by utilizing one or several samples and design a stochastic gradient descent with variance reduction (SVRG) algorithm that is efficient and effective. Then, we conduct a comprehensive numerical experiment on both artificial and public benchmark datasets. The experimental results show LapQSODM has a better generalization performance than most of the well-known benchmark methods and is more robust due to the optimization of the margin distribution. Finally, we apply LapQSODM to three realistic credit risk assessment problems. The promising numerical results demonstrate the great potential and ability of our model in the real practice.

Efficient and Low-Cost Skin Cancer Detection System Implementation with a Comparative Study Between Traditional and CNN-Based Models

Medical image classification is an essential task in the field of combining medical applications with Artificial Intelligence. This study is carried out to introduce an accurate, precise method for skin cancer recognition. This research investigates the performance of classifying skin cancer dataset HAM10000 using ResNet50, MobileNet, and the traditional Support Vector Machines (SVM) model. The dataset combines seven cancer types: Actinic Keratosis, Basal Cell Carcinoma, Benign Keratosis, Dermatofibroma, Melanoma, Melanocytic Nevus, and Vascular Lesion. The SVM classifier is designed to employ a Histogram of Oriented Gradient (HOG) features with Principle Component Analysis (PCA). Moreover, the Synthetic Minority Oversampling Technique is used to balance the dataset. Additionally, six conventional machine learning (ML) methods are used to compare the results with the calculation of precision, recall, F1 Score, and accuracy. The results confirm that the SVM method outperforms the other algorithms with an accuracy of 99.15%. The novelty contribution of this research activity is mainly based on the development of a high accuracy, low computational complex machine method for skin cancer types recognition in the domain of medical image classification.

An accident diagnosis method of pressurized water reactor based on BI-LSTM neural network

Due to the lack of accident data, previous researchers used accident data obtained from system code simulation to train and verify the machine learning models. In this study, a long short-term memory (LSTM) neural network was used to construct a pressurized water reactor (PWR) accident diagnosis model. The model was trained and tested using simulation data of a PWR based on the deterministic analysis method and uncertainty analysis method. The accuracy of the LSTM model based on deterministic analysis and uncertainty analysis are 96.67% and 98.77%, respectively. The LSTM model developed in the present study is superior to traditional mathematical statistics and support vector machine (SVM) models in accident diagnosis, especially for diagnoses with uncertain data. The results of this study show that the LSTM model has practical value in reactor accident diagnosis.

Analysis of Proposed and Traditional Boosting Algorithm with Standalone Classification Methods for Classifying Gene Expresssion Microarray Data Using a Reject Option

ABSTRACT In medical field, accurate decisions are very important as they risk human lives. decision support system (DSS) plays important role in making accurate decisions and used for classification/prediction. In gene expression analysis, genes are not only inflated by the external environmental conditions but also the expression values of certain genes are affected (like cancer, obesity etc). in this study, various traditional (Support Vector Machine, Decision Trees, and Linear Discriminant Analysis, naïve Bayes, logistic regression, and multilayer perceptron) and proposed methods (combination of traditional with ensemble and probabilistic classifiers) are used in order to perform the classification and prediction analysis. In this study we used the publicly available datasets comprised of Lymphoid, Leukemia and Colon Cancer. The classification performance on Colon dataset with traditional methods was obtained with accuracy (56%) and proposed probabilistic ensemble methods with accuracy (88%). For dataset, Leukemia, the accuracy was obtained using traditional methods (78%) and proposed methods (92%). Similarly, on Lymphoid dataset, the traditional methods yielded accuracy (75%) and proposed methods (87%). The results revealed that proposed methods yielded the improved detection performance. The proposed methods can be used as a better predictor for early diagnosis and improved diagnosis to improve the healthcare systems.

Uncertainty Ordinal Multi-Instance Learning for Breast Cancer Diagnosis.

Ordinal multi-instance learning (OMIL) deals with the weak supervision scenario wherein instances in each training bag are not only multi-class but also have rank order relationships between classes, such as breast cancer, which has become one of the most frequent diseases in women. Most of the existing work has generally been to classify the region of interest (mass or microcalcification) on the mammogram as either benign or malignant, while ignoring the normal mammogram classification. Early screening for breast disease is particularly important for further diagnosis. Since early benign lesion areas on a mammogram are very similar to normal tissue, three classifications of mammograms for the improved screening of early benign lesions are necessary. In OMIL, an expert will only label the set of instances (bag), instead of labeling every instance. When labeling efforts are focused on the class of bags, ordinal classes of the instance inside the bag are not labeled. However, recent work on ordinal multi-instance has used the traditional support vector machine to solve the multi-classification problem without utilizing the ordinal information regarding the instances in the bag. In this paper, we propose a method that explicitly models the ordinal class information for bags and instances in bags. Specifically, we specify a key instance from the bag as a positive instance of bags, and design ordinal minimum uncertainty loss to iteratively optimize the selected key instances from the bags. The extensive experimental results clearly prove the effectiveness of the proposed ordinal instance-learning approach, which achieves 52.021% accuracy, 61.471% sensitivity, 47.206% specificity, 57.895% precision, and an 59.629% F1 score on a DDSM dataset.

Discussing the Construction of a Budget Management System Combining Multimedia Technology and Financial Risk Management

The development of modern technology allows enterprises to continuously expand the means of financial management. The use of advanced information technology and modern management means makes the financial management of enterprises continue to improve, but there are also many problems. In the face of various types of risks that enterprises may face at present, in addition to strengthening financial accounting, enterprises should also recognize the various risks that the double-edged sword of financial management informatization may bring to enterprise financial management and carefully analyze the reasons for the emergence of risks, from the ideological and technical means to continuously improve the entire process of financial management informatization, which is an important topic for every financial worker to discuss. This paper mainly studies and innovates the data mining process and the support vector machine model and designs the data preprocessing method in the data mining process to perform feature selection and optimize the parameters of the support vector machine model. The specific process is as follows: based on CRISP-DM, the industry-standard model of the data mining process, a series of data preprocessing, eigenvalue extraction, parameter optimization, training set pruning, and other methods are designed from business understanding, data understanding, data preparation, and other aspects to improve data quality. In the traditional support vector machine, when the test sample is located at the boundary point of the hyperplane, the judgment may be wrong. In the aspect of SVM model improvement, according to the discrimination method of SVM, the weighted K-nearest neighbor algorithm is introduced to redistinguish the qualified test samples in the feature space. From the whole process of data mining, a data mining system is designed, which is “data preprocessing standardization + genetic algorithm feature selection + training set pruning + support vector machine classifier discrimination optimization.” Finally, this paper uses a series of data mining optimization methods designed to mine the actual financial data of listed companies and has achieved good results.

MIFNN: Molecular Information Feature Extraction and Fusion Deep Neural Network for Screening Potential Drugs.

Molecular property prediction is essential for drug screening and reducing the cost of drug discovery. Current approaches combined with deep learning for drug prediction have proven their viability. Based on the previous deep learning networks, we propose the Molecular Information Fusion Neural Network (MIFNN). The features of MIFNN are as follows: (1) we extracted directed molecular information using 1D-CNN and the Morgan fingerprint using 2D-CNN to obtain more comprehensive feature information; (2) we fused two molecular features from one-dimensional and two-dimensional space, and we used the directed message-passing method to reduce the repeated collection of information and improve efficiency; (3) we used a bidirectional long short-term memory and attention module to adjust the molecular feature information and improve classification accuracy; (4) we used the particle swarm optimization algorithm to improve the traditional support vector machine. We tested the performance of the model on eight publicly available datasets. In addition to comparing the overall classification capability with the baseline model, we conducted a series of ablation experiments to verify the optimization of different modules in the model. Compared with the baseline model, our model achieved a maximum improvement of 14% on the ToxCast dataset. The performance was very stable on most datasets. On the basis of the current experimental results, MIFNN performed better than previous models on the datasets applied in this paper.

An ISVM Algorithm Based on High-Dimensional Distance and Forgetting Characteristics

In the face of the batch, dynamic access data, or the flow of data that continuous changes over time, the traditional support vector machine algorithm cannot dynamically adjust the previous classification model. To overcome this shortcoming, the incremental support vector machine (ISVM) algorithm is proposed. However, many incremental support vector algorithms still have shortcomings such as low efficiency, memory limitation, and poor generalization. This paper puts forward the new ISVM algorithm, HDFC-ISVM ∗ algorithm, based on the high-dimensional distance and forgetting characteristics. This paper firstly proposes the original HDFC-ISVM algorithm that first learns the distribution characteristics of the samples according to the distance between the samples and the normative hyperplane. Then, it introduces the forgetting factor. In the incremental learning process, the classifier gradually accumulates the spatial distribution knowledge of samples, eliminates the samples that have no contributions to the classifier, and selectively forgets some useless samples according to the forgetting factor, which overcomes the shortcomings such as low efficiency and poor accuracy of some algorithms. But, the original HDFC-ISVM algorithm is sensitive to parameters, and different settings of the parameters have a great impact on the final classification accuracy of the algorithm. Therefore, on the basis of the original algorithm, an improved algorithm HDFC-ISVM ∗ based on the adjustments to the initialization strategy and updating rules of the forgetting factor is proposed. The initialization strategy and updating rules of the forgetting factor are adjusted to adapt datasets with different distributions in this improved algorithm. The rationality of the improved strategy about the forgetting factor is discussed theoretically. At the same time, the proposed algorithm has better classification accuracy, classification efficiency, and better generalization ability than other algorithms, which is verified by experiments.

ResNet Model Automatically Extracts and Identifies FT-NIR Features for Geographical Traceability of Polygonatum kingianum.

Medicinal plants have incredibly high economic value, and a practical evaluation of their quality is the key to promoting industry development. The deep learning model based on residual convolutional neural network (ResNet) has the advantage of automatic extraction and the recognition of Fourier transform near-infrared spectroscopy (FT-NIR) features. Models are difficult to understand and interpret because of unknown working mechanisms and decision-making processes. Therefore, in this study, artificial feature extraction methods combine traditional partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM) models to understand and compare deep learning models. The results show that the ResNet model has significant advantages over traditional models in feature extraction and recognition. Secondly, preprocessing has a great impact on the feature extraction and feature extraction, and is beneficial for improving model performance. Competitive adaptive reweighted sampling (CARS) and variable importance in projection (VIP) methods screen out more feature variables after preprocessing, but the number of potential variables (LVs) and successive projections algorithm (SPA) methods obtained is fewer. The SPA method only extracts two variables after preprocessing, causing vital information to be lost. The VIP feature of traditional modelling yields the best results among the four methods. After spectral preprocessing, the recognition rates of the PLS-DA and SVM models are up to 90.16% and 88.52%. For the ResNet model, preprocessing is beneficial for extracting and identifying spectral image features. The ResNet model based on synchronous two-dimensional correlation spectra has a recognition accuracy of 100%. This research is beneficial to the application development of the ResNet model in foods, spices, and medicinal plants.

Correntropy long short term memory soft sensor for quality prediction in industrial polyethylene process

A typical challenge for construction of accurate soft sensors in the process industries is that industrial process data often contains various noise and outliers. Inspired by correntropy in tackling non-Gaussian noise effectively, a maximum correntropy criterion-based long short term memory (MCC-LSTM) neural network is proposed to develop a reliable soft sensor model for quality prediction. Without tedious data preprocessing approaches, the MCC-LSTM adopts the objective function using the maximum correntropy criterion (MCC) centered on a Gaussian kernel, which assigns relatively smaller weights to outliers automatically. Once the MCC-LSTM model is constructed, the outliers can be identified and their negative effects on the prediction can be reduced to some extent. Consequently, the prediction performance can be enhanced for modeling of process data with uncertainties. Additionally, an index is introduced to assess the performance of prediction models when data contain outliers and noise. A numerical example and an industrial polyethylene process demonstrate that the MCC-LSTM soft sensor can achieve more reliable predictive performance compared to traditional LSTM and support vector machine candidates.

An uncertain support vector machine based on soft margin method.

Traditional support vector machines (SVMs) play an important role in the classification of precise data. However, due to various reasons, available data are sometimes imprecise. In this paper, uncertain variables are adopted to describe the imprecise data, and an uncertain support vector machine (USVM) is built for linearly alpha-nonseparable sets based on soft margin method, where a penalty coefficient is utilized as the trade-off between the maximum margin and the sum of slack variables. Then the equivalent crisp model is derived based on the inverse uncertainty distributions. Numerical experiments are designed to illustrate the application of the soft margin USVM. Finally, metrics, such as accuracy, precision, and recall are used to evaluate the robustness of the proposed model.

Comparisons of deep learning and machine learning while using text mining methods to identify suicide attempts of patients with mood disorders

BackgroundSuicide attempt is one of the most severe consequences for patients with mood disorders. This study aimed to perform deep learning and machine learning while using text mining to identify patients with suicide attempts and to compare their effectiveness. MethodsA total of 13,100 patients with mood disorders were selected. Two traditional text mining methods, logistic regression and Support vector machine (SVM), and one deep learning model (Convolutional neural network, CNN) were adopted to perform overall analysis and gender-specific subgroup analysis of patients to identify suicide attempts. The classification effectiveness of these models was evaluated by accuracy, F1-value, precision, recall, and the area under Receiver operator characteristic curve (ROC). ResultsCNN's results were greater than the other two for all indicators except recall which was slightly smaller than SVM in male subgroup analysis. The accuracy values of the CNN were 98.4 %, 98.2 %, and 98.5 % in the overall analysis and the subgroup analysis for males and females, respectively. The results of McNemar's test showed that CNN and SVM models' predictions were statistically different from the logistic regression model's predictions in the overall analysis and the subgroup analysis for females (P < 0.050). LimitationsA fixed number of features were selected based on document frequency to train models; this was a single-site study. ConclusionsCNN model was a better way to detect suicide attempts in patients with mood disorders prior to hospital admission, saving time and resources in recognizing high-risk patients and preventing suicide.

Application of Graph Neural Network in Driving Fatigue Detection Based on EEG Signals.

The objective of this article is to solve the current social phenomenon of a large number of fatigue driving, so that social safety becomes more stable in the future, and the detection and application of driving fatigue are more meaningful. This article aims to study the application of graph neural network (GNN) in driving fatigue detection (this article is abbreviated as DFD) based on EEG signals. This article uses a pattern classification method based on a multilayer perceptual overlimit learning machine to find the hidden information of the signal through an unsupervised learning self-encoding structure, which achieves the optimization purpose and has a better classification effect than traditional classifiers. An improved soft threshold (the soft threshold can be used to solve the optimization problem, and the optimization problem solved is similar to the base pursuit noise reduction problem, but it is not the same, and it should be noted that the soft threshold cannot solve the base pursuit noise reduction problem) denoising algorithm is selected, and the collected EEG (a technique for capturing brain activity using electrophysiological markers is the electroencephalogram). The sum of the postsynaptic potentials produced simultaneously by a large number of neurons occurs when the brain is active. It records the process of brain activity in the cerebral cortex or scalp surface) signals are preprocessed, so that the feature extraction efficiency of extracting EEG signals is improved. The final experimental data show that the traditional support vector machine, SVM algorithm, and the KNN convolutional neural (the K-nearest neighbor method, often known as KNN, was first put forth by Cover and Hart in 1968. It is one of the most straightforward machine learning algorithms and a theoretically sound approach) algorithms has a recognition rate of 79% and 81% for fatigue. The improved algorithm in this article has an average recognition rate of 87.5% for driver fatigue, which is greatly improved.

New Results on Classification Modeling of Noisy Tensor Datasets: A Fuzzy Support Tensor Machine Dual Model

In this article, classification problems for a class of tensor datasets with a noisy environment are investigated. To address such issues, a novel fuzzy support tensor machine (FSTM) dual model with robustness is established. First, for each input sample in the noisy tensor dataset, we define three kinds of fuzzy membership functions, such as linear, cosine, and exponential forms. In particular, the reconstruction process from one-dimensional (1-D) vector data to third-order tensor data is also derived in the Appendix. Second, the original optimization model of an FSTM on fuzzy membership is designed by constructing the vector pattern of the traditional support vector machine (SVM) models into a tensor pattern. Next, by introducing the Lagrangian multiplier method and tensor-Tucker decomposition method to the original FSTM model, an FSTM dual model without tensor inner product operation is obtained for the first time. Such a dual model with tensor-Tucker decomposition form can avoid conservativeness caused by the vectorization of tensor data in the traditional SVM model. Furthermore, an FSTM classifier is derived by the designed numerical algorithm, and the classification generalization error bound of the FSTM model with a general form is developed. It is worth noting that a linear least-squares FSTM (LLS-FSTM) equation with tensor-Tucker decomposition is also designed in the Appendix to further reduce the slightly time-consuming problem of the solving the dual optimization model FSTM. Finally, two numerical examples are presented to verify the feasibility and validity of the derived FSTM classifier.