Traditional Support Vector Machine Research Articles

Ant Colony Optimization based Support Vector Machine for Improved Classification of Unbalanced Datasets

Classifying unbalanced datasets is a challenging task, and traditional Support Vector Machine (SVM) algorithms often struggle to achieve accurate results. In this paper, we propose an Ant Colony Optimization based Support Vector Machine (SVM-ACO) algorithm that outperforms SVM on unbalanced datasets. Traditional SVM algorithms tend to be biased towards the majority class, leading to suboptimal performance for the minority class. However, SVM-ACO addresses this issue by incorporating the ant colony optimization approach, which assigns different weights to samples based on their class membership, giving higher importance to the minority class. This enables SVM-ACO to achieve a more balanced classification performance and improved accuracy. We compare the accuracy achieved by SVM-ACO and SVM on various imbalanced datasets from the literature. The experimental results support the effectiveness of SVM-ACO in addressing the challenges of unbalanced datasets. By incorporating ant colony optimization, SVM-ACO achieves improved accuracy and provides a promising alternative for handling unbalanced classification problems.

Stress Recognition in Code-Mixed Social Media Texts using Machine Learning

Stress, being a complex emotional state caused by a variety of multiple sources, has the potential for serious effects if left untreated. The primary goal of this research is to select and consider AI models that effectively recognize stress within the complicated domain of social media posts. The significance of this study is not only the categorization of stress but also the interpretation of the sophisticated methods that serve as the basis for these emotional responses. Among the traditional machine learning models, Random Forest, K-Nearest Neighbor, Logistic Regression, Decision Tree, and Support Vector Machine are used. The deep learning model’s LSTM, BiLSTM, and transformer-based models m-BERT, AL-BERT, XLM-RoBERTa, IndicBERT, and Distil-BERT were used. Of those models, LSTM proved to be the best-performing model, with an F1-score of 0.75.

Deep learning-based classification of anti-personnel mines and sub-gram metal content in mineralized soil (DL-MMD)

De-mining operations are of critical importance for humanitarian efforts and safety in conflict-affected regions. In this paper, we address the challenge of enhancing the accuracy and efficiency of mine detection systems. We present an innovative Deep Learning architecture tailored for pulse induction-based Metallic Mine Detectors (MMD), so called DL-MMD. Our methodology leverages deep neural networks to distinguish amongst nine distinct materials with an exceptional validation accuracy of 93.5%. This high level of precision enables us not only to differentiate between anti-personnel mines, without metal plates but also to detect minuscule 0.2-g vertical paper pins in both mineralized soil and non-mineralized environments. Moreover, through comparative analysis, we demonstrate a substantial 3% and 7% improvement (approx.) in accuracy performance compared to the traditional K-Nearest Neighbors and Support Vector Machine classifiers, respectively. The fusion of deep neural networks with the pulse induction-based MMD not only presents a cost-effective solution but also significantly expedites decision-making processes in de-mining operations, ultimately contributing to improved safety and effectiveness in these critical endeavors.

A novel intelligent risk prediction model for the effectiveness of CO2/N2–ECGD technology

AbstractTo solve the problems of high sampling requirements and low predictive accuracy resulting from the complexity, uniqueness, and randomness of predicting the risk of the CO2 and N2 injection to enhance coal seam gas drainage (CO2/N2–ECGD) technology. The principal component analysis (PCA) method to reduce the dimensionality of the factor data that contribute to the effect risk of the technology was adopted. And the particle swarm optimization (PSO) method was implemented to search for optimal hyperparameters in support vector machine (SVM) by particle search, as a solution to the traditional SVM hyperparameters optimization problem. A novel risk prediction model using machine learning algorithms for gas injection displacement technology was constructed. The prediction results were tested and compared with those of backpropagation (BP), Random Forest (RF), and Decision Tree (DT) models using data from 29 gas injection displacement field projects in China. The results demonstrated that the SVM model had greater accuracy in prediction than the other three models. Additionally, after PSO optimization and dimensionality reduction, the PCA–PSO–SVM model reached 100% prediction accuracy, while requiring less modeling and operation time. The study provided a reliable and reasonable model for predicting technical effects, along with a theoretical basis for risk management and prevention. First, the technology's influencing indicators were analyzed by examining its mechanisms. Second, we utilized the PCA method to reduce the dimensionality of the factor data that contribute to the risk of the technology's effects. Third, we implemented the PSO method to search for optimal hyperparameters in the SVM through particle search, as a solution to the traditional SVM hyperparameters optimization problem. Finally, the prediction results were tested and compared with those of BP, RF, and DT models using data from 29 gas injection displacement field projects in China. The SVM model was found to have greater accuracy in prediction than the other three models. After PSO optimization and dimensionality reduction, the PCA–PSO–SVM model achieved 100% prediction accuracy while requiring less modeling and operation time. The study presents a valid and reasonable model for predicting technical effects and a theoretical basis for risk management and prevention.

Exploring an Intelligent Classification Model for the Recognition of Automobile Sounds Based on EEG Physiological Signals

The advancement of an intelligent automobile sound switching system has the potential to elevate the market standing of automotive products, with the pivotal prerequisite being the selection of automobile sounds based on the driver’s subjective perception. The subjective responses of diverse individuals to sounds can be objectively manifested through EEG signals. Therefore, EEG signals are employed herein to attain the recognition of automobile sounds. A subjective evaluation and EEG signal acquisition experiment are designed involving the stimulation of three distinct types of automobile sounds, namely comfort, power, and technology sounds, and a comprehensive database of EEG signals corresponding to these three sound qualities is established. Then, a specific transfer learning model based on a convolutional neural network (STL-CNN) is formulated, where the method of training the upper layer parameters with the fixed bottom weights is proposed to adaptively extract the EEG features related to automobile sounds. These improvements contribute to improving the generalization ability of the model and realizing the recognition of automobile sounds fused with EEG signals. The results of the comparison with traditional support vector machine (SVM) and convolutional neural network (CNN) models demonstrate that the accuracy of the test set of the STL-CNN model reaches 91.5%. Moreover, its comprehensive performance, coupled with the ability to adapt to individual differences, surpasses that of both SVM and CNN models. The demonstrated method in the recognition of automobile sounds based on EEG signals is of significance for the future implementation of switching driving sound modes fused with EEG signals.

Identifying a group of factors predicting cognitive impairment among older adults.

Cognitive impairment has multiple risk factors spanning several domains, but few studies have evaluated risk factor clusters. We aimed to identify naturally occurring clusters of risk factors of poor cognition among middle-aged and older adults and evaluate associations between measures of cognition and these risk factor clusters. We used data from the National Health and Nutrition Examination Survey (NHANES) III (training dataset, n = 4074) and the NHANES 2011-2014 (validation dataset, n = 2510). Risk factors were selected based on the literature. We used both traditional logistic models and support vector machine methods to construct a composite score of risk factor clusters. We evaluated associations between the risk score and cognitive performance using the logistic model by estimating odds ratios (OR) and 95% confidence intervals (CI). Using the training dataset, we developed a composite risk score that predicted undiagnosed cognitive decline based on ten selected predictive risk factors including age, waist circumference, healthy eating index, race, education, income, physical activity, diabetes, hypercholesterolemia, and annual visit to dentist. The risk score was significantly associated with poor cognitive performance both in the training dataset (OR Tertile 3 verse tertile 1 = 8.15, 95% CI: 5.36-12.4) and validation dataset (OR Tertile 3 verse tertile 1 = 4.31, 95% CI: 2.62-7.08). The area under the receiver operating characteristics curve for the predictive model was 0.74 and 0.77 for crude model and model adjusted for age, sex, and race. The model based on selected risk factors may be used to identify high risk individuals with cognitive impairment.

Traffic accident classification using IndoBERT

<span>Traffic accidents are a widespread concern globally, causing loss of life, injuries, and economic burdens. Efficiently classifying accident types is crucial for effective accident management and prevention. This study proposes a practical approach for traffic accident classification using IndoBERT, a language model specifically trained for Indonesian. The classification task involves sorting accidents into four classes: car accidents, motorcycle accidents, bus accidents, and others. The proposed model achieves a 94% accuracy in categorizing these accidents. To assess its performance, we compared IndoBERT with traditional methods, random forest (RF) and support vector machine (SVM), which achieved accuracy scores of 85% and 87%, respectively. The IndoBERT-based model demonstrates its effectiveness in handling the complexities of the Indonesian language, providing a useful tool for traffic accident classification and contributing to improved accident management and prevention strategies.</span>

Accurate categorization and rapid pathological diagnosis correction with Micro-Raman technique in human lung adenocarcinoma infiltration level.

In the context of surgical interventions for lung adenocarcinoma (LADC), precise determination of the extent of LADC infiltration plays a pivotal role in shaping the surgeon's strategic approach to the procedure. The prevailing diagnostic standard involves the expeditious intraoperative pathological diagnosis of areas infiltrated by LADC. Nevertheless, current methodologies rely on the visual interpretation of tissue images by proficient pathologists, introducing an error margin of up to 15.6%. In this study, we investigated the utilization of Micro-Raman technique on isolated specimens of human LADC with the objective of formulating and validating a workflow for the pathological diagnosis of LADC featuring diverse degrees of infiltration. Our strategy encompasses a thorough pathological characterization of LADC, spanning different tissue types and levels of infiltration. Through the integration of Raman spectroscopy with advanced deep learning models for simultaneous diagnosis, this approach offers a swift, precise, and clinically relevant means of analysis. The diagnostic performance of the convolutional neural network (CNN) model, coupled with the microscopic Raman technique, was found to be exceptional and consistent, surpassing the traditional support vector machine (SVM) model. The CNN model exhibited an area under the curve (AUC) value of 96.1% for effectively distinguishing normal tissue from LADC and an impressive 99.0% for discerning varying degrees of infiltration in LADCs. To comprehensively assess its clinical utility, Raman datasets from patients with intraoperative rapid pathologic diagnostic errors were utilized as test subjects and input into the established CNN model. The results underscored the substantial corrective capacity of the Micro-Raman technique, revealing a misdiagnosis correction rate exceeding 96% in all cases. Ultimately, our discoveries highlight the Micro-Raman technique's potential to augment the intraoperative diagnostic precision of LADC with varying levels of infiltration. And compared to the traditional SVM model, the CNN model has better generalization ability in diagnosing different infiltration levels. This method furnishes surgeons with an objective groundwork for making well-informed decisions concerning subsequent surgical plans.

Research on roller bearing fault diagnosis method based on flexible dynamic adjustable strategy under data imbalance

In engineering practice, the collection of equipment vibration signals is prone to interference from the external environment, resulting in abnormal data and imbalanced data in different states. Traditional support vector machine, support matrix machine and other methods have advantages in balancing sample classification, but have limitations in obtaining low rank information, making it difficult to perform classification tasks under data imbalance. Therefore, a novel classification method that targets matrices as the input, called flexible dynamic matrix machine (FDMM), is proposed in this paper. First, FDMM establishes a regularization term using a flexible low-rank operator and sparse constrain, which can better take into account matrix structure information. Then, the upper bound of the loss function is truncated, reducing the impact of the loss on the construction of the decision hyperplane. Finally, the recognition performance of imbalanced data is improved by adjusting the game values of different categories of samples through dynamic adjustment function. Experimental results demonstrate that superior classification accuracy and generalization performance can be achieved with the FDMM method when applied to two roller bearing datasets.

Machine learning model based on non-convex penalized huberized-SVM

The support vector machine (SVM) is a classical machine learning method. Both the hinge loss and least absolute shrinkage and selection operator (LASSO) penalty are usually used in traditional SVMs. However, the hinge loss is not differentiable, and the LASSO penalty does not have the Oracle property. In this paper, the huberized loss is combined with non-convex penalties to obtain a model that has the advantages of both the computational simplicity and the Oracle property, contributing to higher accuracy than traditional SVMs. It is experimentally demonstrated that the two non-convex huberized-SVM methods, smoothly clipped absolute deviation huberized-SVM (SCAD-HSVM) and minimax concave penalty huberized-SVM (MCP-HSVM), outperform the traditional SVM method in terms of the prediction accuracy and classifier performance. They are also superior in terms of variable selection, especially when there is a high linear correlation between the variables. When they are applied to the prediction of listed companies, the variables that can affect and predict financial distress are accurately filtered out. Among all the indicators, the indicators per share have the greatest influence while those of solvency have the weakest influence. Listed companies can assess the financial situation with the indicators screened by our algorithm and make an early warning of their possible financial distress in advance with higher precision.

E2ETCA: End-to-end training of CNN and attention ensembles for rice disease diagnosis1

Rice is one of the most important crops worldwide. Diseases of the rice plant can drastically reduce crop yield and even lead to complete loss of production. Early diagnosis can reduce the severity and help efforts to establish effective treatment and reduce the usage of pesticides. Traditional machine learning approaches have already been employed for automatic diagnosis. However, they heavily rely on manual preprocessing of images and handcrafted features, which is challenging, time-consuming, and may require domain expertise. Recently, a single end-to-end deep learning (DL)-based approach was employed to diagnose rice diseases. However, it is not highly robust, nor is it generalizable to every dataset. Hence, we propose a novel end-to-end training of convolutional neural network (CNN) and attention (E2ETCA) ensemble framework that fuses the features of two CNN-based state-of-the-art (SOTA) models along with those of an attention-based vision transformer model. These fused features are utilized for diagnosis by the addition of an extra fully connected layer with softmax. The whole procedure is performed end-to-end, which is very important for real-world applications. Additionally, we feed the extracted features into a traditional machine learning approach support vector machine for classification and further analysis. To verify the effectiveness of our proposed E2ETCA framework, we demonstrate it on three publicly available datasets: the Mendeley Rice Leaf Disease Image Samples dataset, the Kaggle Rice Diseases Image dataset, the Bangladesh Rice Research Institute dataset, and a combination of these three datasets. On the basis of various evaluation metrics (accuracy, precision, recall, and F1-score), our proposed E2ETCA framework exhibits superior performance to existing SOTA approaches for rice disease diagnosis, which can also be generalizable in similar other domains.

An Intelligent Fault Detection Method of Industrial Gearboxes With Robustness One-Class Support Matrix Machine Toward Multisource Nonideal Data

Various kinds of sensing data can be acquired for smart fault detection. Each signal source has spatial attributes and strong correlations exist between different data sources. However, most of the existing fault detection models are established in the vector domain, which would destroy the structure information embedded within multisource data. Besides, the nonideal data, especially strong-noise data and mislabeled data, will seriously affect the fault detection performance. Therefore, a matrix-form one-class model called robustness one-class support matrix machine (ROCSMM) is proposed for industrial gearbox fault detection under multisource nonideal data. ROCSMM extends the traditional one-class support vector machine to the matrix domain, which can retain the topological structure information of multisource data while improving the fault detection performance. Besides, an adaptive weight generation strategy is designed for ROCSMM according to the prior distribution of matrix samples. This strategy can eliminate the negative impact of nonideal data on ROCSMM to the greatest extent and improve the model's robustness. The experimental results indicate that the proposed model is superior to other cutting-edge models in the fault detection of industrial gearboxes.

Modeling abrupt changes in mine water inflow trends: A CEEMDAN-based multi-model prediction approach

The global impact of coal mining on groundwater resources is increasingly prominent. Constructing waterproof curtains can mitigate the destruction caused by open-pit coal mining on groundwater resources. However, the construction of these curtains alters the hydrogeological conditions, leading to abrupt changes in mine water inflow that are challenging to predict. Traditional models like Long Short-Term Memory Neural Network (LSTM) and Gated Recurrent Unit Neural Network (GRU) demonstrate poor performance when predicting abrupt changes in data trends. This study proposes a multi-model prediction approach and a Self-Attention-based prediction model to address this issue. The proposed Approach, based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), leverages the strengths of multiple models for prediction. The research results found that the proposed Self-Attention model excels at forecasting abrupt shifts in data trends. Comparatively, the multi-model predictions show a 26.12 % improvement in Mean Absolute Error (MAE) and a 33.47 % improvement in Root Mean Square Error (RMSE) when compared to single-model predictions. This study not only unveils the potential of Self-Attention in predicting abrupt trend changes but also integrates it with traditional LSTM and Support Vector Machine (SVM) models. The multi-model prediction framework proposed in this study integrates the strengths of multiple models, achieving higher accuracy than a single model. Accurate prediction of mine water inflow is essential, as it can provide valuable insights for decision-making by coal mining enterprises and local regulatory authorities, offering a scientific basis for effective management.

Enhancing handwritten numeric string recognition through incremental support vector machines

Handwritten digit recognition systems are integral to diverse applications such as postal services, banking, and document processing in our digitally-driven society. This research addresses the challenges posed by evolving datasets and dynamic scenarios in handwritten digit recognition by proposing an approach based on incremental support vector machines (ISVM). ISVM is an extension of traditional support vector machines (SVM) designed to handle scenarios where new data points become available over time. The dataset includes handwritten images (numbers “0” to “6”) and trials introducing new classes (“7”, “8”, and “9”). Evaluation utilizes k-fold cross-validation for robustness. Digital image processing involves converting images into numeric data using the histogram method. The result showed the positive outcomes of using ISVM in handwritten digit recognition, emphasizing its adaptability to incremental learning and its ability to maintain robust performance in the face of evolving datasets, which is crucial for real-world applications.

Rapid Identification of the Species of Bloodstain Based on Near Infrared Spectroscopy and Convolutional Neural Network-Support Vector Machine Algorithm

As one of the most important types of evidence at the scene of the crime, the rapid identification of the human bloodstain is of great significance to solve the criminal case. In this paper, the spectral data of different species of bloodstain samples including human, chicken and pig were acquired by using a hand-held near-infrared spectrometer. Then, the training models were established via convolutional neural network-support vector machine algorithm. Meanwhile, the traditional support vector machine, genetic algorithm-back propagation and random forest classification algorithms were also compared. The results showed that the prediction accuracy of convolutional neural network-support vector machine algorithm was the highest and the overall performance of the model was the best. The rapid detection method based on a handheld near-infrared spectrometer and convolutional neural network-support vector machine algorithm could identify the species of bloodstain efficiently, non-destructively, quickly and accurately and it provided a new technical reference for bloodstains detection and identification.

Global Model Selection via Solution Paths for Robust Support Vector Machine.

Robust support vector machine (RSVM) using ramp loss provides a better generalization performance than traditional support vector machine (SVM) using hinge loss. However, the good performance of RSVM heavily depends on the proper values of regularization parameter and ramp parameter. Traditional model selection technique with gird search has extremely high computational cost especially for fine-grained search. To address this challenging problem, in this paper, we first propose solution paths of RSVM (SPRSVM) based on the concave-convex procedure (CCCP) which can track the solutions of the non-convex RSVM with respect to regularization parameter and ramp parameter respectively. Specifically, we use incremental and decremental learning algorithms to deal with the Karush-Khun-Tucker violating samples in the process of tracking the solutions. Based on the solution paths of RSVM and the piecewise linearity of model function, we can compute the error paths of RSVM and find the values of regularization parameter and ramp parameter, respectively, which corresponds to the minimum cross validation error. We prove the finite convergence of SPRSVM and analyze the computational complexity of SPRSVM. Experimental results on a variety of benchmark datasets not only verify that our SPRSVM can globally search the regularization and ramp parameters respectively, but also show a huge reduction of computational time compared with the grid search approach.

Support Matrix Machine via Joint l2,1 and Nuclear Norm Minimization Under Matrix Completion Framework for Classification of Corrupted Data.

Traditional support vector machines (SVMs) are fragile in the presence of outliers; even a single corrupt data point can arbitrarily alter the quality of the approximation. If even a small fraction of columns is corrupted, then classification performance will inevitably deteriorate. This article considers the problem of high-dimensional data classification, where a number of the columns are arbitrarily corrupted. An efficient Support Matrix Machine that simultaneously performs matrix Recovery (SSMRe) is proposed, i.e. feature selection and classification through joint minimization of l2,1 (the nuclear norm of L ). The data are assumed to consist of a low-rank clean matrix plus a sparse noisy matrix. SSMRe works under incoherence and ambiguity conditions and is able to recover an intrinsic matrix of higher rank in the presence of data densely corrupted. The objective function is a spectral extension of the conventional elastic net; it combines the property of matrix recovery along with low rank and joint sparsity to deal with complex high-dimensional noisy data. Furthermore, SSMRe leverages structural information, as well as the intrinsic structure of data, avoiding the inevitable upper bound. Experimental results on different real-time applications, supported by the theoretical analysis and statistical testing, show significant gain for BCI, face recognition, and person identification datasets, especially in the presence of outliers, while preserving a reasonable number of support vectors.

An innovative network intrusion detection system (NIDS): Hierarchical deep learning model based on Unsw-Nb15 dataset

With the increasing prevalence of network intrusions, the development of effective network intrusion detection systems (NIDS) has become crucial. In this study, we propose a novel NIDS approach that combines the power of long short-term memory (LSTM) and attention mechanisms to analyze the spatial and temporal features of network traffic data. We utilize the benchmark UNSW-NB15 dataset, which exhibits a diverse distribution of patterns, including a significant disparity in the size of the training and testing sets. Unlike traditional machine learning techniques like support vector machines (SVM) and k-nearest neighbors (KNN) that often struggle with limited feature sets and lower accuracy, our proposed model overcomes these limitations. Notably, existing models applied to this dataset typically require manual feature selection and extraction, which can be time-consuming and less precise. In contrast, our model achieves superior results in binary classification by leveraging the advantages of LSTM and attention mechanisms. Through extensive experiments and evaluations with state-of-the-art ML/DL models, we demonstrate the effectiveness and superiority of our proposed approach. Our findings highlight the potential of combining LSTM and attention mechanisms for enhanced network intrusion detection.

Research on the application technology of Artificial Intelligence in college physical education and training

Abstract Nowadays, with the swift advancement of Artificial Intelligence (AI) technology, its applications have become widespread across diverse fields. AI is no longer a mere abstract idea but has seamlessly integrated into our daily lives, bringing numerous conveniences through its myriad benefits. One such domain where AI has made significant inroads in college physical education and training. The integration of Intelligent Computer-Aided Instruction (ICAI) with computer-assisted teaching systems, AI-powered wearable devices, motion capture systems in sports training, and virtual demonstration technology for simulating athletic movements has greatly enhanced both the precision of physical education and the efficacy of physical training. AI continues to evolve rapidly, indicating vast potential for further development in its integration with physical education and training. This paper highlights the widespread adoption of AI in sports and delves into its specific applications within this domain. The findings reveal that the algorithm employed in this context excels in identifying sports movement features, outperforming the comparison algorithm by 27.65%. Moreover, it precisely pinpoints the edge contours of human movement. In comparison to traditional Support Vector Machines (SVM), Convolutional Neural Networks (CNN) exhibit clear advantages during the later stages of operation, reducing errors by 36.69%. The experimental results underscore the importance of comprehensive human body detection in ensuring stable and accurate sports action tracking.

Research on phosphorite photoelectric sorting process based on fuzzy algorithm

Abstract In this paper, we synthesize the differences in grayscale and texture information of phosphorite X-ray images and extract a total of six feature quantities under their grayscale and texture features, respectively. After feature fusion, a fuzzy support vector machine classifier with a normal plane-type affiliation function is used to optimize the binary classification method of traditional support vector machines, which improves the accuracy of classification. To improve the sorting model’s division of the optimal classification hyperplane, the centroid position of the two types of samples is considered comprehensively. In order to give full play to the classification performance of the sorting model, the particle swarm algorithm is further used to optimize the core parameters of the fuzzy support vector machine, the penalty factor, and the kernel parameters to complete the construction of the classifier model. In the face of the mixed colloidal phosphorite that can not be exploited, based on the PSO-NP-FSVM photoelectric sorting operation, the concentrate yield rate, operation recovery rate, and optical sorting waste rate are 56.52%, 20.87%, and 71.17%, respectively. After photoelectric sorting, the grade of concentrate P2 O5 can be increased to 20.98%, which can meet the requirements for the grade of the processing plant. Therefore, the photoelectric sorting process combined with a fuzzy algorithm is capable of meeting the actual production requirements.