Multi-class Support Vector Machine Research Articles

Considerations for implanting speech brain computer interfaces based on functional magnetic resonance imaging

Objective. Brain-computer interfaces (BCIs) have the potential to reinstate lost communication faculties. Results from speech decoding studies indicate that a usable speech BCI based on activity in the sensorimotor cortex (SMC) can be achieved using subdurally implanted electrodes. However, the optimal characteristics for a successful speech implant are largely unknown. We address this topic in a high field blood oxygenation level dependent functional magnetic resonance imaging (fMRI) study, by assessing the decodability of spoken words as a function of hemisphere, gyrus, sulcal depth, and position along the ventral/dorsal-axis. Approach. Twelve subjects conducted a 7T fMRI experiment in which they pronounced 6 different pseudo-words over 6 runs. We divided the SMC by hemisphere, gyrus, sulcal depth, and position along the ventral/dorsal axis. Classification was performed on in these SMC areas using multiclass support vector machine (SVM). Main results. Significant classification was possible from the SMC, but no preference for the left or right hemisphere, nor for the precentral or postcentral gyrus for optimal word classification was detected. Classification while using information from the cortical surface was slightly better than when using information from deep in the central sulcus and was highest within the ventral 50% of SMC. Confusion matrices where highly similar across the entire SMC. An SVM-searchlight analysis revealed significant classification in the superior temporal gyrus and left planum temporale in addition to the SMC. Significance. The current results support a unilateral implant using surface electrodes, covering the ventral 50% of the SMC. The added value of depth electrodes is unclear. We did not observe evidence for variations in the qualitative nature of information across SMC. The current results need to be confirmed in paralyzed patients performing attempted speech.

Classification of exercise fatigue levels by multi-class SVM from ECG and HRV.

Among the various physiological signals, electrocardiogram (ECG) is a valid criterion for the classification of various exercise fatigue. In this study, we combine features extracted by deep neural networks with linear features from ECG and heart rate variability (HRV) for exercise fatigue classification. First, the ECG signals are converted into 2-D images by using the short-term Fourier transform (STFT), and image features are extracted by the visual geometry group (VGG) . The extracted image and linear features of ECG and HRV are sent to the different types of classifiers to distinguish distinct exercise fatigue level. To validate performance, the proposed methods are tested on (i) an open-source EPHNOGRAM dataset and (ii) a self-collected dataset (n = 51). The results reveal that the classification based on the concatenated features has the highest accuracy, and the calculation time of the system is also significantly reduced. This demonstrates that the proposed novel hybrid approach can be used to assist in improving the accuracy and timeliness of exercise fatigue classification in a real-time exercise environment. The experimental results show that the proposed method outperforms other recent state-of-the-art methods in terms of accuracy 96.90%, sensitivity 96.90%, F1-score of 0.9687 in EPHNOGRAM and accuracy 92.17%, sensitivity 92.63%, F1-score of 0.9213 in self-collected dataset.

Detection of Skin Disease by using Image Processing

There are many types and forms of skin diseases and have many causes, including internal ones related to hormones and body glands such as acne, or external related to air pollution or sensitivity to sunlight such as rashes. The skin disease may be contagious, such as scabies and lice, or it may not be contagious, such as drug allergy and rosacea, or it may be chronic, such as psoriasis and atopic eczema, or it may be rare, such as sweet syndrome and ofuji disease. The outlook of society, in general, reduces the importance of skin disease when it appears and it is preferable not to visit the doctor. Skin diseases are a major and worrying problem in societies due to their physical and psychological effects on patients. Detecting skin diseases at an early stage has an important role in treatment. The process of diagnosing and treating skin injury is related to the skill and experience of the specialist doctor. skin disease prediction by using a neural network based on image analysis. Diagnosis and prediction of skin disease take a very long process because it requires a patient’s history, physical examination, and proper laboratory diagnostic tests. st of such diagnosis is still limited and very expensive. So, image processing techniques help to build automated screening system for dermatology at an initial stage. The extraction of features plays a key role in helping to classify skin disease. proach works on the inputs of a color image. Then resize the of the image to extract features using pre trained convolutional neural network. After that classified feature using Multiclass SVM. Finally, the results are shown to the user, including the type.

A joint learning framework for optimal feature extraction and multi-class SVM

In high-dimensional data classification, effectively extracting discriminative features while eliminating redundancy is crucial for enhancing the performances of classifiers, such as Support Vector Machine (SVM). However, previous studies have decoupled the process of feature extraction from the development of SVM, leading to suboptimal classification accuracy. To address this problem, we propose a novel joint learning framework that combines optimal feature extraction and multi-class SVM, incorporating a generalized regression form to learn a discriminative latent subspace. The projected data in this subspace are more likely to have a larger margin between different classes and align with the properties of the SVM classification mechanism, enhancing the overall classification performance. Three iterative algorithms were presented to obtain optimal solutions with guaranteed convergence, and theoretical analyses were also conducted to reveal their fundamental nature. The optimal linear projection subspace is equivalent to that obtained from Linear Discriminant Analysis (LDA) in some special cases. We conducted extensive experiments using diverse datasets to evaluate the performances of the proposed algorithms. Our algorithms achieved an accuracy improvement of up to 7.55% compared to other conventional methods.

Modelling appearance variations in expressive and neutral face image for automatic facial expression recognition

AbstractIn automatic facial expression recognition (AFER) systems, modelling the spatio‐temporal feature information in a specific manner, coalescing, and its effective utilization is challenging. The state‐of‐the‐art studies have examined integrating multiple features to enhance the recognition rate of AFER systems. However, the feature variations between expressive and neutral face images are not fully explored to identify the expression class. The proposed research presents an innovative approach to AFER by modelling appearance variations in both expressive and neutral face images. The prominent contributions of the work are developing a novel and hybrid feature space by integrating the discriminative feature distribution derived from expressive and neutral face images; preserving the highly discriminative latent feature distribution using autoencoders. Local binary pattern (LBP) and histogram of oriented gradients (HOG) are the feature descriptors employed to derive the discriminative texture and shape information, respectively. The component‐based approach is employed, wherein the features are derived from the salient facial regions instead of the whole face. The three‐stage stacked deep convolutional autoencoder (SDCA) and multi‐class support vector machine (MSVM) are employed to address dimensionality reduction and classification, respectively. The efficacy of the proposed model is substantiated by empirical findings, which establish its superiority in terms of accuracy in AFER tasks on widely recognized benchmark datasets.

Hybrid anomaly detection: Leveraging autoencoder for feature learning and random forest neural network for discriminative classification

Anomaly detection, a critical aspect of data analysis and cybersecurity, aims to identify unusual patterns that deviate from the expected norm. In this study, we propose a hybrid approach that combines the strengths of Autoencoder neural networks and Multiclass Support Vector Machines (SVM) for robust anomaly detection. The Autoencoder is utilized for feature learning and extraction, capturing intricate patterns in the data, while the Multiclass SVM provides a discriminative classification mechanism to distinguish anomalies from normal patterns. Specifically, the Autoencoder is trained on normal data to acquire a compact and efficient representation of the underlying patterns, with the reconstruction errors serving as indicative measures of anomalies. Concurrently, a Multiclass SVM is trained to classify instances into multiple classes, including an anomaly class. The anomaly scores from the Autoencoder and the decision function of the Multiclass SVM, along with that of the Random Forest Neural Network (AE-RFNN), are combined, leveraging their complementary strengths. A thresholding mechanism is then employed to classify instances as normal or anomalous based on the combined scores. The performance of the hybrid model is evaluated using standard metrics such as precision, recall, F1-score, and the area under the Receiver Operating Characteristic (ROC) curve. The proposed hybrid anomaly detection approach demonstrates effectiveness in capturing complex patterns and discerning anomalies across diverse datasets. Additionally, the model offers flexibility for adaptation to evolving data distributions. This study contributes to the advancement of anomaly detection methodologies by presenting a hybrid solution that combines feature learning and discriminative classification for improved accuracy and generalization.

Masked facial recognition using ensemble convolutional neural network and grey-level co-occurrence matrix

The COVID-19 pandemic proved how face masks became necessary to stop the spread of infection. Due to this, effective identification of people wearing face mask became challenging. Masked facial recognition has significantly increased in accuracy because of developments in convolutional neural networks (CNNs). Small size of the dataset of masked facial images has been a problem in earlier research. As would be expected, this results in poorer accuracy when the model tries to identify faces. In this study, a novel model is proposed with textural feature extraction using grey-level co-occurrence matrix (GLCM) and an ensemble of two pre-trained CNNs DenseNet-121 and VGG-16. Using the minimum redundancy and maximum relevance, the model has improved accuracy by choosing the most important features of the image. The model was trained using in-house dataset that included 38,290 photos of 2,500 people with approximately equal distribution of properly masked, partially masked, and unmasked images. In this, we evaluated the performance of the model on different classifiers multi-class logistic regression (LR) and support vector machine (SVM) with one-vs-rest (OvR) classification and artificial neural network (ANN) and applied a soft voting scheme. The model achieved the highest accuracy of 98.56% at a learning rate of 0.001 on the ANN classifier.

MTKSVCR: A novel multi-task multi-class support vector machine with safe acceleration rule

Regularized multi-task learning (RMTL) has shown good performance in tackling multi-task binary problems. Although RMTL can be used to handle multi-class problems based on “one-versus-one” and “one-versus-rest” techniques, the information of the samples is not fully utilized and the class imbalance problem occurs. Motivated by the regularization technique in RMTL, we propose an original multi-task multi-class model termed MTKSVCR based on “one-versus-one-versus-rest” strategy to achieve better testing accuracy. Due to the utilization of the idea of RMTL, the related information included in multiple tasks is mined by setting different penalty parameters before task-common and task-specific regularization terms. However, the proposed MTKSVCR is time-consuming since it employs all samples in each optimization problem. Therefore, a multi-parameter safe acceleration rule termed SA is further presented to reduce the time consumption. It identifies and deletes most of the superfluous samples corresponding to 0 elements in the dual optimal solution before solving. Then, only a reduced dual problem is to be solved and the computational efficiency is improved accordingly. The biggest advantage of the proposed SA lies in safety. Namely, it derives an identical optimal solution to the primal problem without SA. In addition, our method remains effective when multiple parameters change simultaneously. Experiments on different artificial datasets and benchmark datasets verify the validity of the proposed methods.

Multi-Class Support Vector Machine with Maximizing Minimum Margin

Support Vector Machine (SVM) stands out as a prominent machine learning technique widely applied in practical pattern recognition tasks. It achieves binary classification by maximizing the "margin", which represents the minimum distance between instances and the decision boundary. Although many efforts have been dedicated to expanding SVM for multi-class case through strategies such as one versus one and one versus the rest, satisfactory solutions remain to be developed. In this paper, we propose a novel method for multi-class SVM that incorporates pairwise class loss considerations and maximizes the minimum margin. Adhering to this concept, we embrace a new formulation that imparts heightened flexibility to multi-class SVM. Furthermore, the correlations between the proposed method and multiple forms of multi-class SVM are analyzed. The proposed regularizer, akin to the concept of "margin", can serve as a seamless enhancement over the softmax in deep learning, providing guidance for network parameter learning. Empirical evaluations demonstrate the effectiveness and superiority of our proposed method over existing multi-classification methods. Complete version is available at https://arxiv.org/pdf/2312.06578.pdf. Code is available at https://github.com/zz-haooo/M3SVM.

Optimizing precision agriculture: Bayesian-enhanced papaya (Carica papaya L.) fruit disease classification via cubic SVM and ResNet-101 deep features

With the rise of the fruit processing industry, machine learning and image processing have become necessary for quality control and monitoring of fruits. Recently, strong vision-based solutions have emerged in farming industries that make inspections more accurate at a much lower cost. Advanced deep learning methods play a key role in these solutions. In this study, we built an image-based framework that uses the ResNet-101 CNN model to identify different types of papaya fruit diseases with minimal training data and processing power. A case study to identify commonly encountered papaya fruit diseases during harvesting was used to support the results of the suggested methodology. A total of 983 images of both healthy and defective papaya were considered during the experiment. In this study, we initially used the ResNet-101 CNN model for classification and then combined the deep features drawn out from the activation layer (fc1000) of the ResNet-101 CNN along with a multi-class Support Vector Machine (SVM) to classify papaya fruit defect detection. After comparing the performance of both approaches, it was found that Cubic SVM is the best classifier using the deep feature of ResNet-101 CNN, achieved with an accuracy of 99.5% and an area under the curve (AUC) of 1 without any classification error. The findings of this experiment reveal that the ResNet-101 CNN with the cubic SVM model can categorize good, diseased, and defective papaya pictures. Moreover, the suggested model executed the task in a greater way in terms of the F1- Score (0.99), sensitivity (99.50%), and precision (99.71%). The present work not only assists the end user in determining the type of disease but also makes it possible for them to take corrective measures during farming.

Abstract 4907: RanBALL: Identifying B-cell acute lymphoblastic leukemia subtypes based on an ensemble random projection model

Abstract As the most common pediatric malignancy, B-cell acute lymphoblastic leukemia (B-ALL) has multiple distinct subtypes characterized by recurrent and sporadic somatic and germline genetic alterations like chromosomal alteration, transcription factor rearrangement or kinase inhibition. The treatment of B-ALL patients is personalized based on specific subtypes, as the treatment responses for different B-ALL subtypes may vary considerably. Identification of B-ALL subtypes can facilitate risk stratification and enable tailored therapeutic approaches. Existing methods for B-ALL subtyping primarily depend on immunophenotypic, cytogenetic and genomic analyses, which would be costly, complicated, and laborious in clinical practice applications. To overcome these challenges, we present RanBALL (an Ensemble Random Projection-Based Model for Identifying B-Cell Acute Lymphoblastic Leukemia Subtypes), an accurate and cost-effective model for B-ALL subtype identification based on transcriptomic profiling only. RanBALL leverages random projection (RP) to construct an ensemble of dimension-reduced multi-class classifiers for B-ALL subtyping. Specifically, the transcriptomic profiling features were projected onto low-dimensional spaces by random projection matrices whose elements conform to a distribution characterized by zero mean and unit variance. To ensure reliable and robust performance, we selected 20 subspace dimensions ranging from 600 to 2500, with intervals of 100. The transformed low dimensional data matrix was used for training an ensemble of multi-class support vector machine (SVM) classifiers, each corresponding to one of the RP matrices of various dimensions. The predicted probabilistic scores of each B-ALL subtype were integrated for determining the final decision. Results based on 10 times 10-fold cross validation tests for >1700 B-ALL patients demonstrated that the proposed model achieved an accuracy of 93.7%, indicating promising prediction capabilities of RanBALL for B-ALL subtyping. Furthermore, the 30% held-out tests suggested that the model was robust and consistent to maintain high confidence levels for accurate predictions. The high accuracies of RanBALL suggested that our model could effectively capture underlying patterns of transcriptomic profiling for accurate B-ALL subtype identification. To extend the impact of RanBALL, we have established a free and publicly available python package for RanBALL available at https://github.com/wan-mlab/RanBALL. We believe RanBALL will facilitate the discovery of B-ALL subtype-specific marker genes and therapeutic targets, and eventually have consequential positive impacts on downstream risk stratification and tailored treatment design. Citation Format: Lusheng Li, Hanyu Xiao, Joseph D. Khoury, Jieqiong Wang, Shibiao Wan. RanBALL: Identifying B-cell acute lymphoblastic leukemia subtypes based on an ensemble random projection model [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4907.

Rolling bearing fault diagnosis based on fine-grained multi-scale Kolmogorov entropy and WOA-MSVM

In allusion to solve the issue of fault diagnosis for bearing and other rotatory machinery, a technique based on fined-grained multi-scale Kolmogorov entropy and whale optimized multi-class support vector machine (abbreviated as FGMKE-WOA-MSVM) is proposed. Firstly, vibration signals are decomposed by fine-grained multi-scale decomposition, and the Kolmogorov entropy of the sub-signals at different analysis scales is calculated as the multi-dimension feature vector, which quantitatively characterize the complexity of the signal at multi-scales. Aiming at the problem of sensitive parameters selection for multi-class support vector machine model (abbreviated as MSVM), the whale optimization algorithm (abbreviated as WOA) is introduced to optimize the penalty factor and kernel function parameter, and constructing optimal WOA-MSVM model. Finally, an instance analysis is carried out with Jiangnan University bearing datasets to verify the effectiveness and superiority of this technique. The results show that compared with different feature vectors and models such as K nearest neighbors (abbreviated as KNN) and Decision Tree (abbreviated as RF), the proposed technique is superior with fast computation speed and high diagnostic efficiency.

Classifying Coal Mine Pillar Stability Areas with Multiclass SVM on Ensemble Learning Models

Pillars are key structural components in coal mining. The safety requirements of underground coal mines are non-negotiable. Accurately classifying the areas of pillar stability helps ensure safety in coal mines. This study aimed to classify new pillar stability categories and their stability areas. The multiclass support vector machine (SVM) method was implemented with two types of kernel functions (polynomial and radial basis function (RBF) kernels) on pillar stability data with four new categories: failed or intact, either with or without an appropriate safety factor. This classification uses three basic ensemble learning models: Artificial Neural Network-Backpropagation Rectified Linear Unit, Artificial Neural Network-Backpropagation Exponential Linear Unit, and Artificial Neural Network-Backpropagation Gaussian Error Linear Unit. The results with four data proportions and ten experiments had an average accuracy and standard deviation of 92.98% and 0.56%-1.64% respectively. The accuracies of the multiclass SVM method using the polynomial kernel and the RBF kernel with Bayesian parameter optimization to classify the areas of pillar stability were 91% and 92%, respectively. The multiclass SVM method with the RBF kernel captured 96.6% of potentially dangerous pillars. The visualization of classification areas showed that areas with intact pillars may also have failed pillars.

Hybrid whale optimized crow search algorithm and multi-SVM classifier for effective system level test case selection

A novel approach to enhance software testing through intelligent test case selection is proposed in this work. The proposed method combines feature extraction, clustering, and a hybrid optimization algorithm to improve testing effectiveness while reducing resource overhead. It employs a context encoder to extract relevant features from software code, enhancing the accuracy of subsequent testing. Through the use of Fuzzy C-means (FCM) clustering, the test cases are classified into groups, streamlining the testing process by identifying similar cases. To optimize feature selection, a Hybrid Whale Optimized Crow Search Algorithm (HWOCSA), which intelligently combines the strengths of both Whale Optimization Algorithm (WOA) and Crow Search Algorithm (CSA) is introduced. This hybrid approach mitigates limitations while maximizing the selection of pertinent features for testing. The ultimate contribution of this work lies in the proposal of a multi-SVM classifier, which refines the test case selection process. Each classifier learns specific problem domains, generating predictions that guide the selection of test cases with unprecedented precision. Experimental results demonstrate that the proposed method achieves remarkable improvements in testing outcomes, including enhanced performance metrics, reduced computation time, and minimized training data requirements. By significantly streamlining the testing process and accurately selecting relevant test cases, this work paves the way for higher quality software updates at a reduced cost.

Speckle Noise Removal from Biomedical MRI Images and Classification by Multi-Support Vector Machine

INTRODUCTION: Image Processing (IP) methods play a vital role in medical images for diagnosing and predicting illness, as well as monitoring the patient's progress. The IP methods are utilized in many applications for example in the field of medicine.
 OBJECTIVES: The images that are obtained by the MRI magnetic Resonance imaging and x rays are analyzed with the help of image processing.
 METHODS: This application is very costly to the patient. Because of the several non-idealities in the image process, medical images are frequently tainted by impulsive, multiplicative, and addictive noise.
 RESULTS: By replacing some of the original image's pixels with new ones that have luminance values which are less than the allowed dynamic luminance range, noise frequently affects medical images.
 CONCLUSION: In this research work, the Speckle type noises are eliminated with the help of Mean Filter (MF) and classify the images using Multi-SVM classifier.  The entire system developed using python programming.

Decision-Making on the Diagnosis of Oncological Diseases Using Cost-Sensitive SVM Classifiers Based on Datasets with a Variety of Features of Different Natures

This paper discusses the problem of detecting cancer using such biomarkers as blood protein markers. The purpose of this research is to propose an approach for making decisions in the diagnosis of cancer through the creation of cost-sensitive SVM classifiers on the basis of datasets with a variety of features of different nature. Such datasets may include compositions of known features corresponding to blood protein markers and new features constructed using methods for calculating entropy and fractal dimensions, as well as using the UMAP algorithm. Based on these datasets, multiclass SVM classifiers were developed. They use cost-sensitive learning principles to overcome the class imbalance problem, which is typical for medical datasets. When implementing the UMAP algorithm, various variants of the loss function were considered. This was performed in order to select those that provide the formation of such new features that ultimately allow us to develop the best cost-sensitive SVM classifiers in terms of maximizing the mean value of the metric MacroF1−score. The experimental results proved the possibility of applying the UMAP algorithm, approximate entropy and, in addition, Higuchi and Katz fractal dimensions to construct new features using blood protein markers. It turned out that when working with the UMAP algorithm, the most promising is the application of a loss function on the basis of fuzzy cross-entropy, and the least promising is the application of a loss function on the basis of intuitionistic fuzzy cross-entropy. Augmentation of the original dataset with either features on the basis of the UMAP algorithm, features on the basis of the UMAP algorithm and approximate entropy, or features on the basis of approximate entropy provided the creation of the three best cost-sensitive SVM classifiers with mean values of the metric MacroF1−score increased by 5.359%, 5.245% and 4.675%, respectively, compared to the mean values of this metric in the case when only the original dataset was utilized for creating the base SVM classifier (without performing any manipulations to overcome the class imbalance problem, and also without introducing new features).

Ensembled CNN with artificial bee colony optimization method for esophageal cancer stage classification using SVM classifier.

Esophageal cancer (EC) is aggressive cancer with a high fatality rate and a rapid rise of the incidence globally. However, early diagnosis of EC remains a challenging task for clinicians. To help address and overcome this challenge, this study aims to develop and test a new computer-aided diagnosis (CAD) network that combines several machine learning models and optimization methods to detect EC and classify cancer stages. The study develops a new deep learning network for the classification of the various stages of EC and the premalignant stage, Barrett's Esophagus from endoscopic images. The proposed model uses a multi-convolution neural network (CNN) model combined with Xception, Mobilenetv2, GoogLeNet, and Darknet53 for feature extraction. The extracted features are blended and are then applied on to wrapper based Artificial Bee Colony (ABC) optimization technique to grade the most accurate and relevant attributes. A multi-class support vector machine (SVM) classifies the selected feature set into the various stages. A study dataset involving 523 Barrett's Esophagus images, 217 ESCC images and 288 EAC images is used to train the proposed network and test its classification performance. The proposed network combining Xception, mobilenetv2, GoogLeNet, and Darknet53 outperforms all the existing methods with an overall classification accuracy of 97.76% using a 3-fold cross-validation method. This study demonstrates that a new deep learning network that combines a multi-CNN model with ABC and a multi-SVM is more efficient than those with individual pre-trained networks for the EC analysis and stage classification.

Maize seed variety identification model using image processing and deep learning

<p><span>Maize is Ethiopia’s dominant cereal crop regarding area coverage and production level. There are different varieties of maize in Ethiopia. Maize varieties are classified based on morphological features such as shape and size. Due to the nature of maize seed and its rotation variant, studies are still needed to identify Ethiopian maize seed varieties. With expert eyes, identification of maize seed varieties is difficult due to their similar morphological features and visual similarities. We proposed a hybrid feature-based maize variety identification model to solve this problem. <br /> For training and testing the model, images of each maize variety were collected from the adet agriculture and research center (AARC), Ethiopia. A multi-class support vector machine (MCSVM) classifier was employed on a hybrid of handcrafted (i.e., gabor and histogram of oriented gradients) and convolutional neural network (CNN)-based feature selection techniques and achieved an overall classification accuracy of 99%.</span></p>

Land scene classification from remote sensing images using improved artificial bee colony optimization algorithm

The images obtained from remote sensing consist of background complexities and similarities among the objects that act as challenge during the classification of land scenes. Land scenes are utilized in various fields such as agriculture, urbanization, and disaster management, to detect the condition of land surfaces and help to identify the suitability of the land surfaces for planting crops, and building construction. The existing methods help in the classification of land scenes through the images obtained from remote sensing technology, but the background complexities and presence of similar objects act as a barricade against providing better results. To overcome these issues, an improved artificial bee colony optimization algorithm with convolutional neural network (IABC-CNN) model is proposed to achieve better results in classifying the land scenes. The images are collected from aerial image dataset (AID), Northwestern Polytechnical University-Remote Sensing Image Scene 45 (NWPU-RESIS45), and University of California Merced (UCM) datasets. IABC effectively selects the best features from the extracted features using visual geometry group-16 (VGG-16). The selected features from the IABC are provided for the classification process using multiclass-support vector machine (MSVM). Results obtained from the proposed IABC-CNN achieves a better classification accuracy of 96.40% with an error rate 3.6%.

Gear fault diagnosis based on complex network theory and error-correcting output codes: Multi class support vector machine

Gearbox failures have a detrimental effect on the machine performance that affects the production capacity and economic benefits of a manufacturing industry in an adverse manner. Early detection of faults in gears helps to prevent sudden machine failures. Previously, many signal processing and artificial intelligence techniques have been successfully applied to diagnose gear faults by analyzing the machine vibrations. These techniques have repeatedly emphasized on the need to extract quality features from gear vibration signals and using artificial intelligence techniques that can identify multiple types of faults simultaneously. As such, in this article, new features called as visibility graph features based on complex network theory have been proposed to extract fault information from vibration signals. In addition, a new artificial intelligence framework for multifault classification known as error-correcting output codes-multiclass support vector machine is proposed to detect gear faults. To the best of authors’ knowledge, these features along with the error-correcting output codes-artificial intelligence framework have been rarely utilized for fault diagnosis purpose. The proposed approach has been applied on experimental gear vibration data to validate its effectiveness. First, the vibration signals are transformed to graphs, and various graph properties are computed to serve as fault features. Then, the fault features are supplied to train the error-correcting output codes-multiclass support vector machine model and learn the fault patterns. Finally, the gear fault classification accuracies are determined for various gear test conditions and compared with those of existing methods. It is observed that the proposed approach provides an average improvement of 4.22%, 9.93% and 11.8% over the time-domain features, time–frequency domain features and voting-based multiclass support vector machine classifier, respectively, for different operating conditions. Furthermore, the suggested technique augments the classification accuracies by 10%, 7.5% and 6.7%, respectively, as compared with the deep-learning models, namely standard stacked sparse autoencoder, deep neural network and convolution neural network.