Multi Support Vector Machine Research Articles

Enhanced Breast Cancer Diagnosis Through an Integration of MSVM and Genetic Algorithms

Breast cancer remains a major health concern among women, often spreading from the breast to other parts of the body, and is the second leading cause of death in women. While early detection significantly increases the chances of successful treatment, current methods face challenges in accuracy and efficiency. This paper presents an approach for early-stage breast cancer detection using a combination of genetic algorithms and decision trees. The genetic algorithm, an iterative technique, enhances the speed of results, while the decision tree aids in classification. Additionally, a multi-support vector machine (MSVM) is employed for feature extraction and comparison with trained images. The proposed model is evaluated based on classification accuracy, precision, F-score, and recall. Simulation results demonstrate improved performance over existing systems

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.

Comprehensive Classification of Fetal Health Using Cardiotocogram Data Based on Machine Learning

In the realm of obstetrics, the evaluation of fetal health remains a paramount yet challenging endeavor. Traditional approaches, such as electronic fetal monitoring (EFM), despite their widespread adoption, continue to grapple with uncertainties regarding their impact on neonatal outcomes and the reduction of emergency cesarean deliveries. This ambiguity is compounded by a prevailing confusion within the obstetric community about interpreting fetal heart rate patterns, often leading to inconsistent and subjective assessments. Addressing these complexities, our study presents an innovative machine learning-based techniques for the comprehensive classification of fetal health using cardiotocogram (CTG) data, offering a more objective and nuanced alternative to conventional methods. The core of our proposed solution is a novel model employing a sophisticated ensemble of machine learning classifiers, including Multi-Support Vector Machine (Multi-SVM), Decision Tree, Random Forest with Hyperparameter Tuning, XGBoost, and Neural Networks. This model is unique in its application, processing datasets in four different forms: raw datasets, datasets processed with MinMaxScaler, datasets subjected to feature selection using SelectKBest, and a combination of MinMaxScaler processing and SelectKBest feature selection. Such meticulous preprocessing, encompassing normalization and feature selection, is pivotal in ensuring equitable contribution from each feature, thereby optimizing the model's learning process and predictive accuracy. The effectiveness of our model is rigorously evaluated using a dataset comprising 2126 individual records from CTG exams, classified by specialist obstetricians into three types: Normal, Suspect, and Pathological. These records are exhaustively analyzed using various metrics, including Accuracy, Precision, Recall, F1-Score, ROC AUC, and Confusion Matrix. Among the classifiers, XGBoost emerged as the most proficient, consistently outperforming others across multiple metrics. This indicates its superior ability to accurately identify and categorize the different states of fetal health. Our findings thus underscore the significant promise of machine learning in revolutionizing fetal health monitoring, offering a more reliable, objective, and comprehensive method for assessing fetal well-being, with profound implications for prenatal care and clinical decision-making.

Research on Multi-Source Heterogeneous Big Data Fusion Method Based on Feature Level

With the development of research on multi-modal data fusion and its combination with online data management, the application of multi-modal big data fusion in information management systems is more and more extensive. How to integrate multi-modal big data effectively is the key technology to building an efficient information management system. In this paper, based on the combination of a multi-support vector machine and convolution neural network, the feature-level data fusion of multi-source heterogeneous big data is implemented, and it is applied to the real data set to test the relevant model. Experimental results show that this method can not only realize heterogeneous integration of big data, but also has high accuracy and reliability.

An Edge Computing-Oriented Islanding Detection Using Differential Entropy and Multi-Support Vector Machines

This paper presents an edge computing-oriented islanding detection paradigm using multi-support vector machines (SVM) and differential entropy. The proposed methodology can detect islanding events and locate which microgrid was disconnected in a distribution system with microgrid cluster. The proposed method employs multi-SVMs in the cloud to detect and locate potential islanding events in power distribution systems. The accuracy and reliability of the results of SVMs are enhanced by introducing differential entropy for quantitative evaluation. Additionally, the method decomposes the SVM inference process at the edge, enabling edge devices with low computational power and limited resources to support data-driven algorithms without compromising accuracy and speed. An extensive study is performed on the modified IEEE123 distribution system established in real-time digital simulation. Simulation results elucidate that the proposed method can accurately and promptly detect system islanding operations with zero non-detection zone. Comparison with other methods illustrates the superiority of the proposed method in terms of discrimination accuracy, detection time, and reliability.

Image Copy-move Forgery Detection and Classification Using Golden Jackal Optimization Based Multi-Support Vector Machine

Image Copy-move Forgery Detection and Classification Using Golden Jackal Optimization Based Multi-Support Vector Machine

The Involvement of Local Binary Pattern to Improve the Accuracy of Multi Support Vector-Based Javanese Handwriting Character Recognition

Indonesia is a country that is rich in cultural diversity. An example of one such variety is the Javanese language. The letters that are usually used in Javanese are non-Latin letters or are usually known as Javanese script. However, along with advances in technology, the Javanese language is increasingly being forgotten. In the past, the Javanese script was used as a subject in schools, aiming for Indonesian students to continue to gain knowledge about the Javanese script. The initial step in the introduction of the Javanese script starts with the preprocessing process by changing the image of the Javanese script from the RGB image to a grayscale image which is then performed feature extraction, where the feature extraction used in this script recognition is texture extraction with the Local Binary Pattern (LBP) algorithm. The results of this processing are obtained information that can be used as a parameter in the Multi Support Vector Machine (SVM) classification to predict Javanese script images. In this study using the LBP method with the Multi SVM Algorithm as a classification algorithm produces a high accuracy of 90% in the recognition of Javanese script, better than using only Multi SVM with an accuracy of 80%.

Machine Learning based Early Stage Identification of Liver Tumor using Ultrasound Images

Liver cancer is one of the most malignant diseases and its diagnosis requires more computational time. It can be minimized by applying a Machine learning algorithm for the diagnosis of cancer. The existing machine learning technique uses only the color-based methods to classify images which are not efficient. So, it is proposed to use texture-based classification for diagnosis. The input image is resized and pre-processed by Gaussian filters. The features are extracted by applying Gray level co-occurrence matrix (GLCM) and Local binary pattern (LBP in the preprocessed image. The Local Binary Pattern (LBP) is an efficient texture operator which labels the pixels of an image by thresholding the neighborhood of each pixel and considers the result as a binary number. The extracted features are classified by multi-support vector machine (Multi SVM) and K-Nearest Neighbor (K-NN) algorithms. The Advantage of combining SVM with KNN is that SVM measures a large number of values whereas KNN accurately measures point values. The results obtained from the proposed techniques achieved high precision, accuracy, sensitivity and specificity than the existing method.

Multi-Resolution Feature Embedded Level Set Model for Crosshatched Texture Segmentation

In image processing applications, texture is the most important element utilized by human visual systems for distinguishing dissimilar objects in a scene. In this research article, a variational model based on the level set is implemented for crosshatched texture segmentation. In this study, the proposed model’s performance is validated on the Brodatz texture dataset. The cross-hatched texture segmentation in the lower resolution texture images is difficult, due to the computational and memory requirements. The aforementioned issue has been resolved by implementing a variational model based on the level set that enables efficient segmentation in both low and high-resolution images with automatic selection of the filter size. In the proposed model, the multi-resolution feature obtained from the frequency domain filters enhances the dissimilarity between the regions of crosshatched textures that have low-intensity variations. Then, the resultant images are integrated with a level set-based active contour model that addresses the segmentation of crosshatched texture images. The noise added during the segmentation process is eliminated by morphological processing. The experiments conducted on the Brodatz texture dataset demonstrated the effectiveness of the proposed model, and the obtained results are validated in terms of Intersection over the Union (IoU) index, accuracy, precision, f1-score and recall. The extensive experimental investigation shows that the proposed model effectively segments the region of interest in close correspondence with the original image. The proposed segmentation model with a multi-support vector machine has achieved a classification accuracy of 99.82%, which is superior to the comparative model (modified convolutional neural network with whale optimization algorithm). The proposed model almost showed a 0.11% improvement in classification accuracy related to the existing model.

An efficient machine learning approach for classification of diabetic retinopathy stages

Diabetic retinopathy (DR) the prime cause of blindness, develops when glucose levels rise, causing retinal damage. DR can be prevented if the illness is detected early. As a result, early grading, categorization, and diagnosis of DR can help diabetic patients avoid visual loss. Several system methods assist in the classification of DR using high-performance criteria. This work proposes an efficient system-based DR classification. The purpose of efficient machine learning dabetic retinopatyy grading classification (EML-DRGC) design is to recognize DR impulsively with highest accuracy. The proposed technique employs preprocessing methods such as employing the Gaussian filtering approach for removing noise present in retinal fundus images. The segmentation process is followed using K-means segmentation algorithm which is used for segmenting the region of interest (ROI) from background. Moreover, Feature extraction process is done by using gray level co-occurrence matrix (GLCM) in which features are extracted bycapturing the image's visual content and features from acceerated segment test (FAST) design is used as extractor of features. Finally, multi support vector machine is utilized as classifier for detecting severity levels of DR. Performance metrics such as accuracy of 98.38% and specificity of 98.34% are obtained which are superior to existing designs.

Brain Tumor Detection by Modified Particle Swarm Optimization Algorithm and Multi-Support Vector Machine Classifier

Brain Tumor Detection by Modified Particle Swarm Optimization Algorithm and Multi-Support Vector Machine Classifier

Performance Analysis of Cereals Grains using Neural Network & Multi Support Vector Machine

Objectives: To classify cereal grain using a multi-support vector machine and artificial neural network for better accuracy. To build a system for cereals grains classification with the use of image processing techniques. Methods: Using a CCD camera, the method starts with image acquisition. To acquire images Grayscale conversion, noise reduction, binarization, edge detection, and morphological operations are applied. Using the edge detection technique edge of the objects is predictable. The watershed algorithm is used for the segmentation of touching and overlapping cereals kernels. Local Binary Pattern (LBP) texture feature and color features extracted from segmented images. For image classification, the features extraction method is used. Findings: We have incorporated various parameters like shape, size, length, width, major axis length, and minor axis lengths on different cereals like rice, barley, millet, sorghum, wheat, and millet. There are a total of 96 images of the data set are used to train or test the model. Out of that 70% are training and 30% are testing. Improvements: In the proposed MSVM technique, we have achieved 89.7% accuracy and in the ANN technique the accuracy is 92.3% which is higher than the conventional SVM technique. Novelty: The proposed technique is based on the Multi Support Vector Machine (MSVM) and Artificial Neural Network (ANN). We have compared the MSVM and ANN with the SVM technique. Keywords: Segmentation; SVM; Cereals Quality; Watershed Algorithm; Local Binary Pattern; Classification of grain; neural network

Multi support vector machine and image processing for diagnosis of coronary artery disease

Multi support vector machine and image processing for diagnosis of coronary artery disease

Spatio-Temporal Deep Feature Fusion for Human Action Recognition

Action Recognition plays a vital role in many secure applications. The objective of this paper is to identify actions more accurately. This paper focuses on the two stream network in which keyframe extraction method is utilized before extracting spatial features. The temporal features are extracted using Attentive Correlated Temporal Feature (ACTF) which uses Long Short Term Memory (LSTM) for deep features. The spatial and temporal features are fused and classified using multi Support Vector Machine (multiSVM) classifier. Experiments are done on HMDB51 and UCF101 datasets. The results of the proposed method are compared with recent methods in terms of accuracy. The proposed method is proved to work better than other methods by achieving an accuracy of 96% for HMDB51 dataset and 98% for UCF101 dataset.

Alzheimer's Disease Detection using Correlation based Ensemble Feature Selection and Multi Support Vector Machine

Alzheimer's Disease Detection using Correlation based Ensemble Feature Selection and Multi Support Vector Machine

Remote Sensing Scene Type Classification Using Multi-Trial Vector-Based Differential Evolution Algorithm and Multi-Support Vector Machine Classifier

In recent decades, remote sensing scene type classification becomes a challenging task in remote sensing applications. In this paper, a new model is proposed for multi-class scene type classification in remote sensing images. Firstly, the aerial images are collected from the Aerial Image Dataset (AID), University of California Merced (UC Merced) and REmote Sensing Image Scene Classification 45 (RESISC45) datasets. Next, AlexNet, GoogLeNet, ResNet 18, and Visual Geometric Group (VGG) 19 models are used for extracting feature vectors from the collected aerial images. After feature extraction, the Multi-Trial vector based Differential Evolution (MTDE) algorithm is proposed to choose active feature vectors for better classification and to reduce system complexity and time consumption. The selected active features are fed to the Multi Support Vector Machine (MSVM) for final scene type classification. The simulation results showed that the proposed MTDE-MSVM model obtained high classification accuracy of 99.41%, 99.59% and 99.74% on RESISC45, AID and UC Merced datasets.

Classification of Damages in Composite Material using Multi-support Vector Machine

Classification of Damages in Composite Material using Multi-support Vector Machine

Human Gait Recognition: A Deep Learning and Best Feature Selection Framework

Background—Human Gait Recognition (HGR) is an approach based on biometric and is being widely used for surveillance. HGR is adopted by researchers for the past several decades. Several factors are there that affect the system performance such as the walking variation due to clothes, a person carrying some luggage, variations in the view angle. Proposed—In this work, a new method is introduced to overcome different problems of HGR. A hybrid method is proposed or efficient HGR using deep learning and selection of best features. Four major steps are involved in this work-preprocessing of the video frames, manipulation of the pre-trained CNN model VGG-16 for the computation of the features, removing redundant features extracted from the CNN model, and classification. In the reduction of irrelevant features Principal Score and Kurtosis based approach is proposed named PSbK. After that, the features of PSbK are fused in one materix. Finally, this fused vector is fed to the One against All Multi Support Vector Machine (OAMSVM) classifier for the final results. Results—The system is evaluated by utilizing the CASIA B database and six angles 00°, 18°, 36°, 54°, 72°, and 90° are used and attained the accuracy of 95.80%, 96.0%, 95.90%, 96.20%, 95.60%, and 95.50%, respectively. Conclusion—The comparison with recent methods show the proposed method work better.

Fake News Detection System Using Featured-Based Optimized MSVM Classification

Fake News creates erroneous suspense information that can be identified. This spreads dishonesty about a country’s status or overstates the expense of special functions for a government, destroying democracy in certain countries, such as in the Arab Spring. Associations such as the “House of Commons and the Crosscheck project” address concerns such as publisher responsibility. However, since they rely entirely on manual detection by humans, their coverage is minimal. This is neither sustainable nor possible in a world where billions of items are withdrawn or posted every second. The paper produces a deep study on past research work on fake news detection on the selected data-sets and proposes an algorithm with Multi-layered Principal Component Analysis for feature selection followed by firefly-optimized algorithm. Multi-Support Vector Machines(MSVM) are finally used to classify the news. We used ten different data-sets for testing the proposed algorithm. As the number of features in the data-sets are more, feature extration and selection methods help to improve the accuracy in respective data-sets. Only the datasets having less number of features gave a lower performance on our feature extraction algorithms.

Handwritten Character Recognition Using Unsupervised Feature Selection and Multi Support Vector Machine Classifier

Handwritten Character Recognition Using Unsupervised Feature Selection and Multi Support Vector Machine Classifier