Analysis and Classification of ECG Signal based on Whale Optimization with Compact Multiclass Support Vector Machine

Classification is the process of grouping objects that have the same features or characteristics into several classes. The automatic documents classification use words frequency that appears on training data as features. The large number of documents cause the number of words that appears as a feature will increase. Therefore, summaries are chosen to reduce the number of words that used in classification. The classification uses multiclass Support Vector Machine (SVM) method. SVM was considered to have a good reputation in the classification. This research tests the effect of summary as selection features into documents classification. The summaries reduce text into 50%. A result obtained that the summaries did not affect value accuracy of classification of documents that use SVM. But, summaries improve the accuracy of Simple Logistic Classifier. The classification testing shows that the accuracy of Naïve Bayes Multinomial (NBM) better than SVM

Download Full-text

Classification of power quality disturbances in distribution systems using two Mathematical Morpological Filters and multiclass support vector machine

2019 24th Electrical Power Distribution Conference (EPDC) ◽

10.1109/epdc.2019.8903755 ◽

2019 ◽

Author(s):

Siavash Shadpey

Keyword(s):

Support Vector Machine ◽

Power Quality ◽

Distribution Systems ◽

Support Vector ◽

Multiclass Support Vector Machine ◽

Power Quality Disturbances

Download Full-text

Differential Evolution and Multiclass Support Vector Machine for Alzheimer’s Classification

Security and Communication Networks ◽

10.1155/2022/7275433 ◽

2022 ◽

Vol 2022 ◽

pp. 1-13

Author(s):

Jhansi Rani Kaka ◽

K. Satya Prasad

Keyword(s):

Support Vector Machine ◽

Feature Selection ◽

Differential Evolution ◽

Optimization Method ◽

Support Vector ◽

Local Optima ◽

Whale Optimization ◽

Multiclass Support Vector Machine ◽

Poor Convergence

Early diagnosis of Alzheimer’s helps a doctor to decide the treatment for the patient based on the stages. The existing methods involve applying the deep learning methods for Alzheimer’s classification and have the limitations of overfitting problems. Some researchers were involved in applying the feature selection based on the optimization method, having limitations of easily trapping into local optima and poor convergence. In this research, Differential Evolution-Multiclass Support Vector Machine (DE-MSVM) is proposed to increase the performance of Alzheimer’s classification. The image normalization method is applied to enhance the quality of the image and represent the features effectively. The AlexNet model is applied to the normalized images to extract the features and also applied for feature selection. The Differential Evolution method applies Pareto Optimal Front for nondominated feature selection. This helps to select the feature that represents the characteristics of the input images. The selected features are applied in the MSVM method to represent in high dimension and classify Alzheimer’s. The DE-MSVM method has accuracy of 98.13% in the axial slice, and the existing whale optimization with MSVM has 95.23% accuracy.

Download Full-text

Design of a Power Aware Systolic Array based Support Vector Machine Classifier

Advances in Systems Analysis, Software Engineering, and High Performance Computing - Intelligent Applications for Heterogeneous System Modeling and Design ◽

10.4018/978-1-4666-8493-5.ch005 ◽

2015 ◽

pp. 96-138

Author(s):

Bhaswati Mandal ◽

Manash Pratim Sarma ◽

Kandarpa Kumar Sarma

Keyword(s):

Support Vector Machine ◽

Systolic Array ◽

Support Vector ◽

Svm Classifier ◽

Classification Problems ◽

Processing Elements ◽

Array Architecture ◽

Multiclass Support Vector Machine ◽

Kernel Operation

This chapter presents a method for generating binary and multiclass Support Vector Machine (SVM) classifier with multiplierless kernel function. This design provides reduced power, area and reduced cost due to the use of multiplierless kernel operation. Binary SVM classifier classifies two groups of linearly or nonlinearly separable data while the multiclass classification provides classification of three nonlinearly separable data. Here, at first SVM classifier is trained for different classification problems and then the extracted training parameters are used in the testing phase of the same. The dataflow from all the processing elements (PEs) are parallely supported by systolic array. This systolic array architecture provides faster processing of the whole system design.

Download Full-text