scholarly journals An Artificial Intelligence System for Classification of COVID-19 Suspicious Person using Support Vector Machine (SVM) Classifier

2020 ◽  
Vol 176 (37) ◽  
pp. 16-19
Author(s):  
Nitin M. Shivale ◽  
Gauri Virkar ◽  
Tejas L. Bhosale
Keyword(s):  
Artificial Intelligence ◽  
Support Vector Machine ◽  
Support Vector ◽  
Svm Classifier ◽  
Artificial Intelligence System ◽  
Intelligence System

Artificial intelligence system based on the layout effect of both sides in volleyball matches

2020 ◽  
pp. 1-10
Author(s):  
Kai Zhao ◽  
Wei Jiang ◽  
Xinlong Jin ◽  
Xuming Xiao
Keyword(s):  
Artificial Intelligence ◽  
Moving Objects ◽  
Support Vector ◽  
Moving Target ◽  
Artificial Intelligence System ◽  
Fast Detection ◽  
Intelligence System ◽  
Detection And Tracking ◽  
Traditional Sports

The traditional sports match analysis mostly adopts the method of manual observation and recording, which is not only time-consuming and laborious but also has the defects of subjectivity and inaccuracy in the judgment results, resulting in the deviation of the match data analysis and statistical results. The purpose of this paper is to study an artificial intelligence system that can automatically analyze and evaluate the effect of both sides in volleyball matches. In this paper, the system is divided into two steps: detection and tracking of moving objects, recognition, and classification of players’ behaviors and movements. About moving target detection and tracking, this paper proposes a moving target fast detection framework based on a mixture of mainstream technologies and a MeanShift target tracking method based on Kalman filtering and adaptive target region size. For behavior and action recognition and classification, this paper proposes a classifier combining BP neural network and support vector machine. Experimental results show that the proposed algorithm and classifier are effective. By analyzing the performance of the proposed classifier, the classification accuracy is 98%.

Kernel Optimized-Support Vector Machine and MapReduce Framework for Sentiment Classification of Train Reviews

2019 ◽  
Vol 27 (06) ◽  
pp. 1025-1050
Author(s):  
Rashmi K. Thakur ◽  
Manojkumar V. Deshpande
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Public Transportation ◽  
Extraction Process ◽  
Sentiment Classification ◽  
Support Vector ◽  
Svm Classifier ◽  
Mapreduce Framework ◽  
Mapreduce Model

Sentiment analysis is one of the popular techniques gaining attention in recent times. Nowadays, people gain information on reviews of users regarding public transportation, movies, hotel reservation, etc., by utilizing the resources available, as they meet their needs. Hence, sentiment classification is an essential process employed to determine the positive and negative responses. This paper presents an approach for sentiment classification of train reviews using MapReduce model with the proposed Kernel Optimized-Support Vector Machine (KO-SVM) classifier. The MapReduce framework handles big data using a mapper, which performs feature extraction and reducer that classifies the review based on KO-SVM classification. The feature extraction process utilizes features that are classification-specific and SentiWordNet-based. KO-SVM adopts SVM for the classification, where the exponential kernel is replaced by an optimized kernel, finding the weights using a novel optimizer, Self-adaptive Lion Algorithm (SLA). In a comparative analysis, the performance of KO-SVM classifier is compared with SentiWordNet, NB, NN, and LSVM, using the evaluation metrics, specificity, sensitivity, and accuracy, with train review and movie review database. The proposed KO-SVM classifier could attain maximum sensitivity of 93.46% and 91.249% specificity of 74.485% and 70.018%; and accuracy of 84.341% and 79.611% respectively, for train review and movie review databases.

MULTI-CLUSTER SUPPORT VECTOR MACHINE CLASSIFIER FOR THE CLASSIFICATION OF SUSPICIOUS AREAS IN DIGITAL MAMMOGRAMS

2011 ◽  
Vol 10 (04) ◽  
pp. 481-494 ◽  
Author(s):  
PETER MC LEOD ◽  
BRIJESH VERMA
Keyword(s):  
Support Vector Machine ◽  
Support Vector Machine Classifier ◽  
Screening Mammography ◽  
Support Vector ◽  
Svm Classifier ◽  
Anova Analysis ◽  
Novel Technique ◽  
Digital Database ◽  
Benchmark Database

This paper presents a novel technique for the classification of suspicious areas in digital mammograms. The proposed technique is based on clustering of input data into numerous clusters and amalgamating them with a Support Vector Machine (SVM) classifier. The technique is called multi-cluster support vector machine (MCSVM) and is designed to provide a fast converging technique with good generalization abilities leading to an improved classification as a benign or malignant class. The proposed MCSVM technique has been evaluated on data from the Digital Database of Screening Mammography (DDSM) benchmark database. The experimental results showed that the proposed MCSVM classifier achieves better results than standard SVM. A paired t-test and Anova analysis showed that the results are statistically significant.

scholarly journals Feature Extraction and Classification of MRI Using Hybrid RBF Kernel and SVM

2021 ◽  
pp. 418-426
Author(s):  
Suhas S ◽  
Dr. C. R. Venugopal
Keyword(s):  
Support Vector Machine ◽  
Image Retrieval ◽  
Classification Accuracy ◽  
Kernel Functions ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Svm Classifier ◽  
Mr Images ◽  
Rbf Kernel

An enhanced classification system for classification of MR images using association of kernels with support vector machine is developed and presented in this paper along with the design and development of content-based image retrieval (CBIR) system. Content of image retrieval is the process of finding relevant image from large collection of image database using visual queries. Medical images have led to growth in large image collection. Oriented Rician Noise Reduction Anisotropic Diffusion filter is used for image denoising. A modified hybrid Otsu algorithm termed is used for image segmentation. The texture features are extracted using GLCM method. Genetic algorithm with Joint entropy is adopted for feature selection. The classification is done by support vector machine along with various kernels and the performance is validated. A classification accuracy of 98.83% is obtained using SVM with GRBF kernel. Various features have been extracted and these features are used to classify MR images into five different categories. Performance of the MC-SVM classifier is compared with different kernel functions. From the analysis and performance measures like classification accuracy, it is inferred that the brain and spinal cord MRI classification is best done using MC- SVM with Gaussian RBF kernel function than linear and polynomial kernel functions. The proposed system can provide best classification performance with high accuracy and low error rate.

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

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.

Spectral-Spatial Classification of Hyperspectral Image Based on Support Vector Machine

2021 ◽  
Vol 16 (1) ◽  
pp. 56-74
Author(s):  
Weiwei Yang ◽  
Haifeng Song
Keyword(s):  
Support Vector Machine ◽  
Classification Accuracy ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
State Of The Art ◽  
Support Vector ◽  
Svm Classifier ◽  
Spatial Classification ◽  
Homogeneous Regions

Recent research has shown that integration of spatial information has emerged as a powerful tool in improving the classification accuracy of hyperspectral image (HSI). However, partitioning homogeneous regions of the HSI remains a challenging task. This paper proposes a novel spectral-spatial classification method inspired by the support vector machine (SVM). The model consists of spectral-spatial feature extraction channel (SSC) and SVM classifier. SSC is mainly used to extract spatial-spectral features of HSI. SVM is mainly used to classify the extracted features. The model can automatically extract the features of HSI and classify them. Experiments are conducted on benchmark HSI dataset (Indian Pines). It is found that the proposed method yields more accurate classification results compared to the state-of-the-art techniques.

Classification of Medical Text Data Using Convolutional Neural Network-Support Vector Machine Method

2020 ◽  
Vol 10 (7) ◽  
pp. 1746-1753
Author(s):  
Lan Liu ◽  
Xiankun Sun ◽  
Chengfan Li ◽  
Yongmei Lei
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Convolutional Neural Network ◽  
Support Vector ◽  
Svm Classifier ◽  
Text Data ◽  
Machine Method ◽  
Medical Text ◽  
Conventional Methods

Conventional methods of medical text data classification, neglect of context among different words and semantic information, has a poor text description, classification effect and generalization capability and robustness. To tackle the inefficiencies and low precision in the classification of medical text data, in this paper, we presented a new classification method with improved convolutional neural network (CNN) and support vector machine (SVM), i.e., CNN-SVM method. In the method, some convolution kernel filters that contribute greatly to the CNN model are first selected by the average response energy (ARE) value, and then used to simplify and reconstruct the CNN model. Next, the SVM classifier was optimized by firefly algorithm (FA) and context information to overcome the disadvantages of over-saturation and over-training in SVM classification. Finally, the presented CNN-SVM method is tested by the simulation experiment and the true classification of medical text data. The experimental results show that the presented CNN-SVM method in this paper can significantly reduce the complexity and amount of computation compared to the conventional methods, and further promote the computational efficiency and classification accuracy of medical text data.

scholarly journals Comparison of Random Forest and Support Vector Machine for Indonesian Tweet Complaint Classification

2019 ◽  
pp. 202-207 ◽  
Author(s):  
Desi Ramayanti
Keyword(s):  
Support Vector Machine ◽  
Random Forest ◽  
Text Classification ◽  
Research Area ◽  
Computational Time ◽  
Support Vector ◽  
Svm Classifier ◽  
Text Documents ◽  
Case Organization

In digital business, the managerial commonly need to process text so that it can be used to support decision-making. The number of text documents contained ideas and opinions is progressing and challenging to understand one by one. Whereas if the data are processed and correctly rendered using machine learning, it can present a general overview of a particular case, organization, or object quickly. Numerous researches have been accomplished in this research area, nevertheless, most of the studies concentrated on English text classification. Every language has various techniques or methods to classify text depending on the characteristics of its grammar. The result of classification among languages may be different even though it used the same algorithm. Given the greatness of text classification, text classification algorithms that can be implemented is the support vector machine (SVM) and Random Forest (RF). Based on the background above, this research is aimed to find out the performance of support vector machine algorithm and random forest in classification of Indonesian text. 1. Result of SVM classifier with cross validation k-10 is derived the best accuracy with value 0.9648, however, it spends computational time as long as 40.118 second. Then, result of RF classifier with values, i.e. 'bootstrap': False, 'min_samples_leaf': 1, 'n_estimators': 10, 'min_samples_split': 3, 'criterion': 'entropy', 'max_features': 3, 'max_depth': None is achieved accuracy is 0.9561 and computational time 109.399 second.

scholarly journals Wavelet based Extraction of Features from EEG Signals and Classification of Novel Emotion Recognition Using SVM and HMM Classifier and to Measure its Accuracy

2021 ◽  
Vol 36 (1) ◽  
pp. 727-732
Author(s):  
M. Mohanambal ◽  
Dr.P. Vishnu Vardhan
Keyword(s):  
Support Vector Machine ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Mean Value ◽  
Support Vector ◽  
Svm Classifier ◽  
Emotion Detection ◽  
Eeg Signals

Aim: The study aims to extract features from EEG signals and classify emotion using Support Vector Machine (SVM) and Hidden Markov Model (HMM) classifier. Materials and methods: The study was conducted using the Support Vector Machine (SVM) and Hidden Markov Model (HMM) programs to analyze and compare the recognition of emotions classified under EEG signals. The results were computed using the MATLAB algorithm. For each group, ten samples were used to compare the efficiency of SVM and HMM classifiers. Result: The study’s performance exhibits the HMM classifier’s accuracy over the SVM classifier and the emotion detection from EEG signals computed. The mean value of the HMM classifier is 52.2, and the SVM classifier is 22.4. The accuracy rate of 35% with the data features is found in HMM classifier. Conclusion: This study shows a higher accuracy level of 35% for the HMM classifier when compared with the SVM classifier. In the detection of emotions using the EEG signal. This result shows that the HMM classifier has a higher significant value of P=.001 < P=.005 than the SVM classifier.

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