Classification of Welding Flaws in Gamma Radiography Images Based on Multi-scale Wavelet Packet Feature Extraction Using Support Vector Machine

2015 ◽  
Vol 34 (4) ◽  
Author(s):  
Mohamed S. El-Tokhy ◽  
Imbaby I. Mahmoud
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Wavelet Packet ◽  
Support Vector ◽  
Multi Scale

Identification and Classification of Brain Tumor from MRI with Feature Extraction by Support Vector Machine

2020 ◽  
Author(s):  
Alok Sarkar ◽  
Md. Maniruzzaman ◽  
Md. Shamim Ahsan ◽  
Mohiuddin Ahmad ◽  
Mohammad Ismat Kadir ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Brain Tumor ◽  
Support Vector

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.

scholarly journals Digit Classification of Majapahit Relic Inscription using GLCM-SVM

2018 ◽  
Vol 1 (2) ◽  
pp. 46
Author(s):  
Tri Septianto ◽  
Endang Setyati ◽  
Joan Santoso
Keyword(s):  
Image Processing ◽  
Support Vector Machine ◽  
Feature Extraction ◽  
Template Matching ◽  
Support Vector ◽  
Gray Level ◽  
Class 1 ◽  
Occurrence Matrix ◽  
Handwritten Recognition

A higher level of image processing usually contains some kind of classification or recognition. Digit classification is an important subfield in handwritten recognition. Handwritten digits are characterized by large variations so template matching, in general, is inefficient and low in accuracy. In this paper, we propose the classification of the digit of the year of a relic inscription in the Kingdom of Majapahit using Support Vector Machine (SVM). This method is able to cope with very large feature dimensions and without reducing existing features extraction. While the method used for feature extraction using the Gray-Level Co-Occurrence Matrix (GLCM), special for texture analysis. This experiment is divided into 10 classification class, namely: class 1, 2, 3, 4, 5, 6, 7, 8, 9, and class 0. Each class is tested with 10 data so that the whole data testing are 100 data number year. The use of GLCM and SVM methods have obtained an average of classification results about 77 %.

Automatic Prostate Lesions Detection on MR Images Based on the Ising Model

2019 ◽  
Vol 16 (2) ◽  
pp. 341-350
Author(s):  
Artur Bernardo Silva Reis ◽  
Aristófanes Corrêa Silva ◽  
Anselmo Cardoso de Paiva ◽  
Marcelo Gattass
Keyword(s):  
Prostate Cancer ◽  
Support Vector Machine ◽  
Feature Extraction ◽  
Ising Model ◽  
Support Vector ◽  
Mr Images ◽  
Male Population ◽  
Prostate Imaging ◽  
Wolff Algorithm

Prostate cancer is the second most prevalent type of cancer in the male population worldwide. Prostate imaging tests have adopted for the prevention, diagnosis, and treatment. It is known that early detection increases the chances of an effective treatment, improving the prognosis of the disease. This paper proposes an automatic methodology for prostate lesions detection. It consists of the following steps: Extracting candidates for lesions with Wolff algorithm; feature extraction using the Ising model measures and finally the uses support vector machine in the classification of a lesion or healthy tissue. The methodology was validated using a set of 28 exams containing the lesion markings and obtained a sensitivity of 95.92%, specificity of 93.89% and accuracy of 94.16%. These are promising since they were more significant than other methods compared.

Classification of Surface EMGs Using Wavelet Packet Energy Analysis and a Genetic Algorithm-Based Support Vector Machine

2013 ◽  
Vol 45 (1) ◽  
pp. 39-48 ◽  
Author(s):  
Y. Rong ◽  
D. Hao ◽  
X. Han ◽  
Y. Zhang ◽  
J. Zhang ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Energy Analysis ◽  
Wavelet Packet ◽  
Support Vector

scholarly journals Classification of Sleep Apnea Based on Sub-Band Decomposition of EEG Signals

Diagnostics ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 1571
Author(s):  
Rajeswari Jayaraj ◽  
Jagannath Mohan
Keyword(s):  
Support Vector Machine ◽  
Sleep Apnea ◽  
Support Vector Machine Classifier ◽  
Wavelet Packet ◽  
Brain Perfusion ◽  
Support Vector ◽  
Eeg Signals ◽  
Data Format ◽  
Electroencephalogram Eeg

To classify between normal and sleep apnea subjects based on sub-band decomposition of electroencephalogram (EEG) signals. This study comprised 159 subjects obtained from the ISRUC (Institute of System and Robotics—University of Coimbra), Sleep-EDF (European Data Format), and CAP (Cyclic Alternating Pattern) Sleep database, which consists of normal and sleep apnea subjects. The wavelet packet decomposition method was incorporated to categorize the EEG signals into five frequency bands, namely, alpha, beta, delta, gamma, and theta. Entropy and energy (non-linear) for all bands was calculated and as a result, 10 features were obtained for each EEG signal. The ratio of EEG bands included four parameters, including heart rate, brain perfusion, neural activity, and synchronization. In this study, a support vector machine with kernels and random forest classifiers was used for classification. The performance measures demonstrated that the improved results were obtained from the support vector machine classifier with a kernel polynomial order 2. The accuracy (90%), sensitivity (100%), and specificity (83%) with 14 features were estimated using the data obtained from ISRUC database. The proposed study is feasible and seems to be accurate in classifying the subjects with sleep apnea based on the extracted features from EEG signals using a support vector machine classifier.

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