Automatic Detection and Classification of Tomato Pests Using Support Vector Machine Based on HOG and LBP Feature Extraction Technique

2020 ◽  
pp. 49-55
Author(s):  
Gayatri Pattnaik ◽  
K. Parvathi
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Automatic Detection ◽  
Extraction Technique ◽  
Support Vector ◽  
Tomato Pests ◽  
Feature Extraction Technique

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.

A Novel Feature Extraction from Genome Sequences For Taxonomic Classification Of Living Organisms

2021 ◽  
Vol 12 (2) ◽  
pp. 1436-1451
Author(s):  
Muthulakshmi M, Et. al.
Keyword(s):  
Feature Extraction ◽  
Genome Sequence ◽  
Taxonomic Classification ◽  
Extraction Technique ◽  
Genome Sequences ◽  
National Centre ◽  
History Of ◽  
Living Organisms ◽  
Feature Extraction Technique

Genome sequencing aids in understanding the nature, characteristics, habitat and evolutionary history of all living organisms. Apart from sequencing, the more important task is to correctly place the sequenced genome in the taxonomy. Generally, the taxonomic classification of the living organisms is done by observing their morphological, behavioral, genetic and biochemical characteristics. Among them, taxonomic classification using genetic observation is more accurate scientifically as the Genome sequence analysis exploits the complete characteristics of the organism. In this paper, we developed a novel Frequency based Feature Extraction Technique (FFET) which extracts 120 features and helps to analyze the genome sequence of the organism and to classify them in the taxonomy accordingly. We performed a kingdom level taxonomic classification using the proposed FFET. The proposed FFET extracts features based on storage, frequency of nucleotide bases, pattern arrangement and amino acid composition of genome sequences. The feature extraction technique is applied to 150 samples of genome sequences of various organisms which were downloaded from National Centre for Biotechnology and Information (NCBI) database. The extracted features are classified using various Machine learning and Deep learning classifiers. From the results, it is evident that FFET performs well for classification with Convolutional Neural Network (CNN) classifier with an accuracy of 96.73 %.

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