scholarly journals Orthogonal Moment Extraction and Classification of Melanoma Images

2018 ◽  
Author(s):  
Sudhakar Singh ◽  
Shabana Urooj
Keyword(s):  
Support Vector Machine ◽  
Zernike Moments ◽  
Support Vector ◽  
Moment Invariants ◽  
Swarm Optimization ◽  
Orthogonal Moments ◽  
Equal Interval ◽  
The Moment ◽  
Phase Angles

This paper provides orthogonal moments (OM) such as, Zernike Moments(ZM), Psuedo Zernike Moments(PZM) and Orthogonal Fourier Mellin Moments(OFMM) for the analysis of melanoma images. The moment invariants may vary with respect to geometric variations. For the analysis of orthogonal moments hundred random melanoma images and hundred non-melanoma images have been taken into consideration from the database of 570 melanoma images and 250 non-melanoma images respectively. Orthoganal moments have been computed by varying the phase angles from 10° to 40° with an equal interval of 10° degree for the orders 2, 4,8,16,32,64,128,256 respectively. For the optimal OMs Particle Swarm Optimization (PSO) technique have been used. These set of extracted optimal OMs have been further applied to classify melanoma images. Support Vector Machine (SVM) has been used for the classification of [1]sensitivity=88.78%.

Zernike moments-based fingerprint recognition using weighted-support vector machine

2019 ◽  
Vol 33 (21) ◽  
pp. 1950245 ◽  
Author(s):  
Harinder Kaur ◽  
Husanbir Singh Pannu
Keyword(s):  
Support Vector Machine ◽  
Zernike Moments ◽  
Fingerprint Recognition ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Kappa Statistics ◽  
Feature Selection Technique ◽  
Orthogonal Moments ◽  
Sensitivity Specificity ◽  
Invariant Properties

Moments play an important role in image analysis and invariant pattern recognition. There are two types: orthogonal moments and non-orthogonal moments. Orthogonal moments perform better than non-orthogonal moments, they have properties such as robustness to image noise and geometrical invariant properties such as scale, rotation and translation. In this paper, an improvement in fingerprint recognition is done by using the Non-subsampled contourlet transform (NSCT) and the Zernike moments (ZMs). NSCT decomposes the fingerprint images into NSCT subbands. Thereafter, ZMs are used to evaluate the features of fingerprint images. Thereafter, feature selection technique is applied to select potential features from the obtained features using coefficient of determination. Thereafter, a well-known weighted-support vector machine is also used to train and test the evaluated features. Extensive experiments reveal that the proposed technique achieves significant improvement over the existing techniques in terms of accuracy, sensitivity, specificity, [Formula: see text]-measure, kappa statistics and execution time.

scholarly journals Perbandingan Algoritma SVM dan SVM Berbasis Particle Swarm Optimization Pada Klasifikasi Beras Mekongga

2021 ◽  
Vol 5 (2) ◽  
pp. 102-108
Author(s):  
Emilia Ayu Wijayanti ◽  
Tania Rahmadanti ◽  
Ultach Enri
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
The Other ◽  
Support Vector ◽  
Rice Varieties ◽  
Swarm Optimization ◽  
Accuracy Level ◽  
Auc Value

Rice is the most important staple food in Indonesia. There are various types of varieties available, one of them is Inpari Mekongga variety. In Karawang, Mekongga rice type is the most popular and superior compared to others. However, this type of rice is often mixed with the other types because there are too many varieties and various other problems. Classifying varieties of rice types can be done to identify the types of rice. The classification of rice varieties in this research is divided into 2 classes, Mekongga and not Mekongga. The method that used in this reserach is Support Vector Machine (SVM) and Particle Swarm Optimatizon (PSO). SVM method was chosen because it basically handles the classification of two classes. Meanwhile, PSO method used to optimize the accuracy level of the SVM method. Combination from the two methods is very well used in classification data because it can increase the level of accuracy better. The purpose of this reserach is compare the accuracy of the 2 methods that used. The results from research is mekongga rice classification with Support Vector Machine has accuracy value 46.67% and  AUC value 0.475. Meanwhile, using Support Vector Machine based on Particle Swarm Optimization (PSO) can help improve the classification of this mekongga rice with accuracy value 70.83% and AUC value 0.671.

Classification of Fatigued Bills Based on a Support Vector Machine by Using the Amplitude Difference Features of Acoustic Signals

2011 ◽  
Vol 131 (8) ◽  
pp. 1495-1501
Author(s):  
Dongshik Kang ◽  
Masaki Higa ◽  
Hayao Miyagi ◽  
Ikugo Mitsui ◽  
Masanobu Fujita ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Acoustic Signals ◽  
Support Vector ◽  
Amplitude Difference
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