Classification of hyper-spectral images with Probabilistic Fuzzy Kernel based Fuzzy C-Means clustering and Support Vector Machine

2016 ◽  
Author(s):  
Shuqiong Xu ◽  
Conggui Yuan
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Fuzzy C Means ◽  
Hyper Spectral ◽  
Fuzzy C Means Clustering

scholarly journals Classification of Brain MRI Tumor Images Using Fuzzy C Means Clustering With Firefly Algorithms Optimized Support Vector Machine

2021 ◽  
Vol 23 (11) ◽  
pp. 70-77
Author(s):  
M.R. Thiyagupriyadharsan ◽  
◽  
Dr.S. Suja ◽  
Keyword(s):  
Health Care ◽  
Support Vector Machine ◽  
Brain Tumor ◽  
Health Care System ◽  
Support Vector ◽  
Human Beings ◽  
Fuzzy C Means ◽  
Mri Brain ◽  
Care System

In the contemporary world, many dangerous disease which are affecting human beings and new pandemic disease is also raising alarm to have an effective health care system. In this aspect the technology plays a major role in improving and optimizing the health care system. The diagnostic is done by taking blood test, urine test, and medical imaging like X-ray, CT scan, Ultrasound scan and MRI scan system. Among these, the paper focus will be emphasized on MRI imaging in identifying the brain tumor using image processing. In the proposed work the fuzzy C means(FCM) algorithm along with firefly algorithm optimized support vector machine (SVM) are used to classify the MRI brain tumor images. The results of these works are compared using the performance metrics such as accuracy, sensitivity, specificity and precision. The proposed method gives best results for the classification of MRI brain tumor images.

scholarly journals Assessment of Geospatial Approaches Used for Classification of Crops

2018 ◽  
Vol 3 (3) ◽  
pp. 271-279
Author(s):  
Suraj Kumar Singh ◽  
Shruti Kanga ◽  
Sudhanshu
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
General Survey ◽  
Processing Strategies ◽  
Hyper Spectral ◽  
Satellite Sensors ◽  
The Government ◽  
Spot 5 ◽  
Work Done

Harvests distinguishing proof from remotely detected pictures is fundamental because of utilization of remote identifying images as a contribution for rural and monetary arranging by the government and private offices. Accessible satellite sensors like IRS AWIFS, LISS, SPOT 5 and furthermore LANDSAT, MODIS are great wellsprings of multispectral information with various spatial resolutions and Hyperion, Hy-Map, AVIRIS are great wellsprings of hyper-Spectral. The technique for current research is choice of satellite information; utilization of appropriate strategy for arrangement and checking the accuracy. From most recent four decades different specialists have been taking a shot at these issues up to some degree yet at the same time a few difficulties are there like numerous products distinguishing proof, separation of harvests of the same sort this paper gives a general survey of the work done in this vital zone. Multispectral and hyper-spectral images contain spectral data about the crops. Good delicate registering and examination aptitudes are required to order and distinguish the class of enthusiasm from that datasets. Various specialists have worked with supervised and unsupervised arrangement alongside hard classifiers and also delicate processing strategies like fuzzy C mean, support vector machine and they have been discovered distinctive outcomes with various datasets.

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