Analysis and identification of kidney stone using Kth nearest neighbour (KNN) and support vector machine (SVM) classification techniques

2017 ◽  
Vol 27 (3) ◽  
pp. 574-580 ◽  
Author(s):  
Jyoti Verma ◽  
Madhwendra Nath ◽  
Priyanshu Tripathi ◽  
K. K. Saini
Keyword(s):  
Support Vector Machine ◽  
Kidney Stone ◽  
Support Vector ◽  
Nearest Neighbour ◽  
Classification Techniques ◽  
Svm Classification

scholarly journals A Comparative Study of Support Vector Machine and Maximum Likelihood Classification to Extract Land Cover of Lahore District, Punjab, Pakistan

2021 ◽  
Vol 64 (3) ◽  
pp. 265-274
Author(s):  
Fatima Mushtaq ◽  
Khalid Mahmood ◽  
Mohammad Chaudhry Hamid ◽  
Rahat Tufail
Keyword(s):  
Support Vector Machine ◽  
Maximum Likelihood ◽  
Land Cover ◽  
Support Vector ◽  
Kappa Statistics ◽  
Error Matrix ◽  
Classification Techniques ◽  
Machine Learning Classification ◽  
Hyper Plane ◽  
Better Than

The advent of technological era, the scientists and researchers develop machine learning classification techniques to classify land cover accurately. Researches prove that these classification techniques perform better than previous traditional techniques. In this research main objective is to identify suitable land cover classification method to extract land cover information of Lahore district. Two supervised classification techniques i.e., Maximum Likelihood Classifier (MLC) (based on neighbourhood function) and Support Vector Machine (SVM) (based on optimal hyper-plane function) are compared by using Sentinel-2 data. For this optimization, four land cover classes have been selected. Field based training samples have been collected and prepared through a survey of the study area at four spatial levels. Accuracy for each of the classifier has been assessed using error matrix and kappa statistics. Results show that SVM performs better than MLC. Overall accuracies of SVM and MLC are 95.20% and 88.80% whereas their kappa co-efficient are 0.93 and 0.84 respectively.  

scholarly journals An Improved Dual Clustering Method for Classification of Microarray Image Segementation

2019 ◽  
Vol 8 (4) ◽  
pp. 2514-2519
Keyword(s):  
Support Vector Machine ◽  
Ct Scanning ◽  
Support Vector ◽  
Nearest Neighbour ◽  
Resonance Imaging ◽  
Computerised Tomography ◽  
Medical Field ◽  
Microarray Image ◽  
Image Segementation

Microarray is a fast and rapid growing technology which plays dynamic role in the medical field. It is an advanced than MRI (Magnetic Resonance Imaging) and CT scanning (Computerised Tomography). The purpose of this work is to make fine perfection against the gene expression. In this study the two clustering are used which fuzzy c means and k means and also it classifies with better results. The microarray data base indicates the classification in support vector machine. Segmentation is most important step in microarray image. The classification in support vector machine is compared with other two classifiers which means the k nearest neighbour and with the Bayes classifiers.

scholarly journals Improved Instance Selection Methods for Support Vector Machine Speed Optimization

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Andronicus A. Akinyelu ◽  
Aderemi O. Adewumi
Keyword(s):  
Support Vector Machine ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Instance Selection ◽  
Training Time ◽  
Svm Classification ◽  
Support Vectors ◽  
Increase With Increase ◽  
Dataset Size ◽  
Machine Speed

Support vector machine (SVM) is one of the top picks in pattern recognition and classification related tasks. It has been used successfully to classify linearly separable and nonlinearly separable data with high accuracy. However, in terms of classification speed, SVMs are outperformed by many machine learning algorithms, especially, when massive datasets are involved. SVM classification speed scales linearly with number of support vectors, and support vectors increase with increase in dataset size. Hence, SVM classification speed can be enormously reduced if it is trained on a reduced dataset. Instance selection techniques are one of the most effective techniques suitable for minimizing SVM training time. In this study, two instance selection techniques suitable for identifying relevant training instances are proposed. The techniques are evaluated on a dataset containing 4000 emails and results obtained compared to other existing techniques. Result reveals excellent improvement in SVM classification speed.

Sentiment Analysis with Social Media Analytics, Methods, Process, and Applications

2017 ◽  
pp. 192-208
Author(s):  
Karteek Ramalinga Ponnuru ◽  
Rashik Gupta ◽  
Shrawan Kumar Trivedi
Keyword(s):  
Social Media ◽  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Support Vector ◽  
Social Media Analytics ◽  
Nearest Neighbour ◽  
Huge Amount ◽  
Word Clouds

Firms are turning their eye towards social media analytics to get to know what people are really talking about their firm or their product. With the huge amount of buzz being created online about anything and everything social media has become ‘the' platform of the day to understand what public on a whole are talking about a particular product and the process of converting all the talking into valuable information is called Sentiment Analysis. Sentiment Analysis is a process of identifying and categorizing a piece of text into positive or negative so as to understand the sentiment of the users. This chapter would take the reader through basic sentiment classifiers like building word clouds, commonality clouds, dendrograms and comparison clouds to advanced algorithms like K Nearest Neighbour, Naïve Biased Algorithm and Support Vector Machine.

Study about Recognition of Digital Meter Dial Reading Based on SVM

2014 ◽  
Vol 615 ◽  
pp. 194-197
Author(s):  
Zhen Yuan Tu ◽  
Fang Hua Ning ◽  
Wu Jia Yu
Keyword(s):  
Support Vector Machine ◽  
Cross Validation ◽  
Recognition Rate ◽  
Classification Model ◽  
Support Vector ◽  
Feature Points ◽  
Svm Classification ◽  
Performance Factors ◽  
Model Combining ◽  
Fast Recognition

In practice, it is difficult for Support Vector Machine (SVM) to have a relatively high recognition rate as well as a quite fast recognition speed. In order to resolve this defect, in this paper we build a SVM classification model combining numerical characteristics. We use readings of rotary natural meters as the test temple, do positioning, preprocessing, feature points extracting, classifying and other series of operations to the numeric region of the dial. Then with the idea of cross-validation, we keep doing parameter optimation to SVM. At last, after making a comprehensive contrast of the effects which numerous performance factors make on the experimental outputs, we try to give our explanation of the outputs from different perspectives.

scholarly journals Cardiovascular Disease Prediction System Using Extra Trees Classifier

2019 ◽  
Author(s):  
Rahman Shafique ◽  
Arif Mehmood ◽  
Saleem ullah ◽  
Gyu Sang Choi
Keyword(s):  
Data Mining ◽  
Cardiovascular Disease ◽  
Health Care ◽  
Support Vector Machine ◽  
Prediction Models ◽  
Support Vector ◽  
Prediction System ◽  
Classification Techniques ◽  
Data Mining Techniques ◽  
Tree Classifier

Abstract Heart Disease as cardiovascular disease is the leading cause of death for both men and women. It is the major cause of morbidity and mortality in present society. Therefore, researchers are working to help health care professionals in diagnosing process by using data mining techniques. Although the health care industry is richer in the database this data is not properly mined in order to discover hidden patterns and can able to make decisions based on these patterns. The major goal of this learning refers the extraction of hidden layers by applying numerous data mining techniques that probably give remarkable results in order to ensure the presence of cardiovascular disease among peoples. Data mining classification techniques are used to discover these patterns for research in medical industry. The dataset containing 13 attributes has analyzed for prediction system. The dataset contains some commonly used medical terms like blood pressure, cholesterol level, chest pain and 11 other attributes used to predict cardiovascular disease. The most common and effective classification techniques that are used in mining process are Verdict Tree commonly known as Decision Tree, Extra Trees Classifier, Random Forest, Support Vector Machine, Naive Bays and Logistic Regression has analyzed in this paper. Diagnosing and controlling ratio of deaths from cardiovascular disease Extra classifier trees consider is the best approach. We evaluate these prediction models by using evaluation parameters which are Accuracy, Precision, Recall, and F1-score. As per our experimental results shows accuracy of Extra trees classifier, Logistic Model tree classifier, support vector machine, and naive bays classifiers are 90%, 88%, 87%, 86% respectively. So as per our experiment analysis Extra Tree classifier with highest accuracy considered best approach for predication cardiovascular disease.

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