Bio
Dr. Tarun Rao is a researcher affiliated with Acharya Nagarjuna University and Dayananda Sagar College of Engineering in India. His work focuses on data mining and machine learning, with notable publications including 'A Hybrid Random Forest based Support Vector Machine Classification Supplemented by Boosting', 'Supervised Classification of Remote Sensed Data using Support Vector Machine', and 'Crop Coverage Data Classification using Support Vector Machine'. Dr. Rao holds a PhD and has contributed as a reviewer for the Global Journal of Computer Science and Technology (GJCST). His research interests span classification algorithms, remote sensing data analysis, and agricultural data applications. With 5 publications and 8 citations, his work has an h-index of 2 and an i10-index of 0.
Educational Journey
PhD
Experience
Editors Role
Reviewer
GJCST
2016 -Research
Crop Coverage Data Classification using Support Vector Machine
A statistical tool which can be used in various applications ranging from medical science to agricultural science is support vector machines. The proposed methodology used is support vector machine and it isused to classify a raster map. The dataset used herein is of Gujarat state agriculture map. The proposed approach is used to classify raster map into groups based on crop coverage of various crops. One group represents rice crop coverageand the othermillets crop coverage and yet another that of cotton crop coverage.Various statistical parameters are used to measure the efficacy of the proposed methodology employed.
A Hybrid Random Forest based Support Vector Machine Classification Supplemented by Boosting
This paper presents an approach to classify remote sensed data using a hybrid classifier. Random forest, Support Vector machines and boosting methods are used to build the said hybrid classifier. The central idea is to subdivide the input data set into smaller subsets and classify individual subsets. The individual subset classification is done using support vector machines classifier. Boosting is used at each subset to evaluate the learning by using a weight factor for every data item in the data set. The weight factor is updated based on classification accuracy. Later the final outcome for the complete data set is computed by implementing a majority voting mechanism to the individual subset classification outcomes.
Supervised Classification of Remote Sensed Data using Support Vector Machine
Support vector machines have been used as a classification method in various domains including and not restricted to species distribution and land cover detection. Support vector machines offer many key advantages like its capacity to handle huge feature spaces and its flexibility in selecting a similarity function. In this paper the support vector machine classification method is applied to remote sensed data. Two different formats of remote sensed data is considered for the same. The first format is a comma separated value format wherein a classification model is developed to predict whether a specific bird species belongs to Darjeeling area or any other region. The second format used is raster format which contains image of Andhra Pradesh state in India. Support vector machine classification method is used herein to classify the raster image into categories. One category represents land and the other water wherein green color is used to represent land and light blue color is used to represent water. Later the classifier is evaluated using kappa statistics and accuracy parameters.
