Home > Computer Science > Other > Volume-3 > Issue-5 > Classification of Paddy Types using Naïve Bayesian Classifiers

Classification of Paddy Types using Naïve Bayesian Classifiers

Call for Papers

Volume-8 | Issue-6

Last date : 27-Dec-2024

Best International Journal
Open Access | Peer Reviewed | Best International Journal | Indexing & IF | 24*7 Support | Dedicated Qualified Team | Rapid Publication Process | International Editor, Reviewer Board | Attractive User Interface with Easy Navigation

Journal Type : Open Access

First Update : Within 7 Days after submittion

Submit Paper Online

For Author

Research Area


Classification of Paddy Types using Naïve Bayesian Classifiers


Mie Mie Aung | Su Mon Ko | Win Myat Thuzar | Su Pan Thaw



Mie Mie Aung | Su Mon Ko | Win Myat Thuzar | Su Pan Thaw "Classification of Paddy Types using Naïve Bayesian Classifiers" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5, August 2019, pp.1355-1359, URL: https://www.ijtsrd.com/papers/ijtsrd26585.pdf

Classification is a form of data analysis that can be used extract models describing important data classes or to predict future data trends. Classification is the process of finding a set of models that describe and distinguish data classes or concepts, for the purpose of being able to use the model to predict the class of objects whose class label is unknown. In classification techniques, Naïve Bayesian Classifier is one of the simplest probabilistic classifiers. This paper is to study the Naïve Bayesian Classifier and to classify class label of paddy type data using Naïve Bayesian Classifier. This paper predicts four class labels and displays the selected impacts attribute of each class label by using Naïve Bayesian classifier. Moreover, this paper can predict the types of paddy for paddy dataset by using other classification methods such as Decision Tree and Artificial Neural Network. Furthermore, this system can be used to predict production rate and display the selected impacts attribute of other crops such as soybeans, corns, cottons. This paper focuses on paddy dataset and decides paddy types are Lasbar or Yar Sabar or Yenat Khan Sabar or Sar Ngan Khan Sabar.

Naïve Bayesian, Paddy types, Classification, Large dataset


IJTSRD26585
Volume-3 | Issue-5, August 2019
1355-1359
IJTSRD | www.ijtsrd.com | E-ISSN 2456-6470
Copyright © 2019 by author(s) and International Journal of Trend in Scientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)

International Journal of Trend in Scientific Research and Development - IJTSRD having online ISSN 2456-6470. IJTSRD is a leading Open Access, Peer-Reviewed International Journal which provides rapid publication of your research articles and aims to promote the theory and practice along with knowledge sharing between researchers, developers, engineers, students, and practitioners working in and around the world in many areas like Sciences, Technology, Innovation, Engineering, Agriculture, Management and many more and it is recommended by all Universities, review articles and short communications in all subjects. IJTSRD running an International Journal who are proving quality publication of peer reviewed and refereed international journals from diverse fields that emphasizes new research, development and their applications. IJTSRD provides an online access to exchange your research work, technical notes & surveying results among professionals throughout the world in e-journals. IJTSRD is a fastest growing and dynamic professional organization. The aim of this organization is to provide access not only to world class research resources, but through its professionals aim to bring in a significant transformation in the real of open access journals and online publishing.

Thomson Reuters
Google Scholer
Academia.edu

ResearchBib
Scribd.com
archive

PdfSR
issuu
Slideshare

WorldJournalAlerts
Twitter
Linkedin