Design of Paddy Crop Production Technique
Using K-Mean, Naive Bayes, KNN and SVM Classifiers
(Sprache: Englisch)
Crop production analysis is one of the applications of prediction analysis. This study is related to paddy production. In the previous research work, the SVM and KNN algorithm is implemented to analyze prediction. To improve the accuracy of the paddy...
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Klappentext zu „Design of Paddy Crop Production Technique “
Crop production analysis is one of the applications of prediction analysis. This study is related to paddy production. In the previous research work, the SVM and KNN algorithm is implemented to analyze prediction. To improve the accuracy of the paddy production, the hybrid classifier will be designed based on K-mean clustering and Naive Bayes classifier. The presented and earlier algorithms will be applied in python and it is expected that accuracy will be improved with a reduction in execution time. The performance of SVM, KNN, and Naive Bayes is compared for the wheat production prediction. Naive Bayes is the best classifier for the wheat production prediction as per the obtained analytic results.
Autoren-Porträt von Pankaj Bhambri
Bhambri, PankajDr. Pankaj Bhambri is working as Assistant Professor in the Department of Information Technology at Guru Nanak Dev Engineering College, Ludhiana, Punjab, INDIA. He obtained his Ph.D. in CSE from IKGPTU, Jalandhar. He has more than 15 years of teaching experience and he contributed for many articles in renowned journals indexed in SCIE and Scopus.
Bibliographische Angaben
- Autor: Pankaj Bhambri
- 76 Seiten, Maße: 22 cm, Kartoniert (TB), Englisch
- Verlag: LAP Lambert Academic Publishing
- ISBN-10: 6202683309
- ISBN-13: 9786202683302
Sprache:
Englisch
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