Fisher Linear Discriminant Modeling for Crop Classifications Based on Soil Attributes | Chapter 9 | Emerging Issues in Agricultural Sciences Vol. 9

 This study working multivariate statistical techniques to resolve soil nutrient data for crop categorization, focusing on the "Potato" and "Raagi" crops. The reasoning revealed highly meaningful differences in soil nutrient descriptions between these crop types, with distinguishing soil nutrients exhibiting solid variability. The Fisher Uninterrupted Discriminant Analysis demonstrated irregular discriminative power, gaining perfect crop separation. The confusion forge indicated extreme classification accuracy, accompanying "Potato" reaching 100% veracity and "Ragi" at 96.15%. The ROC value of 0.992 further validated the model's influence in crop discrimination. These findings focal point the utility of multivariate statistical approaches for crop categorization and selection based on soil mineral characteristics.

Author(s) Details:

Rajarathinam A.,
Department of Statistics, Manonmaniam Sundaranar University, Tirunelvel-627 012, India.

Please see the link here: https://stm.bookpi.org/EIAS-V9/article/view/12651

Study on Opinion Mining Framework Using Proposed RB-Bayes Model for Text Classification| Chapter 8 | New Approaches in Engineering Research Vol. 9

 Everyone is on social media, and their motivation is not only to be active but also to generate knowledge. We always read reviews on social media before making a purchase. Information mining is a capable concept with enormous potential for predicting future patterns and behaviour. It refers to the process of extracting hidden information from large data sets using techniques such as factual investigation, machine learning, grouping, neural systems, and genetics.algorithms. There is a problem of zero likelihood in naïve bayes. To solve the problem of zero likelihood, this work proposed the RB-Bayes technique, which is based on Baye's theorem. We also compare our strategy to a few other approaches, such as naive bayes and SVM. We show that this technique is superior to several existing strategies, and that it can analyse data sets more effectively. When the proposed approach is applied to real-world data sets, the results are generally more accurate. The precision of the RB-Bayes computation is 83,333.

Author (S) Details

Dr.Rajni Bhalla
Department of Computer Application, Lovely Professional University, India.

Dr. Amandeep Bagga
Department of Computer Application, Lovely Professional University, India.


View Book :- https://stm.bookpi.org/NAER-V9/article/view/2821