Logistic Regression for Intelligent Email Spam Detection: A Practical Approach | Chapter 3 | Mathematics and Computer Science: Research Updates Vol. 2

This paper presents an experiment on spam filters using Logistic Regression, where the filter's effectiveness is influenced by the characteristics of the token frequency distribution. The focus of the discussion is on the importance of data cleaning before model development. It emphasizes the necessity of excluding inconsistent features prior to their inclusion in the model. The experiment utilizes the UCI dataset, which shows the percentage of token counts in each email. The model’s discriminative performance is evaluated through the use of an ROC curve. The use of the UCI dataset provided valuable insights into how token counts influence spam classification. The ROC curve analysis reinforced the importance of evaluating model performance comprehensively, offering a clear view of its discriminative power.

 

Author (s) Details

 

K. Srikanth
Department of Data Science, Malla Reddy University, Telangana, India.

 

Please see the book here:- https://doi.org/10.9734/bpi/mcsru/v2/3819

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