Developing A New Dimension of an Applied Exponential Model: Application in Biological Sciences

Modeling of exponential growth or decay is a nonlinear regression technique. In the real world, the exponential growth is often used to model population growth while the exponential decay is often used to a model declining population or a decreasing size. In this study, we try to improve the performance of exponential growth by adding bootstrap and fuzzy techniques. This gives us the option to perform analysis even when there is not enough data. The aim of the current work is to develop a new dimension of an applied exponential analysis with improved results. The suggested method was tested and applied to biological data. The gathered data was compared by measuring the average width of the predicted interval using least squares method and fuzzy method. The result shows that the average width of the predicted interval using least squares method was 0.522 while using fuzzy method was 0.082. This indicated the superiority of the fuzzy regression methodology. Besides that, this paper provides the algorithm for the prediction of cell growth and inferences.