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Researcher at Indian Institute of Science Education and Research Bhopal.

In this article, we will briefly discuss the SVR model. We will discuss three types of SVR namely, S-SVR (Scaling-SVR), Z-SVR (Z-score-SVR), and R-SVR (Range-SVR). Afterwards, we will discuss its application in predicting the Average Localisation Error (ALE) in node localisation process in Wireless Sensor Network (WSNs).

You can download our paper for more details. You can write to me (abhilash.singh@ieee.org)if you have any question or visit my web page for more updates.

Introduction


The final output from the code.

In this article, we will discuss a simple code to plot a Linear Regression (LR) curve. The code is written in MATLAB and can be downloaded from my MATLAB repository. You can write to me (abhilash.singh@ieee.org) if you have any question or visit my web page for more updates.

In LR, our main objective is to find the best fitting straight line through the observed values. The best fitting line is called the regression line. The formula for LR is

y = m *x + c

where y is the predicted value, m is the slope of the line, and…


In this article, we will discuss the detailed process of surface soil moisture (top 5 cm) estimation using satellite images. This article is divided into five sections. First, we will see the satellite images used then we will see the study area. Afterwards, we will go through the models. Then we will see the detailed methodology. Lastly, we will see the results, discussion and conclusion section.

You can download the paper from MDPI website. You can write to me (abhilash.singh@ieee.org)if you have any question or visit my web page for more updates

Satellite images


Graphical User Interface for PCA in MATLAB

In this article, we will first discuss the basics of PCA and how we can use PCA in MATLAB. After that, we will try to answer a fundamental question in PCA. You can download the MATLAB code from my MATLAB repository.

Question. What is PCA?

PCA is a mathematical procedure that transforms a no. of possibly correlated variables into smaller no. of uncorrelated variables called principal components (PC’s).
The first PC accounts for the highest variability in the data and the succeeding components have less variability than the preceding one.

Question. How we can use PCA in MATLAB?

[coeff, score

Abhilash Singh

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