Mathematical Modeling using Rough Set and Random Forest Model to Predict Wind Speed

Das, Ratnakar; Mishra, Jibitesh; Mishra, Sumit; Pattnaik, P. K.; Das, Subhalaxmi

doi:10.23919/indiacom54597.2022.9763275

Cited by 7 publications

(1 citation statement)

References 39 publications

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“…RST hybrid with various machine learning tools useful in predicting various meteorological phenomena have been discussed in this research. Das et al [4] developed a novel model by combining RST and Random Forest Model (RFM) to predict wind speed in this context. Das et al [5] designed a model for predicting rainfall with significant accuracy.…”

Section: Introductionmentioning

confidence: 99%

Detection of HIV by using Rough Set and Homotopy Analysis Method

Subrata Kumar Nayak

2023

IJRITCC

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The significant objective of this research is to recognize how to calculate the classification process using rough set theory (RST) for the Human immunodeficiency virus infection and acquired immune deficiency syndrome (HIV & AIDS) symptoms dataset. RST has a multi-dimensional concept with multiple approaches. In this paper, our main objective is to find the symptoms of (HIV & AIDS) using basic RST and Homotopy Analysis Method (HAM) to validate our claim using statistical techniques. We prefer RST & HAM over other soft computing techniques and Mathematical Modelling as both RST and Homotopy Analysis (HAM) because RST can handle vague and imprecise data efficiently, and HAM is a suitable technique for finding analytical solutions. We have used the chi-squared test to validate our claim.

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Section: Introductionmentioning

confidence: 99%