Linear Regression and Artificial Neural Networks based Efficient Sales Forecasting Model with Increased Prediction Accuracy

Reddy, S. Ravi Teja; Malathi, P.

doi:10.1109/icirca54612.2022.9985619

Cited by 2 publications

(1 citation statement)

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“…After testing the models and recording the expectations for each model, the anticipated worth and the genuine worth is looked at and the outcomes are examined utilizing R2 score as precision. Since an assortment of AI calculations and procedures can be applied to take care of the forest coverage inclusion issue, the expectation strategies considered for examination in this review [9], [11], [20] are Linear-Regression, [13], [15] Decision-Trees, [16], [17] Random-Forest-Regressor, and [20] Support-Vector-Machines. The main goal is to identify the optimum regressor with the most accurate results.…”

Section: Assessment Of Global Forest Coverage Through Machine Learnin...mentioning

confidence: 99%

Assessment of Global Forest Coverage through Machine Learning Algorithms

Metkewar,

Chauhan,

Prasanth

et al. 2024

ICST Transactions on Scalable Information Systems

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This exploration of paper presents an investigation of the Forest Region Inclusion Dataset that gives data on the backwoods inclusion of different nations overall from 1990 to 2020. The dataset contains country-wise information on population, population density, population development rate, total population rate, and forest region inclusion. We examined this dataset to decide the patterns in woodland region inclusion across various nations and mainlands, as well as the connection among populace and backwoods region inclusion. Our discoveries show that while certain nations have essentially expanded their forest region inclusion, others have encountered a decline. Besides, we found that population density and development rate are adversely related with forest area coverage. Authors have implemented four machine learning algorithms that are Linear Regression, Decision Tree, Random Forest and Support Vector Machine on the dataset.

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