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Imneuron: AI Powered Neural Network Solutions for Regression Tasks

It offers a sophisticated and versatile tool for creating and evaluating artificial intelligence based neural network models tailored for regression analysis on datasets with continuous target variables. Leveraging the power of neural networks, it allows users to experiment with various hidden neuron configurations across two layers, optimizing model performance through "5 fold"" or "10 fold"" cross validation. The package normalizes input data to ensure efficient training and assesses model accuracy using key metrics such as R squared (R2), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Percentage Error (PER). By storing and visualizing the best performing models, it provides a comprehensive solution for precise and efficient regression modeling making it an invaluable tool for data scientists and researchers aiming to harness AI for predictive analytics.

Version: 0.1.0
Depends: R (≥ 2.10)
Imports: MLmetrics, ggplot2, neuralnet
Published: 2024-06-20
DOI: 10.32614/CRAN.package.Imneuron
Author: M Iqbal Jeelani ORCID iD [aut, cre], Fehim Jeelani Wani ORCID iD [aut]
Maintainer: M Iqbal Jeelani <jeelani.miqbal at gmail.com>
License: GPL (≥ 3)
NeedsCompilation: no
CRAN checks: Imneuron results

Documentation:

Reference manual: Imneuron.pdf

Downloads:

Package source: Imneuron_0.1.0.tar.gz
Windows binaries: r-devel: Imneuron_0.1.0.zip, r-release: Imneuron_0.1.0.zip, r-oldrel: Imneuron_0.1.0.zip
macOS binaries: r-release (arm64): Imneuron_0.1.0.tgz, r-oldrel (arm64): Imneuron_0.1.0.tgz, r-release (x86_64): Imneuron_0.1.0.tgz, r-oldrel (x86_64): Imneuron_0.1.0.tgz

Linking:

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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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