This page explains what is directly reproducible from the repository, what has been reconstructed for presentation only, and how the paper's cloud ERP integration ideas should be interpreted without overstating them as completed deployment code.
docs/assets/papers/AI-Cloud-ERP-Accepted-Manuscript-Encrypted.zip is the protected PDF download.docs/assets/papers/AI-Cloud-ERP-Accepted-Manuscript-LaTeX-Source-Encrypted.zip is the protected LaTeX-source download.paper/ remains the repository working directory used to curate and refresh source assets.docs/index.html is the main research portal.docs/report.html, docs/poster.html, and docs/implementation.html provide alternate views.docs/assets/ contains published site images, charts, data, and the encrypted manuscript archives.data/baseline_metrics.csv and data/attention_scores.csv are exact table exports.data/reconstructed_training_curves.csv is presentation support data only.data/research_data.json feeds the docs site and keeps provenance in one place.scripts/generate_visualizations.py copies assets, writes data exports, and builds the SVG gallery.scripts/refresh_assets.sh reruns the generator.scripts/check-js.sh checks the website JavaScript syntax with Node.The environment above is reported in the manuscript, but the repository does not contain runnable notebooks, package lockfiles, or the ERP dataset needed to replay training end-to-end.
The repo therefore focuses on faithful publication packaging rather than claiming full training reproducibility that the source bundle does not support.
R_i = alpha_i * C_iInterpreted as attention-informed scaling of module compute demand.
D_k = sum I(M_k in T_i) * alpha_ikUsed to reason about modules that may require stronger failover strategies.
f_theta: T -> c -> y_hatRepresents the prediction engine as a stateless AI service within cloud-native ERP architecture.
The paper describes a formal integration pathway for cloud ERP use, but it also states that a full physical deployment was not performed in the current study. The website preserves that distinction everywhere.
bash scripts/refresh_assets.shThis updates the CSV exports, combined JSON, copied paper assets, and SVG visualizations.
bash scripts/check-js.shIf Node is installed, the script validates docs/script.js with node --check.
The repository aligns the public citation and site metadata to the official IEEE indexing record because the local LaTeX bundle still shows a stale conference header from an older template.
The interactive line charts are useful for the portal, but they are explicitly marked as reconstructions because the repo includes only the rendered figures, not raw epoch logs.
Settings -> Pages.Deploy from a branch./docs folder.https://anis151993.github.io/AI-and-Cloud-Computing-in-Business-Systems-A-Hybrid-Model-for-Enhancing-Enterprise-Resource-Planning/The root repository also includes a markdown deployment guide in the GitHub repo for contributors who want the non-site version of the same workflow.