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Repository Structure

Organized for Research, Reproducibility, and Deployment

The repository is structured to clearly separate methodology, experiments, documentation, and deployment components.

This separation improves clarity for reviewers, supports reproducibility, and allows scalable project extension.


High-Level Architecture

  • Research Layer

Methodology, modeling, evaluation, and results documentation.

  • Documentation Layer

Ethical analysis, future directions, and structured project reporting.

  • Deployment Layer

Application usage, installation, and optional API interface.


Directory Overview

Path Purpose
docs/index.md Landing page
docs/introduction.md Background and research motivation
docs/objectives.md Research objectives
docs/dataset.md Dataset structure and access
docs/ethics.md Ethical framework and safeguards
docs/datasets/ Synthetic dataset files
docs/methodology/data.md Data generation and preprocessing
docs/methodology/models.md Embedding model and SVM classifiers
docs/methodology/evaluation.md Experimental setup and metrics
docs/results/findings.md Quantitative results
docs/results/error-analysis.md Error patterns and interpretation
docs/results/limitations.md Technical and experimental limitations
docs/deployment/app.md Prototype application usage
docs/deployment/api.md API interface documentation
docs/deployment/install.md Installation instructions
docs/paper/paper.md IEEE-style research paper
docs/paper/slides.md Presentation materials
docs/paper/artifacts.md Supporting research artifacts
docs/documentation/future-work.md Extended research roadmap
docs/documentation/repo-structure.md Repository organization reference
docs/documentation/requirements.md Software and hardware requirements

Structural Principles

  • Modular Separation

Methodology, results, and deployment are isolated to prevent cross-dependency clutter.

  • Reproducibility Focus

Experimental steps are documented in sequence to enable replication.

  • Reviewer-Friendly Navigation

Academic content is separated from implementation details for clarity.

  • Scalability

Additional datasets, models, or deployment layers can be added without restructuring the project.


Alignment with Research Workflow

The repository mirrors the lifecycle of the project:

  1. Problem formulation
  2. Dataset construction
  3. Model development
  4. Evaluation and validation
  5. Alert framework design
  6. Ethical analysis
  7. Deployment demonstration

This structured organization supports both academic review and future system expansion.