https://github.com/womb0comb0/csc461-research-paper-cnn-and-ai

https://github.com/womb0comb0/csc461-research-paper-cnn-and-ai

github-pages jupyter-notebook machine-learning python r-programming research-project shell-script

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• Host: GitHub
• URL: https://github.com/womb0comb0/csc461-research-paper-cnn-and-ai
• Owner: WomB0ComB0
• Created: 2024-04-28T20:33:13.000Z (8 months ago)
• Default Branch: master
• Last Pushed: 2024-10-20T17:00:53.000Z (2 months ago)
• Last Synced: 2024-10-20T20:26:09.203Z (2 months ago)
• Topics: github-pages, jupyter-notebook, machine-learning, python, r-programming, research-project, shell-script
• Language: TeX
• Homepage:
• Size: 2.3 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Advancements in Deep Learning for Image Categorization

This repository contains the code and resources for the research paper "Advancements in Deep Learning Techniques for Enhanced Image Categorization: A Comprehensive Literature Study" by Mike Odnis.

## Overview

This project investigates recent advancements in deep learning techniques for improving image categorization accuracy. The research covers various approaches including data augmentation, transfer learning, and convolutional neural networks (CNNs).

## Repository Structure

- `src/`

  - `scripts/`: R scripts for data analysis and visualization

    - `visualization.r`

    - `statistical_analysis.r`

    - `integration_with_deep_learning.r`

    - `exploratory_data_analysis.r`

    - `data_preprocessing.r`

    - `benchmarking_and_comparison.r`

  - `python/`: Python scripts for deep learning models

    - `transfer_learning.py`

    - `data_augmentation.py`

    - `convolutional_neural_networks.py`

  - `notebooks/`: Jupyter notebooks for interactive analysis

    - `01_data_augmentation.ipynb`

    - `02_transfer_learning.ipynb`

    - `03_convolutional_neural_networks.ipynb`

- `paper.tex`: LaTeX source for the research paper

- `paper.pdf`: Compiled PDF of the research paper

- `references.bib`: Bibliography file for the paper

## Key Findings

The research examines ten foundational studies in image categorization, focusing on:

1. Object detection

2. Medical imaging

3. Face recognition

4. Hyperspectral classification

5. Individual cattle recognition

## Future Work

Future research should focus on developing more robust and efficient deep learning models to address challenges such as:

- Background clutter

- Object orientations

- Inconsistent illumination

## Acknowledgements

Special thanks to Professor Mohammad Alshibli at the Department of Computer Science, Farmingdale State College for his support and insights throughout this research.

## Author

Mike Odnis - Department of Computer Science, Farmingdale State College