research-cnn-and-ai-24-1

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