https://github.com/robertovicario/bsc-computer-science-thesis

Research thesis in Machine Learning for the achievement of Bachelor of Science Degree in Computer Science.
https://github.com/robertovicario/bsc-computer-science-thesis

artificial-intelligence bachelor-thesis computer-science jupyter machine-learning python stress-detection university-of-insubria

Last synced: about 4 hours ago
JSON representation

Research thesis in Machine Learning for the achievement of Bachelor of Science Degree in Computer Science.

• Host: GitHub
• URL: https://github.com/robertovicario/bsc-computer-science-thesis
• Owner: robertovicario
• License: mit
• Created: 2024-01-02T20:15:13.000Z (11 months ago)
• Default Branch: main
• Last Pushed: 2024-06-01T10:25:14.000Z (6 months ago)
• Last Synced: 2024-06-01T11:43:20.839Z (6 months ago)
• Topics: artificial-intelligence, bachelor-thesis, computer-science, jupyter, machine-learning, python, stress-detection, university-of-insubria
• Language: Jupyter Notebook
• Homepage: https://raw.githubusercontent.com/robertovicario/BSc-Computer-Science-Thesis/main/Applicare_il_Machine_Learning_per_il_Rilevamento_dello_Stress_negli_Ambienti_di_Lavoro_di_Ufficio.pdf
• Size: 9.19 MB
• Stars: 2
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE
  • Citation: CITATION.cff

Awesome Lists containing this project

README

        
# Apply Machine Learning for Stress Detection in Office Work Environments

## Abstract

Detecting stress in workplace environments represents an innovative element for promoting employee health and well-being, particularly in office settings. The use of machine learning in this context offers an effective approach to identifying early signs of stress, allowing organizations to intervene promptly to mitigate associated risks. Of particular interest is examining whether the results obtained through unsupervised learning methods can be comparable to those derived from supervised approaches, as highlighted in the research *"Exploring Unsupervised Machine Learning Classification Methods for Physiological Stress Detection"* by Iqbal et al. (2022) in the journal Frontiers in Medical Technology.

|  |

| :--: |

| [ ITA ] |

## License

This project is distributed under the [MIT License](https://opensource.org/licenses/MIT). You can find the complete text of the license in the project repository.

## Contacts

For any questions, feedback, or inquiries about this project, feel free to contact me:

- Email: [[email protected]](mailto:[email protected])