This repository explores the use of Text and Data Mining (TDM) techniques to enhance the evaluation mechanisms for research proposals. The project aligns with the Oman 2040 Vision, focusing on leveraging advanced analytics to ensure fairness, efficiency, and relevance in the proposal evaluation process.
Features
Text Analysis: Processes and analyzes research proposal texts to extract key insights.
Data Mining: Applies machine learning techniques to evaluate proposal quality and alignment with strategic goals.
Goal Alignment: Provides tools to assess proposals against the Oman 2040 Vision objectives.
Objectives
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Enhance decision-making in research funding by introducing automated evaluation tools.
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Identify trends, themes, and gaps in research proposals.
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Develop metrics for alignment with strategic objectives.
Approach
Preprocessing:
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Tokenization, stemming, and lemmatization.
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Feature extraction using TF-IDF and word embeddings.
Evaluation Framework:
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Classification models to predict proposal relevance and impact.
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Clustering for thematic analysis.
Alignment Metrics:
- Customized scoring to match proposals with Oman 2040 goals.
Usage
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Clone the repository.
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Install the required libraries.
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Run the notebook and follow the instructions in the markdown cells.
Future Directions
This study sets the groundwork for building an automated, scalable system to evaluate research proposals, incorporating advanced AI and NLP techniques.