Methodology

Data Collection and Analysis
- Collaborate with industry and academic institutions to gather diverse datasets.
- Use advanced data pre-processing and feature engineering techniques.
Model Development
- Train and validate AI/ML models using state-of-the-art algorithms.
- Focus on interpretable AI to improve user trust and compliance.
System Integration and Testing
- Develop scalable architectures for real-time applications.
- Test models in simulated and real-world scenarios for robustness.
Feedback and Improvement
- Continuous evaluation and enhancement based on user and stakeholder feedback.