This page avoids self-scored percentages. Each capability is connected to work that demonstrates how it was applied.

Capability Applied tools and methods Evidence
Reliable RAG Hybrid retrieval, BM25, TF-IDF, text normalization, citations, controlled generation GovRAG Copilot, Asthma RAG
Model evaluation Baselines, repeated seeds, ROC/PR, macro F1, class-level errors, cross-dataset testing Deepfake robustness, Malicious URLs
Computer vision PyTorch, CNN transfer learning, YOLOv8, OpenCV, privacy blurring, DCT features PPE detection, Cashew disease
NLP and text analytics Arabic/English preprocessing, TF-IDF, Word2Vec/AraVec, LSTM/BiLSTM, transformers Arabic sentiment, Galaxy sentiment
Data and decision modeling Pandas, feature engineering, imbalance review, segmentation, backtesting, interpretation Retail analytics, Market regimes
Prototyping and implementation Python, Git/GitHub, testing, Gradio, Streamlit, reusable project structures Selected work

Working principles

  • Define the decision and user before choosing the model.
  • Compare against clear baselines and report tradeoffs, not one metric.
  • Treat citations, privacy, monitoring, and failure modes as system requirements.
  • Keep repositories inspectable, reproducible, and documented.