A comprehensive entity resolution and security monitoring system that unifies campus data sources to provide proactive threat detection and explainable AI predictions.

security_monitoring_system/ -> Core Application Files -> security_dashboard.py ( # Main Streamlit UI)

-> production_predictor.py (# ML prediction backend) -> pipeline.py (# ML training pipeline) -> EntityResolver.py ( # Entity resolution pipeline)

Machine Learning -> trained_model.joblib # trained ML model -> predictive_features.json # Entity features for ML ->predictive_features_code_file.py # Feature generation code

Entity Resolution -> Entity_resolution_map.json # Entity mapping data . Entity_resolution_map_code_file.py # Resolution logic

Raw Data Sources . campus card_swipes.csv # Card swipe access logs .wifi_associations_logs.csv # WiFi connection data . cctv_frames.csv # CCTV face recognition data . face_embeddings.csv # Facial feature vectors . library_checkouts.csv #lab record records . lab_bookings.csv # Laboratory reservations . free_text_notes.csv # Helpdesk tickets & RSVPs .student or staff profiles.csv # Entity master data

├Documentation ├── README.md # This file └── report.pdf # Technical report

RAW DATA SOURCES->

DATA INGESTION LAYER • CSV File Parsing • Data Validation
• Timestamp Normalization->

ENTITY RESOLUTION ENGINE • Direct Matching (student_id, card_id, face_id) • Fuzzy Matching (Name, Email variants) • Cross-Source Linking (Temporal, Spatial)

MULTI-MODAL FUSION LAYER • Temporal Alignment (5-min windows) • Confidence-weighted Data Fusion • Activity Timeline Generation

MACHINE LEARNING PIPELINE
• Feature Engineering (Temporal, Spatial, Behavioral, Sequential) • Model Training (XGBoost, Random Forest, Ensemble) • Prediction & Evidence Generation

SECURITY DASHBOARD • Real-time Entity Monitoring • Predictive Location Insights • 12-hour Inactivity Alerts