Please enable JavaScript.

Coggle requires JavaScript to display documents.

Research Areas - Coggle Diagram

- - - - Analysis: We will analyze the current paper-based barangay workflows to identify bottlenecks, define data privacy compliance requirements under RA 10173, and map out the geospatial data needs for incident visualization.
        
        Design: We will design the mobile-first user interface for residents and the comprehensive web-based admin dashboard, while structuring the Supabase relational database and edge functions for secure data handling.
        
        Implementation: We will code the React Native resident portal and the web admin dashboard by integrating Supabase for real-time data synchronization, automated SLA timelines, and secure role-based access control.
        
        Testing: We will perform system testing to ensure real-time database updates reflect instantly, and conduct User Acceptance Testing (UAT) with barangay staff to verify the accuracy of the geospatial mapping and document tracking.
        
        Deployment: We will officially launch the BARANGAYAN platform in Barangay Ampid 1, San Mateo, Rizal, and conduct comprehensive training sessions for local officials and community residents.
        
        Maintenance: We will provide ongoing technical support, monitor server performance, and release periodic updates to improve the geospatial analytics and address any security vulnerabilities.
  - - - System Design: Create a blueprint mapping the React Native UI to a Firebase cloud database for resident profiles. Design an admin panel for profile verification and plan a GPS API geofence to verify users live within barangay boundaries.
        
        Implementation (Coding): Program the software using React Native Expo for mobile interfaces and Google Firebase for database management. Code the interactive scheduling calendar, document request forms, and automated push notification modules.
        
        Testing / Verification: Verify that the booking system restricts daily slots to prevent health center overcrowding. Test the phone's GPS feature to confirm it successfully blocks non-residents from exploiting local services.
        
        Deployment: Hand over the completed application to the local Barangay Council and administrative staff. Publish the app on app stores so everyday citizens can download it directly to their smartphones.
        
        Support / Maintenance: Maintain the push notification and SMS text backup systems for urgent community weather alerts. Assist local staff with database management and troubleshoot automated account verification errors.
  - - - Analysis: We will analyze local disaster response protocols and geographical hazard maps to determine how to effectively store critical data locally and synchronize it with the cloud when connectivity is restored.
        
        Design: We will design the offline-first mobile user interface and the GIS-based web dashboard for LGUs, while planning the database structure to handle QR check-ins and real-time evacuation center capacity monitoring.
        
        Implementation: We will develop the React Native application with local storage capabilities for offline maps, integrating the GPS routing, QR code scanner, and Firebase backend for cloud synchronization.
        
        Testing: We will conduct rigorous field testing in low-connectivity environments to verify offline map functionality, and simulate disaster scenarios to test the family reunification and QR check-in systems.
        
        Deployment: We will install the system with the local DRRMO and partner evacuation centers, providing comprehensive training to personnel on how to manage evacuee records and broadcast emergency alerts.
        
        Maintenance: We will update the offline hazard maps and evacuation center data regularly, monitor the cloud synchronization logs, and patch any bugs to ensure the app remains reliable during actual emergencies.
- - - - Analysis: We will evaluate various computer vision models and medical image datasets to determine the most accurate and lightweight AI architecture suitable for mobile deployment.
        
        Design: We will design a user-friendly, accessible mobile interface that clearly displays risk levels and medical disclaimers, while architecting the backend pipeline for secure image processing and AI inference..
        
        Implementation: We will train and optimize the image classification model, then integrate it into the Android application alongside the camera capture, gallery upload, and scan history features
        
        Testing: We will test the AI model's prediction accuracy and confidence scoring using a diverse dataset of skin condition images, followed by usability testing to ensure the medical disclaimers are clearly understood.
        
        Deployment: We will deploy the application to partner Rural Health Units and Barangay Health Centers to assist health workers and residents in preliminary skin condition screening.
        
        Maintenance: We will regularly retrain the AI model with new, anonymized image data to improve detection accuracy and update the educational content based on the latest medical guidelines.
- - - - System Design: Architect a convolutional neural network classification pipeline. Configure mathematical model logic to output condition classifications, confidence scores, and risk levels.
        
        Implementation (Coding): Code a native Android mobile application package. Integrate the trained image model file, camera captures, and educational medical text arrays.
        
        Testing / Verification: Validate model accuracy against known clinical image datasets. Run usability field tests with Barangay Health Workers.
        
        Deployment: Install application onto testing tablets across local Rural Health Units. Provide access to underserved patients for preliminary disease screening.
        
        Support / Maintenance: Refine AI model training parameters to maintain prediction accuracy. Update medical disclaimers to ensure the tool remains a screening resource, not a replacement for a doctor.