DISAADE

This presentation details DISAADE, an intelligent neonatal incubator using AI to analyze baby cries, movements, and predict long-term health for premature infants.

React Python Django PostgreSQL Pandas

Overview

DISAADE — Là où la technologie rencontre l’amour de la vie.

Couveuse néonatale intelligente connectée, conçue en Guinée pour protéger les bébés prématurés.

Protéger les plus fragiles, accompagner les mamans, aider les médecins.

Tech stack

React

React is an open-source JavaScript library for building dynamic user interfaces (UIs).

React is a component-based JavaScript library, developed by Meta (Facebook), engineered for building fast, declarative UIs. It mandates a one-way data flow and utilizes a Virtual DOM mechanism to ensure efficient, predictable updates to the user interface. Developers construct complex UIs by composing small, encapsulated components; this architecture promotes code reusability and simplifies state management across large applications. The library employs JSX (a syntax extension) to integrate HTML-like markup directly within JavaScript logic, supporting development for both web (React DOM) and native mobile platforms (React Native).

https://react.dev/

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Python

Python: The high-level, general-purpose language built for readability, powering everything from web backends to advanced machine learning models.

Python is the high-level, general-purpose language prioritizing clear, readable syntax (via significant indentation), ensuring rapid development for any team . Its ecosystem is massive: use it for robust web development with frameworks like Django and Flask, or leverage its power in data science with libraries such as Pandas and NumPy . The Python Package Index (PyPI) provides thousands of community-contributed modules, offering immediate solutions for tasks from network programming to GUI creation . The language is actively maintained by the Python Software Foundation (PSF), with the stable release currently at Python 3.14.0 (as of November 2025) .

https://python.org

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Django

Django is the 'batteries included' Python web framework: high-level, secure, and engineered for rapid, pragmatic development.

Django is a high-level Python web framework, built on the 'Don't Repeat Yourself' (DRY) principle, designed to expedite the creation of complex, database-driven applications. It ships with a comprehensive suite of components out-of-the-box: a powerful Object-Relational Mapper (ORM), a robust authentication system, URL routing, and a dynamic, automated admin interface (CRUD operations). This integrated approach minimizes boilerplate code and development time, which is why major platforms like Instagram, Mozilla, and Disqus trust Django for scalable, secure web services.

https://www.djangoproject.com/

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PostgreSQL

PostgreSQL (Postgres): The world's most advanced, open-source object-relational database (ORDBMS), built for reliability and extensibility.

PostgreSQL is the premier open-source ORDBMS, proven over 35+ years of active development. It adheres strictly to ACID properties (Atomicity, Consistency, Isolation, Durability), ensuring data integrity for mission-critical workloads. Key features include robust SQL compliance, Multi-Version Concurrency Control (MVCC), and superior extensibility (e.g., custom data types, functions in multiple languages). Advanced capabilities like native JSON/JSONB support and the PostGIS extension (geospatial data) make it a powerful, flexible choice for complex enterprise applications.

https://www.postgresql.org/

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Pandas

Pandas is the core Python library for high-performance data manipulation and analysis: it introduces the DataFrame and Series structures for fast, flexible handling of labeled data.

Pandas is your go-to, open-source Python library for data science, engineered for efficient data manipulation and analysis. It centers on two primary data structures: the two-dimensional DataFrame (like a spreadsheet or SQL table) and the one-dimensional Series. This toolkit handles everything from loading diverse file formats (CSV, Excel, JSON) to complex operations: data cleaning, transformation, statistical analysis, and time series processing. Developed by Wes McKinney in 2008, Pandas builds directly on NumPy and provides an intuitive API, making it the industry standard for turning raw data into actionable insights with minimal code.

https://pandas.pydata.org

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