Week #1 #

Team Formation and Project Proposal #

Team Members: #

Team Member (Lead): Vladislav Pershko - @Vlad4Per - v.pershko@innopolis.university
Team Member 2: Sergey Dzyuba - @Sergey_Dzyuba - s.dzyuba@innopolis.university
Team Member 3: Vlad Vechkanov - @Spiliv8ler - v.vechkanov@innopolis.university
Team Member 4: Mikhail Voronin - @VM1522 - m.voronin@innopolis.university
Team Member 5: Suleiman Karim Eddin - @Suleiman_Karim - s.karimeddin@innopolis.university
Team Member 6: Hadi Salloum - @Hadi7Salloum - h.salloum@innopolis.university

Value Proposition: #

Identify the Problem: The project addresses the need for advanced feature selection in machine learning.
Solution Description: Integrates classical and quantum computing methods using metrics like Generalized Mean Information Coefficient and Pearson Correlation Coefficient for optimal feature subsets.
Benefits to Users: Improved predictive model performance, accuracy, efficiency, and simplicity in feature selection.
Differentiation: Combining classical and quantum computing for enhanced feature selection capabilities.
User Impact: Empowering users with a user-friendly interface and advanced algorithms for better machine learning outcomes.

Lean Questionnaire: #

What problem or need does your software project address? Addressing the need for advanced feature selection in machine learning.
Who are your target users or customers? Machine learning practitioners, data scientists, and researchers.
How will you validate and test your assumptions about the project? Through user feedback, testing with real-world datasets, and comparative analysis with existing methods.
What metrics will you use to measure the success of your project? User adoption rates, improvement in model performance metrics, and user satisfaction surveys.
How do you plan to iterate and pivot if necessary based on user feedback? Regularly gathering and analyzing user feedback to make iterative improvements and pivot if needed to better align with user needs.

Leveraging AI, Open-Source, and Experts: #

Quantum Technology: The team will utilize advanced quantum technology, specifically D-Wave quantum annealers, for advanced feature selection using quantum annealing techniques.
AI Integration: AI technologies will be integrated into the platform to enhance feature selection algorithms and improve predictive model performance.
Open-Source Tools: Open-source tools and libraries will be used throughout the development process to ensure efficiency and access to a wide range of resources.
Collaboration with Experts: Collaboration with experts in quantum computing, machine learning, and software development to gain insights and guidance for optimizing the platform.

Defining the Vision for Your Project: #

Overview: Our project aims to develop both a cutting-edge website and a mobile app, offering feature selection services for machine learning applications. This platform will integrate classical and quantum computing methods to provide advanced feature selection capabilities. Schematic Drawings:

+------------------+
|                  |
|                  |
| Authentification |
|       page       |
|                  |
+--------+---------+
         |
         |
         |
+-----------------+
|                 |
|                 |
|   Import data   |
|                 |
|                 |
+--------+--------+
         |
         |
         |
+-----------------+
|                 |
|                 |
|   Report page   |
|                 |
|                 |
+--------+--------+
         |
         |
         |
+-----------------+
|                 |
|                 |
|   Price list    |
|                 |
|                 |
+--------+--------+
         |
         |
         |
+-----------------+
|                 |
|                 |
|    Previous     |
|    reports      |
|                 |
+--------+--------+

Tech Stack: The technologies and frameworks planned for our project include:

Front-end: Flutter
Back-end: Laravel - Flask - Postgress
Quantum Computing Integration: D-Wave quantum annealers - Ocean
AI Integration: TensorFlow, Keras