Definition

The Strant AI Fusion Modules are designed according to a layered architecture, ensuring complex processing capabilities and high accuracy:

Input Layer:

• Receives data from the DL Modules.

• Data can originate from various formats, such as numerical data, text, or images.

Intermediate Processing Layer:

• This stage performs data standardization and weighting. The main steps include:

• Standardization: Converts all data into a unified format (e.g., converting all data into numerical form for easier processing).

  • Weighting: Assigns weights to data sources or modules based on their reliability and importance. For example:

  • Financial news from a reputable source may be assigned a higher weight.

  • Data from technical charts may be down-weighted during periods of high market volatility.

Convergence Layer:

• The standardized and weighted data is aggregated using advanced algorithms:

Weighted Averaging: Uses weights to combine the outputs.

• Voting Mechanism: When modules produce differing results, the Fusion Module selects the most "voted" result.

• Stacking Models: A meta-model learns how to combine the outputs from the DL Modules to optimize the final result.

Output Layer:

Provides the final aggregated output, representing the optimal result from the fusion process.