# Definition

**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.