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In technical terms, "deep features" are complex patterns extracted from data (like text or images) by deep learning models. For criminal investigations involving Arabic content, deep features are used to:

: Because Arabic is morphologically rich, deep features (such as those from ELMo or AraBERT embeddings) are used to capture hierarchical relationships and grammatical dependencies that simpler models might miss.

: Deep learning architectures, such as Transformers or CNN-LSTMs, extract deep semantic features to understand the context and nuance of unstructured citizen reports or social media posts to identify potential criminal activities.