COMPARATIVE STUDY OF FEATURE-LEVEL AND DECISION-LEVEL FUSION STRATEGIES IN NEURAL NETWORK MODELS FOR MULTIMODAL PSYCHODIAGNOSTICS
DOI:
https://doi.org/10.47390/ts-v3i8y2025No3Abstract
This paper examines the effectiveness of feature-level and decision-level fusion strategies in neural network models for multimodal psychodiagnostics. Findings indicate that feature-level fusion enhances information extraction, while decision-level fusion improves diagnostic accuracy. A hybrid approach ensures greater reliability and expands applications in clinical practice
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