Authors : Maxwell Nortey

Volume/Issue : Volume 11 - 2026, Issue 4 - April

Google Scholar : https://tinyurl.com/ynvpwbey

Scribd : https://tinyurl.com/53n39wbv

DOI : https://doi.org/10.38124/ijisrt/26apr1888

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : High-stakes public service operations such as emergency response coordination, disaster management, healthcare triage, and national security monitoring require rapid, data-driven decision-making under conditions of uncertainty, time pressure, and information overload. This paper presents a novel Adaptive Multi-Modal Visualization Optimization Framework (AMVOF) designed to enhance decision-making quality by dynamically integrating heterogeneous data streams into cognitively efficient visual representations. The framework leverages a hybrid architecture combining Graph Neural Networks (GNNs) for relational data structuring, Temporal Convolutional Networks (TCNs) for real-time trend extraction, and a newly proposed Cognitive Load-Aware Visualization Selection Algorithm (CLVSA) that adaptively selects optimal visualization formats (e.g., heatmaps, network graphs, temporal dashboards) based on operator context and task criticality.

Keywords : Data Visualization Optimization; Decision Intelligence Systems; Cognitive Load-Aware Algorithms; Public Service Operations; Real-Time Analytics.

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