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John Rover R. Sinag

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Conference Jul 2026

A Capability–Compliance–Sustainability Framework for Future Multimedia Design Ecosystems

The transformation of multimedia design is experiencing structural shifts based on AI, interoperability, and regulatory considerations. Previous research focused on each of these factors individually; however, little attention was paid to their interactions in an integrated approach. In this paper, the Capability-Compliance-Sustainability (CCS) framework was proposed to discuss the evolution of multimedia design from 2026 onwards. The framework represents a result of a comprehensive review of current trends in AI architecture design, neural rendering, web-based GPUs, interoperability, accessibility, and AI governance tools. The three interconnected constructs were formed based on a synthesis of the reviewed literature: Capability, which implies the development of computational capabilities through AI; Compliance, which refers to regulation, accessibility, and provenance; and Sustainability, which includes infrastructure resilience, energy-efficient computation, and scalability. To illustrate the analytical utility of the conceptual model, selected multimedia design use cases are presented. The paper presents the potential of the CCS framework for multimedia creation for education, generative content, and 3D/XR. From a theoretical standpoint, the framework describes multimedia evolution as an ecosystem equilibrium management process.

John Rover R. Sinag · 0 citations
Conference Jul 2026

Bias-Aware Systematic Review of AI-Based Mental Health Detection Using Social Media: A PRISMA and PROBAST(+AI) Analysis

This study presents a bias-aware systematic review of artificial intelligence (AI)-based mental health detection using social media data from 2015 to 2025. Guided by PRISMA 2020 and PROBAST(+AI), records from IEEE Xplore, Scopus, and Web of Science were screened from 3,861 initial records to 308 included studies. Results show a strong shift toward transformer and hybrid models, depression-focused tasks, and Twitter/X and Reddit datasets. However, the corpus also shows major methodological gaps, including absent external validation, limited reporting of class imbalance handling, low explainable AI adoption, and underreported platform sources. The review argues that future systems require transparent data provenance, bias-aware validation, explainable decision support, and privacy-preserving crossplatform evaluation before clinical or public-health deployment.

John Rover R. Sinag, Janela Reis Babaran-Sinag · 0 citations

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