Journal of Multimedia Technology & Recent Advancements
Volume 13, Issue 2 (2026)
Table of contents
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Intelligent Big Data Processing: Optimizing Analytics Pipelines Through Ensemble Machine Learning, Distributed Computing, and Adaptive AI Architectures
Abstract: Traditional data analytics methods are facing significant challenges due to the explosive rise of digital data. Global data generation is expected to reach nearly 175 zettabytes by 2025. This puts significant pressure on existing computing infrastructure. Conventional analytics methods often struggle with scalability, processing speed, and smart decision-making. Rule-based systems and standalone machine learning models frequently cannot handle the complexity and scale of modern big data environments effectively. To tackle …
Published in Journal of Multimedia Technology & Recent Advancements · Vol. 13, Issue 2, 2026 · pp. 1–11 Read article
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HabituraX: A Comprehensive Machine Learning System for Lifestyle-Based Disease Risk Prediction
Abstract: The increased prevalence of lifestyle-related non-communicable diseases such as diabetes, obesity, and sleep disorders in the world requires the development of proactive risk-stratification tools. Recent studies indicate that there is a significant shift in favor of multimodal artificial intelligence (AI) models that comprise deep learning models such as CNNs and LSTMs, which often demonstrate high predictive accuracy, along with complex ensemble approaches. However, the necessary clinical interpretability to perform open …
Published in Journal of Multimedia Technology & Recent Advancements · Vol. 13, Issue 2, 2026 · pp. 12–23 Read article
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AI-Driven Real-Time Traffic Monitoring and Intelligent Control System: A Unified Deep Learning Framework for Smart Urban Mobility Management
Abstract: Urban traffic congestion imposes staggering economic and environmental costs on modern cities, with the INRIX Global Traffic Scorecard estimating annual per-driver productivity losses exceeding $1,348 in major metropolitan areas. Conventional traffic management infrastructure—fixed-cycle signal controllers, manual surveillance, and threshold-based incident detection—is fundamentally ill-equipped to address the dynamic, non-linear complexity of contemporary urban vehicular flows. This paper introduces TrafficNet-DRL, a unified deep learning and deep reinforcement learning framework for real-time traffic …
Published in Journal of Multimedia Technology & Recent Advancements · Vol. 13, Issue 2, 2026 · pp. 24–33 Read article
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Crowdsourced Real-Time Response with AR Assistance
Abstract: The rapid growth of digital technologies has significantly transformed the way people interact with both virtual systems and the physical world. Among these technologies, crowdsourcing and augmented reality (AR) have emerged as two powerful innovations that are reshaping real-time information sharing, decision-making, and user experiences. Crowdsourcing enables organizations and systems to collect valuable data from a large number of users, while AR enhances the user's perception by overlaying digital information …
Published in Journal of Multimedia Technology & Recent Advancements · Vol. 13, Issue 2, 2026 · pp. 34–40 Read article
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Attention-Enhanced YOLO Framework for Robust Real-Time Object Detection in Autonomous Driving Environments
Abstract: Accurate object detection is essential for autonomous driving systems, where vehicles must continuously perceive surrounding road users and infrastructure in real time. Although the You Only Look Once (YOLO) family of deep learning detectors has achieved significant success due to its high speed and efficiency, detecting small or partially occluded objects in complex traffic environments remains challenging. This paper presents an attention-enhanced YOLO framework designed to improve feature representation and …
Published in Journal of Multimedia Technology & Recent Advancements · Vol. 13, Issue 2, 2026 · pp. 41–52 Read article