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23 articles for “graph-based neural networks”
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AI-Driven Framework for Accelerating Polymer Nanocomposite Commercialization in Computational Materials Engineering
Abstract: The remarkable mechanical strength increased functional qualities, lightweight structure, and thermal stability of polymer nanocomposites have prompted modern materials research to prioritize their rapid commercialization. Advanced materials can be created by adding nanoscale fillers such as carbon nanotubes, graphene, silica, and metal oxides to polymer matrices. These materials have applications in biomedical engineering, aerospace, electronics, packaging, and automobile manufacture. Research and development of polymer nanocomposites has traditionally relied on costly …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 1–19 Read article
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Development of Neuromorphic Polymer Composites Using IoT Sensing and Brain-Inspired Learning Algorithms
Abstract: This research aims to develop neuromorphic polymer composites by combining conductive sensing materials, IoT-based sensing data collection and brain-inspired learning models for adaptive response. Hybrid conductive polymer composites were developed by adding carbon nanofibers and graphene Nano platelets to a thermoplastic polymer. IoT sensors (strain, temperature) were employed to collect real-time sensing data that was combined with environmental data. A material-aware neuromorphic learning algorithm was created with event-driven spike coding …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 755–784 Read article
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Real-Time Object Detection and Tracking in Traffic Surveillance: Implementing Algorithms That Can Process Video Streams for Immediate Traffic Monitoring
Abstract: The rapid growth in urban development and traffic congestion calls for adopting high standards of traffic surveillance systems for monitoring. This paper reviews the current advancement and future trends of real-time object detection and tracking technology and its implications for traffic surveillance. Conventional approaches to traffic monitoring can provide more or less accurate data, but they are not easily scalable and cannot cope with rapidly changing conditions typical within urban …
Published in International Journal of Algorithms Design and Analysis Review · Vol. 3, Issue 1, 2025 · pp. 18–39 Read article