A Deep Learning-Based Robot Analysis Model for Semantic Context Capturing by Using Predictive Models in Public Management

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Author(s): Zixuan Li (School of Public Policy and Management, China University of Mining and Technology, China)and Chengli Wang (School of Public Policy and Management, China University of Mining and Technology, China)
Copyright: 2024
Volume: 32
Issue: 1
Pages: 23
Source title: Journal of Global Information Management (JGIM)
Editor(s)-in-Chief: Zuopeng (Justin) Zhang (University of North Florida, USA)
DOI: 10.4018/JGIM.335900

Keywords: Computer Science & IT / Global Information Technology / Information Science Reference / IT Research and Theory

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Abstract

In the realm of robotics, the ability to comprehend intricate semantic contexts within diverse environments is paramount for autonomous decision-making and effective human-robot collaboration. This article delves into the realm of enhancing robotic semantic understanding through the fusion of deep learning techniques. This work presents a pioneering approach: integrating several neural network models to analyze robot images, thereby capturing nuanced environmental semantic contexts. The authors augment this analysis with predictive models, enabling the robot to adapt the changing contexts intelligently. Through rigorous experimentation, our model demonstrated a substantial 25% increase in accuracy when compared to conventional methods, showcasing its robustness in real-world applications. This research marks a significant stride toward imbuing robots with sophisticated visual comprehension, paving the way for more seamless human-robot interactions and a myriad of practical applications in the evolving landscape of robotics.

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A Deep Learning-Based Robot Analysis Model for Semantic Context Capturing by Using Predictive Models in Public Management

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