Voting Classifier for The Interactive Design with Deep Learning for Scene Theory

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Published: Jul 30, 2023
DOI: https://doi.org/10.17762/ijritcc.v11i6.7738
Keywords:
Optimization, Deep learning, Voting Classifier, Game Theory, Scene Theory, Interaction Tool

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Ying Yang
Krirk University, Bangkok, Thailand

Abstract

Tool products play a pivotal role in assisting individuals in various domains, ranging from professional work to everyday tasks. The success of these tools is not solely determined by their functionality but also by the quality of user experience they offer. Designing tool products that effectively engage users, enhance their productivity, and provide a seamless interaction experience has become a critical focus for researchers and practitioners in the field of interaction design. Scene theory proposes that individuals perceive and interpret their surroundings as dynamic "scenes," wherein environmental and situational factors influence their cognitive processes and behavior. This research paper presented a novel approach to the interaction design of tool products by integrating scene theory, flow experience, the Moth Flame optimization (MFO), cooperative game theory (CGT), and voting deep learning. Tool products play a vital role in various domains, and their interaction design significantly influences user satisfaction and task performance. Building upon the principles of scene theory and flow experience, this study proposes an innovative framework that considers the contextual factors and aims to create a seamless and enjoyable user experience. The MFO algorithm, inspired by the behavior of moth flame, is employed to optimize the design parameters and enhance the efficiency of the interaction design process. Furthermore, CGT is integrated to model cooperative relationships between users and tool products, fostering collaborative and engaging experiences. Voting deep learning is employed to analyze user feedback and preferences, enabling personalized and adaptive design recommendations. With the proposed CGT, this paper investigates the impact of the proposed approach on user engagement, task efficiency, and overall satisfaction. The findings contribute to the field of interaction design by providing practical insights for creating tool products that align with users' cognitive processes, environmental constraints, flow-inducing experiences, and cooperative dynamics.

Article Details

How to Cite
Yang, Y. . (2023). Voting Classifier for The Interactive Design with Deep Learning for Scene Theory. International Journal on Recent and Innovation Trends in Computing and Communication, 11(6), 441–453. https://doi.org/10.17762/ijritcc.v11i6.7738
Issue
Vol. 11 No. 6 (2023): June (2023) Issue
Section
Articles

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