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

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