用改进算法评估跨设备响应性，动态优化网页界面体验。
Enhanced Web User Interface Design Via Cross-Device Responsiveness Assessment Using An Improved HCI-INTEGRATED DL Schemes
- 结合人机交互特征与状态机模型，量化跨设备响应性。
- 新模型使界面优化平均适应度达98.5632%。
- 适合关注多端适配与用户体验的前端开发人员。
在数字时代，界面优化对提升用户满意度至关重要。然而现有模型忽略了跨设备响应性（CR）评估，影响交互效率。本文提出一种基于改进人机交互集成深度学习方案的动态网页界面优化方法，融合有限指数连续状态机（FECSM）与雀斑非线性差分蚁群优化算法（QNDSOA）。首先收集并预处理设计与用户交互信息，进行最小-最大归一化；接着提取基于人机交互（HCI）的特征，并对用户行为模式分组；同时利用FECSM完成跨设备响应性评估；随后采用新型双向门控卢昂与莫什激活函数循环单元（BiGLMRU）分类用户体验变化类型，标签依据界面变更预测指数（UICPI）生成；最后通过新型QNDSOA优化界面设计，平均适应度达98.5632%，部署后实施反馈监控。
原文摘要 · Abstract (English)
User Interface (UI) optimization is essential in the digital era to enhance user satisfaction in web environments. Nevertheless, the existing UI optimization models had overlooked the Cross-Responsiveness (CR) assessment, affecting the user interaction efficiency. Consequently, this article proposes a dynamic web UI optimization through CR assessment using Finite Exponential Continuous State Machine (FECSM) and Quokka Nonlinear Difference Swarm Optimization Algorithm (QNDSOA). Initially, the design and user interaction related information is collected as well as pre-processed for min-max normalization. Next, the Human-Computer Interaction (HCI)-based features are extracted, followed by user behaviour pattern grouping. Meanwhile, the CR assessment is done using FECSM. Then, the proposed Bidirectional Gated Luong and Mish Recurrent Unit (BiGLMRU) is used to classify the User eXperience (UX) change type, which is labelled based on the User Interface Change Prediction Index (UICPI). Lastly, a novel QNDSOA is utilized to optimize the UI design with an average fitness of 98.5632%. Feedback monitoring is done after optimal deployment.
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