Enterprise AI Analysis
Enhanced Web User Interface Design Via Cross-Device Responsiveness Assessment Using An Improved HCI-INTEGRATED DL Schemes
This research proposes a dynamic web UI optimization through Cross-Responsiveness (CR) assessment using Finite Exponential Continuous State Machine (FECSM) and Quokka Nonlinear Difference Swarm Optimization Algorithm (QNDSOA). It focuses on enhancing user satisfaction by extracting HCI-based features, grouping user behavior patterns, classifying UX change types with BiGLMRU, and optimizing UI design with QNDSOA. The approach significantly improves UI adaptability and performance metrics.
Executive Impact at a Glance
Key performance indicators derived from this research highlight the potential for significant improvements in web UI design and user experience.
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Average Fitness
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Deep Analysis & Enterprise Applications
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Problem Statement
Proposed Methodology
Performance Results
Conclusion
The Challenge: Inefficient UI Optimization
Current UI optimization models often overlook Cross-Responsiveness (CR) assessment, leading to suboptimal user interaction across diverse devices. This research addresses key limitations of prevailing works, including the lack of focus on CR assessment, insufficient concentration on user behavior (UB) patterns, inefficient UI design updates, and failure to consider the improvement level of UI on web development processes.
Traditional techniques have struggled to provide a holistic solution for dynamic UI optimization that adapts to continuous changes in device configurations and user preferences, reducing overall effectiveness and user satisfaction.
Innovative Approach to Web UI Optimization
This study introduces a novel model for optimizing web UI based on an improved HCI-INTEGRATED DL Scheme. It integrates Finite Exponential Continuous State Machine (FECSM) for CR assessment and Quokka Nonlinear Difference Swarm Optimization Algorithm (QNDSOA) for optimal UI design.
The methodology involves: data collection and pre-processing, HCI-based feature extraction, user behavior pattern grouping using HPPDBSCAN, UX change type classification via BiGLMRU (Bidirectional Gated Luong and Mish Recurrent Unit), and QNDSOA for UI optimization with continuous feedback.
Superior Performance & Adaptability
Experimental results demonstrate the proposed system's superior performance:
- Proposed FECSM: Achieved 94.2356% state coverage, 95.2312% transition efficiency, and 93.6532% loop detection rate, outperforming traditional FSM models in CR assessment.
- Proposed FDWIS: Showed significantly lower fuzzification, defuzzification, and rule generation times compared to ANFIS, TFL, and RBP for UX change labeling.
- Proposed BiGLMRU: Attained 99.2315% accuracy in UX change type classification, surpassing BiGRU, LSTM, RNN, and DLNN by effectively handling longer dependencies and mitigating overfitting with Mish activation.
- Proposed QNDSOA: Achieved an average fitness of 98.5632% for UI optimization, demonstrating higher efficiency and adaptability than existing algorithms like QSOA, ESOA, SSOA, and GWOA.
Optimized UI for Future Web Environments
This work successfully designed an enhanced web UI through CR assessment using advanced HCI-integrated FECSM and QNDSOA. The system provides detailed insights into transitions across different screen sizes, achieving high state coverage and optimal UI design with an average fitness of 98.5632%.
The continuous feedback monitoring mechanism ensures model trustworthiness and adaptability. Future work will focus on integrating explainable AI and cognitive load factors to further improve reliability and trust in the UI-enhancement process.
98.56%
Average Fitness of QNDSOA for UI Optimization
Enterprise Process Flow
Data Collection & Pre-processing
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HCI Feature Extraction
→
User Behaviour Pattern Grouping (HPPDBSCAN)
→
CR Assessment (FECSM)
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UX Change Type Classification (BiGLMRU)
→
Optimal UI Design (QNDSOA)
| Algorithm | State Coverage (%) | Transition Efficiency (%) | Loop Detection Rate (%) |
|---|---|---|---|
| Proposed FECSM | 94.2356 | 95.2312 | 93.6532 |
| FSM | 86.5402 | 86.2356 | 84.6375 |
| HMM | 79.0364 | 78.0326 | 77.6023 |
Impact of Enhanced UI on E-commerce UX
The proposed BiGLMRU model, when applied to an e-commerce platform, demonstrated a significant improvement in user experience by accurately classifying UX change types. This led to a 20% reduction in bounce rate and a 15% increase in conversion rate through adaptive UI adjustments based on real-time user behavior analysis and cross-device responsiveness. The precise categorization enabled proactive UI enhancements, ensuring optimal user engagement across various devices.
Estimate Your Enterprise's AI Impact
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Estimated Annual Savings
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Implementation Roadmap
A structured approach ensures seamless integration and maximum impact for your enterprise.
Phase 1: Data Integration & Pre-processing
Integrate web crawler and session replay tools for data collection. Implement min-max normalization for standardization.
Phase 2: Feature Engineering & Pattern Grouping
Extract HCI-based features and apply HPPDBSCAN for robust user behavior pattern grouping.
Phase 3: Cross-Responsiveness Assessment
Utilize FECSM to model interface responsiveness across devices and identify friction points.
Phase 4: UX Change Classification
Deploy BiGLMRU to classify UI change requirements (Low, Medium, High) based on UICPI and FD WIS.
Phase 5: Optimal UI Design & Deployment
Optimize UI design using QNDSOA with continuous feedback for real-time adjustments and deploy to live environment.
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