报告详情
Dynamic NeuroFusion Ensemble Prediction System for Retail Sales and Customer Behaviour Prediction
编号：76
访问权限：仅限参会人
更新：2026-07-22 16:09:42 浏览：14次
In-person
报告开始：2026年07月31日 12:25（Asia/Kolkata）
报告时间：15min
所在会场：[S6] Artificial Intelligence Use Cases [S6-4] Artificial Intelligence Use Cases
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摘要
Abstract—The increasing complexity of retail operations and evolving market patterns has created a significant need for intelligent prediction models that support data-driven decision-making. However, conventional Machine Learning (ML) and Deep Learning (DL) models often encounter difficulties in handling heterogeneous retail data, nonlinear feature relationships, redundant variables and complex predictive patterns. This research proposes a Dynamic NeuroFusion Ensemble Prediction System (DNEPS) for retail product-demand classification. DNEPS incorporates a NeuroWeave Feature Intelligence Layer (NFIL) for adaptive feature-interaction learning, an Adaptive Performance-Guided Ensemble Layer (APGEL) for assigning validation-based weights to individual learners and an Intelligent Fusion and Decision Layer for generating integrated predictive insights. The framework combines Random Forest, XGBoost, LightGBM and Deep Neural Network (DNN) models within unified ensemble architecture. The proposed system was evaluated using a large-scale synthetic Retail Sales and Customer Behaviour Analysis dataset, with product demand categorized into low, medium and high-demand classes. Leakage-free preprocessing and training-derived percentile thresholds were used during model development. Experimental results indicate that DNEPS achieves 98.6% accuracy, outperforming the selected baseline models. These findings demonstrate the effectiveness of adaptive feature intelligence and performance-guided ensemble fusion for reliable retail demand classification and decision support.
关键词
Keywords—Adaptive Ensemble Learning, Decision Intelligence, NeuroWeave Feature Intelligence, Organizational Forecasting, Predictive Analytics, Retail Analytics
报告人
Dr Agila G
Professor Sri Ramakrishna College of Arts & Science稿件作者
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重要日期
-
会议日期
07月30日
2026
至08月01日
2026
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07月28日 2026
初稿截稿日期
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08月03日 2026
注册截止日期
主办单位
The United Societies of Science
承办单位
Kongunadu College of Engineering and Technology
协办单位
IEEE Section
IEEE Madras Section
IEEE Madras Section
联系方式
- ICNCCom 2026
- ic******@usssociety.org
历届会议
-
2026年07月30日 印度 Trichy
2026 International Conference on Networks Computers and Communications
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