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The proposed framework integrates user behavior analysis, feature engineering, collaborative filtering, content similarity, and ML algorithms such as Random Forests, Gradient Boosting, Support Vector Machines, Neural Networks, and hybrid models to generate accurate recommendations.
An Intelligent Human-Centric Cyber-Physical System (HC-CPS) framework comprising six interconnected layers for real-time monitoring, predictive maintenance, adaptive production scheduling, quality optimization, and human-centered decision support is proposed, providing a scalable, resilient, and sustainable foundation...
A Large Language Model-Augmented Machine Learning Pipeline that integrates data acquisition, intelligent preprocessing, semantic feature engineering, automated model selection, hyperparameter optimization, explainable AI, continuous monitoring, and feedback-driven refinement within a unified framework is proposed.
This work forms a prior method based on Deep Deterministic Policy Gradient controller and a adaptive unscented Kalman filter which continuously providing constantly estimating robot states and improving the motion primitives in hazardous operating conditions to validate that end-to-end AI navigation architectures deliv...
A Digital Twin-Assisted Optimization Framework for Electric Vehicle Charging Infrastructure (DTO-EVCI) that integrates IoT, cloud computing, artificial intelligence (AI), machine learning, and optimization techniques to enable real-time monitoring, predictive analytics, and intelligent charging management is proposed.
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