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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.
The proposed Explainable Graph Artificial Intelligence Framework for Financial Risk Forecasting (XGraph-FR) integrates Graph Neural Networks (GNNs) and Explainable AI (XAI) to deliver accurate and interpretable financial risk predictions. It models financial markets as a heterogeneous graph connecting companies, sector...
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...
An AI-based dynamic task allocation framework that integrates machine learning, reinforcement learning, swarm intelligence, and optimization techniques to enable intelligent and adaptive decision-making in heterogeneous multi-robot systems 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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