Tensor-based factorial hidden markov model for Cyber-Physical-Social services

With the rapid development and widespread application of information, computer, and communication technologies, Cyber-Physical-Social Systems (CPSS) have gained increasing importance and attention. To enable intelligent applications and provide better services for CPSS users, efficient data analytical models are crucial. This paper presents a novel data analytic framework for CPSS services. First, a Tensor-Based Factorial Hidden Markov Model (T-FHMM) is introduced to comprehensively analyze multi-user activity features, enhancing CPSS activity analytics. A tensor-based Forward-Backward algorithm is then designed for T-FHMM to efficiently perform evaluation tasks using multiple probabilistic computing micro-services. Additionally, a tensor-based Baum-Welch algorithm is developed to accurately learn model parameters via parameter optimization micro-services. Furthermore, a tensor-based Viterbi algorithm is implemented with specific micro-services to improve prediction tasks. Finally, the comprehensive performance of the proposed model and algorithms is validated on three open datasets through self-comparison and other-comparison. Experimental results demonstrate that the proposed method outperforms compared methods in terms of accuracy, precision, recall, and F1-score.

Text 124879.pdf - Published Version Restricted to Repository staff only Download (3MB) Official URL or Download Paper: https://ieeexplore.ieee.org/document/10980022/

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