Instantaneous mental workload assessment using time–frequency analysis and semi-supervised learning

Abstract

The real-time assessment of mental workload (MWL) is critical for development of intelligent human–machine cooperative systems in various safety–critical applications. Although data-driven machine learning (ML) approach has shown promise in MWL recognition, there is still difficulty in acquiring a sufficient number of labeled data to train the ML models. This paper proposes a semi-supervised extreme learning machine (SS-ELM) algorithm for MWL pattern classification requiring only a small number of labeled data. The measured data analysis results show that the proposed SS-ELM paradigm can effectively improve the accuracy and efficiency of MWL classification and thus provide a competitive ML approach to utilizing a large number of unlabeled data which are available in many real-world applications.

Advantages of Combining Factorization Machine with Elman Neural Network for Volatility Forecasting of Stock Market

“…Artificial neural network has the advantages on learning from sample data and capturing the nonlinear relations among interconnected neurons through training mode [3]. It is capable of dealing with nonlinear highdimensional data and approximating any nonlinear functions with arbitrary precision [4][5][6][7]. Particularly, the simple recurrent network, i.e., Elman neural network (Elman NN) [8] has shown its stronger ability as it has the characteristic of time-varying.…”