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This work investigated the use of an ensemble deep random vector functional link (edRVFL) network for electroencephalogram (EEG)-based driver fatigue recognition. Against the low feature learning capability of the edRVFL network from raw EEG signals, two strategies were exploited in this work. Specifically, the first one was to exploit the advantages of the feature extractor module in CNNs, i.e. , use CNN features as the input of the edRVFL network. The second one was to improve the feature learning capability of the edRVFL network. An enhanced edRFVL network named FGloWD-edRVFL was proposed, in which four enhancements were implemented, including random forest-based F eature selection, Glo bal output layer, W eighting and entropy-based D ynamic ensemble. The proposed FGloWD-edRVFL network was evaluated on the challenging cross-subject driver fatigue recognition tasks. The results indicated that the proposed model could boost the recognition performance, significantly outperforming all strong baselines. The step-wise analysis further demonstrated the effectiveness of the proposed enhancements in the edRVFL network. • An enhanced edRVFL input with CNN features is proposed. • RF-based feature selection is used to remove inferior enhancement features. • Weighting and a global output layer are implemented to diversify ensemble learning. • A dynamic ensemble is used to reduce the impact of low-competence classifiers. [ABSTRACT FROM AUTHOR]

Emotion recognition based on electroencephalogram (EEG) signals is helpful in various fields, including medical healthcare. One possible medical application is to diagnose emotional disorders in patients. Humans tend to work and communicate efficiently when in a good mood. On the other hand, negative emotions can harm physical and mental health. Traditional EEG-based methods usually extract time-domain and frequency-domain features before classifying them. Convolutional Neural Networks (CNN) enables us to extract features and classify them end-to-end. However, most CNN methods use backpropagation to train their models, which can be computationally expensive, primarily when a complex model is used. Inspired by the successes of Random Vector Functional Link and Convolutional Random Vector Functional Link, we propose using a randomized CNN model for emotion recognition that removes the need for a backpropagation method. Also, we expand our randomized CNN method to a deep and ensemble version to improve emotion recognition performance. We do experiments on the commonly used publicly available Database for Emotion Analysis using the Physiological Signals (DEAP) dataset to evaluate our randomized CNN models. Results on the DEAP dataset show our models outperform all other models, with at least 95% accuracy for all subjects. Our ensemble version outperforms our shallow version, winning the shallow version in most subjects. • A randomized CNN is proposed to classify EEG signals. • Randomized CNN are expanded into deep and ensemble version for improved performance. • Randomized CNN outperforms other EEG-based models. • Deep and ensemble version substantially outperforms shallow Randomized CNN. [ABSTRACT FROM AUTHOR]

Time charter rates must be predicted accurately to assist sensible decisions in the global, highly volatile shipping market. Time charter rates are affected by multiple factors, such as second-hand ship prices, order book, Libor interest rate, etc. However, not all factors convey predictive features to anticipate the future of time charter rates. Therefore, extracting predictive features from multiple driving time series from the shipping market is crucial for forecasting purposes. Accordingly, this paper proposes a novel convolutional recurrent neural network for time charter rates forecasting under the multi-variate phenomenon. The proposed network first extracts features from the monthly time series using a novel convolutional filter, the annual dilated filter. The annual dilated convolutional filter can extract the predictive features effectively and impose a sparse input structure to prevent overfitting. Then, a recurrent neural network learns the temporal information from the convoluted features. An extensive comparison study with many baseline models, including the persistence (Naïve I), statistical models, and the state-of-art networks, is conducted on the time charter rates of six kinds of ships. The empirical results demonstrate the proposed model's superiority in forecasting the time charter rates. • A novel dilated convolution LSTM neural network is proposed to forecast time charter rates. • The proposed convolutional filters can extract temporal features of monthly charter rates efficiently. • The proposed model outperforms the baselines in terms of three error metrics. [ABSTRACT FROM AUTHOR]