Manual classification of sleep stage is a time-consuming but necessary step in the diagnosis and treatment of sleep disorders, and its automation has been an area of active study. The previous works have shown that low dimensional fast Fourier transform (FFT) features and many machine learning algorithms have been applied. In this paper, we demonstrate utilization of features extracted from EEG signals via FFT to improve the performance of automated sleep stage classification through machine learning methods. Unlike previous works using FFT, we incorporated thousands of FFT features in order to classify the sleep stages into 2–6 classes. Using the expanded version of Sleep-EDF dataset with 61 recordings, our method outperformed other state-of-the art methods. This result indicates that high dimensional FFT features in combination with a simple feature selection is effective for the improvement of automated sleep stage classification.

In many animal-related studies, a high-performance animal behavior recognition system can help researchers reduce or get rid of the limitation of human assessments and make the experiments easier to reproduce. Recently, although deep learning models are holding state-of-the-art performances in human action recognition tasks, these models are not well-studied in applying to animal behavior recognition tasks. One reason is the lack of extensive datasets which are required to train these deep models for good performances. In this research, we investigated two current state-of-the-art deep learning models in human action recognition tasks, the I3D model and the R(2 + 1)D model, in solving a mouse behavior recognition task. We compared their performances with other models from previous researches and the results showed that the deep learning models that pre-trained using human action datasets then fine-tuned using the mouse behavior dataset can outperform other models from previous researches. It also shows promises of applying these deep learning models to other animal behavior recognition tasks without any significant modification in the models' architecture, all we need to do is collecting proper datasets for the tasks and fine-tuning the pre-trained models using the collected data.

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To classify DNA sequences, k-mer frequency is widely used since it can convert variablelength sequences into fixed-length and numerical feature vectors. However, in case of fixedlength DNA sequence classification, subsequences starting at a specific position of the given sequence can also be used as categorical features. Through the performance evaluation on six datasets of fixed-length DNA sequences, our algorithm based on the above idea achieved comparable or better performance than other state-of-the art algorithms.

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