Animal sound classification (ASC) refers to the automatic identification of animal categories by sound, and is useful for monitoring rare or elusive wildlife. Thus far, deep-learning-based models have shown good performance in ASC when training data is sufficient, but suffer from severe performance degradation if not. Recently, generative adversarial networks (GANs) have shown the potential to solve this problem by generating virtual data. However, in a multi-class environment, existing GAN-based methods need to construct separate generative models for each class. Additionally, they only consider the waveform or spectrogram of sound, resulting in poor quality of the generated sound. To overcome these shortcomings, we propose a two-step sound augmentation scheme using a class-conditional GAN. First, common features are learned from all classes of animal sounds, and multiple classes of animal sounds are generated based on the features that consider both waveforms and spectrograms using class-conditional GAN. Second, we select data from the generated data based on the confidence of the pretrained ASC model to improve classification performance. Through experiments, we show that the proposed method improves the accuracy of the basic ASC model by up to 18.3%, which corresponds to a performance improvement of 13.4% compared to the second-best augmentation method.

In order to minimize the impact of influenza on public health, accurate early forecasting is essential. Various deep-learning-based models have been proposed to predict future influenza occurrences by capturing temporal/regional patterns from past occurrence time-series data. However, the prediction performance of these unimodal approaches is limited because they extract knowledge only from collected data, and users cannot input contextual information and domain knowledge to them. Recently, large language models (LLMs) have demonstrated the potential to improve prediction accuracy by linking contextual text information to time-series predictions. In this paper, we propose MIFlu, a multimodal influenza forecasting scheme that can fuse contextual text information to time-series influenza occurrences using two LLMs. It first extracts text embeddings from the user's text prompts that contain contextual information using a text-embedding LLM. Then, MIFlu fuses the text embeddings and time-series embeddings and uses the fused embeddings to predict future occurrences using a forecasting LLM. In extensive experiments using public national/regional influenza datasets, MIFlu outperforms other predictive models, improving prediction performance by up to 26.2% compared to state-of-the-art models. We also analyze the effect of various textual input embedders, hyperparameters, and the amount of training data on forecasting accuracy.

Recently, energy procurement by renewable energy sources has increased. In particular, as solar power generation has a high penetration rate among them, solar radiation predictions at the site are attracting much attention for efficient operation. Various approaches have been proposed to forecast solar radiation accurately. Recently, hybrid models have been proposed to improve performance through forecasting in the frequency domain using past solar radiation. Since solar radiation data have a pattern, forecasting in the frequency domain can be effective. However, forecasting performance deteriorates on days when the weather suddenly changes. In this paper, we propose a domain hybrid forecasting model that can respond to weather changes and exhibit improved performance. The proposed model consists of two stages. In the first stage, forecasting is performed in the frequency domain using wavelet transform, complete ensemble empirical mode decomposition, and multilayer perceptron, while forecasting in the sequence domain is accomplished using light gradient boosting machine. In the second stage, a multilayer perceptron-based domain hybrid model is constructed using the forecast values of the first stage as the input. Compared with the frequency-domain model, our proposed model exhibits an improvement of up to 36.38% in the normalized root-mean-square error.