Neural networks : the official journal of the International Neural Network Society
Oct 18, 2024
Spatiotemporal Graph (STG) forecasting is an essential task within the realm of spatiotemporal data mining and urban computing. Over the past few years, Spatiotemporal Graph Neural Networks (STGNNs) have gained significant attention as promising solu...
Neural networks : the official journal of the International Neural Network Society
Oct 17, 2024
Transformer-based models demonstrate tremendous potential for Multivariate Time Series (MTS) forecasting due to their ability to capture long-term temporal dependencies by using the self-attention mechanism. However, effectively modeling the spatial ...
The recent COVID-19 pandemic has thrown the importance of accurately forecasting contagion dynamics and learning infection parameters into sharp focus. At the same time, effective policy-making requires knowledge of the uncertainty on such prediction...
This paper delves into the engineering applications of lime-stabilized red clay, a highly water-sensitive material, particularly in the context of the climatic conditions prevalent in the Dalian region. We systematically investigate the impact of wat...
This paper proposes a model called X-LSTM-EO, which integrates explainable artificial intelligence (XAI), long short-term memory (LSTM), and equilibrium optimizer (EO) to reliably forecast solar power generation. The LSTM component forecasts power ge...
This study focuses on improving short-term power load forecasting, a critical aspect of power system planning, control, and operation, especially within the context of China's "dual-carbon" policy. The integration of renewable energy under this polic...
Crop price forecasting is difficult in that supply is not as elastic as demand, therefore, supply and demand should be stabilized through long-term forecasting and pre-response to the price. In this study, we propose a Parametric Seasonal-Trend Autor...
Neural networks : the official journal of the International Neural Network Society
Sep 23, 2024
In data analysis and forecasting, particularly for multivariate long-term time series, challenges persist. The Transformer model in deep learning methods has shown significant potential in time series forecasting. The Transformer model's dot-product ...
Collective migration is an important component of many biological processes, including wound healing, tumorigenesis, and embryo development. Spatial agent-based models (ABMs) are often used to model collective migration, but it is challenging to thor...
While substantial investment has been made in the early identification of mental and behavioural health disorders in service members, rates of depression, substance abuse and suicidality continue to climb. Objective and persistent measures are needed...
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