AIMC Topic: Seasons

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Integrated codec decomposed Transformer for long-term series forecasting.

Neural networks : the official journal of the International Neural Network Society
Recently, Transformer-based and multilayer perceptron (MLP) based architectures have formed a competitive landscape in the field of time series forecasting. There is evidence that series decomposition can further enhance the model's ability to percei...

Temporally boosting neural network for improving dynamic prediction of PM concentration with changing and unbalanced distribution.

Journal of environmental management
Increasing medical research evidence suggests that even low PM concentrations may trigger significant health issues. Hence, an accurate prediction of PM holds immense significance in securing public health safety. However, current data-drive predicti...

Delayed flowering phenology of red-flowering plants in response to hummingbird migration.

Current biology : CB
The radiation of angiosperms is marked by a phenomenal diversity of floral size, shape, color, scent, and reward. The multi-dimensional response to selection to optimize pollination has generated correlated suites of these floral traits across distan...

Hydraulic and thermal performance trigger the deterministic assembly of water microbiomes: From biogeographical homogenization to machine learning model.

Water research
Water quality at the point of consumption has long been a health issue because of the potential for microbial ecology. However, research on water hydraulic performance remains in its infancy, and in particular, little is known about the effects of th...

Analyzing the impact of COVID-19 on seasonal infectious disease outbreak detection using hybrid SARIMAX-LSTM model.

Journal of infection and public health
BACKGROUND: This study estimates the incidence of seasonal infectious diseases, including influenza, norovirus, severe fever with thrombocytopenia syndrome (SFTS), and tsutsugamushi disease, in the Republic of Korea from 2005 to 2023. It also examine...

Forewarning the seasonal dynamics of corn leafhopper and mollicutes through neural networks.

International journal of biometeorology
The corn leafhopper (CL), Dalbulus maidis (DeLong & Wolcott) (Hemiptera: Cicadellidae), has become the most important corn pest in Brazil and other corn-producing countries. This highly efficient insect vector transmits corn stunting pathogens result...

Cluster-based downscaling of precipitation using Kolmogorov-Arnold Neural Networks and CMIP6 models: Insights from Oman.

Journal of environmental management
Accurate precipitation predictions are crucial for addressing climate change impacts on water resources, especially in arid regions like Oman. Therefore, this study evaluates three machine learning models-Random Forest (RF), Multilayer Perceptron Neu...

Forecasting invasive mosquito abundance in the Basque Country, Spain using machine learning techniques.

Parasites & vectors
BACKGROUND: Mosquito-borne diseases cause millions of deaths each year and are increasingly spreading from tropical and subtropical regions into temperate zones, posing significant public health risks. In the Basque Country region of Spain, changing ...

Developing a seasonal-adjusted machine-learning-based hybrid time‑series model to forecast heatwave warning.

Scientific reports
Heatwaves pose a significant threat to environmental sustainability and public health, particularly in vulnerable regions and rapidly growing cities. They cause water shortages, stress on plants, and an overall drying out of landscapes, reducing plan...

Quantitative analysis and evaluation of winter and summer landscape colors in the Yangzhou ancient Canal utilizing deep learning.

Scientific reports
Color is an important index for human visual evaluation of landscape, and it is also a key factor affecting people's recognition and experience of heritage landscape. In this study, five important sites of the Yangzhou Grand Canal were selected for t...