AIMC Topic: Spatio-Temporal Analysis

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Machine learning to detect schedules using spatiotemporal data of behavior: A proof of concept.

Journal of the experimental analysis of behavior
Traditionally, the experimental analysis of behavior has relied on the single discrete response paradigm (e.g., key pecks, lever presses, screen clicks) to identify behavioral patterns. However, the development and availability of new technology allo...

Spatio-temporal crash severity analysis with cost-sensitive multi-graphs attention network.

Accident; analysis and prevention
Most conventional crash severity models attempt to achieve a low classification error rate, implicitly assuming the same losses for all classification errors. In this paper, we suggest that this setting has limitations in terms of reasonableness, as ...

Improved surface NO Retrieval: Double-layer machine learning model construction and spatio-temporal characterization analysis in China (2018-2023).

Journal of environmental management
As an important atmospheric pollutant causing serious harm to human health and the natural environment, monitoring of surface NO (SNO) level is of critical importance. However, the current SNO retrieval models neglect to consider the influence of NO ...

A deep learning framework leveraging spatiotemporal feature fusion for electrophysiological source imaging.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Electrophysiological source imaging (ESI) is a challenging technique for noninvasively measuring brain activity, which involves solving a highly ill-posed inverse problem. Traditional methods attempt to address this challen...

Spatial differences, dynamic evolution and influencing factors of the coupling and coordination relationship between health resources allocation and health service utilization in China.

BMC public health
BACKGROUND: With the progress of economic and social development, the demand for healthcare has increased, making the rational allocation of health resources and the efficient utilization of services crucial for public health. However, mismatches bet...

Unravelling tumour spatiotemporal heterogeneity using spatial multimodal data.

Clinical and translational medicine
Analysing the genome, epigenome, transcriptome, proteome, and metabolome within the spatial context of cells has transformed our understanding of tumour spatiotemporal heterogeneity. Advances in spatial multi-omics technologies now reveal complex mol...

Spatiotemporal Variation Assessment and Improved Prediction Of Cyanobacteria Blooms in Lakes Using Improved Machine Learning Model Based on Multivariate Data.

Environmental management
Cyanobacterial blooms in shallow lakes pose a significant threat to aquatic ecosystems and public health worldwide, highlighting the urgent need for advanced predictive methodologies. As impounded lakes along the Eastern Route of the South-to-North W...

Spatio-temporal changes of small protist and free-living bacterial communities in a temperate dimictic lake: insights from metabarcoding and machine learning.

FEMS microbiology ecology
Microbial communities, which include prokaryotes and protists, play an important role in aquatic ecosystems and influence ecological processes. To understand these communities, metabarcoding provides a powerful tool to assess their taxonomic composit...

A Novel Early Warning Model for Hand, Foot and Mouth Disease Prediction Based on a Graph Convolutional Network.

Biomedical and environmental sciences : BES
OBJECTIVES: Hand, foot and mouth disease (HFMD) is a widespread infectious disease that causes a significant disease burden on society. To achieve early intervention and to prevent outbreaks of disease, we propose a novel warning model that can accur...

Single feature spatio-temporal architecture for EEG Based cognitive load assessment.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
The study of electroencephalography (EEG) data for cognitive load analysis plays an important role in identification of stress-inducing tasks. This can be useful in applications such as optimal work allocation, increasing efficiency in the workplace ...