Forecasting and analyzing influenza activity in Hebei Province, China, using a CNN-LSTM hybrid model
G Li, Y Li, G Han, C Jiang, M Geng, N Guo, W Wu… - BMC Public Health, 2024 - Springer
Background Influenza, an acute infectious respiratory disease, presents a significant global
health challenge. Accurate prediction of influenza activity is crucial for reducing its impact …
health challenge. Accurate prediction of influenza activity is crucial for reducing its impact …
Combining evolutionary computation with machine learning technique for improved short-term prediction of UT1-UTC and length-of-day
Over the years, prediction techniques for the highly variable angular velocity of the Earth
represented by Earth's rotation (UT1-UTC) and length-of-day (LOD) have been continuously …
represented by Earth's rotation (UT1-UTC) and length-of-day (LOD) have been continuously …
Artificial intelligence-based forecasting model for incinerator in sulfur recovery units to predict SO2 emissions
Pollutant emissions from chemical plants are a major concern in the context of
environmental safety. A reliable emission forecasting model can provide important …
environmental safety. A reliable emission forecasting model can provide important …
Medium-and long-term prediction of length-of-day changes with the combined singular spectrum analysis and neural networks
Y Lei, D Zhao, M Guo - Studia Geophysica et Geodaetica, 2023 - Springer
Real-time estimates of the Earth orientation parameters (EOP) are currently unavailable for
users owing to the delay caused by complex data processing and heavy computation …
users owing to the delay caused by complex data processing and heavy computation …
Advancing polar motion prediction with derivative information
Abstract Earth Orientation Parameters (EOP) are essential for monitoring Earth's rotational
irregularities, impacting satellite navigation, space exploration, and climate forecasting. This …
irregularities, impacting satellite navigation, space exploration, and climate forecasting. This …
Machine Learning Optimized Graphene and MXene-Based Surface Plasmon Resonance Biosensor Design for Cyanide Detection
Cyanide, a highly toxic chemical compound, presents severe risks to both human health and
the environment. Its presence is particularly concerning in various industrial sectors …
the environment. Its presence is particularly concerning in various industrial sectors …
[PDF][PDF] Neural network analysis of neutron and X-ray reflectivity data incorporating prior knowledge
Due to the ambiguity related to the lack of phase information, determining the physical
parameters of multilayer thin films from measured neutron and X-ray reflectivity curves is, on …
parameters of multilayer thin films from measured neutron and X-ray reflectivity curves is, on …
[HTML][HTML] Development of a Deep Learning Model for Predicting Speech Audiometry Using Pure-Tone Audiometry Data
Speech audiometry is a vital tool in assessing an individual's ability to perceive and
comprehend speech, traditionally requiring specialized testing that can be time-consuming …
comprehend speech, traditionally requiring specialized testing that can be time-consuming …
[HTML][HTML] Forecasting Copper Prices Using Deep Learning: Implications for Energy Sector Economies
R Derakhshani, A GhasemiNejad, N Amani Zarin… - Mathematics, 2024 - mdpi.com
Energy is a foundational element of the modern industrial economy. Prices of metals play a
crucial role in energy sectors' revenue evaluations, making them the cornerstone of effective …
crucial role in energy sectors' revenue evaluations, making them the cornerstone of effective …
Neural network analysis of neutron and X-ray reflectivity data: Incorporating prior knowledge for tackling the phase problem
Due to the lack of phase information, determining the physical parameters of multilayer thin
films from measured neutron and X-ray reflectivity curves is, on a fundamental level, an …
films from measured neutron and X-ray reflectivity curves is, on a fundamental level, an …