AIMC Topic: Forecasting

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A networked station system for high-resolution wind nowcasting in air traffic operations: A data-augmented deep learning approach.

PloS one
This study introduces a high-resolution wind nowcasting model designed for aviation applications at Madeira International Airport, a location known for its complex wind patterns. By using data from a network of six meteorological stations and deep le...

Enhancing stock index prediction: A hybrid LSTM-PSO model for improved forecasting accuracy.

PloS one
Stock price prediction is a challenging research domain. The long short-term memory neural network (LSTM) widely employed in stock price prediction due to its ability to address long-term dependence and transmission of historical time signals in time...

The Future of Artificial Intelligence Using Images and Clinical Assessment for Difficult Airway Management.

Anesthesia and analgesia
Artificial intelligence (AI) algorithms, particularly deep learning, are automatic and sophisticated methods that recognize complex patterns in imaging data providing high qualitative assessments. Several machine-learning and deep-learning models usi...

Integrating artificial intelligence with mechanistic epidemiological modeling: a scoping review of opportunities and challenges.

Nature communications
Integrating prior epidemiological knowledge embedded within mechanistic models with the data-mining capabilities of artificial intelligence (AI) offers transformative potential for epidemiological modeling. While the fusion of AI and traditional mech...

Enhancing air quality predictions in Chile: Integrating ARIMA and Artificial Neural Network models for Quintero and Coyhaique cities.

PloS one
In this comprehensive analysis of Chile's air quality dynamics spanning 2016 to 2021, the utilization of data from the National Air Quality Information System (SINCA) and its network of monitoring stations was undertaken. Quintero, Puchuncaví, and Co...

Predictive modeling of air quality in the Tehran megacity via deep learning techniques.

Scientific reports
Air pollution is a significant challenge in metropolitan areas, where increasing amounts of air pollutants threaten public health and environmental safety. The present study aims to forecast the concentrations of various air pollutants, including CO,...

Enhancing door-to-door waste collection forecasting through ML.

Waste management (New York, N.Y.)
We explore the application of machine learning (ML) techniques to forecast door-to-door waste collection, addressing the challenges in municipal solid waste (MSW) management. ML models offer a promising solution to optimize waste collection operation...

Novel deep neural network architecture fusion to simultaneously predict short-term and long-term energy consumption.

PloS one
Energy is integral to the socio-economic development of every country. This development leads to a rapid increase in the demand for energy consumption. However, due to the constraints and costs associated with energy generation resources, it has beco...

Energy consumption forecasting for oil and coal in China based on hybrid deep learning.

PloS one
The consumption forecasting of oil and coal can help governments optimize and adjust energy strategies to ensure energy security in China. However, such forecasting is extremely challenging because it is influenced by many complex and uncertain facto...

Forecasting cardiovascular disease mortality using artificial neural networks in Sindh, Pakistan.

BMC public health
Cardiovascular disease (CVD) is a leading cause of death and disability worldwide, and its incidence and prevalence are increasing in many countries. Modeling of CVD plays a crucial role in understanding the trend of CVD death cases, evaluating the e...