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Forecasting

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Time-varying compartmental models with neural networks for pandemic infection forecasting.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
The emergence and spread of deadly pandemics has repeatedly occurred throughout history, causing widespread infections and life loss. Forecasting the progression of pandemics is crucial for decision-makers to achieve its mitigation. This predictive t...

Advancing pearl millet yield forecasting: Comparative analysis of individual and ensemble machine learning approaches over Rajasthan, India.

PloS one
Pearl millet (Pennisetum glaucum L.) is a resilient crop known for its ability to thrive in arid and semi-arid regions, making it a crucial staple in regions prone to drought. Rajasthan, a state in India, emerged as the top producer of pearl millet. ...

Exploring the achievements and forecasting of SDG 3 using machine learning algorithms: Bangladesh perspective.

PloS one
BACKGROUND: Sustainable Development Goal 3 (SDG 3), focusing on ensuring healthy lives and well-being for all, holds global significance and is particularly vital for Bangladesh. Neonatal Mortality Rate (NMR), Under-5 Mortality Rate (U5MR), Maternal ...

Migrative armadillo optimization enabled a one-dimensional quantum convolutional neural network for supply chain demand forecasting.

PloS one
Demand forecasting is a quite challenging task, which is sensitive to several factors such as endogenous and exogenous parameters. In the context of supply chain management, demand forecasting aids to optimize the resources effectively. In recent yea...

AI-driven approaches for air pollution modelling: A comprehensive systematic review.

Environmental pollution (Barking, Essex : 1987)
In recent years, air quality levels have become a global issue with the rise of harmful pollutants and their effects on climate change. Urban areas are especially affected by air pollution, resulting in a deterioration of the environment and a surge ...

Flood resilience through hybrid deep learning: Advanced forecasting for Taipei's urban drainage system.

Journal of environmental management
The escalating impacts of climate change have intensified extreme rainfall events, placing urban drainage systems under unprecedented pressure and increasing flood risks. Addressing these challenges requires advanced flood mitigation strategies, opti...

Interconnections, trend analysis and forecasting of water-air temperature with water level dynamics in Blue Moon Lake Valley: A statistical and machine learning approach.

Journal of environmental management
Glacier-fed lakes serve as vital indicators of climate change, yet their temperature and water level dynamics are insufficiently studied, particularly in high-altitude basins. Examining these interactions is fundamental for the effective management o...

Forecasting deforestation and carbon loss across New Guinea using machine learning and cellular automata.

The Science of the total environment
The island of New Guinea harbors some of the world's most biologically diverse and highly endemic tropical ecosystems. Nevertheless, progressing land-use change in the region threatens their integrity, which will adversely affect their biodiversity a...

Forecasting trends of rising emergency department chest imaging using machine learning.

Emergency radiology
INTRODUCTION: Imaging studies in the acute care setting, such as the emergency room, have been increasing. In this report, we use the Centers for Medicare and Medicaid services (CMS) database to assess trends in ED chest CT and chest CTA imaging in E...

Enhanced EEG Forecasting: A Probabilistic Deep Learning Approach.

Neural computation
Forecasting electroencephalography (EEG) signals, that is, estimating future values of the time series based on the past ones, is essential in many real-time EEG-based applications, such as brain-computer interfaces and closed-loop brain stimulation....