AIMC Topic: Forecasting

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Artificial intelligence in the NICU to predict extubation success in prematurely born infants.

Journal of perinatal medicine
OBJECTIVES: Mechanical ventilation in prematurely born infants, particularly if prolonged, can cause long term complications including bronchopulmonary dysplasia. Timely extubation then is essential, yet predicting its success remains challenging. Ar...

Medical imaging and multimodal artificial intelligence models for streamlining and enhancing cancer care: opportunities and challenges.

Expert review of anticancer therapy
INTRODUCTION: Artificial intelligence (AI) has the potential to transform oncologic care. There have been significant developments in AI applications in medical imaging and increasing interest in multimodal models. These are likely to enable improved...

The Basics of Artificial Intelligence in Nursing: Fundamentals and Recommendations for Educators.

The Journal of nursing education
BACKGROUND: Artificial intelligence (AI) offers exciting possibilities; however, AI is a double-edged sword. The adoption of this technology offers many benefits but also presents risks to academic integrity and appropriately prepared graduates. Many...

A novel hybrid model based on two-stage data processing and machine learning for forecasting chlorophyll-a concentration in reservoirs.

Environmental science and pollution research international
The accurate and efficient prediction of chlorophyll-a (Chl-a) concentration is crucial for the early detection of algal blooms in reservoirs. Nevertheless, predicting Chl-a concentration in multivariate time series poses a significant challenge due ...

Hybrid deep learning models with multi-classification investor sentiment to forecast the prices of China's leading stocks.

PloS one
The prediction of stock prices has long been a captivating subject in academic research. This study aims to forecast the prices of prominent stocks in five key industries of the Chinese A-share market by leveraging the synergistic power of deep learn...

Forecasting water quality variable using deep learning and weighted averaging ensemble models.

Environmental science and pollution research international
Water quality variables, including chlorophyll-a (Chl-a), play a pivotal role in comprehending and evaluating the condition of aquatic ecosystems. Chl-a, a pigment present in diverse aquatic organisms, notably algae and cyanobacteria, serves as a val...

Deep learning framework for epidemiological forecasting: A study on COVID-19 cases and deaths in the Amazon state of ParĂ¡, Brazil.

PloS one
Modeling time series has been a particularly challenging aspect due to the need for constant adjustments in a rapidly changing environment, data uncertainty, dependencies between variables, volatile fluctuations, and the need to identify ideal hyperp...

Big data and artificial intelligence in cancer research.

Trends in cancer
The field of oncology has witnessed an extraordinary surge in the application of big data and artificial intelligence (AI). AI development has made multiscale and multimodal data fusion and analysis possible. A new era of extracting information from ...

Advances, challenges, and future research needs in machine learning-based crash prediction models: A systematic review.

Accident; analysis and prevention
Accurately modelling crashes, and predicting crash occurrence and associated severities are a prerequisite for devising countermeasures and developing effective road safety management strategies. To this end, crash prediction modelling using machine ...

Development and performance comparison of optimized machine learning-based regression models for predicting energy-related carbon dioxide emissions.

Environmental science and pollution research international
Accurate prediction of CO emissions for the countries has become a crucial task in decision-making processes for planning energy conversion and usage, supporting the design of effective emissions reduction strategies, and helping to achieve the goal ...